research papers on e health

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• Published: 27 October 2021

A narrative review on the validity of electronic health record-based research in epidemiology

• Milena A. Gianfrancesco 1 &
• Neal D. Goldstein   ORCID: orcid.org/0000-0002-9597-5251 2  

BMC Medical Research Methodology volume  21 , Article number:  234 ( 2021 ) Cite this article

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Electronic health records (EHRs) are widely used in epidemiological research, but the validity of the results is dependent upon the assumptions made about the healthcare system, the patient, and the provider. In this review, we identify four overarching challenges in using EHR-based data for epidemiological analysis, with a particular emphasis on threats to validity. These challenges include representativeness of the EHR to a target population, the availability and interpretability of clinical and non-clinical data, and missing data at both the variable and observation levels. Each challenge reveals layers of assumptions that the epidemiologist is required to make, from the point of patient entry into the healthcare system, to the provider documenting the results of the clinical exam and follow-up of the patient longitudinally; all with the potential to bias the results of analysis of these data. Understanding the extent of as well as remediating potential biases requires a variety of methodological approaches, from traditional sensitivity analyses and validation studies, to newer techniques such as natural language processing. Beyond methods to address these challenges, it will remain crucial for epidemiologists to engage with clinicians and informaticians at their institutions to ensure data quality and accessibility by forming multidisciplinary teams around specific research projects.

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The proliferation of electronic health records (EHRs) spurred on by federal government incentives over the past few decades has resulted in greater than an 80% adoption-rate at hospitals [ 1 ] and close to 90% in office-based practices [ 2 ] in the United States. A natural consequence of the availability of electronic health data is the conduct of research with these data, both observational and experimental [ 3 ], due to lower overhead costs and lower burden of study recruitment [ 4 ]. Indeed, a search on PubMed for publications indexed by the MeSH term “electronic health records” reveals an exponential growth in biomedical literature, especially over the last 10 years with an excess of 50,000 publications.

An emerging literature is beginning to recognize the many challenges that still lay ahead in using EHR data for epidemiological investigations. Researchers in Europe identified 13 potential sources of “bias” (bias was defined as a contamination of the data) in EHR-based data covering almost every aspect of care delivery, from selective entrance into the healthcare system, to variation in care and documentation practices, to identification and extraction of the right data for analysis [ 5 ]. Many of the identified contaminants are directly relevant to traditional epidemiological threats to validity [ 4 ]. Data quality has consistently been invoked as a central challenge in EHRs. From a qualitative perspective, healthcare workers have described challenges in the healthcare environment (e.g., heavy workload), imperfect clinical documentation practices, and concerns over data extraction and reporting tools, all of which would impact the quality of data in the EHR [ 6 ]. From a quantitative perspective, researchers have noted limited sensitivity of diagnostic codes in the EHR when relying on discrete codings, noting that upon a manual chart review free text fields often capture the missed information, motivating such techniques as natural language processing (NLP) [ 7 ]. A systematic review of EHR-based studies also identified data quality as an overarching barrier to the use of EHRs in managing the health of the community, i.e. “population health” [ 8 ]. Encouragingly this same review also identified more facilitators than barriers to the use of EHRs in public health, suggesting that opportunities outweigh the challenges. Shortreed et al. further explored these opportunities discussing how EHRs can enhance pragmatic trials, bring additional sophistication to observational studies, aid in predictive modeling, and be linked together to create more comprehensive views of patients’ health [ 9 ]. Yet, as Shortreed and others have noted, significant challenges still remain.

It is our intention with this narrative review to discuss some of these challenges in further detail. In particular, we focus on specific epidemiological threats to validity -- internal and external -- and how EHR-based epidemiological research in particular can exacerbate some of these threats. We note that while there is some overlap in the challenges we discuss with traditional paper-based medical record research that has occurred for decades, the scale and scope of an EHR-based study is often well beyond what was traditionally possible in the manual chart review era and our applied examples attempt to reflect this. We also describe existing and emerging approaches for remediating these potential biases as they arise. A summary of these challenges may be found in Table 1 . Our review is grounded in the healthcare system in the United States, although we expect many of the issues we describe to be applicable regardless of locale; where necessary, we have flagged our comments as specific to the U.S.

Challenge #1: Representativeness

The selection process for how patients are captured in the EHR is complex and a function of geographic, social, demographic, and economic determinants [ 10 ]. This can be termed the catchment of the EHR. For a patient record to appear in the EHR the patient must have been registered in the system, typically to capture their demographic and billing information, and upon a clinical visit, their health details. While this process is not new to clinical epidemiology, what tends to separate EHR-based records from traditional paper-based records is the scale and scope of the data. Patient data may be available for longer periods of time longitudinally, as well as have data corresponding to interactions with multiple, potentially disparate, healthcare systems [ 11 ]. Given the consolidation of healthcare [ 12 ] and aggregated views of multiple EHRs through health information networks or exchanges [ 11 ] the ability to have a complete view of the patients’ total health is increasing. Importantly, the epidemiologist must ascertain whether the population captured within the EHR or EHR-derived data is representative of the population targeted for inference. This is particularly true under the paradigm of population health and inferring the health status of a community from EHR-based records [ 13 ]. For example, a study of Clostridium difficile infection at an urban safety net hospital in Philadelphia, Pennsylvania demonstrated notable differences in risk factors in the hospital’s EHR compared to national surveillance data, suggesting how catchment can influence epidemiologic measures [ 14 ]. Even health-related data captured through health information exchanges may be incomplete [ 15 ].

Several hypothetical study settings can further help the epidemiologist appreciate the relationship between representativeness and validity in EHR research. In the first hypothetical, an EHR-based study is conducted from a single-location federally qualified health center, and in the second hypothetical, an EHR-based study is conducted from a large academic health system. Suppose both studies occur in the same geographic area. It is reasonable to believe the patient populations captured in both EHRs will be quite different and the catchment process could lead to divergent estimates of disease or risk factor prevalence. The large academic health system may be less likely to capture primary care visits, as specialty care may drive the preponderance of patient encounters. However, this is not a bias per se : if the target of inference from these two hypothetical EHR-based studies is the local community, then selection bias becomes a distinct possibility. The epidemiologist must also consider the potential for generalizability and transportability -- two facets of external validity that respectively relate to the extrapolation of study findings to the source population or a different population altogether -- if there are unmeasured effect modifiers, treatment interference, or compound treatments in the community targeted for inference [ 16 ].

There are several approaches for ascertaining representativeness of EHR-based data. Comparing the EHR-derived sample to Census estimates of demography is straightforward but has several important limitations. First, as previously described, the catchment process may be driven by discordant geographical areas, especially for specialty care settings. Second and third, the EHR may have limited or inaccurate information on socioeconomic status, race, and ethnicity that one may wish to compare [ 17 , 18 ], and conversely the Census has limited estimates of health, chiefly disability, fertility, and insurance and payments [ 19 ]. If selection bias is suspected as a result of missing visits in a longitudinal study [ 20 ] or the catchment process in a cross-sectional study [ 21 ], using inverse probability weighting may remediate its influence. Comparing the weighted estimates to the original, non-weighted estimates provides insight into differences in the study participants. In the population health paradigm whereby the EHR is used as a surveillance tool to identify community health disparities [ 13 ], one also needs to be concerned about representativeness. There are emerging approaches for producing such small area community estimates from large observational datasets [ 22 , 23 ]. Conceivably, these approaches may also be useful for identifying issues of representativeness, for example by comparing stratified estimates across sociodemographic or other factors that may relate to catchment. Approaches for issues concerning representativeness specifically as it applies to external validity may be found in these references [ 24 , 25 ].

Challenge #2: Data availability and interpretation

Sub-challenge #2.1: billing versus clinical versus epidemiological needs.

There is an inherent tension in the use of EHR-based data for research purposes: the EHR was never originally designed for research. In the U.S., the Health Information Technology for Economic and Clinical Health Act, which promoted EHRs as a platform for comparative effectiveness research, was an attempt to address this deficiency [ 26 ]. A brief history of the evolution of the modern EHR reveals a technology that was optimized for capturing health details relevant for billing, scheduling, and clinical record keeping [ 27 ]. As such, the availability of data for fundamental markers of upstream health that are important for identifying inequities, such as socioeconomic status, race, ethnicity, and other social determinants of health (SDOH), may be insufficiently captured in the EHR [ 17 , 18 ]. Similarly, behavioral risk factors, such as being a sexual minority person, have historically been insufficiently recorded as discrete variables. It is only recently that such data are beginning to be captured in the EHR [ 28 , 29 ], or techniques such as NLP have made it possible to extract these details when stored in free text notes (described further in “ Unstructured data: clinical notes and reports ” section).

As an example, assessing clinical morbidities in the EHR may be done on the basis of extracting appropriate International Classification of Diseases (ICD) codes, used for billing and reimbursement in the U.S. These codes are known to have low sensitivity despite high specificity for accurate diagnostic status [ 30 , 31 ]. Expressed as predictive values, which depend upon prevalence, presence of a diagnostic code is a likely indicator of a disease state, whereas absence of a diagnostic code is a less reliable indicator of the absence of that morbidity. There may further be variation by clinical domain in that ICD codes may exist but not be used in some specialties [ 32 ], variation by coding vocabulary such as the use of SNOMED for clinical documentation versus ICD for billing necessitating an ontology mapper [ 33 ], and variation by the use of “rule-out” diagnostic codes resulting in false-positive diagnoses [ 34 , 35 , 36 ]. Relatedly is the notion of upcoding, or the billing of tests, procedures, or diagnoses to receive inflated reimbursement, which, although posited to be problematic in EHRs [ 37 ] in at least one study, has not been shown to have occurred [ 38 ]. In the U.S., the billing and reimbursement model, such as fee-for-service versus managed care, may result in varying diagnostic code sensitivities and specificities, especially if upcoding is occurring [ 39 ]. In short, there is potential for misclassification of key health data in the EHR.

Misclassification can potentially be addressed through a validation study (resources permitting) or application of quantitative bias analysis, and there is a rich literature regarding the treatment of misclassified data in statistics and epidemiology. Readers are referred to these texts as a starting point [ 40 , 41 ]. Duda et al. and Shepherd et al. have described an innovative data audit approach applicable to secondary analysis of observational data, such as EHR-derived data, that incorporates the audit error rate directly in the regression analysis to reduce information bias [ 42 , 43 ]. Outside of methodological tricks in the face of imperfect data, researchers must proactively engage with clinical and informatics colleagues to ensure that the right data for the research interests are available and accessible.

Sub-challenge #2.2: Consistency in data and interpretation

For the epidemiologist, abstracting data from the EHR into a research-ready analytic dataset presents a host of complications surrounding data availability, consistency and interpretation. It is easy to conflate the total volume of data in the EHR with data that are usable for research, however expectations should be tempered. Weiskopf et al. have noted such challenges for the researcher: in their study, less than 50% of patient records had “complete” data for research purposes per their four definitions of completeness [ 44 ]. Decisions made about the treatment of incomplete data can induce selection bias or impact precision of estimates (see Challenges #1 , #3 , and #4 ). The COVID-19 pandemic has further demonstrated the challenge of obtaining research data from EHRs across multiple health systems [ 45 ]. On the other hand, EHRs have a key advantage of providing near real-time data as opposed to many epidemiological studies that have a specific endpoint or are retrospective in nature. Such real-time data availability was leveraged during COVID-19 to help healthcare systems manage their pandemic response [ 46 , 47 ]. Logistical and technical issues aside, healthcare and documentation practices are nuanced to their local environments. In fact, researchers have demonstrated how the same research question analyzed in distinct clinical databases can yield different results [ 48 ].

Once the data are obtained, choices regarding operationalization of variables have the potential to induce information bias. Several hypothetical examples can help demonstrate this point. As a first example, differences in laboratory reporting may result in measurement error or misclassification. While the order for a particular laboratory assay is likely consistent within the healthcare system, patients frequently have a choice where to have that order fulfilled. Given the breadth of assays and reporting differences that may differ lab to lab [ 49 ], it is possible that the researcher working with the raw data may not consider all possible permutations. In other words, there may be lack of consistency in the reporting of the assay results. As a second example, raw clinical data requires interpretation to become actionable. A researcher interested in capturing a patient’s Charlson comorbidity index, which is based on 16 potential diagnoses plus the patient’s age [ 50 ], may never find such a variable in the EHR. Rather, this would require operationalization based on the raw data, each of which may be misclassified. Use of such composite measures introduces the notion of “differential item functioning”, whereby a summary indicator of a complexly measured health phenomenon may differ from group to group [ 51 ]. In this case, as opposed to a measurement error bias, this is one of residual confounding in that a key (unmeasured) variable is driving the differences. Remediation of these threats to validity may involve validation studies to determine the accuracy of a particular classifier, sensitivity analysis employing alternative interpretations when the raw data are available, and omitting or imputing biased or latent variables [ 40 , 41 , 52 ]. Importantly, in all cases, the epidemiologists should work with the various health care providers and personnel who have measured and recorded the data present in the EHR, as they likely understand it best.

Furthermore and related to “Billing versus Clinical versus Epidemiological Needs” section, the healthcare system in the U.S. is fragmented with multiple payers, both public and private, potentially exacerbating the data quality issues we describe, especially when linking data across healthcare systems. Single payer systems have enabled large and near-complete population-based studies due to data availability and consistency [ 53 , 54 , 55 ]. Data may also be inconsistent for retrospective longitudinal studies spanning many years if there have been changes to coding standards or practices over time, for example due to the transition from ICD-9 to ICD-10 largely occurring in the mid 2010s or the adoption of the Patient Protection and Affordable Care Act in the U.S. in 2010 with its accompanying changes in billing. Exploratory data analysis may reveal unexpected differences in key variables, by place or time, and recoding, when possible, can enforce consistency.

Sub-challenge #2.3: Unstructured data: clinical notes and reports

There may also be scenarios where structured data fields, while available, are not traditionally or consistently used within a given medical center or by a given provider. For example, reporting of adverse events of medications, disease symptoms, and vaccinations or hospitalizations occurring at different facility/health networks may not always be entered by providers in structured EHR fields. Instead, these types of patient experiences may be more likely to be documented in an unstructured clinical note, report (e.g. pathology or radiology report), or scanned document. Therefore, reliance on structured data to identify and study such issues may result in underestimation and potentially biased results.

Advances in NLP currently allow for information to be extracted from unstructured clinical notes and text fields in a reliable and accurate manner using computational methods. NLP utilizes a range of different statistical, machine learning, and linguistic techniques, and when applied to EHR data, has the potential to facilitate more accurate detection of events not traditionally located or consistently used in structured fields. Various NLP methods can be implemented in medical text analysis, ranging from simplistic and fast term recognition systems to more advanced, commercial NLP systems [ 56 ]. Several studies have successfully utilized text mining to extract information on a variety of health-related issues within clinical notes, such as opioid use [ 57 ], adverse events [ 58 , 59 ], symptoms (e.g., shortness of breath, depression, pain) [ 60 ], and disease phenotype information documented in pathology or radiology reports, including cancer stage, histology, and tumor grade [ 61 ], and lupus nephritis [ 32 ]. It is worth noting that scanned documents involve an additional layer of computation, relying on techniques such as optical character recognition, before NLP can be applied.

Hybrid approaches that combine both narrative and structured data, such as ICD codes, to improve accuracy of detecting phenotypes have also demonstrated high performance. Banerji et al. found that using ICD-9 codes to identify allergic drug reactions in the EHR had a positive predictive value of 46%, while an NLP algorithm in conjunction with ICD-9 codes resulted in a positive predictive value of 86%; negative predictive value also increased in the combined algorithm (76%) compared to ICD-9 codes alone (39%) [ 62 ]. In another example, researchers found that the combination of unstructured clinical notes with structured data for prediction tasks involving in-hospital mortality and 30-day hospital readmission outperformed models using either clinical notes or structured data alone [ 63 ]. As we move forward in analyzing EHR data, it will be important to take advantage of the wealth of information buried in unstructured data to assist in phenotyping patient characteristics and outcomes, capture missing confounders used in multivariate analyses, and develop prediction models.

Challenge #3: Missing measurements

While clinical notes may be useful to recover incomplete information from structured data fields, it may be the case that certain variables are not collected within the EHR at all. As mentioned above, it is important to remember that EHRs were not developed as a research tool (see “ Billing versus clinical versus epidemiological needs ” section), and important variables often used in epidemiologic research may not be typically included in EHRs including socioeconomic status (education, income, occupation) and SDOH [ 17 , 18 ]. Depending upon the interest of the provider or clinical importance placed upon a given variable, this information may be included in clinical notes. While NLP could be used to capture these variables, because they may not be consistently captured, there may be bias in identifying those with a positive mention as a positive case and those with no mention as a negative case. For example, if a given provider inquires about homelessness of a patient based on knowledge of the patient’s situation or other external factors and documents this in the clinical note, we have greater assurance that this is a true positive case. However, lack of mention of homelessness in a clinical note should not be assumed as a true negative case for several reasons: not all providers may feel comfortable asking about and/or documenting homelessness, they may not deem this variable worth noting, or implicit bias among clinicians may affect what is captured. As a result, such cases (i.e. no mention of homelessness) may be incorrectly identified as “not homeless,” leading to selection bias should a researcher form a cohort exclusively of patients who are identified as homeless in the EHR.

Not adjusting for certain measurements missing from EHR data can also lead to biased results if the measurement is an important confounder. Consider the example of distinguishing between prevalent and incident cases of disease when examining associations between disease treatments and patient outcomes [ 64 ]. The first date of an ICD code entered for a given patient may not necessarily be the true date of diagnosis, but rather documentation of an existing diagnosis. This limits the ability to adjust for disease duration, which may be an important confounder in studies comparing various treatments with patient outcomes over time, and may also lead to reverse causality if disease sequalae are assumed to be risk factors.

Methods to supplement EHR data with external data have been used to capture missing information. These methods may include imputation if information (e.g. race, lab values) is collected on a subset of patients within the EHR. It is important to examine whether missingness occurs completely at random or at random (“ignorable”), or not at random (“non-ignorable”), using the data available to determine factors associated with missingness, which will also inform the best imputation strategy to pursue, if any [ 65 , 66 ]. As an example, suppose we are interested in ascertaining a patient's BMI from the EHR. If men were less likely to have BMI measured than women, the probability of missing data (BMI) depends on the observed data (gender) and may therefore be predictable and imputable. On the other hand, suppose underweight individuals were less likely to have BMI measured; the probability of missing data depends on its own value, and as such is non-predictable and may require a validation study to confirm. Alternatively to imputing missing data, surrogate measures may be used, such as inferring area-based SES indicators, including median household income, percent poverty, or area deprivation index, by zip code [ 67 , 68 ]. Lastly, validation studies utilizing external datasets may prove helpful, such as supplementing EHR data with claims data that may be available for a subset of patients (see Challenge #4 ).

As EHRs are increasingly being used for research, there are active pushes to include more structured data fields that are important to population health research, such as SDOH [ 69 ]. Inclusion of such factors are likely to result in improved patient care and outcomes, through increased precision in disease diagnosis, more effective shared decision making, identification of risk factors, and tailoring services to a given population’s needs [ 70 ]. In fact, a recent review found that when individual level SDOH were included in predictive modeling, they overwhelmingly improved performance in medication adherence, risk of hospitalization, 30-day rehospitalizations, suicide attempts, and other healthcare services [ 71 ]. Whether or not these fields will be utilized after their inclusion in the EHR may ultimately depend upon federal and state incentives, as well as support from local stakeholders, and this does not address historic, retrospective analyses of these data.

Challenge #4: Missing visits

Beyond missing variable data that may not be captured during a clinical encounter, either through structured data or clinical notes, there also may be missing information for a patient as a whole. This can occur in a variety of ways; for example, a patient may have one or two documented visits in the EHR and then is never seen again (i.e. right censoring due to lost to follow-up), or a patient is referred from elsewhere to seek specialty care, with no information captured regarding other external issues (i.e. left censoring). This may be especially common in circumstances where a given EHR is more likely to capture specialty clinics versus primary care (see Challenge #1 ). A third scenario may include patients who appear, then are not observed for a long period of time, and then reappear: this case is particularly problematic as it may appear the patient was never lost to follow up but simply had fewer visits. In any of these scenarios, a researcher will lack a holistic view of the patient’s experiences, diagnoses, results, and more. As discussed above, assuming absence of a diagnostic code as absence of disease may lead to information and/or selection bias. Further, it has been demonstrated that one key source of bias in EHRs is “informed presence” bias, where those with more medical encounters are more likely to be diagnosed with various conditions (similar to Berkson’s bias) [ 72 ].

Several solutions to these issues have been proposed. For example, it is common for EHR studies to condition on observation time (i.e. ≥n visits required to be eligible into cohort); however, this may exclude a substantial amount of patients with certain characteristics, incurring a selection bias or limiting the generalizability of study findings (see Challenge #1 ). Other strategies attempt to account for missing visit biases through longitudinal imputation approaches; for example, if a patient missed a visit, a disease activity score can be imputed for that point in time, given other data points [ 73 , 74 ]. Surrogate measures may also be used to infer patient outcomes, such as controlling for “informative” missingness as an indicator variable or using actual number of missed visits that were scheduled as a proxy for external circumstances influencing care [ 20 ]. To address “informed presence” bias described above, conditioning on the number of health-care encounters may be appropriate [ 72 ]. Understanding the reason for the missing visit may help identify the best course of action and before imputing, one should be able to identify the type of missingness, whether “informative” or not [ 65 , 66 ]. For example, if distance to a healthcare location is related to appointment attendance, being able to account for this in analysis would be important: researchers have shown how the catchment of a healthcare facility can induce selection bias [ 21 ]. Relatedly, as telehealth becomes more common fueled by the COVID-19 pandemic [ 75 , 76 ], virtual visits may generate missingness of data recorded in the presence of a provider (e.g., blood pressure if the patient does not have access to a sphygmomanometer; see Challenge #3 ), or necessitate a stratified analysis by visit type to assess for effect modification.

Another common approach is to supplement EHR information with external data sources, such as insurance claims data, when available. Unlike a given EHR, claims data are able to capture a patient’s interaction with the health care system across organizations, and additionally includes pharmacy data such as if a prescription was filled or refilled. Often researchers examine a subset of patients eligible for Medicaid/Medicare and compare what is documented in claims with information available in the EHR [ 77 ]. That is, are there additional medications, diagnoses, hospitalizations found in the claims dataset that were not present in the EHR. In a study by Franklin et al., researchers utilized a linked database of Medicare Advantage claims and comprehensive EHR data from a multi-specialty outpatient practice to determine which dataset would be more accurate in predicting medication adherence [ 77 ]. They found that both datasets were comparable in identifying those with poor adherence, though each dataset incorporated different variables.

While validation studies such as those using claims data allow researchers to gain an understanding as to how accurate and complete a given EHR is, this may only be limited to the specific subpopulation examined (i.e. those eligible for Medicaid, or those over 65 years for Medicare). One study examined congruence between EHR of a community health center and Medicaid claims with respect to diabetes [ 78 ]. They found that patients who were older, male, Spanish-speaking, above the federal poverty level, or who had discontinuous insurance were more likely to have services documented in the EHR as compared to Medicaid claims data. Therefore, while claims data may help supplement and validate information in the EHR, on their own they may underestimate care in certain populations.

Research utilizing EHR data has undoubtedly positively impacted the field of public health through its ability to provide large-scale, longitudinal data on a diverse set of patients, and will continue to do so in the future as more epidemiologists take advantage of this data source. EHR data’s ability to capture individuals that traditionally aren’t included in clinical trials, cohort studies, and even claims datasets allows researchers to measure longitudinal outcomes in patients and perhaps change the understanding of potential risk factors.

However, as outlined in this review, there are important caveats to EHR analysis that need to be taken into account; failure to do so may threaten study validity. The representativeness of EHR data depends on the catchment area of the center and corresponding target population. Tools are available to evaluate and remedy these issues, which are critical to study validity as well as extrapolation of study findings. Data availability and interpretation, missing measurements, and missing visits are also key challenges, as EHRs were not specifically developed for research purposes, despite their common use for such. Taking advantage of all available EHR data, whether it be structured or unstructured fields through NLP, will be important in understanding the patient experience and identifying key phenotypes. Beyond methods to address these concerns, it will remain crucial for epidemiologists and data analysts to engage with clinicians and informaticians at their institutions to ensure data quality and accessibility by forming multidisciplinary teams around specific research projects. Lastly, integration across multiple EHRs, or datasets that encompass multi-institutional EHR records, add an additional layer of data quality and validity issues, with the potential to exacerbate the above-stated challenges found within a single EHR. At minimum, such studies should account for correlated errors [ 79 , 80 ], and investigate whether modularization, or submechanisms that determine whether data are observed or missing in each EHR, exist [ 65 ].

The identified challenges may also apply to secondary analysis of other large healthcare databases, such as claims data, although it is important not to conflate the two types of data. EHR data are driven by clinical care and claims data are driven by the reimbursement process where there is a financial incentive to capture diagnoses, procedures, and medications [ 48 ]. The source of data likely influences the availability, accuracy, and completeness of data. The fundamental representation of data may also differ as a record in a claims database corresponds to a “claim” as opposed to an “encounter” in the EHR. As such, the representativeness of the database populations, the sensitivity and specificity of variables, as well as the mechanisms of missingness in claims data may differ from EHR data. One study that evaluated pediatric quality care measures, such as BMI, noted inferior sensitivity based on claims data alone [ 81 ]. Linking claims data to EHR data has been proposed to enhance study validity, but many of the caveats raised in herein still apply [ 82 ].

Although we focused on epidemiological challenges related to study validity, there are other important considerations for researchers working with EHR data. Privacy and security of data as well as institutional review board (IRB) or ethics board oversight of EHR-based studies should not be taken for granted. For researchers in the U.S., Goldstein and Sarwate described Health Insurance Portability and Accountability Act (HIPAA)-compliant approaches to ensure the privacy and security of EHR data used in epidemiological research, and presented emerging approaches to analyses that separate the data from analysis [ 83 ]. The IRB oversees the data collection process for EHR-based research and through the HIPAA Privacy Rule these data typically do not require informed consent provided they are retrospective and reside at the EHR’s institution [ 84 ]. Such research will also likely receive an exempt IRB review provided subjects are non-identifiable.

Conclusions

As EHRs are increasingly being used for research, epidemiologists can take advantage of the many tools and methods that already exist and apply them to the key challenges described above. By being aware of the limitations that the data present and proactively addressing them, EHR studies will be more robust, informative, and important to the understanding of health and disease in the population.

Availability of data and materials

All data and materials used in this review are described herein.

Abbreviations

Body Mass Index

Electronic Health Record

International Classification of Diseases

Institutional review board/ethics board

Health Insurance Portability and Accountability Act

Natural Language Processing

Social Determinants of Health

Socioeconomic Status

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Acknowledgements

The authors thank Dr. Annemarie Hirsch, Department of Population Health Sciences, Geisinger, for assistance in conceptualizing an earlier version of this work.

Research reported in this publication was supported in part by the National Institute of Arthritis and Musculoskeletal and Skin Diseases of the National Institutes of Health under Award Number K01AR075085 (to MAG) and the National Institute Of Allergy And Infectious Diseases of the National Institutes of Health under Award Number K01AI143356 (to NDG). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Gianfrancesco, M.A., Goldstein, N.D. A narrative review on the validity of electronic health record-based research in epidemiology. BMC Med Res Methodol 21 , 234 (2021). https://doi.org/10.1186/s12874-021-01416-5

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Barriers and facilitators to the use of e-health by older adults: a scoping review

• Jessica Wilson 1 ,
• Milena Heinsch 1 ,
• David Betts 2 ,
• Debbie Booth 3 &
• Frances Kay-Lambkin 1  

BMC Public Health volume  21 , Article number:  1556 ( 2021 ) Cite this article

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Limited attention has been paid to how and why older adults choose to engage with technology-facilitated health care (e-health), and the factors that impact on this. This scoping review sought to address this gap.

Databases were searched for papers reporting on the use of e-health services by older adults, defined as being aged 60 years or older, with specific reference to barriers and facilitators to e-health use.

14 papers were included and synthesised into five thematic categories and related subthemes. Results are discussed with reference to the Unified Theory of Acceptance and Use of Technology2. The most prevalent barriers to e-health engagement were a lack of self-efficacy, knowledge, support, functionality, and information provision about the benefits of e-health for older adults. Key facilitators were active engagement of the target end users in the design and delivery of e-health programs, support for overcoming concerns privacy and enhancing self-efficacy in the use of technology, and integration of e-health programs across health services to accommodate the multi-morbidity with which older adults typically present.

E-health offers a potential solution to overcome the barriers faced by older adults to access timely, effective, and acceptable health care for physical and mental health. However, unless the barriers and facilitators identified in this review are addressed, this potential will not be realised.

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Introduction

In recent years, rapid population ageing has become a worldwide phenomenon. In 2018, older people outnumbered children for the first time in history. By 2050, they are expected to make up 22% of the global population [ 1 ]. Commensurate with this growth is the need to ensure proper planning and delivery of health services and supports to facilitate full and happy lives across the age spectrum.

The wellbeing of older adults is diverse. While some lead physically active lives free of major health concerns, population ageing has also coincided with a sharp increase in non-communicable diseases (e.g., diabetes, cancer, and heart disease) [ 2 ], and in some older populations, the co-occurrence of multiple chronic conditions is as high as 77% [ 3 ]. Age-related factors, such as changes in social roles and familial relationships, retirement, and deteriorating physical health are also associated with increased mental health challenges [ 4 , 5 ]. Crucially, 15% of older adults experience a mental health disorder [ 6 ], and a further 15% experience clinically significant depressive symptoms [ 7 ]. This makes the promotion and maintenance of mental health an equally important consideration alongside physical health for older adults. Facilitating access to health and mental health services and supports for older people is, thus, a global imperative.

Currently, health systems are not well aligned with the complex needs of older adults [ 8 ]; there is a tendency to focus on individual diagnoses rather than on treatment of the whole person [ 9 , 10 , 11 ]. Widespread endorsement of this ‘single disease framework’ by current health systems has arguably hindered the provision of integrated, ‘patient-centred care’ for older adults [ 11 ]. Consequently, and despite growing health and medical advances, the rate of mild-to-moderate disability of older adults has remained stable over the past three decades [ 6 , 12 ], resulting in increased health service utilization [ 3 , 11 , 13 , 14 , 15 ]. At the same time, older adults often face unique challenges to accessing health services, including limited income or insurance, reduced mobility or disability, rural or remote location, and negative self-perceptions of ageing (associated with lower health-related quality of life) [ 16 , 17 ].

e-Health (defined as any health service, platform, tool, or intervention delivered electronically) [ 18 ] has substantial potential to improve access to, as well as support the provision of efficient and effective care for older adults [ 19 , 20 ]. Research shows that adoption of information and communication technology by older adults is increasing [ 21 ], and is perceived to be positive and essential to their everyday lives [ 22 ]. This creates significant potential to better support the health care needs of older aged adults within the current limitations of our health service systems. To date, two systematic reviews have explored the benefits of e-health for older adults, finding clinically significant improvements in health behaviors (increased physical activity and healthy eating) as well as psychological and health outcomes (memory and blood pressure) [ 23 , 24 ] associated with the use of these technologies.

Despite the availability and potential benefits of e-health for older adults [ 25 ] barriers to uptake and use remain [ 23 , 26 ]. Limited attention has been paid to how and why older adults choose to engage with e-health services, and the factors that impact on this. We sought to address this gap by reviewing the existing literature on barriers and facilitators to the use of e-health by older adults, with a view to informing the development and implementation of a targeted e-health intervention for older adults. The results of this review are discussed with reference to the key constructs of the Unified Theory of Acceptance and Use of Technology2 [ 27 ].

This review follows the Preferred Reporting Items for Systematic Review and Meta-Analyses, Scoping Review extension (PRISMA-ScR) guidelines [ 28 ], and uses a scoping review methodology outlined by Arksey and O’Malley [ 29 ], and Levac et al. [ 30 ]. The choice to conduct a scoping review rather than a systematic review was informed by Munn et al. [ 31 ], who explains that systematic reviews focus on the synthesis of quantitative outcomes assessing the effectiveness of treatments and practice. In contrast, a scoping review is an appropriate method to a) identify the scope of available literature on a given topic; b) provide an overview of concepts relating to the topic; and c) identify gaps in the literature. Given the limited literature exploring barriers and facilitators to e-health use by older adults, a scoping review of the available evidence, and evidence gaps, was considered most appropriate.

Eligibility

Individual studies were included in the review if they: (i) were published in the English language; (ii) constituted outputs of empirical research (either quantitative, qualitative or mixed methods); (iii) were published in a peer-reviewed journal; and (iv) reported on participants aged 60 years and over. Studies were excluded if they: (i) were not written in the English language; (ii) constituted grey literature; (iii) were not published in a peer-reviewed journal; and (iv) reported on populations aged under 60 years. Sixty years was selected as the key age criterion, based on the United Nations definition of an “older” person, regardless of that person’s individual history or where in the world they live [ 32 ]. Articles which met the eligibility criteria were included regardless of journal rank and impact factor, to ensure identification of a wide range of methodologies; particularly qualitative methodologies, which remain underrepresented in high impact biomedical journals [ 33 ]. Studies were included if they made any form of reference to uptake, acceptance, attitudes, benefits, influences, perceptions, usefulness, determinants of use, experiences, expectations, and beliefs in relation to e-health use by older people. e-Health was defined as any electronic, mobile, online-delivered health or mental health service, including passive (e.g., health information webpage or patient portal) and active (e.g., clinician-moderated) therapy [ 18 ].

Search strategy

A search of databases: CINAHL, Embase, Medline, Psychology and Behavioral Sciences, PsycINFO, and Scopus, was conducted by DBooth on 4th August 2020. No limit was placed on the date of databases searched. A combination of subject headings and keywords specific to each database was used in Medline, PsycINFO, Embase, and CINAHL. Keyword searches were used in Psychology and Behavior Science Collection and Scopus databases. See supplementary file for search strategy.

Figure  1 shows the PRISMA flow chart. A total of 3536 papers were identified and were uploaded to Covidence ( https://www.covidence.org/ ), where all screening and data management was completed against the inclusion and exclusion criteria. After screening titles by the predefined eligibility criteria, 3012 were excluded, resulting in 542 papers. Following this, a further 457 papers were excluded based on abstract screening, leaving 85 papers for full text review, resulting in a total of 14 papers for extraction. It should be noted that the preliminary search for appropriate papers identified two studies with participants aged 50 years and older, which provided valuable information relating directly to the research question [ 34 , 35 ]. A decision was made to include these studies, as the mean group age was greater than 60 years. JW performed the initial title and abstract screening phases of the review. Both JW and DBetts reviewed the full text publications for inclusions, with MH resolving any conflicts.

PRISMA flow chart of study selection

Data was extracted from the 14 included studies according to the following fields: author and year, field (e.g., chronic disease or mental health), study design, study focus e.g., (prevention of diabetes or depression intervention), description of population including important demographics such as rural location or physical disability, age range and mean, recruitment country, technology type (e.g., tablet or PC), service or intervention (e.g., pain management application), barriers to access, and facilitators to access. Tables  1 and 2 displays these data. After familiarisation with each of the papers, preliminary coding of three papers was completed by JW and DBetts, and a codebook was created to guide the analysis of the remaining 12 papers by JW. Following this, codes were cross-referenced and synthesised into five thematic categories by JW and DBetts, with consultation from MH to resolve discrepancies. Key themes were discussed with reference to the Unified Theory of Acceptance and Technology Use 2, briefly outlined below.

Theoretical framework

The Unified Theory of Acceptance and Technology Use (UTAUT) is one of the most comprehensive and widely used technology acceptance models [ 47 ]. UTAUT proposes that behavioural intention to use technology is affected by an individual’s effort expectancy (degree to which the technology is perceived to be easy to use), performance expectancy (degree to which the technology is perceived to be useful), social influence (degree to which using the technology is supported by an individual’s social network), facilitating conditions (the degree to which an individual believes to have the resources to use the technology) [ 48 ]. UTAUT2 adds three additional constructs to the original UTAUT—hedonic motivation (degree to which the technology is perceived to be enjoyable), price value (degree to which the technology is perceived to be affordable and cost-effective) and habit (the degree to which technology use is influenced by the passage of time) [ 49 ]. UTAUT and UTAUT2 are most commonly applied using quantitative approaches. However, in this review UTAUT2 was applied as an analytical framework to facilitate deeper insights into the key findings from this review and identify areas for further research.

Of the 14 papers identified, 12 reported on barriers, and 13 reported on facilitators of e-health use in older adults. The characteristics of these papers are summarized in Table  1 .

The barriers and facilitators to older adults accessing e-health were each mapped into five thematic categories (1) individual , including intrinsic and extrinsic; (2) technological , including functionality, content, and availability; (3) relational , including technological support and social support; (4) environmental , including location; and (5) organizational , including privacy, trust, and the sharing of data (see Table  2 ).

Individual ( n  = 14)

Intrinsic barriers (including physical, sensory, intellectual ability, and motivation) were discussed by nine of the included studies. Physical ageing was the most prevalent barrier to accessing e-health, with hearing and sight limitations being the most common [ 34 , 36 , 37 , 38 ]. Concerns about memory were also reported [ 38 ], particularly with remembering passwords, and the acquisition of new information [ 39 ]. Additionally, the reduction of fine motor control (i.e., trembling hands) made it difficult to interact with devices, particularly those with small screens [ 34 , 37 ]. Perceived self-efficacy regarding the use of technology was discussed as a barrier by four of the included studies. Discussion about perceived efficacy focused on: i) the difficulties of using technology [ 38 ] and e-health [ 40 ]; ii) concerns about the use of digital mental health technologies [ 35 ]; and iii) feelings of incompetence [ 41 ]. Other intrinsic barriers included a lack of interest in learning, and a fear or dislike of technology [ 37 , 42 ].

Intrinsic facilitators were discussed by seven studies. Of these, five highlighted a willingness and desire to learn [ 34 , 36 , 37 , 38 , 41 ], finding that participants who articulated an innate sense of curiosity and interest in technology were more willing to use e-health, and more likely to engage and explore various e-health platforms. Other facilitators were a motivation and desire to make a lifestyle change [ 19 , 43 ] and a desire to contribute to scientific progress by trialling e-health programs in the context of research [ 19 , 41 , 43 ].

Extrinsic barriers (external factors outside the individual) were discussed by nine studies. These included inexperience with e-health [ 35 , 37 ] or with computers/technology in general [ 36 , 38 , 41 ], and an overall lack of awareness of e-health opportunities [ 34 , 35 ]. Some studies reported that participants had previous negative experiences [ 40 ] or unmet expectations [ 37 ] in relation to e-health services; a preference for traditional health care services [ 34 , 36 , 37 , 39 ]; or a genuine fear that, if unused, traditional health services may cease to exist [ 37 ]. Stigma around e-health services in some studies extended to a disbelief in the reported advantages of technology [ 37 ], lack of confidence in the use of technology as a health service [ 42 ], and a belief that telephones (smart phones) are for telephone communication only and not for health services [ 39 ]. Other studies reported that the perceived lack of routine and structure (external accountability) provided by e-health services [ 44 , 45 ] created a barrier to incorporate e-health into daily routines [ 44 ], and a perception that learning to engage with e-health involves more effort than reward [ 35 , 38 ]. Cultural barriers, including second language difficulties and the cultural value of technologies detracting from time with family were also noted [ 38 ].

Extrinsic facilitators were identified by eleven studies. These included a perception that e-health services are of benefit [ 19 , 34 , 37 , 40 ] and have the potential to support health care management [ 37 ], independent living [ 40 ], and self-managed care [ 39 , 43 , 46 ]. One study identified the convenience afforded by e-health programs, allowing participants to progress their care at their own pace and accommodating issues such as reduced mobility [ 45 ]. Three studies found that the ability to incorporate e-health into participant routines facilitated their use of these services [ 40 , 44 , 46 ].

Six studies focused on participants’ previous experiences of, and skills relating to, e-health programs [ 19 , 35 , 36 , 40 ], finding that prior exposure to, or experience of, e-health [ 35 ] and previous positive experiences with technology more generally [ 37 ], facilitated the use of e-health in the future. A related finding was that for some participants the opportunity to learn new information acted as a facilitator for engaging with e-health [ 43 ].

Technological ( n  = 11)

Six studies discussed functional barriers related to the design of e-health programs and their interface with older end users. Problematic features included small screen and text [ 44 ]; small icons and lack of colour contrast between text and background [ 36 ]; and complex functionality that assumes the user has experience with the technology [ 42 ]. Poorly functioning platforms, including problems with logging in and navigation [ 41 ], and faulty IT systems that did not function as intended [ 35 , 37 ] were also barriers to use.

Functionality (ease of use) was identified as a facilitator to e-health use in four studies [ 35 , 36 , 40 , 41 ]. For example, de Veer et al. highlighted the importance of platforms that are ‘pleasant’ to interact with, and Cajita et al. identified useful features for older adults, such as a large visual display and audio feedback for users [ 36 , 40 ].

Five studies discussed barriers relating to content, such as built-in reminder systems to reinforce e-health use. Lack of alerts or reminders was a barrier reported by van Middelaar et al. [ 41 ]. On the other hand, participants trialling a medication adherence application reported ‘alert fatigue’, from too many reminders [ 46 ]. Participants in this study also reported condescending communication (praise for taking medication), impersonal messages, and an inability to respond to messages (facilitates memory) as barriers to continued use [ 46 ]. Regarding service content, the large amount of information offered across e-health services was perceived as overwhelming and difficult to understand [ 35 ], particularly when the information included complex medical terminology [ 38 ]. Additionally, having too much content on one page was a barrier to use [ 44 ].

Five studies discussed the content of e-health services as facilitators, highlighting the need for specifically curated, personalized content, that aligns closely with user needs [ 37 , 44 , 45 , 46 ]. Additionally, three studies found that e-health use was facilitated by reminders and alerts about content [ 41 , 44 , 46 ], and the use of images to facilitate memory and attention in relation to medication [ 46 ].

Availability

Barriers relating to e-health availability were discussed by three studies. These included a lack of access to the required electronic equipment (i.e., smart phone, tablet, or computer) [ 38 ] and the cost to purchase and upgrade this equipment, as well as the cost of an internet/mobile data or wi-fi service [ 34 ]. In particular, cost was a barrier for older adults who were on a limited or fixed income such as a pension [ 36 ]. Participants in Cajita et al. stated that the cost of the required equipment outweighed the perceived benefit of engaging with e-health [ 36 ]. In contrast, one study found that free, or low-cost, electronic equipment such as a computer or smart phone facilitated the use of e-health by older adults [ 36 ].

Relational ( n  = 7)

Technological support.

Three studies found that a lack of technological support (e.g., training, troubleshooting, and guidance) provided alongside e-health programs was a barrier to uptake. For example, participants in two studies stated that they would have felt more encouraged to use e-health if they were given adequate training and support in using the technology [ 36 , 38 ]. Participants in another study were discouraged from using an online counseling platform because there was no support to troubleshoot issues [ 41 ]. Reliance on family for support and guidance, and a lack of patience and understanding from family members while participants were learning to use the mobile technology, was also highlighted as a barrier [ 38 ].

Seven studies identified technological support as a facilitator to e-health use by older adults. Five studies found that uptake was facilitated by training and support in relation to the technical aspects of a program [ 36 , 37 , 38 , 39 , 41 ]. Findings highlighted the need for a dedicated coach to provide training, and continued feedback, to support participant engagement and progress through the e-health program [ 41 ]. Additionally, Bhattarai et al. found that peer-to-peer based platforms allowed participants to share knowledge and experience, thereby facilitating e-health engagement [ 44 ]; while Mishuris et al. found that family and carer support could facilitate e-health use [ 34 ].

Social support

Lack of social interaction was discussed in three studies as a barrier to e-health use. Not seeing a person face-to-face, whether it be a doctor or peers in a group setting, was a key deterrent to e-health uptake [ 37 , 45 ]. For participants using a mobile-based mental health intervention, the lack of interpersonal communication was perceived to detract from the therapeutic process, with communication via technology considered an ‘inauthentic’ experience for this age group [ 35 ].

One study found that inclusive, community-based approaches to designing and implementing e-health supported uptake by participants, such as peer-led health information sessions, and receiving information from the community was particularly important for diverse ethnocultural groups [ 38 ].

Environmental ( n  = 1)

Unreliable or unavailable internet services in rural and remote locations, were discussed as barriers in one study [ 45 ]. On the other hand, one study focusing on older adults in rural and remote communities [ 45 ] addressed environmental factors relating to location, finding that e-health reduced the need to travel long-distances to health care appointments.

Organisational ( n  = 10)

Concerns about privacy and security were raised by participants in three studies [ 35 , 42 , 46 ]. In one study, 28% of respondents surveyed viewed privacy as a barrier to using e-health [ 42 ]. Additionally, participants using a mental health intervention expressed concerns about who was accessing their health information, and how information was being shared with practitioners [ 35 ]. No studies identified specific facilitators relating to privacy.

Mistrust of e-health was reported across four studies, with a lack of trust in the accuracy of the information contained in e-health being the greatest concern [ 37 , 38 , 42 ]. Other issues of trust related to participants’ uncertainty about who they were communicating with, particularly about mental health issues [ 35 ]; and appropriate management of emergency situations [ 37 ]. Additionally, Chinese and Punjabi immigrants in Zibrik et al.’s study expressed a distrust in e-health due to a perceived association with Western medicine’s prioritization of medication over natural therapies [ 38 ].

Five studies discussed trust, with two identifying that e-health services recommended by a physician were more likely to be used by older adults [ 36 , 43 ]. In one study, this recommendation took the form of a letter inviting patients to participate in an e-health program from their trusted practitioner [ 43 ]. Further, participants were more likely to trust e-health services that were designed by experts in the field [ 45 ], provided access to specialists [ 34 ], and provided a clear purpose and transparent credentials [ 35 ].

Data sharing

One paper identified a lack of information communication between health platforms and professionals as a barrier, with participants expressing a desire for e-health services to be streamlined, and information to be shared [ 37 ]. Supporting this finding, [ 37 ] three studies in which e-health platforms had the capability to share data with health services found that this facilitated the use of e-health [ 42 ].

This scoping review sought to explore barriers and facilitators to the use of e-health by older adults, with the aim of informing future development and uptake of digital health and mental health interventions for this age group. The Unified Theory of Acceptance and Use of Technology2 (UTAUT2) was used as an analytical framework to further examine the findings and identify opportunities for future research.

Analysis of the five thematic categories resulted in three broad implications for the development of future e-health services for older adults. These relate to the 1) design of the e-health service; 2) training and education provided to increase e-health literacy; and 3) perceived authenticity of the service. Contextual implications are discussed as a sub-theme.

Design of the e-health service

Consideration of the specific needs of older adults in the design of digital health services was one of the most significant factors impacting uptake and ongoing use of e-health services in this review. Consistent barriers related to the functionality of e-health platforms and problems with the user interface, such as small screens, text, and images. These barriers reflect a lack of consideration of physical difficulties associated with ageing, such as poor eyesight, hearing, and memory, which can hinder older people’s engagement. Findings also showed that older people can become overwhelmed by new information and alerts, and by challenges associated with altering or customising the user interface to their individual needs, creating barriers to uptake. Conversely, when the design of e-health services addresses the needs of older adults, engagement increases. Specifically, e-health services that were accessible, pleasant to use, had larger screens, such as a tablet or desktop/laptop, larger font size, audio features, notifications, and diverse, curated content showed greater uptake. Based on these findings, the following features should be considered in the design of e-health services: i) offering services that are accessible across multiple technologies including tablets and computers; ii) features such was audio feedback, large text size, and a notification system that allows users to set how and when they are notified, enabling engagement with platforms in a manner that best suits the individual; and iii) including wide and diverse information that can be curated for the user based on their circumstances, reducing the need for navigation through content that may be irrelevant and overwhelming, while still offering a platform that addresses multiple health needs without requiring users to engage with different platforms, services, or professionals.

Findings from this review suggest that both useability and usefulness are important factors to consider when designing future e-health services. These factors align with the constructs of individual effort expectancy and performance expectancy in the UTAUT2 framework. In fact, one study included in this review applied the UTAUT, finding that effort expectancy and performance expectancy were both highly related to older people’s intention to use e-health [ 40 ]. It should be noted that findings from other studies differ, suggesting that for older people, effort expectancy is more important than performance expectancy in predicting the uptake of digital technologies [ 50 ], however, this study did not specifically focus on the use of e-health services.

Useability and usefulness have been recognized as important components of successful e-health uptake in the wider literature [ 51 ], with De Rouck et al. [ 52 ] noting that a thorough understanding of the factors that impact on the useability and usefulness of e-health services for specific end users would support technological design and effectiveness. Since older adults are not a homogenous group [ 2 ], their physical needs and ability to engage with digital platforms can vary. Consideration of age-related factors and allowing older adults to customize platform interfaces would provide them with more options to engage. To address these issues, findings from this review suggest that future e-health developers should not only consider the design elements described earlier in this discussion but should actively incorporate the feedback of older adults in their design, engagement, and delivery strategies. This process of consultation can be achieved using focus groups, individual interviews or surveys, and pilot studies – all of which can occur both pre- and post-development of e-health platforms.

Training and education to increase e-health literacy

Alongside design, a significant factor influencing the successful uptake of e-health by older adults was training and education in how best to use the technology to their advantage. The ability to use and benefit from e-health, known as e-health literacy, is an important part of ensuring the effectiveness of e-health program engagement and outcomes across the lifespan [ 53 , 54 ]. In this review, effective training, and education to develop e-health literacy took two distinct forms—providing practical skills to support older adults’ use of e-health programs and addressing misconceptions or previous negative experiences with e-health programs.

In relation to practical skills, common barriers were a lack of i) previous experience, ii) training on how to use the technological features of the program, and iii) access to formal or informal supports to troubleshoot problems. Yet, older adults who were provided with support, guidance, and training were more likely to express positive associations with e-health. Specific examples of successful training and support included; addressing issues of discouragement and inexperience by providing a dedicated coach for initial and ongoing guidance; helping to build trust, encouragement, and motivation [ 41 ]; addressing a lack of basic computer skills by facilitating and offering low-cost group computer classes [ 38 ]; and including family and carers in initial training sessions so they could provide informal ongoing support [ 34 ]. Additionally, where support and training focused on the potential benefits of e-health, older adults were less likely to perceive it as difficult, incompatible with their current health and lifestyle needs, or ineffective as a treatment platform.

Application of the UTAUT2 suggests that providing older people with training and education in the use of e-health technologies may facilitate effort expectancy, performance expectancy and facilitating conditions. They also suggest that social influence may play a role in supporting ongoing engagement with e-health, supporting findings from a previous systematic review [ 55 ]. In contrast, de Veer et al. [ 40 ] found that social influence had no impact on e-health uptake, after beliefs about performance expectancy and effort expectancy had been taken into account. According to Venkatesh et al. [ 48 ] social influence only plays a role in a mandatory context. However, findings from this review suggest that receiving information and support from community members was important for older people, particularly those from diverse ethnocultural groups [ 38 ]. Future research should therefore explore the impact of social influence on e-health uptake by older people from specific cultural groups.

Authenticity

Findings from this review suggested that e-health uptake is enhanced when e-health services and service providers are perceived to be authentic (trustworthy and credible). Older adults were less likely to engage with e-health services when they were concerned about how their privacy would be protected. Additionally, some older adults expressed uncertainty about the appropriate sharing of their personal information with other health services, with one study reporting that participants favoured e-health programs that were streamlined across traditional service settings and shared pertinent information with appropriate health professionals across these settings [ 37 ]. The importance of establishing trust in e-health has been increasingly recognised as a key challenge for the field, with previous research suggesting that consumer confidence in information security and privacy is likely to influence how they choose to engage [ 56 ]. These concerns could be addressed by employing strategies to strengthen the authenticity of the e-health program. Strategies could include referrals to e-health services from a trusted source such as a general practitioner or mental health service provider [ 36 , 43 ], providing access to health and mental health specialists [ 34 ], and ensuring that e-health services practice effective collaboration in the management and sharing of relevant health information [ 39 , 44 , 46 ].

Findings from this review suggest that the impact of variables such as perceived credibility and trustworthiness on e-health uptake by older adults may warrant further exploration. While the UTAUT2 does not include a specific construct relating to trust, a recent study by [ 27 ] extended the UTAUT2 by adding two important factors, mass media (channels of communication—whether written, broadcast, or spoken—that reach a large audience) and trust (the subjective expectation with which consumers believe that a specific transaction occurs in a way consistent with their expectations). Application of these constructs in a small sample of Jordanian community members ( n  = 7) found that the adoption of a mobile banking technology was positively and significantly influenced by the mass media (television, radio and internet promotion) and trust (security and privacy of the mobile banking service) [ 27 ]. These additional constructs shed new light on the findings of this review. Viewed in combination, is possible to infer that targeted public health media campaigns to raise the profile, relevance, and credibility of e-health services, and articulate how to evaluate the credibility and utility of these services, may be effective in addressing some of the barriers to e-health uptake by older adults.

Contextual considerations

So far, this discussion has focused on three broad implications for the development of e-health services for older adults. While not as prominent in the literature, the sub-theme of contextual considerations nonetheless offered important insights for future development of e-health programs for older adults.

In some studies, financial factors were highlighted as a barrier to accessing e-health programs. Older adults who were retired, on a fixed income, or who lived in a remote location were less likely to engage with e-health programs. The ability to use technology was also restricted by the type of access to the internet, the cost of owning or upgrading a computer, or a perception that the cost of accessing e-health programs outweighed the benefits. Analysis of these findings using the UTAUT2 suggest that price value may be an important facilitating condition that plays a role in the uptake of e-health technologies by older people. Further research applying the UTAUT2 with this population is needed to determine the predictive power of this construct.

Findings from this review highlight the important issue of equity in accessing e-health, where a possible digital divide exists beyond age or generational issues [ 18 ]. The notion of a digital divide broadly refers to the separation that can exist between those who have access to, and the ability to understand diverse technological resources, and those who do not [ 57 ]. Research in this area has found that structural inequalities such as low socioeconomic status, ethnicity, and education levels, often contribute to such disparities in the use of e-health programs [ 58 , 59 ]. Further, Beard [ 12 ] suggested that challenges of appropriate resourcing and access to technology are likely to be more significant for older adults than for other groups; an observation supported by this review. Conversely, e-health also holds great potential for enhancing access to health and mental health programs for older adults, particularly those with disabilities or those who live in remote locations, with limited transport options.

Gaps in the literature and opportunities for future research

While literature on the impacts and efficacy of e-health for older adults is growing [ 19 , 20 ], to date, few studies have focused on understanding the practical and conceptual barriers and facilitators for older adults in accessing e-health services. Given the rapid increase in population ageing, and the complex health and mental health challenges older people can experience, future research exploring the potential for e-health to respond to these challenges is essential. Research with a focus on digital mental health interventions for older people is needed, as this review identified only one study that focused on the use of e-mental health by older people. This finding is concerning given the prevalence of mental health concerns in older populations [ 4 , 5 ], the increased risk of physical health problems in older adults with mental health problems [ 60 ], and Australian data indicating that older adults are the least likely of all age groups to access mental health services [ 61 ]. Future research is also needed to explore broader environmental and contextual factors impacting on e-health use by older people, as the existing literature tended to focus on individual, relational and design-related factors. Findings from one study suggested that including older people in the process of designing and developing e-health services may enhance their relevance for, and use by, this population. More research is needed to explore how older adults can best be included in the e-health design process.

Additional gaps in the literature were highlighted when applying the UTAUT2. Notably, findings from this review did not find evidence that specifically supported the constructs of habit and hedonic motivation. While three studies did find that e-health uptake was enhanced when participants were able to integrate the e-health service into their pre-existing routines, this finding does not directly address the construct of habit (the length of time from initially adopting and using e-health). Further research could address this gap by exploring whether the passage of time has an impact on e-health engagement by older people. While e-health services are not conventionally designed to be enjoyable, future research could also investigate what aspects of hedonic motivation might support engagement with these services. Finally, findings from this review suggested that the constructs of price value and social influence may facilitate the uptake of e-health services by older people. Of particular importance was the finding that these constructs may impact on specific groups of older people who are already experiencing higher levels of disadvantage, such as older people on low or fixed incomes, or older people from cultural or ethnic minority groups. This highlights an urgent need for future research examining factors that facilitate or hinder the use of e-health services by specific groups of older people, who may be particularly vulnerable or marginalised. Combining UTAUT2 with normative theories of social justice and equity may facilitate such efforts [ 47 ].

Limitations

This review has several limitations. Firstly, as non-English publications were excluded, any pertinent non-English language publications are likely to have been missed, possibly resulting in a culturally biased review. Secondly, while the inclusion criteria for this review enabled identification of a wide range of literature the use of broader search terms means that studies focused on more specific, narrow subject areas may have been missed. Finally, while PRISMA-ScR guidelines were adhered to at every stage of this review, the protocol was not registered.

Consideration of the specific barriers and facilitators that influence the use of e-health by older adults is critical to improve their use of e-health programs, and to realise the potential of technology to ameliorate the challenges associated with traditional healthcare for this group. Findings from this review suggested that older adults are more likely to use e-health services that are cognizant of their physical and functional needs, provide appropriate education and training to engage with e-health, address previous negative experiences of, and misconceptions about, digital health technologies; and employ strategies to enhance the perceived trustworthiness and credibility of e-health. Further research is needed to explore the practical and conceptual barriers and facilitators for older adults in accessing e-health.

Availability of data and materials

All data generated or analysed during this study are included in this published article [and its supplementary information files], including PRISMA checklist, and raw extraction file.

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This work was supported by Suicide Prevention Australia [grant number G1801238].

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J.W and D. Betts completed the literature search for included studies. J.W wrote the introduction, methods, and results (barriers), and prepared all tables, figures, and references. M.H wrote the discussion with D. Betts, supported J. W and D. Betts with the thematic analysis, reviewed and provided feedback on the whole article. D.Betts wrote the results (facilitators), and the discussion with M.H. D.Booth completed the database search. F.KL reviewed and provided feedback. The author(s) read and approved the final manuscript.

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Wilson, J., Heinsch, M., Betts, D. et al. Barriers and facilitators to the use of e-health by older adults: a scoping review. BMC Public Health 21 , 1556 (2021). https://doi.org/10.1186/s12889-021-11623-w

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Analysis of E-mental health research: mapping the relationship between information technology and mental healthcare

• Tatsawan Timakum   ORCID: orcid.org/0000-0002-9877-0323 1 ,
• Qing Xie   ORCID: orcid.org/0000-0003-1926-1457 2 &
• Min Song   ORCID: orcid.org/0000-0003-3255-1600 3  

BMC Psychiatry volume  22 , Article number:  57 ( 2022 ) Cite this article

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E-mental healthcare is the convergence of digital technologies with mental health services. It has been developed to fill a gap in healthcare for people who need mental wellbeing support that may not otherwise receive psychological treatment. With an increasing number of e-mental healthcare and research, this study aimed to investigate the trends of an e-mental health research field that integrates interdisciplinary fields and to examine the information technologies is being used in mental healthcare. To achieve the research objectives, bibliometric analysis, information extraction, and network analysis were applied to analyze e-mental health research data.

E-mental health research data were obtained from 3663 bibliographic records from the Web of Science (WoS) and 3172 full-text articles from PubMed Central (PMC). The text mining techniques used for this study included bibliometric analysis, information extraction, and visualization.

The e-mental health research topic trends primarily involved e-health care services and medical informatics research. The clusters of research comprised 16 clusters, which refer to mental sickness, e-health, diseases, information technology (IT), and self-management. The information extraction analysis revealed a triple relation with IT and biomedical domains. Betweenness centrality was used as a measure of network graph centrality, based on the shortest path to rank the important entities and triple relation; nodes with higher betweenness centrality had greater control over the network because more information passes through that node. The IT entity-relations of “mobile” had the highest score at 0.043466. The top pairs were related to depression, mobile health, and text message.

Conclusions

E-mental related publications were associated with various research fields, such as nursing, psychology, medical informatics, computer science, telecommunication, and healthcare innovation. We found that trends in e-mental health research are continually rising. These trends were related to the internet of things (IoT) and mobile applications (Apps), which were applied for mental healthcare services. Moreover, producing AI and machine learning for e-mental healthcare were being studied. This work supports the appropriate approaches and methods of e-mental health research that can help the researcher to identify important themes and choose the best fit with their own survey work.

Peer Review reports

The demand for mental health services has been growing globally in recent years. Mental health conditions arise as a result of genetic factors, life circumstances, and other illnesses. This is especially prevalent in people with chronic diseases such as cancer, diabetes, and high blood pressure [ 1 ]. Lower social and economic barriers have led to more individuals coming forward for help with mental illness [ 2 ]. However, mental health service resources have not been progressing to meet the growing demand. This has led to pressure on service capacity [ 3 , 4 ], resulting in longer waiting times for people in need of support. Additionally, there are extensive gaps between service engagement and treatment, which is reflected in the high number of people who never receive any treatment.

Digital health is an interdisciplinary field that integrates digital technologies with health, healthcare, and society to enhance the efficiency of healthcare delivery and make medicines more personalized and precise. As part of this, electronic mental (e-mental) health care has been developed to support mental wellbeing by preventing mental illness and the need for psychological treatment [ 5 ]. It is part of a solution to directly help individuals in need by utilizing information and communication technology (ICT) via the internet [ 6 , 7 ]. E-mental health care includes digital technology-based treatments and new media, such as web-based, mobile phone-based, email, and virtual reality-based interventions, and text messaging services for mental health self-management. Additionally, these services are used to distribute health promotion, screening, prevention, and treatment information [ 8 ]. They can improve the health care of patients, provide professional education through e-learning, and contribute to electronic research in mental health care [ 9 ], cognitive behavioral therapy (CBT) [ 10 ], mental health diagnosis, and treat illness-related mental health conditions [ 11 , 12 ]. Thus, e-mental health care refers to the use of technology and the internet to deliver mental health information and services [ 13 ]. This concept covers the range of e-health or digital health services, which are derived from the evolution of technologies like smartphones and mobile devices [ 14 ].

Additionally, e-mental health systems can collect individual data to detect mental health symptoms and develop personalized programs that overcome the barriers to seeking help [ 15 ]. Tailored treatments may include supportive feedback, CBT, psychoeducation, and acceptance commitment therapy [ 5 ]. In recent years, e-mental health treatment programs have incorporated various applications and technologies for smartphone or tablet users that are available to the public. These can be found via apps such as the NHS applications library, which is provided by NHS in England ( https://www.nhs.uk/apps-library/category/mental-health/ ). These applications are assessed against a range of NHS standards. Moreover, there are several mental health services apps for purchasing, such as Moodfit ( https://www.getmoodfit.com/ ), MoodMission ( https://moodmission.com/ ), and Talkspace ( https://www.talkspace.com/ ). Most of these apps allow users to access their features anywhere and anytime. They offer a way to stay engaged, such as feelings trackers and daily reminders. Therefore, the use of IT can assist medical professionals in screening, assessing, monitoring, and delivering treatment interventions as well as supporting the patients with access to mental health care [ 16 ].

E-mental health has been studied across various sciences, such as the medicine, psychology, information science, media, and technology related fields. The field has gained a steady increase in interest among researchers. One study has investigated different types of e-mental health self-management interventions [ 8 ]. Other studies have analyzed the effectiveness of technology-based self-help therapies and behavioral interventions for mental health problems [ 17 , 18 ], such as computerized treatment for addictive disorders [ 5 ], online treatment for depression [ 10 ], and web-based interventions for alcoholism [ 19 ]. Another study has analyzed the benefits and negative impact of social network services on the internet [ 20 ]. These studies have shown some potential benefits to e-mental health, which have developed with the dynamics of new scientific discipline.

However, in this research field, the analysis of the trends and the IT elements in e-mental health has not been performed. Therefore, to facilitate future research and practice in this field, it is essential to comprehend the emerging subjects and knowledge structures in e-mental health such as medical informatics and artificial intelligence (AI). Moreover, an understanding of the relationship between the domains of biomedical and IT is vital for mental health interventions that can enable a better understanding of the association between diseases and treatments. In this study, mental health is defined as a mental disease and/or a set of mental conditions or symptoms which could correspond with physical sickness. Therefore, the associated diseases and symptoms also should be examined.

Consequently, this study aimed to identify the key attributes of e-mental health by examining bibliographic information and full-text papers. We sought to detect research clusters, entities, and their connections between the biomedical (diseases, symptoms, and treatments) and IT domains. In particular, the present study was designed to answer, 1) What are the research clusters and trends in e-mental health? 2) What kind of IT is being used in e-mental health? and 3) What are the common diseases connected with mental health conditions, and how can IT be used for treatment?

Data collection

The data used for visualizing the research clusters and time series were downloaded under the research topic of e-mental health from Web of Science (WoS) in bibliographic records. The data for information extraction was obtained from full-text papers from PubMed Central (PMC) databases. Both datasets were retrieved on December 16, 2019.

We included “ehealth” OR “mhealth” in the search words because they are widely used in the electronic health domain; therefore, we included these words to collect all data related to electronic mental health. However, to obtain the appropriate data for our study, we limited our searching criteria by using AND “mental” OR “emental.”

The WoS data were collected by limiting the topic search to “eHealth” OR “mHealth” AND “mental” OR “eMental,” selecting English as the language, and limiting the document types to “Article OR Review OR Meeting Abstract OR Editorial Material OR Proceeding paper.” We selected the timespan from 1990 to 2019, and the collection indexes included were the Science Citation Index Expanded, Social Science Citation Index, Arts and Humanities Citation Index, and Emerging Sources Citation Index. The WoS dataset included 3663 records (Bibliographic record).

For the PMC data, the keywords were limited to “emental” [Body - All Words] OR “ehealth” [Body - All Words] OR “emental health” [Body - All Words] OR “mhealth” [Body - All Words] AND “mental” [Body - All Words]. The full-text results included 3172 records (full-text XML format).

Data processing

The research was designed to analyze the clusters and trends of e-mental health research and extract the entities of biomedical (diseases, symptoms, and treatments) and IT domains. Further, we investigated the association between those entities. We used visualization tools to present the research clusters and entity co-occurrence. The proposed approach of this study is explained, as follows.

First, bibliometric analysis was performed. This is a quantitative statistical analysis approach that is used in the study of scientific citations in academic communication systems [ 21 ]. This approach explores the research topics and trends, then evaluates the productivity of authors and cooperative networks in specific fields [ 22 ]. Using this method, the collaboration patterns between authors, institutions, and publishers are captured to better understand global trends and discover research frontiers [ 23 ]. Mapping science helps to depict the knowledge structure in scientific networks and discover growing discipline areas. This study used the WoS data to obtain a comprehensive understanding of the clusters of e-mental health research. In addition, we created a time series and analyzed the trends in the data. The WoS analysis tool (Clarivate Analytics) was used to observe the research trends. Citespace.5.5.R2 [ 24 ] was used to examine and visualize the clusters of e-mental health and time-series of them. The period from 1990 to 2019 was selected for the study. The bibliography data from WoS was analyzed based on collaboration relationships using a reference and cited author. The sources were represented by the title, abstract, author keywords (DE), keywords plus (ID), and node type was shown by the term and keyword. The top 50 most cited or occurring items were selected for visualization from each year slice. This process was applied to answer research question (RQ) 1.

Second, information extraction (IE) was performed. This is a text mining technique to pull useful information from text documents. It is a part of Natural Language Processing (NLP) that is used for tasks, including Named Entity Recognition (NER) and Relation Extraction (RE) [ 25 ]. The NER system recognizes a named entity that occurs in the text, such as the name of a person, organization, or specific category. The RE system detects and classifies the relationship between entities in the text. The shared functions in the IE approach have significantly contributed to identifying patterns of knowledge in health and biomedicine. We applied this method to identify the biomedical and IT entities and their connections in the full-text papers from PMC using the PKDE4J 2.0 knowledge discovery tool [ 26 ]. This system integrates dictionary-based entity extraction and rule-based relation extraction. To identify the entities of e-mental Health, four types (dictionaries) were selected, including “Information Technology”, “Disease”, “Symptom”, and “Treatment.” These were used as inputs for the tool. In the treatment dictionary, we included medical procedure terms that are used to measure, diagnose, monitor, and treat a problem or disease. Therefore, they were classified into the same category. To use these dictionaries, we considered a description of self-management [ 27 ], which relates to the management of symptoms, treatments, and physical and mental conditions (diseases). The biomedical dictionary was already included in the PKDE4J biomedical entity and relation extraction package [ 26 ]. The corpus used in PKDE4J was compiled from clinical healthcare terminologies such as the National Library of Medicine’s controlled vocabulary thesaurus (MeSH) [ 28 ], the National Center for Biotechnology Information (NCBI) disease corpus [ 29 ], and clinical terms [ 30 ]. Bio entities with three entity types (Disease, Symptom, and Treatment) were constructed from these collections.

The IT lexicon was collected from IT resources, such as the TechTerms [ 31 ] and Computer Hope [ 32 ], which provide support related to the internet, software, artificial intelligence, cell phones, internet, smartphones, sound, video, and IT security. A summary of the dictionary data is displayed in Table  1 . In addition, the system incorporated biomedical verbs, which were extracted from the unified Medical Language System [ 33 ].

Finally, we performed network analysis and visualization to present the connections of each entity type. Network analysis is a method derived from network theory. It emerged from computer science to illustrate the influence of social networks [ 34 ]. It allows researchers to describe relationships between entities [ 35 ]. Social network analysis has been applied in several fields, including the science of citation, which are presented in graph theory [ 36 ]. Concept graphs consist of sets of nodes and edges that are used to represent text documents [ 37 ]. These create visual links and measure the impact of each node based on their pairs in a network. This technique determines which terms are used as a bridge in a network. Therefore, it is necessary to calculate metrics such as the weighted degree of nodes [ 38 ] and betweenness centrality [ 39 ], which are crucial to analyzing the co-occurrence network. This analysis was performed to answer RQ 2.

Data pre-processing included abbreviation resolution, tokenization, sentence splitting, POS tagging, lemmatization, and string normalization [ 26 ]. These techniques were applied to perform a sentence-level analysis of both datasets. They were then processed through Named Entity Recognition (NER), which is a dictionary-based approach. The NER consists of N-gram matching, approximate string match, regex NER, candidate entity filtering, and labeling. Lastly, data were delivered to post-preprocessing and rule generation to be assigned an entity name and entity type.

After extracting and receiving the entity and relation results from 3172 full-text papers, the relationships were used to construct two different networks of entities to be analyzed. First, a network of four entity types and their connections were examined to provide an overview and visualize the prominent pairs in the PMC dataset. Second, a graph of common diseases was created to illustrate the connections between the IT, symptoms, and treatments entities. The nodes (entities) and edges (relations/connections) were evaluated based on betweenness centrality and weight degree. Later, Gephi 0.9.2 [ 40 ] was used to visualize those networks. An overview of all data processing is shown in Fig.  1 .

Research framework

E-mental Health Research trends

The number of studies in e-mental health research has been increasing steadily (Fig.  2 ). In 2015, there was a significant change in the number of publications related to e-mental health, rising to 359 records that year and peaking at 789 records in 2019.

Number of published papers related to e-mental health between 2000 and 2019, indexed by Clarivate Analytics tools

Based on the WoS data analysis, the first publication related to e-health research, indexed by the Clarivate Analytics tools, was published in 2000. This was a study on electronic patient records using an internet-based approach [ 41 ]. In the same year, a study on the ethics of e-health was published [ 42 ]. In 2006, e-health research regarding e-health literacy was prominent. The most cited research paper assessed e-health literacy skills for consumer health [ 43 ]. This upward trend in e-health research continued and reached the highest number of studies in 2015. Research included topics such as internet resources for health care [ 44 ], mobile apps [ 45 ], mobile phone sensors [ 46 ], and web 2.0 [ 47 ]. One study focused on treatment delivery via mobile apps for bipolar disorder [ 48 ]. Since 2019, telehealth services, especially mobile and smartphone applications for mental health and health monitoring, have been well-established in this research field. A telerehabilitation study [ 49 ] was the most cited. These results reflect the growing research trends in e-mental health care.

Research area examination by WoS categories showed that the top-ranking field for published research papers associated with e-mental health was health care sciences services (1366 records), followed by medical informatics (1106 records), and computer science (399 records). Other related areas included public health, psychiatry, nursing, telecommunication, and biomedical social sciences as shown in Table  2 .

Mapping the clusters of E-mental Health Research

Bibliographic analysis revealed that the most frequent document type in the co-citation network was articles, with a total of 10,834 items. This was followed by review papers (2211 items), early access articles (1140 items), meeting abstracts (575 items), editorial material (404 items), and reviews of early access (210 items). The network included 1392 nodes and 2784 edges.

The system detected meaningful research clusters from the co-citation network in 16 groups (Fig. 3 ). The cluster name was determined using the keywords in the corresponding cluster, which is shown in a full result at the DOI link (Additional file 1 : Appendix 1). Cluster #0, “depression,” was the largest research cluster with 82 papers. This cluster is associated with mental health, noncommunicable diseases, breast cancer survivors, and cognitive-behavioral. This cluster was followed by cluster #1, “mhealth,” (74 papers), which included papers on mental health, user engagement, alcohol consumption, mobile apps, and e-mental health. Cluster #2, “health literacy,” had 56 papers. This included papers addressing the theory and technique of psychological knowledge evaluation, shown in terms of psychometrics, classical test theory, item response theory, and social support.

E-mental health care research clusters

The rest of the clusters were #3 “smoking cessation,” #4 “physical activity,” #5 “obesity,” #6 “telehealth,” #7 “older adults,” #8 “cancer,” #9 “health information,” #10 “psychosis,” #11 “usability testing,” #12 “msm,” #13 “artificial intelligence,” #14 “self-management,” and #15 “ehealth.” Some clusters refer to mental disorders and related diseases and symptoms, including smoking cessation, obesity, cancer, and psychosis, which are associated with mental health challenges. Some clusters represent a treatment using IT for self-management, such as the clusters for physical activity, telehealth, health information, self-management, and ehealth. Other clusters represent usability design for the participants, as demonstrated by the cluster for usability testing and artificial intelligence. The most common target demographics were elderly people and men who have sex with men (MSM).

IT and biomedical entities and relations

In this study, four entity types were considered: IT, treatment, disease, and symptom. The relation extraction process was performed to detect co-occurrences between these entities within a sentence of the data corpus. Table  3 shows the results of the named entity extraction in the collected dataset. The most frequently occurring entity type was IT , with 667,291 entities and 2290 unique entity names. This was followed by the treatment (106,519), disease (78,622), and symptom (17,474). The disease entity had the most unique names (2765). By contrast, the symptom entity had only 106 unique names. The top 30 entity names for each entity type are shown in Table  5 , along with their influence degree in the created network.

After extracting the entities, the system determined the connections between any two entities found in the same sentence and connected them with a relation verb. Table  4 displays the relation extraction results with the total numbers of co-occurrences. The connection between IT and IT was perceived the most frequently (777,788 counts). However, we did not focus on this co-occurrence since the study aimed to investigate the association of IT with diseases, symptoms, and treatments. The co-occurrence of IT and treatments occurred 125,199 times, followed by IT and diseases (96,732), and IT and symptoms (18081).

The relation extraction process identifies the co-occurrence of entities at the sentence-level, in which the entity extraction module has extracted two or more entities. The relationship analysis module takes a list of verbs and nominalization words that are used to identify relationships of interest. For example, the Entity 1 “text messaging” (IT) connects to Entity 2 “smoking cessation” (Disease) with the relation verb “target,” which were extracted from the same sentence. This result was detected from the following sentence in the dataset.

“Thus, SMS text messaging (Entity 1) might be an appropriate way to target (Relation verb) smoking cessation (Entity 2) in low SES and African American smokers”.

After we obtained the results of entity and relation extraction, we further employed them in the network analysis to determine the degree of each entity (node) and relation (edge). These are displayed as a graph for easier interpretation.

E-mental health entities network analysis

The entity and relation extraction results were passed into a graphML formatting process to create a network and were exported to Gephi for visualization. The network was evaluated using betweenness centrality to produce a bigger graph that combines all of the entities, relations, and weighted degrees for a specific network, such as a specific disease with IT. Betweenness centrality for a node represents the degree to which the nodes are mutually connected [ 50 ]. Thus, a node with higher betweenness centrality will be more important than other nodes because more information will pass through that node. In other words, the higher weighted entities indicate a higher impact.

IT and biomedical entities network

The network was processed according to the shortest path between each entity pair to produce the graph. The network shows the entities and connections of four entity types, including technology (used for IT), disease, symptom, and treatment. The network is an indirect graph that integrates the related research of e-mental health in a total of 7025 nodes and 105,621 edges (Fig.  4 ). Each node refers to the extracted entities from 3172 full-text papers. The edge indicates the connections between nodes.

Network of entities and relations in the e-mental health research field

Among the 7025 nodes and 105,621 edges, the unigram entity “depression” was the biggest node with a betweenness centrality at 0.046869, followed by “mobile” (0.043466), “cancer” (0.041167), and “screening” (0.028047). The “depression” node had a high influence on other nodes, including “cancer,” “diabetes,” “mobile,” “online,” “measures,” “content,” “screening,” and “discussion” (Fig.  5 ).

Influent entities connections in the e-mental health network

As shown in the large network of e-mental health entities and relations (Fig. 4 ), the results indicate that the technology (IT) nodes strongly occurred with “mobile,” “measure,” “online,” “content,” “video,” “protocol” (communication protocol) , and “security.” In addition, the entities of multimedia for self-monitoring and facilitation in therapy were visualized, including “video,” “sites,” “email,” “social media,” “virtual,” “image,” and “text messages”. Smart devices were frequently identified, such as “mobile phones,” “smartphones,” and “sensors.” The operating system entities such as “protocol,” “remote,” “algorithm”, and “android” were discovered in the network.

Figure  5 shows various associations between IT entity nodes and other nodes. Analysis showed that the node “measures” was associated with the IT entity type itself (mobile, online, content, smartphone, and interactive), disease entities (cancer, diabetes, secondary, hypertension, arthritis, stroke, and blood pressure), and with symptom entities (depression, fatigue, and discharge). Moreover, it is linked to treatment nodes, including “screening,” “discussion,” “measurement,” “examination,” and “advice.” Interestingly, the treatment entity type is frequently represented by the entities “screening,” “surgery,” “discussion,” “measurement,” and “examination.” As displayed in Fig. 5 , “screening” co-occurred with other nodes (diseases, symptoms, and technology), such as cancer, depression, and measures, respectively. In terms of supporting treatment, IT was most frequently used as a tool for “screening” to assist first-line physicians and identify potential health problems or diseases, such as detecting mild cognitive impairment and diseases at an early stage. The entity “surgery” was also a significant node, which implied that this treatment type was associated with telemedicine in surgery and online counseling for specific diseases. Moreover, the network illustrated that talk therapy was a conventional treatment in the e-mental health research field, as demonstrated by high-frequency entities such as “discussion,” “consultation,” and “advice.”

Following the entities network result, Table 5 presents the top 30 highest ranked nodes based on the betweenness centrality degree are reported for each entity type.

Common diseases with IT entities network

In this process, we sought to investigate the top diseases associated with mental health and identify the IT used for intervention. After the common disease entities with high-frequency detection in the dataset were selected from entity extraction results, the relation extraction results were filtered out. Only the top 20 common diseases and their relations remained for the network analysis. In this report, the numbers in parentheses are the frequency of each disease that was detected in the PMC dataset. The top 20 disease entities were “diabetes (5424),” “cancer (4898),” “smoking cessation (1733),” “dementia (1656),” “blood pressure (1508),” “stroke (1269),” “breast cancer (1200),” “obesity (1198),” “hypertension (1132),” “schizophrenia (1088),” “type 2 diabetes (860),” “psychosis (798),” “asthma (739),” “arthritis (714),” “bipolar (697),” “chronic pain (697),” “insomnia (502),” “cardiovascular disease (498),” “heart failure (497),” and “anxiety disorders (479).”

Network analysis showed the prominent IT entities related to e-mental health were “online” (weighted degree of 2478), “measure” (2365), “mobile” (2358), “content” (2242), and “video” (2160) (Table  6 ). In addition, there were other IT terms associated with the top diseases that were discovered in the e-mental health dataset. These included privacy and security concerns (informed consent, security), user interface design (interactive, utility, interface user satisfaction, limited English proficiency), hardware, and software-related terms. A full list is available via the DOI link (Additional file 2 : Appendix 2).

Entity Association in a Specific Disease Network

We investigated the entities and relations of the top 20 common diseases in the e-mental health research field. This analysis focused on the relations of four entities (disease, symptom, IT, and treatment) to explore what types of IT were used to treat illnesses and their symptoms. Figure  6 displays the diabetes network, where signs of “depression” (weighted degree score of 219) occurred most frequently for this disease. The significant technology entities were “measures,” “mobile phone,” “content,” “text messaging,” and “smartphone.” The technology applications associated with treatment entities included “screening,” “counseling,” “advice,” “surgery,” and “empowerment.” More information on the results of all 20 diseases is available at the DOI link (Additional file 3 : Appendix 3).

Diabetes entity network

Figure  7 shows the graphs of four mental illnesses that were detected as the top 20 common diseases, including schizophrenia, psychosis, bipolar, and anxiety disorders. The graphs were adjusted by the weighted degree of entity node for the best view. In these networks, the significant symptoms were “depression,” “suicidal thoughts,” “suicide attempt,” “hyperactivity,” and “discharge.” The prominent treatment entities included “psychoeducation,” and “screening,” and “suicide prevention.” Technology entities were frequently associated, such as “smartphone,” “mobile phone,” and “online.” Additionally, IT media entities were identified, such as text messaging, email, image, video, virtual reality, and social networking. Moreover, the IT for data processing was revealed in these networks (e.g., data mining, machine learning, and streaming). The top five entities for each entity type are shown in each graph.

Mental diseases with their associated entities

Of the 20 network graphs of diseases, the treatment entity nodes (including medical procedures) were related to the entity nodes of online counseling (e.g., email, chat, message, and video) for discussion, advice, shared decision making, meetings, and scheduling. Additionally, they were related to patient education, which was shown in the nodes for “skills training” and “psychoeducation.” Other treatment-related nodes identified were “an intervention of cognitive behavior,” “supportive care,” “psychosocial assessment and therapies,” and “clinical trials.” Moreover, specific physical symptoms entity nodes mainly occurred within a specific disease. For example, diabetes and blood pressure entities were linked with “insulin.” The smoking cessation entity was related to “chest pain.”

In addition, the entity and relation networks demonstrated that IT entity nodes were connected with the entities of treatments and medical procedures. These were used for evaluating and diagnosing patients’ conditions and for treatments such as “measuring body mass index,” “insulin therapy,” “glucose tolerance test,” “total cholesterol,” “blood glucose monitoring,” “heart rate,” “diastolic blood pressure,” and “systolic blood pressure” nodes.

Taken together, our data showed that related technologies were applied for physical and psychological interventions and therapies. Furthermore, web-based internet interventions and mobile applications were commonly used, as demonstrated by several of the IT entity nodes.

In this study, there are two dimensions to discuss. First, the analysis of e-mental health research clusters and trends using bibliometrics. Second, the co-occurrences between e-mental health biomedical-related entities and IT entities ranked by weighted degree and betweenness centrality. Mental health in this analysis was considered as a single disease and as symptoms that occurred with physical sickness. Therefore, other biomedical entities were identified to observe the relations between them and to link them with IT entities in this research field.

What are the research clusters and trends in e-mental health?

In order to overview and understand the scope of the e-mental health research field, we collected WoS bibliographic dataset with the timestamp between 2000 and 2019. We analyzed the trends and observed the related research areas using WoS analysis tools (Clarivate Analytics). We found that interest in this research field is increasing; there was significant progress in 2015, with many publications that were ranked highly. We investigated the e-mental health research topics between 2015 and 2019 and found that the trends were associated with various research fields, such as nursing, psychology, medical informatics, computer science, telecommunication, and healthcare innovation. Furthermore, the research trends at this time were related to the internet of things (IoT) and mobile applications (Apps), which were used in mental healthcare services. Our data showed that in the era of digital behavior, the smart home research topic played a role in contributing a mental health intervention.

Smartphone-based mental health interventions have been studied for the screening, monitoring, diagnosis, and reducing symptoms of depression and anxiety. Mobile health apps were designed for digital self-help interventions that allowed patients to interact with providers remotely and for physicians to deliver therapy. In 2019, we found that research related to artificial intelligence (AI), such as brain-computer interfaces, was more prevalent in this research field.

We further applied the co-citation network analysis to explore the significant e-mental health research clusters that had developed in this research area between 2015 and 2019. The finding demonstrated that 10,834 e-mental related papers could be classified into 16 clusters. The biggest cluster was depression, which is a mental symptom and disease. Other signs and sicknesses were discovered, including smoking cessation, obesity, cancer, and psychosis. These are linked to mental illness; quitting smoking is associated with an increased risk of depression [ 51 , 52 ]. Moreover, cluster-related health information technology was established in this research area. This comprised of mhealth, health literacy, telehealth, health information, and self-management. In addition, the visualization showed a cluster-related application design in the clusters of usability testing and artificial intelligence. We hypothesized that other clusters in this finding were correlated to a target demographic for e-mental health, such as the older adults and MSM clusters.

Our data analysis enabled an overview of the hidden e-mental health research clusters. From these, we could predict the research trends from the top terms and highest citation year. When this research field was first being established (2008–2010), it was associated with cancer survivors and older adults. This indicates that the first intention of e-mental health research was to focus on the use of IT for self-management and the mitigation of mental health impacts from physical sickness. In 2012, research tended to focus on digital mental health interventions related to psychological and lifestyle interventions; the biggest cluster was “depression,” which was a top term in mental health and behavioral therapy. This observation was confirmed by the research clusters, “physical activity” and “psychosis,” which obtained high citations in the same year.

Between 2013 and 2015, research topics of health information, health literacy, telemedicine, and mobile apps were studied frequently. These topics aimed to support mental health education and mental condition prevention. Moreover, in 2015, the research topic related to artificial intelligence (AI) for mental health and mental illnesses became more prevalent as a mental health digital solution. This was interpreted by the cluster and top terms of “artificial intelligence,” “deep learning,” and “medical informatics.” Many existing studies utilized AI to treat and reduce the burden of mental sickness [ 53 , 54 , 55 , 56 ].

To summarize, e-mental health research has been developed as part of the electronic health field that initially focused on electronic patient records. Following this, e-mental health was designed to deliver the right care solutions and mental health services in a timely and effective way by using the internet and other related technologies. With these aims, the researchers in this field have been seeking the appropriate digital technologies for people who lived with a mental health difficulty to access mental health services. As shown in our results, the advance of technologies such as AI applications in medicine could be developed into virtual therapists. Improvements in smart devices and natural language processing offers the ability to learn and monitor the moods and thoughts of patients and deliver therapy. However, to develop the smart tools for mental health care, the technology itself needs further study in addition to further integration of different fields of expertise. For example, it would be important to integrate the technology with the science of medicine, psychology, computing, machine engineering, informatics, communication technology, media and usability design, social networking, or health information education. Therefore, to improve e-mental health platforms and make them ‘smarter,’ the complexities of several sciences should be considered as part of this interdisciplinary research. In summary, this research field has relative interactions across biological, psychological, social systems, and technology research fields. Therefore, the current challenge is to study the principles of e-mental health and generate the cooperation of researchers from various sciences to improve interdisciplinary studies.

What kind of IT is being used in e-mental health?

To understand the linking of biomedical and IT domains in e-mental health, the diseases, symptoms, treatments, and IT entities and relations were analyzed. We used an information extraction system that integrated dictionary-based entity extraction and rule-based relation extraction. This process classified and identified the associations between biomedical and IT entities. Our overview shows the common diseases, including cancer and diabetes; IT of mobile, online, measures, and content; and the treatments related to screening, surgery, and discussion.

After extracting the disease entities, we found 2765 unique disease names. These included physical and mental diseases in the dataset, which confirmed that the physical condition was also assessed in studies of e-mental health. When examining the uses of IT, we used the top 20 diseases to visualize the associated IT for e-mental health. Our findings show 283 IT entity names. These were assessed and classified into four major groups, multimedia, information system, programming, and disease management and therapy. Multimedia referred to a type of content. The media used in e-mental health was shown by related entities, such as video, email, website, text message, virtual, games, video conferencing, and clinical trials. Moreover, the devices mentioned in the data corpus were smartphones, monitors, androids, sensors, tablets, mobile devices, and iPhones.

The information systems deliver and support users with the information needed for their activities effectively and efficiently. The related entities in this study included information security, clinical decision support system, information systems, information and communication technologies, health information systems, computerized decision support, and personal health management. Next, programming occurs, which is the development of a set of instructions for a computer to perform a task. Our results included the following nodes related to programming: algorithm, analysis of variance (ANOVA), query, segment, machine learning, least squares, embedded, and data mining.

Disease management and therapy refer to a system that coordinates healthcare interventions and communications. The following IT entities were associated with disease management and therapy: measures, treatment as usual, instructions, disease management, hivaids, body mass index, cognitive behavioral therapy, heart rate, cardiac rehabilitation, remote monitoring, real-time, case management, interactive voice response, diagnostics, clinical practice guidelines, emergency room, behavior change techniques, health-related quality of life (HRQoL), and test of functional health literacy in adults (TOFHLA).

In this study, we showed that the uses of IT in e-mental health care were developed for healthcare providers, people who have mental illnesses, and people who are physically sick who report mental conditions. These were developed for physical treatment, mental screening, and prevention. For example, people who inject themselves with medication, such as people with diabetes who require insulin, are affected by trypanophobia (needle phobia). This requires a collaboration between psychological medicine and diabetes teams [ 55 ]. Cigarette smoking can cause social phobia and anxiety disorder [ 58 , 59 ]. People who live with chronic pain have an increased risk for suicide [ 60 , 61 ]. IT was also utilized for patients with mental disorders who have a higher risk of physical difficulties. For example, the elderly with a history of bipolar disorder have a significant risk of developing dementia [ 62 ]. Additionally, major depressive and bipolar disorder can lead to accelerated atherosclerosis and early cardiovascular disease in adolescence [ 63 ]. Some IT devices were used for specific diseases and symptoms, such as high blood pressure, which can affect mood disorders [ 64 , 65 ]. Further, low blood pressure is associated with suicidal ideation [ 66 ].

In summary, we used entities and relations analysis in e-mental health research papers to describe the connection between physical and mental illness and the uses of IT for specific diseases, symptoms, and treatments.

Limitations

We acknowledge that our study has a limitation relating to the use of a dictionary for entity extraction because the text mining tools integrate dictionary-based terms to automatically tag bio-entities according to their types. In our method, we relied on MeSH and SNOMED terms to develop our dictionaries for the information extraction system. However, some entities could be incorrectly labelled. For example, some terms could be either a disease name or a symptom; “blood pressure” and “smoking cessation” could be labelled as diseases, and “discharge” “insulin,” and “insight” as symptoms. These errors were due to the ambiguous concepts of an entity. Sometimes they occurred because of a lexical error that the system failed to extract the entire entity. For example, quitting smoking can lead to symptoms of nicotine withdrawal, such as depression, anxiety, and irritability. Consequently smoking cessation was identified as a disease. At the same time, depression, anxiety, and irritability could be diseases or symptoms. Therefore, the context of entities should be carefully checked manually to prevent errors. In the process of data mining, manual annotation is the essential task of preselecting a document for training and evaluating new natural language processing algorithms. Therefore, we plan to improve an automatic method for disambiguating terms in future work.

Dictionaries and rules-based NER are the classical methods that linguists use to manually create a specific rule or special dictionaries according to the characteristics of datasets. However, the diversity and ambiguity of named entity representations create significant challenges to the understanding of natural language. Under different cultures, domains, and backgrounds, the denotations of named entities differ. This is the fundamental problem that named entity recognition technology needs to solve. Different granularity of knowledge representation in a large amount of text data can lead to different degrees of confidence and a lack of normative constraint; therefore, various expressions and unclear references of named entities appear. It is necessary to fully understand the context semantics to further elucidate the entity semantics for recognition.

In future work, deep learning will be applied in NER. Transfer learning versus remote supervised learning will be fully utilized to solve the problem of named entity identification in resource-poor areas and reduce the workload of manual annotation.

This paper utilized bibliometric and information extraction methods to report on the landscape of the e-mental health research field and the use of IT for mental health treatments. The data on the topic of e-mental health was obtained from WoS and PMC. WoS and Citespace were used as tools to identify research trends and for cluster analysis. The PKDE4J tool was utilized for the IT and biomedical information extraction, which combined the entities of diseases, symptoms, and treatments. Following this, the results of the entities and relations compilation were processed via network analysis. This was visualized using the Gephi tool.

The results indicated that e-mental health research has been increasing, and most studies relate to health care sciences services and medical informatics. The research was comprised of 16 clusters, which included e-health, diseases, IT, and self-management. Additionally, entities for IT, diseases, symptoms, and treatments and their connections were illustrated in network graphs. The most frequently occurring entity was IT, which was categorized as a mobile entity. Relation extraction showed that the most frequent entity association was depression paired with cancer, diabetes, mobile, online, measures, and screening. Overall, our data showed that e-mental health research focused on disease-related depression, suicidal thoughts, and suicide attempts. IT was used, primarily via online and mobile devices, to deliver health content, text messaging, and audio for screening, psychoeducation, advice, and suicide prevention.

The clusters and research trend analysis results demonstrate the key disciplines in e-mental health research. This is useful for researchers to understand the knowledge structure, relevant cross-discipline, and topic trends in this field. These data can support the correct approaches and methods for further e-mental health research and to identify important themes that fit best with future work. Moreover, the biomedical and IT entity and relation extraction results are beneficial for physicians, patients, and their proximal family members to understand and optimally treat patients with mental disorders in both physical and psychological therapy modalities using IT. The use of IT supports physicians to deliver psychological services as well as health promotion. At the same time, patients have accessibility and flexibility for self-monitoring integrated into treatment. In addition, healthcare providers and IT developers could use the data in this study to support e-mental health design. Furthermore, the outcomes of the entity and relation extraction could be utilized for disease prevention because they identify the diseases that could potentially cause mental health problems.

We expected that our findings can increase our understanding of e-mental health and related research areas. Moreover, promotes the variety of disciplines, especially in emerging research fields, such as medical informatics and AI, which can facilitate early mental disease detection and enable a better understanding of disease and treatments. In future studies, we seek to explore the impact of the global covid-19 pandemic on e-mental health.

Availability of data and materials

Appendix 1: E-mental health research knowledge clusters. https://doi.org/10.6084/m9.figshare.16957621.v1

Appendix 2: The top 20 diseases with the IT used in e-mental health (283 entity names). https://doi.org/10.6084/m9.figshare.16958221.v1

Appendix 3: Top 20 Disease entities and relations in e-mental health. https://doi.org/10.6084/m9.figshare.16958263.v1

Abbreviations

Analysis of variance

Electronic mental health care

Information and communication technology

Information extraction

Internet of things

Information technology

Natural language processing

Named entity recognition

Relation extraction

Web of Science

PubMed Central

Health-related quality of life

Test of functional health literacy in adults

Mobile health

Sex with men

Cognitive behavioral therapy

Treatment as usual

Randomized controlled trial

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This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2020S1A5B1104865).

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Tatsawan Timakum

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Timakum, T., Xie, Q. & Song, M. Analysis of E-mental health research: mapping the relationship between information technology and mental healthcare. BMC Psychiatry 22 , 57 (2022). https://doi.org/10.1186/s12888-022-03713-9

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Implementing electronic health records in hospitals: a systematic literature review

• Albert Boonstra 1 ,
• Arie Versluis 2 &
• Janita F J Vos 1  

BMC Health Services Research volume  14 , Article number:  370 ( 2014 ) Cite this article

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The literature on implementing Electronic Health Records (EHR) in hospitals is very diverse. The objective of this study is to create an overview of the existing literature on EHR implementation in hospitals and to identify generally applicable findings and lessons for implementers.

A systematic literature review of empirical research on EHR implementation was conducted. Databases used included Web of Knowledge, EBSCO, and Cochrane Library. Relevant references in the selected articles were also analyzed. Search terms included Electronic Health Record (and synonyms), implementation, and hospital (and synonyms). Articles had to meet the following requirements: (1) written in English, (2) full text available online, (3) based on primary empirical data, (4) focused on hospital-wide EHR implementation, and (5) satisfying established quality criteria.

Of the 364 initially identified articles, this study analyzes the 21 articles that met the requirements. From these articles, 19 interventions were identified that are generally applicable and these were placed in a framework consisting of the following three interacting dimensions: (1) EHR context, (2) EHR content, and (3) EHR implementation process.

Conclusions

Although EHR systems are anticipated as having positive effects on the performance of hospitals, their implementation is a complex undertaking. This systematic review reveals reasons for this complexity and presents a framework of 19 interventions that can help overcome typical problems in EHR implementation. This framework can function as a reference for implementers in developing effective EHR implementation strategies for hospitals.

Peer Review reports

In recent years, Electronic Health Records (EHRs) have been implemented by an ever increasing number of hospitals around the world. There have, for example, been initiatives, often driven by government regulations or financial stimulations, in the USA [ 1 ], the United Kingdom [ 2 ] and Denmark [ 3 ]. EHR implementation initiatives tend to be driven by the promise of enhanced integration and availability of patient data [ 4 ], by the need to improve efficiency and cost-effectiveness [ 5 ], by a changing doctor-patient relationship toward one where care is shared by a team of health care professionals [ 5 ], and/or by the need to deal with a more complex and rapidly changing environment [ 6 ].

EHR systems have various forms, and the term can relate to a broad range of electronic information systems used in health care. EHR systems can be used in individual organizations, as interoperating systems in affiliated health care units, on a regional level, or nationwide [ 1 , 2 ]. Health care units that use EHRs include hospitals, pharmacies, general practitioner surgeries, and other health care providers [ 7 ].

The implementation of hospital-wide EHR systems is a complex matter involving a range of organizational and technical factors including human skills, organizational structure, culture, technical infrastructure, financial resources, and coordination [ 8 , 9 ]. As Grimson et al. [ 5 ] argue, implementing information systems (IS) in hospitals is more challenging than elsewhere because of the complexity of medical data, data entry problems, security and confidentiality concerns, and a general lack of awareness of the benefits of Information Technology (IT). Boonstra and Govers [ 10 ] provide three reasons why hospitals differ from many other industries, and these differences might also affect EHR implementations. The first reason is that hospitals have multiple objectives, such as curing and caring for patients, and educating new physicians and nurses. Second, hospitals have complicated and highly varied structures and processes. Third, hospitals have a varied workforce including medical professionals who possess high levels of expertise, power, and autonomy. These distinct characteristics justify a study that focuses on the identification and analysis of the findings of previous studies on EHR implementation in hospitals.

Study aim, theoretical framework, and terminology

In dealing with the complexity of EHR implementation in hospitals, it is helpful to know which factors are seen as important in the literature and to capture the existing knowledge on EHR implementation in hospitals. As such, the objective of this research is to identify, categorize, and analyze the existing findings in the literature on EHR implementation processes in hospitals. This could contribute to greater insight into the underlying patterns and complex relationships involved in EHR implementation and could identify ways to tackle EHR implementation problems. In other words, this study focusses on the identification of factors that determine the progress of EHR implementation in hospitals. The motives behind implementing EHRs in hospitals and the effects on performance of implemented EHR systems are beyond the scope of this paper.

To our knowledge, there have been no systematic reviews of the literature concerning EHR implementation in hospitals and this article therefore fills that gap. Two interesting related review studies on EHR implementation are Keshavjee et al. [ 11 ] and McGinn et al. [ 12 ]. The study of Keshavjee et al. [ 11 ] develops a literature based integrative framework for EHR implementation. McGinn et al. [ 12 ] adopt an exclusive user perspective on EHR and their study is limited to Canada and countries with comparable socio-economic levels. Both studies are not explicitly focused on hospitals and include other contexts such as small clinics and national or regional EHR initiatives.

This systematic review is explicitly focused on hospital-wide, single hospital EHR implementations and identifies empirical studies (that include collected primary data) that reflect this situation. The categorization of the findings from the selected articles draws on Pettigrew’s framework for understanding strategic change [ 13 ]. This model has been widely applied in case study research into organizational contexts [ 14 ], as well as in studies on the implementation of health care innovations [ 15 ]. It generates insights by analyzing three interactive dimensions – context , content , and process – that together shape organizational change. Pettigrew’s framework [ 13 ] is seen as applicable because implementing an EHR artefact is an organization-wide effort. This framework was specifically selected for its focus on organizational change, its ease of understanding, and its relatively general dimensions allowing a broad range of findings to be included. The framework structures and focusses the analysis of the findings from the selected articles.

An organization’s context can be divided into internal and external components. External context refers to the social, economic, political, and competitive environments in which an organization operates. The internal context refers to the structure, culture, resources, capabilities, and politics of an organization. The content covers the specific areas of the transformation under examination. In an EHR implementation, these are the EHR system itself (both hardware and software), the work processes, and everything related to these (e.g. social conditions). The process dimension concerns the processes of change, made up of the plans, actions, reactions, and interactions of the stakeholders, rather than work processes in general. It is important to note that Pettigrew [ 13 ] does not see strategic change as a rational analytical process but rather as an iterative, continuous, multilevel process. This highlights that the outcome of an organizational change will be determined by the context, content, and process of that change. The framework with its three categories, shown in Figure  1 , illustrates the conceptual model used to categorize the findings of this systematic literature review.

Pettigrew ’ s framework [ 13 ] ] and the corresponding categories.

In the literature, several terms are used to refer to electronic medical information systems. In this article, the term Electronic Health Record (EHR) is used throughout. Commonly used terms identified by ISO (the International Organization for Standardization) [ 16 ] plus another not identified by ISO are outlined below and used in our search. ISO considers Electronic Health Record (EHR) to be an overall term for “ a repository of information regarding the health status of a subject of care , in computer processable form ” [ 16 ], p. 13. ISO uses different terms to describe various types of EHRs. These include Electronic Medical Record (EMR), which is similar to an EHR but restricted to the medical domain. The terms Electronic Patient Record (EPR) and Computerized Patient Record (CPR) are also identified. Häyrinen et al. [ 17 ] view both terms as having the same meaning and referring to a system that contains clinical information from a particular hospital. Another term seen is Electronic Healthcare Record (EHCR) which refers to a system that contains all the available health information on a patient [ 17 ] and can thus be seen as synonymous with EHR [ 16 ]. A term often found in the literature is Computerized Physician Order Entry (CPOE). Although this term is not mentioned by ISO [ 16 ] or by Häyrinen et al. [ 17 ], we included CPOE for three reasons. First, it is considered by many to be a key hospital-wide function of an EHR system e.g. [ 8 , 18 ]. Second, from a preliminary analysis of our initial results, we found that, from the perspective of the implementation process, comparable issues and factors emerged from both CPOEs and EHRs. Third, the implementation of a comprehensive electronic medical record requires physicians to make direct order entries [ 19 ]. Kaushal et al. define a CPOE as “ a variety of computer - based systems that share the common features of automating the medication ordering process and that ensure standardized , legible , and complete orders ” [ 18 ], p. 1410. Other terms found in the literature were not included in this review as they were considered either irrelevant or too broadly defined. Examples of such terms are Electronic Client Record (ECR), Personal Health Record (PHR), Digital Medical Record (DMR), Health Information Technology (HIT), and Clinical Information System (CIS).

Search strategies

In order for a systematic literature review to be comprehensive, it is essential that all terms relevant to the aim of the research are covered in the search. Further, we need to include relevant synonyms and related terms, both for electronic medical information systems and for hospitals. By adding an * to the end of a term, the search engines pick out other forms, and by adding “ “ around words one ensures that only the complete term is searched for. Further, by including a ? as a wildcard character, every possible combination is included in the search.

The search used three categories of keywords. The first category included the following terms as approximate synonyms for hospital: “hospital*”, “healthcare”, and “clinic*”. The second category concerned implementation and included the term “implement*”. For the third category, electronic medical information systems, the following search terms were used: “Electronic Health Record*”, “Electronic Patient Record*”, “Electronic Medical Record*”, “Computeri?ed Patient Record*”, “Electronic Healthcare Record*”, “Computeri?ed Physician Order Entry”.

This relatively large set of keywords was necessary to ensure that articles were not missed in the search, and required a large number of search strategies to cover all those keywords. As we were seeking papers about the implementation of electronic medical information systems in hospitals , the search strategies included the terms shown in Table  1 .

The following three search engines were chosen based on their relevance to the field and their accessibility by the researcher: Web of knowledge, EBSCO, and The Cochrane Library. Most search engines use several databases but not all of them were relevant for this research as they serve a wide range of fields. Appendix A provides an overview of the databases used. The reference lists included in articles that met the selection criteria were checked for other possibly relevant studies that had not been identified in the database search.

The articles identified from the various search strategies had to be academic peer-reviewed articles if they were to be included in our review. Further, they were assessed and had to satisfy the following criteria to be included: (1) written in English, (2) full text available online, (3) based on primary empirical data, (4) focused on hospital-wide EHR implementation, and (5) meeting established quality criteria. A long list of abstracts was generated, and all of them were independently reviewed by two of the authors. They independently reviewed the abstracts, eliminated duplicates and shortlisted abstracts for detailed review. When opinions differed, a final decision over inclusion was made following a discussion between the researchers.

Data analysis

The quality of the articles that survived this filtering was assessed by the first two authors using the Standard Quality Assessment Criteria for Evaluating Primary Research Papers [ 18 ]. In other words, the quality of the articles was jointly assessed by evaluating whether specific criteria had been addressed, resulting in a rating of 2 (fully addressed), 1 (partly addressed), or 0 (not addressed) for each criteria. Different questions are posed for qualitative and quantitative research and, in the event of a mixed-method study, both questionnaires were used. Papers were included if they received at least half of the total possible points, admittedly a relatively liberal cut-off point given comments in the Standard Quality Assessment Criteria for Evaluating Primary Research Papers [ 20 ].

The next step was to extract the findings of the reviewed articles and to analyze these with the aim of reaching general findings on the implementation of EHR systems in hospitals. Categorizing these general findings can increase clarity. The earlier introduced conceptual model, based on Pettigrew’s framework for understanding strategic change, includes three categories: context (A), content (B), and process (C). As our review is specifically aimed at identifying findings related to the implementation process, possible motives for introducing such a system, as well as its effects and outcomes, are outside its scope. The authors held frequent discussions between themselves to discuss the meaning and the categorization of the general findings.

Paper selection

Applying the 18 search strategies listed in Table  1 with the various search engines resulted in 364 articles being identified. The searches were carried out on 12 March 2013 for search strategies 1–15 and on 18 April 2013 for search strategies 16–18. The latter three strategies were added following a preliminary analysis of the first set of results which highlighted several other terms and descriptions for information technology in health care. Not surprisingly, many duplicates were included in the 364 articles, both within and between search engines. Using the Refworks functions for identifying exact and close duplicates, 160 duplicates were found. However, this procedure did not identify all the duplicates present and the second author carried out a manual check that identified an additional 23 duplicates. When removing duplicates, we retained the link to the first search engine that identified the article and, as the Web of Knowledge was the first search engine used, most articles appear to have stemmed from this search engine. This left 181 different articles which were screened on title and abstract to check whether they met the selection criteria. When this was uncertain, the contents of the paper were further investigated. This screening resulted in just 13 articles that met all the selection criteria. We then performed two additional checks for completeness. First, checking the references of these articles identified another nine articles. Second, as suggested by the referees of this paper, we also used the term “introduc*” instead of “implement*”, together with the other two original categories of terms, and the term “provider” instead of “physician”, as part of CPOE. Each of these two searches identified one additional article (see Table  1 ). Of these resulting 24 articles, two proved to be almost identical so one was excluded, resulting in 23 articles for a final quality assessment.The results of the quality assessment can be found in Appendix B. The results show that two articles failed to meet the quality threshold and so 21 articles remained for in-depth analysis. Figure  2 displays the steps taken in this selection procedure.

Selection procedure.

To provide greater insight into the context and nature of the 21 remaining articles, an overview is provided in Table  2 . All the studies except one were published after 2000. This reflects the recent increase in effort to implement organization-wide information systems, such as EHR systems, and also increasing incentives from governments to make use of EHR systems in hospitals. Of the 21 studies, 14 can be classified as qualitative, 6 as quantitative, and 1 as a mixed-method study. Most studies were conducted in the USA, with eight in various European countries. Teaching and non-teaching hospitals are almost equally the subject of inquiry, and some researchers have focused on specific types of hospitals such as rural, critical access, or psychiatric hospitals. Ten of the articles were in journals with a five-year impact factor in the Journal Citation Reports 2011 database. There is a huge difference in the number of citations but one should never forget that newer studies have had fewer opportunities to be cited.

Theoretical perspectives of reviewed articles

In research, it is common to use theoretical frameworks when designing an academic study [ 41 ]. Theoretical frameworks provide a way of thinking about and looking at the subject matter and describe the underlying assumptions about the nature of the subject matter [ 42 ]. By building on existing theories, research becomes focused in aiming to enrich and extend the existing knowledge in that particular field [ 42 ]. To provide a more thorough understanding of the selected articles, their theoretical frameworks, if present, are outlined in Table  3 .

It is striking that no specific theoretical frameworks have been used in the research leading to 13 of the 21 selected articles. Most articles simply state their objective as gaining insight into certain aspects of EHR implementation (as shown in Table  1 ) and do not use a particular theoretical approach to identify and categorize findings. As such, these articles add knowledge to the field of EHR implementation but do not attempt to extend existing theories.

Aarts et al. [ 21 ] introduce the notion of the sociotechnical approach: emphasizing the importance of focusing both on the social aspects of an EHR implementation and on the technical aspects of the system. Using the concept of emergent change, they argue that an implementation process is far from linear and predictable due to the contingencies and the organizational complexity that influences the process. A sociotechnical approach and the concept of emergent change are also included in the theoretical framework of Takian et al. [ 37 ]. Aarts et al. [ 21 ] elaborate on the sociotechnical approach when stating that the fit between work processes and the information technology determines the success of the implementation. Aarts and Berg [ 22 ] introduce a model of success or failure in information system implementation. They see creating synergy among the medical work practices, the information system, and the hospital organization as necessary for implementation, and argue that this will only happen if sufficient people accept a change in work practices. Cresswell et al.’s study [ 26 ] is also influenced by sociotechnical principles and draws on Actor-Network Theory. Gastaldi et al. [ 28 ] perceive Electronic Health Records as knowledge management systems and question how such systems can be used to develop knowledge assets. Katsma et al. [ 31 ] focus on implementation success and elaborate on the notion that implementation success is determined by system quality and acceptance through participation. As such, they adopt more of a social view on implementation success rather than a sociotechnical approach. Rivard et al. [ 34 ] examine the difficulties in EHR implementation from a cultural perspective. They not only view culture as a set of assumptions shared by an entire collective (an integration perspective) but also expect subcultures to exist (a differentiation perspective), as well as individual assumptions not shared by a specific (sub-) group (fragmentation perspective). Ford et al. [ 27 ] focus on an entirely different topic and investigate the IT adoption strategies of hospitals using a framework that identifies three strategies. These are the single-vendor strategy (in which all IT is purchased from a single vendor), the best-of-breed strategy (integrating IT from multiple vendors), and the best-of-suit strategy (a hybrid approach using a focal system from one vendor as the basis plus other applications from other vendors).

To summarize, the articles by Aarts et al. [ 21 ], Aarts and Berg [ 22 ], Cresswell et al. [ 26 ], and Takian et al. [ 37 ] apply a sociotechnical framework to focus their research. Gastaldi et al. [ 28 ] see EHRs as a means to renew organizational capabilities. Katsma et al. [ 31 ] use a social framework by focusing on the relevance of an IT system as perceived by the user and the participation of users in the implementation process. Rivard et al. [ 34 ] analyze how organizational cultures can be receptive to EHR implementation. Ford et al. [ 27 ] look at adoption strategies, leading them to focus on the selection procedure for Electronic Health Records. The 13 other studies did not use an explicit theoretical lens in their research.

Implementation-related findings

The process of categorization started by assessing whether a specific finding from a study should be placed in Category A, B, or C. Thirty findings were placed in Category A (context), 31 in Category B (content), and 66 in Category C (process). Comparing and combining the specific findings resulted in several general findings within each category. The general findings are each given a code (category character plus number) and the related code is indicated alongside each specific finding in Appendix C. Findings that were only seen in one article, and thus were lacking support, were discarded.

Category A - context

The context category of an EHR implementation process includes both internal variables (such as resources, capabilities, culture, and politics) and external variables (such as economic, political, and social variables). Six general findings were identified, all but one related to internal variables. An overview of the findings and corresponding articles can be found in Table  4 . The lack of general findings related to external variables reflects our decision to exclude the underlying reasons (e.g. political or social pressures) for implementing an EHR system from this review. Similarly, internal findings related to aspects such as perceived financial benefits or improved quality of care, are outside our scope.

A1: Large (or system-affiliated), urban, not-for-profit, and teaching hospitals are more likely to have implemented an EHR system due to having greater financial capabilities, a greater change readiness, and less focus on profit

The research reviewed shows that larger or system-affiliated hospitals are more likely to have implemented an EHR system, and that this can be explained by their easier access to the large financial resources required. Larger hospitals have more financial resources than smaller hospitals [ 30 ] and system-affiliated hospitals can share costs [ 27 ]. Hospitals situated in urban areas more often have an EHR system than rural hospitals, which is attributed to less knowledge of EHR systems and less support from medical staff in rural hospitals [ 29 ]. The fact that not-for-profit hospitals more often have an EHR system fully implemented and teaching hospitals slightly more often than private hospitals is attributed to the latter’s more wait-and-see approach and the more progressive change-ready nature of public and teaching hospitals [ 27 , 32 ].

A2: EHR implementation requires the selection of a mature vendor who is committed to providing a system that fits the hospital’s specific needs

Although this finding is not a great surprise, it is relevant to discuss it further. A hospital selecting its own vendor can ensure that the system will match the specific needs of that hospital [ 32 ]. Further, it is important to deal with a vendor that has proven itself on the EHR market with mature and successful products. The vendor must also be able to identify hospital workflows and adapt its product accordingly, and be committed to a long-term trusting relationship with the hospital [ 33 ]. With this in mind, the initial price of the system should not be the overriding consideration: the organization should be willing to avoid purely cost-oriented vendors [ 28 ], as costs soon mount if problems arise.

A3: The presence of hospital staff with previous experience of health information technology increases the likelihood of EHR implementation as less uncertainty is experienced by the end-users

In order to be able to work with an EHR system, users must be capable of using information technology such as computers and have adequate typing skills [ 19 , 32 ]. Knowledge of, and previous experience with, EHR systems or other medical information systems reduces uncertainty and disturbance for users, and this results in a more positive attitude towards the system [ 29 , 32 , 37 , 38 ].

A4: An organizational culture that supports collaboration and teamwork fosters EHR implementation success because trust between employees is higher

The influence of organizational culture on the success of organizational change is addressed in almost all the popular approaches to change management, as well as in several of the articles in this literature review. Ash et al. [ 23 , 24 ] and Scott et al. [ 35 ] highlight that a strong culture with a history of collaboration, teamwork, and trust between different stakeholder groups minimizes resistance to change. Boyer et al. [ 25 ] suggest creating a favorable culture that is more adaptive to EHR implementation. However, creating a favorable culture is not necessarily easy: a comprehensive approach including incentives, resource allocation, and a responsible team was used in the example of Boyer et al. [ 25 ].

A5: EHR implementation is most likely in an organization with little bureaucracy and considerable flexibility as changes can be rapidly made

A highly bureaucratic organizational structure hampers change: it slows the process and often leads to inter-departmental conflict [ 19 ]. Specifically, appointing a multidisciplinary team to deal with EHR-related issues can prevent conflict and stimulate collaboration [ 25 ].

A6: EHR system implementation is difficult because cure and care activities must be ensured at all times

During the process of implementing an EHR system, it is of the utmost importance that all relevant information is always available [ 28 , 34 , 39 ]. Ensuring the continuity of quality care while implementing an EHR system is difficult and is an important distinction from many other IT implementations.

Category B - content

The content of the EHR implementation process consists of the EHR system and the corresponding objectives, assumptions, and complementary services. Table  5 lists the five extracted general findings. These focus on both the hardware and software of the EHR system, and its relation to work practices and privacy.

B1: Creating a fit by adapting both the technology and work practices is a key factor in the implementation of EHR

This finding elaborates on the sociotechnical approach identified in the earlier section on the theories adopted in the articles. Several authors [ 21 , 26 , 31 , 37 ] make clear that creating a fit between the EHR system and the existing work practices requires an initial acknowledgement that an EHR implementation is not just a technical project and that existing work practices will change due to the new system. By customizing and adapting the system to meet specific needs, users will become more open to using it [ 19 , 26 , 28 ].

B2: Hardware availability and system reliability, in terms of speed, availability, and a lack of failures, are necessary to ensure EHR use

In several articles, authors highlight the importance of having sufficient hardware. A system can only be used if it is available to the users, and a system will only be used if it works without problems. Ash et al. [ 24 ], Scott et al. [ 35 ], and Weir et al. [ 19 ] refer to the speed of the system as well as to the availability of a sufficient number of adequate terminals see also [ 40 ] in various locations. Systems must be logically structured [ 29 ], reliable [ 32 ], and provide safe information access [ 37 ]. Boyer et al. [ 25 ] also mention the importance of technical aspects but add that these are not sufficient for EHR implementation.

B3: To ensure EHR implementation, the software needs to be user-friendly with regard to ease of use, efficiency in use, and functionality

Some authors distinguish between technical availability and reliability, and the user-friendliness of the software [ 19 , 24 , 32 ]. They argue that it is not sufficient for a system to be available and reliable, it should also be easy and efficient in use, and provide the functionality required for medical staff to give good care. If a system fails to do this, staff will not use the system and will stick to their old ways of working.

B4: An EHR implementation should contain adequate safeguards for patient privacy and confidentiality

Concerns over privacy and confidentiality are recognized by Boyer et al. [ 25 ] and Houser and Johnson [ 29 ] and are considered as a barrier to EHR implementation. Yoon-Flannery et al. [ 40 ] and Takian et al. [ 37 ] also recognize the importance of patient privacy and the need to address this issue by providing training and creating adequate safeguards.

B5: EHR implementation requires a vendor who is willing to adapt its product to hospital work processes

A vendor must be responsive and enable the hospital to develop its product to ensure a good and usable EHR system [ 32 , 33 ]. By so doing, dependence on the vendor decreases and concerns that arise within the hospital can be addressed [ 32 ]. This finding is related to A2 in the sense that an experienced, cooperative, and flexible vendor is needed to deal with the range of interest groups found in hospitals.

Category C - process

This category refers to the actual process of implementing the EHR system. Variables considered are time, change approach, and change management. In our review, this category produced the largest number of general findings (see Table  6 ), as might be expected given our focus on the implementation process. EHR implementation often leads to anxiety, uncertainty, and concerns about a possible negative impact of the EHR on work processes and quality. The process findings, including leadership, resource availability, communication and participation are explicitly aimed at overcoming resistance to EHR implementation. These interventions help to create a positive atmosphere of goal directedness, co-creation and partnership.

C1: Due to their influential position, management’s active involvement and support is positively associated with EHR implementation, and also counterbalances the physicians’ medical dominance

Several authors note the important role that managers play in EHR implementation. Whereas some authors refer to supportive leadership [ 19 , 24 ], others emphasize that strong and active management involvement is needed [ 25 , 32 – 35 ]. Strong leadership is relevant as it effectively counterbalances the physicians’ medical dominance. For instance, Rivard et al. [ 34 ] observe that physicians’ medical dominance and the status and autonomy of other health professionals hinder collaboration and teamwork, and that this complicates EHR implementation. Poon et al. [ 33 ] acknowledge this aspect and argue for strong leadership in order to deal with the otherwise dominant physicians. They also claim that leaders have to set an example and use the system themselves. At the same time, it is motivating that the implementation is managed by leaders who are recognized by the medical staff, for instance by head nurses and physicians or by former physicians and nurses [ 25 , 33 ]. Ovretveit et al. [ 32 ] argue that it helps the implementation if senior management repeatedly declares the EHR implementation to be of the highest priority and supports this with sufficient financial and human resources. Poon et al. [ 33 ] add to this by highlighting that, especially during uncertainties and setbacks, the common vision that guides the EHR implementation has to be communicated to hospital staff. Sufficient human resources include the selection of competent and experienced project leaders who are familiar with EHR implementation. Scott et al. [ 35 ] identify leadership styles for different phases: participatory leadership is valued in selection decisions, whereas a more hierarchical leadership style is preferable in the actual implementation.

C2: Participation of clinical staff in the implementation process increases support for and acceptance of the EHR implementation

Participation of end-users (the clinical staff) generates commitment and enables problems to be quickly solved [ 25 , 26 , 36 ]. Especially because it is very unlikely that the system will be perfect for all, it is important that the clinical staff become the owner, rather than customers, of the system. Clinical staff should participate at all levels and in all steps [ 19 , 28 , 32 , 36 ] from initial system selection onwards [ 35 ]. Ovretveit et al. [ 32 ] propose that this involvement should have an extensive timeframe, starting in the early stages of implementation, when initial vendor requirements are formulated (‘consultation before implementation’), through to the beginning of the use phase. Creating multidisciplinary work groups which determine the content of the EHR and the rules regarding the sharing of information contributes to EHR acceptance [ 25 ] and ensures realistic approaches acceptable to the clinical staff [ 36 ].

C3: Training end-users and providing real-time support is important for EHR implementation success

Frequently, the end-users of a new EHR system lack experience with the specific EHR system or with EHR systems in general. Although it is increasingly hard to imagine society or workplaces without IT, a large specific system, such as an EHR, still requires considerable training on how to use it properly. The importance of training is often underestimated, and inadequate training will create a barrier to EHR use [ 19 , 29 ]. Consequently, adequate training, of appropriate quantity and quality, must be provided at the right times and locations [ 19 , 32 , 36 ]. Simon et al. [ 36 ] add to this the importance of real-time support, preferably provided by peers and super-users.

C4: A comprehensive implementation strategy, offering both clear guidance and room for emergent change, is needed for implementing an EHR system

Several articles highlight aspects of an EHR implementation strategy. A good strategy facilitates EHR implementation [ 19 , 25 ] and consists of careful planning and preparation [ 36 ], a sustainable business plan, effective communication [ 28 , 40 ] and mandatory implementation [ 19 ]. Emergent change is perceived as a key characteristic of EHR implementation in complex organizations such as hospitals [ 21 ], and this suggests an implementation approach based on a development paradigm [ 31 ], which may initially even involve parallel use of paper [ 26 ]. The notion of emergent change has been variously applied, including in the theoretical frameworks of Aarts et al. [ 21 ] and Katsma et al. [ 31 ]. These studies recognize that EHR implementation is relatively unpredictable due to unforeseen contingencies for which one cannot plan. With their emphasis on emergent change with unpredictable outcomes, Aarts et al. [ 21 ] make a case for acknowledging that unexpected and unplanned contingencies will influence the implementation process. They argue that the changes resulting from these contingencies often manifest themselves unexpectedly and must then be dealt with. Additionally, Takian et al. [ 37 ] state that it is crucial to contextualize an EHR implementation so as to be better prepared for unexpected changes.

C5: Establishing an interdisciplinary implementation group consisting of developers, members of the IT department, and end-users fosters EHR implementation success

In line with the arguments for management support and for the participation of clinical staff, Ovretveit et al. [ 32 ], Simon et al. [ 36 ] and Weir et al. [ 19 ] build a case for using an interdisciplinary implementation group. By having all the direct stakeholders working together, a better EHR system can be delivered faster and with fewer problems.

C6: Resistance of clinical staff, in particular of physicians, is a major barrier to EHR implementation, but can be reduced by addressing their concerns

Clinical staff’s attitude is a crucial factor in EHR implementation [ 36 ]. Particularly, the physicians constitute an important group in hospitals. As such, their possible resistance to EHR implementation will form a major barrier [ 29 , 33 ] and may lead to workarounds [ 26 ]. Whether physicians accept or reject an EHR implementation depends on their acceptance of their work practices being transformed [ 22 ]. The likelihood of acceptance will be increased if implementers address the concerns of physicians [ 24 , 28 , 32 , 33 ], but also of other members of clinical staff [ 36 ].

C7: Identifying champions among clinical staff reduces resistance

The previous finding already elaborated on clinical staff resistance and suggested reducing this by addressing their concerns. Another way to reduce their resistance is related to the process of implementation and involves identifying physician champions, typically physicians that are well respected due to their knowledge and contacts [ 32 , 33 ]. Simon et al. [ 36 ] emphasize the importance of identifying champions among each stakeholder group. These champions can provide reassurance to their peers.

C8: Assigning a sufficient number of staff and other resources to the EHR implementation process is important in adequately implementing the system

Implementing a large EHR system requires considerable resources, including human ones. Assigning appropriate people, such as super-users [ 36 ] and a sufficient number of them to that process will increase the likelihood of success [ 19 , 32 , 33 , 36 ]. Further, it is important to have sufficient time and financial resources [ 26 , 32 ]. This finding is also relevant in relation to finding A6 (ensuring good care during organizational change).

These 19 general findings have been identified from the individual findings within the 20 analyzed articles. These findings are all related to one of the three main and interacting dimensions of the framework: six to context, five to content, and eight to process. This identification and explanation of the general findings concludes the results section of this systematic literature review and forms the basis for the discussion below.

This review of the existing academic literature sheds light on the current knowledge regarding EHR implementation. The 21 selected articles all originate from North America or Europe, perhaps reflecting a greater governmental attention to EHR implementation in these regions and, of course, our inclusion of only articles written in English. Two articles were rejected for quality reasons [ 43 , 44 ], see Appendix B. All but one of the selected articles have been published since 2000, reflecting the growing interest in implementing EHR systems in hospitals. Eight articles built their research on a theoretical framework, four of which use the same general lens of the sociotechnical approach [ 21 , 22 , 26 , 37 ]. Katsma et al. [ 31 ] and Rivard et al. [ 34 ] focus more on the social and cultural aspects of EHR implementation, the former on the relevance for, and participation of, users, the latter on three different cultural perspectives. Ford et al. [ 27 ] researched adoption strategies for EHR systems and Gastaldi et al. [ 26 ] consider them as a means to renew organizational capabilities. It is notable that the other reviewed articles did not use a theoretical framework to analyze EHR implementation and made no attempt to elaborate on existing theories.

A total of 127 findings were extracted from the articles, and these findings were categorized using Pettigrew’s framework for strategic change [ 13 ] as a conceptual model including the three dimensions of context, content, and process. To ensure a tight focus, the scope of the review was explicitly limited to findings related to the EHR implementation process, thus excluding the reasons for, barriers to, and outcomes of an EHR implementation.

Some of the findings require further interpretation. Contextual finding A1 relates to the demographics of a hospital. One of the assertions is that privately owned hospitals are less likely than public hospitals to invest in an EHR. The former apparently perceive the costs of EHR implementation to outweigh the benefits. This seems remarkable given that there is a general belief that information technology increases efficiency and reduces process costs, so more than compensating for the high initial investments. It is however important to note that the literature on EHR is ambivalent when it comes to efficiency; several authors record a decrease in the efficiency of work practices [ 25 , 33 , 35 , 38 ], whereas others mention an increase [ 29 , 31 ]. Finding A2 is a reminder of the importance of carefully selecting an appropriate vendor, taking into account experience with the EHR market and the maturity of their products rather than, for example, focussing on the cost price of the system. Given the huge investment costs, the price of an EHR system tends to have a major influence on vendor selection, an aspect that is also promoted by the current European tendering regulations that oblige (semi-) public institutions, like many hospitals, to select the lowest bidder, or the bidder that is economically the most preferable [ 45 ]. The finding that EHR system implementation is difficult because good medical care needs to be ensured at all times (A6) also deserves mention. Essentially, many system implementations in hospitals are different from IT implementations in other contexts because human lives are at stake in hospitals. This not only complicates the implementation process because medical work practices have to continue, it also requires a system to be reliable from the moment it is launched.

The findings regarding the content of the EHR system (Category B) highlight the importance of a suitable software product. A well-defined selection process of the software package and its associated vendor (discussed in A2) is seen as critical (B5). Selection should be based on a careful requirements analysis and an analysis of the experience and quality of the vendor. An important requirement is a sufficient degree of flexibility to customize and adapt the software to meet the needs of users and the work practices of the hospital (finding B1). At the same time the software product should challenge the hospital to rethink and improve its processes. A crucial condition for the acceptance by the diverse user groups of hospitals is the robustness of the EHR system in terms of availability, speed, reliability and flexibility (B2). This also requires adequate hardware in terms of access to computers, and mobile equipment to enable availability at all the locations of the hospital. Perceived ease of use of the system (B4) and the protection of patients’ privacy (B4) are other content factors that can make or break EHR implementation in hospitals.

The findings on the implementation process, our Category C, highlight four aspects that are commonly mentioned in change management approaches as important success factors in organizational change. The active involvement and support of management (C1), the participation of clinical staff (C2), a comprehensive implementation strategy (C4), and using an interdisciplinary implementation group (C5) correspond with three of the ten guidelines offered by Kanter et al. [ 46 ]. These three guidelines are: (1) support a strong leader role; (2) communicate, involve people, and be honest; and (3) craft an implementation plan. As the implementation of an EHR system is an organizational change process it is no surprise that these commonalities are identified in several of the analyzed articles. Three Category C findings (C2, C6, and C7) concern dealing with clinical staff given their powerful positions and potential resistance. Physicians are the most influential medical care providers, and their resistance can delay an EHR implementation [ 23 ], lead to at least some of it being dropped [ 21 , 22 , 34 ], or to it not being implemented at all [ 33 ]. Thus, there is ample evidence of the crucial importance of physicians’ acceptance of an EHR for it to be implemented. This means that clinicians and other key personnel should be highly engaged and motivated to contribute to EHR. Prompt feedback on requests, and high quality support during the implementation, and an EHR that clearly supports clinical work are key issues that contribute to a motivated clinical staff.

Analyzing and comparing the findings enables us to categorize them in terms of subject matter (see Table  7 ). By categorizing the findings in terms of subject, and by totaling the number of articles related to the individual findings on that subject, one can deduce how much attention has been given in the literature to the different topics. This analysis highlights that the involvement of physicians in the implementation process, the quality of the system, and a comprehensive implementation strategy are considered the crucial elements in EHR implementation.

Notwithstanding the useful results, this review and analysis has some limitations. Although we carefully developed and executed the search strategy, we cannot be sure that we found all the relevant articles. Since we focused narrowly on keywords, and these had to be part of an article’s title, we could have excluded relevant articles that used different terminology in their titles. Although searching the reference lists of identified articles did result in several additional articles, some relevant articles might still have been missed. Another limitation is the exclusion of publications in languages other than English. Further, the selection and categorization of specific findings, and the subsequent extraction of general findings, is subjective and depends on the interpretations of the authors, and other researchers might have made different choices. A final limitation is inherent to literature reviews in that the authors of the studies included may have had different motives and aims, and used different methods and interpretative means, in drawing their conclusions.

The existing literature fails to provide evidence of there being a comprehensive approach to implementing EHR systems in hospitals that integrates relevant aspects into an ‘EHR change approach’. The literature is diffuse, and articles seldom build on earlier ones to increase the theoretical knowledge on EHR implementation, notable exceptions being Aarts et al. [ 21 ], Aarts and Berg [ 22 ], Cresswell et al. [ 26 ], and Takian et al. [ 37 ]. The earlier discussion on the various results summarizes the existing knowledge and reveals gaps in the knowledge associated with EHR implementation. The number of EHR implementations in hospitals is growing, as well as the body of literature on this subject. This systematic review of the literature has produced 19 general findings on EHR implementation, which were each placed in one of three categories. A number of these general findings are in line with the wider literature on change management, and others relate to the specific nature of EHR implementation in hospitals.

The findings presented in this article can be viewed as an overview of important subjects that should be addressed in implementing an EHR system. It is clear that EHR systems have particular complexities and should be implemented with great care, and with attention given to context, content, and process issues and to interactions between these issues. As such, we have achieved our research goal by creating a systematic review of the literature on EHR implementation. This paper’s academic contribution is in providing an overview of the existing literature with regard to important factors in EHR implementation in hospitals. Academics interested in this specific field can now more easily access knowledge on EHR implementation in hospitals and can use this article as a starting point and build on the existing knowledge. The managerial contribution lies in the general findings that can be applied as guidelines when implementing EHR in hospitals. We have not set out to provide a single blueprint for implementing an EHR system, but rather to provide guidelines and to highlight points that deserve attention. Recognizing and addressing these aspects can increase the likelihood of getting an EHR system successfully implemented.

Appendix A - List of databases

This appendix provides an overview of all databases included in the used search engines. The databases in italic were excluded for the research as these databases focus on fields not relevant for the subject of EHR implementations.

Web of Knowledge

Web of Science

Biological Abstracts

Journal Citation Reports

Academic Search Premier

AMED - The Allied and Complementary Medicine Database

America : History & Life

American Bibliography of Slavic and East European Studies

Arctic & Antarctic Regions

Art Full Text ( H.W. Wilson )

Art Index Retrospective ( H.W. Wilson )

ATLA Religion Database with ATLASerials

Business Source Premier

Communication & Mass Media Complete

eBook Collection ( EBSCOhost )

Funk & Wagnalls New World Encyclopedia

Historical Abstracts

L ’ Annéephilologique

Library, Information Science & Technology Abstracts

MAS Ultra - School Edition

Military & Government Collection

MLA Directory of Periodicals

MLA International Bibliography

New Testament Abstracts

Old Testament Abstracts

Philosopher ’ s Index

Primary Search

PsycARTICLES

PsycCRITIQUES

Psychology and Behavioral Sciences Collection

Regional Business News

Research Starters - Business

RILM Abstracts of Music Literature

The Cochrane Library

Cochrane Database of Systematic Reviews

Cochrane Central Register of Controlled Trials

Cochrane Methodology Register

Database of Abstracts of Reviews of Effects

Health Technology Assessment Database

NHS Economic Evaluation Database

About The Cochrane Collaboration

Appendix B - Quality assessment

The quality of the articles was assessed with the Standard Quality Assessment Criteria for Evaluating Primary Research Papers [ 18 ]. Assessment was done by questioning whether particular criteria had been addressed, resulting in a rating of 2 (completely addressed), 1 (partly addressed), or 0 (not addressed) points. Table  8 provides the overview of the scores of the articles, (per question) for qualitative studies; Table  9 for quantitative studies; and Table  10 for mixed methods studies. Articles were included if they scored 50% or higher of the total amount of points possible. Based on this assessment, two articles were excluded from the search.

Appendix C - All findings

Table  11 displays all findings from the selected articles. The category number is related to the general finding as discussed in the Results section.

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We acknowledge the Master degree program Change Management at the University of Groningen for supporting this study. We also thank the referees for their valuable comments.

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Boonstra, A., Versluis, A. & Vos, J.F.J. Implementing electronic health records in hospitals: a systematic literature review. BMC Health Serv Res 14 , 370 (2014). https://doi.org/10.1186/1472-6963-14-370

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The electronic cigarette ( e-cigarette ), for many considered as a safe alternative to conventional cigarettes, has revolutionised the tobacco industry in the last decades. In e-cigarettes , tobacco combustion is replaced by e-liquid heating, leading some manufacturers to propose that e-cigarettes have less harmful respiratory effects than tobacco consumption. Other innovative features such as the adjustment of nicotine content and the choice of pleasant flavours have won over many users. Nevertheless, the safety of e-cigarette consumption and its potential as a smoking cessation method remain controversial due to limited evidence. Moreover, it has been reported that the heating process itself can lead to the formation of new decomposition compounds of questionable toxicity. Numerous in vivo and in vitro studies have been performed to better understand the impact of these new inhalable compounds on human health. Results of toxicological analyses suggest that e-cigarettes can be safer than conventional cigarettes, although harmful effects from short-term e-cigarette use have been described. Worryingly, the potential long-term effects of e-cigarette consumption have been scarcely investigated. In this review, we take stock of the main findings in this field and their consequences for human health including coronavirus disease 2019 (COVID-19).

Electronic nicotine dispensing systems (ENDS), commonly known as electronic cigarettes or e-cigarettes , have been popularly considered a less harmful alternative to conventional cigarette smoking since they first appeared on the market more than a decade ago. E-cigarettes are electronic devices, essentially consisting of a cartridge, filled with an e-liquid, a heating element/atomiser necessary to heat the e-liquid to create a vapour that can be inhaled through a mouthpiece, and a rechargeable battery (Fig.  1 ) [ 1 , 2 ]. Both the electronic devices and the different e-liquids are easily available in shops or online stores.

Effect of the heating process on aerosol composition. Main harmful effects documented. Several compounds detected in e-cigarette aerosols are not present in e-liquid s and the device material also seems to contribute to the presence of metal and silicate particles in the aerosols. The heating conditions especially on humectants, flavourings and the low-quality material used have been identified as the generator of the new compounds in aerosols. Some compounds generated from humectants (propylene glycol and glycerol) and flavourings, have been associated with clear airways impact, inflammation, impairment of cardiovascular function and toxicity. In addition, some of them are carcinogens or potential carcinogens

The e-liquid typically contains humectants and flavourings, with or without nicotine; once vapourised by the atomiser, the aerosol (vapour) provides a sensation similar to tobacco smoking, but purportedly without harmful effects [ 3 ]. However, it has been reported that the heating process can lead to the generation of new decomposition compounds that may be hazardous [ 4 , 5 ]. The levels of nicotine, which is the key addictive component of tobacco, can also vary between the commercially available e-liquids, and even nicotine-free options are available. For this particular reason, e-cigarettes are often viewed as a smoking cessation tool, given that those with nicotine can prevent smoking craving, yet this idea has not been fully demonstrated [ 2 , 6 , 7 ].

Because e-cigarettes are combustion-free, and because most of the damaging and well-known effects of tobacco are derived from this reaction, there is a common and widely spread assumption that e-cigarette consumption or “vaping” is safer than conventional cigarette smoking. However, are they risk-free? Is there sufficient toxicological data on all the components employed in e-liquids ? Do we really know the composition of the inhaled vapour during the heating process and its impact on health? Can e-cigarettes be used to curb tobacco use? Do their consumption impact on coronavirus disease 2019 (COVID-19)? In the present review, we have attempted to clarify these questions based on the existing scientific literature, and we have compiled new insights related with the toxicity derived from the use of these devices.

Effect of e-cigarette vapour versus conventional cigarette exposure: in vivo and in vitro effects

Numerous studies have been performed to evaluate the safety/toxicity of e-cigarette use both in vivo and in in vitro cell culture.

One of the first studies in humans involved the analysis of 9 volunteers that consumed e-cigarettes , with or without nicotine, in a ventilated room for 2 h [ 8 ]. Pollutants in indoor air, exhaled nitric oxide (NO) and urinary metabolite profiles were analysed. The results of this acute experiment revealed that e-cigarettes are not emission-free, and ultrafine particles formed from propylene glycol (PG) could be detected in the lungs. The study also suggested that the presence of nicotine in e-cigarettes increased the levels of NO exhaled from consumers and provoked marked airway inflammation; however, no differences were found in the levels of exhaled carbon monoxide (CO), an oxidative stress marker, before and after e-cigarette consumption [ 8 ]. A more recent human study detected significantly higher levels of metabolites of hazardous compounds including benzene, ethylene oxide, acrylonitrile, acrolein and acrylamide in the urine of adolescent dual users ( e-cigarettes and conventional tobacco consumers) than in adolescent e-cigarette -only users (Table 1 ) [ 9 ]. Moreover, the urine levels of metabolites of acrylonitrile, acrolein, propylene oxide, acrylamide and crotonaldehyde, all of which are detrimental for human health, were significantly higher in e-cigarette -only users than in non-smoker controls, reaching up to twice the registered values of those from non-smoker subjects (Table 1 ) [ 9 ]. In line with these observations, dysregulation of lung homeostasis has been documented in non-smokers subjected to acute inhalation of e-cigarette aerosols [ 10 ].

Little is known about the effect of vaping on the immune system. Interestingly, both traditional and e-cigarette consumption by non-smokers was found to provoke short-term effects on platelet function, increasing platelet activation (levels of soluble CD40 ligand and the adhesion molecule P-selectin) and platelet aggregation, although to a lesser extent with e-cigarettes [ 11 ]. As found with platelets, the exposure of neutrophils to e-cigarette aerosol resulted in increased CD11b and CD66b expression being both markers of neutrophil activation [ 12 ]. Additionally, increased oxidative stress, vascular endothelial damage, impaired endothelial function, and changes in vascular tone have all been reported in different human studies on vaping [ 13 , 14 , 15 , 16 , 17 ]. In this context, it is widely accepted that platelet and leukocyte activation as well as endothelial dysfunction are associated with atherogenesis and cardiovascular morbidity [ 18 , 19 ]. In line with these observations the potential association of daily e-cigarettes consumption and the increased risk of myocardial infarction remains controversial but benefits may occur when switching from tobacco to chronic e-cigarette use in blood pressure regulation, endothelial function and vascular stiffness (reviewed in [ 20 ]). Nevertheless, whether or not e-cigarette vaping has cardiovascular consequences requires further investigation.

More recently, in August 2019, the US Centers for Disease Control and Prevention (CDC) declared an outbreak of the e-cigarette or vaping product use-associated lung injury (EVALI) which caused several deaths in young population (reviewed in [ 20 ]). Indeed, computed tomography (CT scan) revealed local inflammation that impaired gas exchange caused by aerosolised oils from e-cigarettes [ 21 ]. However, most of the reported cases of lung injury were associated with use of e-cigarettes for tetrahydrocannabinol (THC) consumption as well as vitamin E additives [ 20 ] and not necessarily attributable to other e-cigarette components.

On the other hand, in a comparative study of mice subjected to either lab air, e-cigarette aerosol or cigarette smoke (CS) for 3 days (6 h-exposure per day), those exposed to e-cigarette aerosols showed significant increases in interleukin (IL)-6 but normal lung parenchyma with no evidence of apoptotic activity or elevations in IL-1β or tumour necrosis factor-α (TNFα) [ 22 ]. By contrast, animals exposed to CS showed lung inflammatory cell infiltration and elevations in inflammatory marker expression such as IL-6, IL-1β and TNFα [ 22 ]. Beyond airway disease, exposure to aerosols from e-liquids with or without nicotine has also been also associated with neurotoxicity in an early-life murine model [ 23 ].

Results from in vitro studies are in general agreement with the limited number of in vivo studies. For example, in an analysis using primary human umbilical vein endothelial cells (HUVEC) exposed to 11 commercially-available vapours, 5 were found to be acutely cytotoxic, and only 3 of those contained nicotine [ 24 ]. In addition, 5 of the 11 vapours tested (including 4 that were cytotoxic) reduced HUVEC proliferation and one of them increased the production of intracellular reactive oxygen species (ROS) [ 24 ]. Three of the most cytotoxic vapours—with effects similar to those of conventional high-nicotine CS extracts—also caused comparable morphological changes [ 24 ]. Endothelial cell migration is an important mechanism of vascular repair than can be disrupted in smokers due to endothelial dysfunction [ 25 , 26 ]. In a comparative study of CS and e-cigarette aerosols, Taylor et al . found that exposure of HUVEC to e-cigarette aqueous extracts for 20 h did not affect migration in a scratch wound assay [ 27 ], whereas equivalent cells exposed to CS extract showed a significant inhibition in migration that was concentration dependent [ 27 ].

In cultured human airway epithelial cells, both e-cigarette aerosol and CS extract induced IL-8/CXCL8 (neutrophil chemoattractant) release [ 28 ]. In contrast, while CS extract reduced epithelial barrier integrity (determined by the translocation of dextran from the apical to the basolateral side of the cell layer), e-cigarette aerosol did not, suggesting that only CS extract negatively affected host defence [ 28 ]. Moreover, Higham et al . also found that e-cigarette aerosol caused IL-8/CXCL8 and matrix metallopeptidase 9 (MMP-9) release together with enhanced activity of elastase from neutrophils [ 12 ] which might facilitate neutrophil migration to the site of inflammation [ 12 ].

In a comparative study, repeated exposure of human gingival fibroblasts to CS condensate or to nicotine-rich or nicotine-free e-vapour condensates led to alterations in morphology, suppression of proliferation and induction of apoptosis, with changes in all three parameters greater in cells exposed to CS condensate [ 29 ]. Likewise, both e-cigarette aerosol and CS extract increased cell death in adenocarcinomic human alveolar basal epithelial cells (A549 cells), and again the effect was more damaging with CS extract than with e-cigarette aerosol (detrimental effects found at 2 mg/mL of CS extract vs. 64 mg/mL of e-cigarette extract) [ 22 ], which is in agreement with another study examining battery output voltage and cytotoxicity [ 30 ].

All this evidence would suggest that e-cigarettes are potentially less harmful than conventional cigarettes (Fig.  2 ) [ 11 , 14 , 22 , 24 , 27 , 28 , 29 ]. Importantly, however, most of these studies have investigated only short-term effects [ 10 , 14 , 15 , 22 , 27 , 28 , 29 , 31 , 32 ], and the long-term effects of e-cigarette consumption on human health are still unclear and require further study.

Comparison of the degree of harmful effects documented from e-cigarette and conventional cigarette consumption. Human studies, in vivo mice exposure and in vitro studies. All of these effects from e-cigarettes were documented to be lower than those exerted by conventional cigarettes, which may suggest that e-cigarette consumption could be a safer option than conventional tobacco smoking but not a clear safe choice

Consequences of nicotine content

Beyond flavour, one of the major issues in the e-liquid market is the range of nicotine content available. Depending on the manufacturer, the concentration of this alkaloid can be presented as low , medium or high , or expressed as mg/mL or as a percentage (% v/v). The concentrations range from 0 (0%, nicotine-free option) to 20 mg/mL (2.0%)—the maximum nicotine threshold according to directive 2014/40/EU of the European Parliament and the European Union Council [ 33 , 34 ]. Despite this normative, however, some commercial e-liquids have nicotine concentrations close to 54 mg/mL [ 35 ], much higher than the limits established by the European Union.

The mislabelling of nicotine content in e-liquids has been previously addressed [ 8 , 34 ]. For instance, gas chromatography with a flame ionisation detector (GC-FID) revealed inconsistencies in the nicotine content with respect to the manufacturer´s declaration (average of 22 ± 0.8 mg/mL vs. 18 mg/mL) [ 8 ], which equates to a content ~ 22% higher than that indicated in the product label. Of note, several studies have detected nicotine in those e-liquids labelled as nicotine-free [ 5 , 35 , 36 ]. One study detected the presence of nicotine (0.11–6.90 mg/mL) in 5 of 23 nicotine-free labelled e-liquids by nuclear magnetic resonance spectroscopy [ 35 ], and another study found nicotine (average 8.9 mg/mL) in 13.6% (17/125) of the nicotine-free e-liquids as analysed by high performance liquid chromatography (HPLC) [ 36 ]. Among the 17 samples tested in this latter study 14 were identified to be counterfeit or suspected counterfeit. A third study detected nicotine in 7 of 10 nicotine-free refills, although the concentrations were lower than those identified in the previous analyses (0.1–15 µg/mL) [ 5 ]. Not only is there evidence of mislabelling of nicotine content among refills labelled as nicotine-free, but there also seems to be a history of poor labelling accuracy in nicotine-containing e-liquids [ 37 , 38 ].

A comparison of the serum levels of nicotine from e-cigarette or conventional cigarette consumption has been recently reported [ 39 ]. Participants took one vape from an e-cigarette , with at least 12 mg/mL of nicotine, or inhaled a conventional cigarette, every 20 s for 10 min. Blood samples were collected 1, 2, 4, 6, 8, 10, 12 and 15 min after the first puff, and nicotine serum levels were measured by liquid chromatography-mass spectrometry (LC–MS). The results revealed higher serum levels of nicotine in the conventional CS group than in the e-cigarette group (25.9 ± 16.7 ng/mL vs. 11.5 ± 9.8 ng/mL). However, e-cigarettes containing 20 mg/mL of nicotine are more equivalent to normal cigarettes, based on the delivery of approximately 1 mg of nicotine every 5 min [ 40 ].

In this line, a study compared the acute impact of CS vs. e-cigarette vaping with equivalent nicotine content in healthy smokers and non-smokers. Both increased markers of oxidative stress and decreased NO bioavailability, flow-mediated dilation, and vitamin E levels showing no significant differences between tobacco and e-cigarette exposure (reviewed in [ 20 ]). Inasmuch, short-term e-cigarette use in healthy smokers resulted in marked impairment of endothelial function and an increase in arterial stiffness (reviewed in [ 20 ]). Similar effects on endothelial dysfunction and arterial stiffness were found in animals when they were exposed to e-cigarette vapor either for several days or chronically (reviewed in [ 20 ]). In contrast, other studies found acute microvascular endothelial dysfunction, increased oxidative stress and arterial stiffness in smokers after exposure to e-cigarettes with nicotine, but not after e-cigarettes without nicotine (reviewed in [ 20 ]). In women smokers, a study found a significant difference in stiffness after smoking just one tobacco cigarette, but not after use of e-cigarettes (reviewed in [ 20 ]).

It is well known that nicotine is extremely addictive and has a multitude of harmful effects. Nicotine has significant biologic activity and adversely affects several physiological systems including the cardiovascular, respiratory, immunological and reproductive systems, and can also compromise lung and kidney function [ 41 ]. Recently, a sub-chronic whole-body exposure of e-liquid (2 h/day, 5 days/week, 30 days) containing PG alone or PG with nicotine (25 mg/mL) to wild type (WT) animals or knockout (KO) mice in α7 nicotinic acetylcholine receptor (nAChRα7-KO) revealed a partly nAChRα7-dependent lung inflammation [ 42 ]. While sub-chronic exposure to PG/nicotine promote nAChRα7-dependent increased levels of different cytokines and chemokines in the bronchoalveolar lavage fluid (BALF) such as IL-1α, IL-2, IL-9, interferon γ (IFNγ), granulocyte-macrophage colony-stimulating factor (GM-CSF), monocyte chemoattractant protein-1 (MCP-1/CCL2) and regulated on activation, normal T cell expressed and secreted (RANTES/CCL5), the enhanced levels of IL-1β, IL-5 and TNFα were nAChRα7 independent. In general, most of the cytokines detected in BALF were significantly increased in WT mice exposed to PG with nicotine compared to PG alone or air control [ 42 ]. Some of these effects were found to be through nicotine activation of NF-κB signalling albeit in females but not in males. In addition, PG with nicotine caused increased macrophage and CD4 + /CD8 + T-lymphocytes cell counts in BALF compared to air control, but these effects were ameliorated when animals were sub-chronically exposed to PG alone [ 42 ].

Of note, another study indicated that although RANTES/CCL5 and CCR1 mRNA were upregulated in flavour/nicotine-containing e-cigarette users, vaping flavour and nicotine-less e-cigarettes did not significantly dysregulate cytokine and inflammasome activation [ 43 ].

In addition to its toxicological effects on foetus development, nicotine can disrupt brain development in adolescents and young adults [ 44 , 45 , 46 ]. Several studies have also suggested that nicotine is potentially carcinogenic (reviewed in [ 41 ]), but more work is needed to prove its carcinogenicity independently of the combustion products of tobacco [ 47 ]. In this latter regard, no differences were encountered in the frequency of tumour appearance in rats subjected to long-term (2 years) inhalation of nicotine when compared with control rats [ 48 ]. Despite the lack of carcinogenicity evidence, it has been reported that nicotine promotes tumour cell survival by decreasing apoptosis and increasing proliferation [ 49 ], indicating that it may work as a “tumour enhancer”. In a very recent study, chronic administration of nicotine to mice (1 mg/kg every 3 days for a 60-day period) enhanced brain metastasis by skewing the polarity of M2 microglia, which increases metastatic tumour growth [ 50 ]. Assuming that a conventional cigarette contains 0.172–1.702 mg of nicotine [ 51 ], the daily nicotine dose administered to these animals corresponds to 40–400 cigarettes for a 70 kg-adult, which is a dose of an extremely heavy smoker. We would argue that further studies with chronic administration of low doses of nicotine are required to clearly evaluate its impact on carcinogenicity.

In the aforementioned study exposing human gingival fibroblasts to CS condensate or to nicotine-rich or nicotine-free e-vapour condensates [ 29 ], the detrimental effects were greater in cells exposed to nicotine-rich condensate than to nicotine-free condensate, suggesting that the possible injurious effects of nicotine should be considered when purchasing e-refills . It is also noteworthy that among the 3 most cytotoxic vapours for HUVEC evaluated in the Putzhammer et al . study, 2 were nicotine-free, which suggests that nicotine is not the only hazardous component in e-cigarettes [ 24 ] .

The lethal dose of nicotine for an adult is estimated at 30–60 mg [ 52 ]. Given that nicotine easily diffuses from the dermis to the bloodstream, acute nicotine exposure by e-liquid spilling (5 mL of a 20 mg/mL nicotine-containing refill is equivalent to 100 mg of nicotine) can easily be toxic or even deadly [ 8 ]. Thus, devices with rechargeable refills are another issue of concern with e-cigarettes , especially when e-liquids are not sold in child-safe containers, increasing the risk of spilling, swallowing or breathing.

These data overall indicate that the harmful effects of nicotine should not be underestimated. Despite the established regulations, some inaccuracies in nicotine content labelling remain in different brands of e-liquids . Consequently, stricter regulation and a higher quality control in the e-liquid industry are required.

Effect of humectants and their heating-related products

In this particular aspect, again the composition of the e-liquid varies significantly among different commercial brands [ 4 , 35 ]. The most common and major components of e-liquids are PG or 1,2-propanediol, and glycerol or glycerine (propane-1,2,3-triol). Both types of compounds are used as humectants to prevent the e-liquid from drying out [ 2 , 53 ] and are classified by the Food and Drug Administration (FDA) as “Generally Recognised as Safe” [ 54 ]. In fact, they are widely used as alimentary and pharmaceutical products [ 2 ]. In an analysis of 54 commercially available e-liquids , PG and glycerol were detected in almost all samples at concentrations ranging from 0.4% to 98% (average 57%) and from 0.3% to 95% (average 37%), respectively [ 35 ].

With regards to toxicity, little is known about the effects of humectants when they are heated and chronically inhaled. Studies have indicated that PG can induce respiratory irritation and increase the probability of asthma development [ 55 , 56 ], and both PG and glycerol from e-cigarettes might reach concentrations sufficiently high to potentially cause irritation of the airways [ 57 ]. Indeed, the latter study established that one e-cigarette puff results in a PG exposure of 430–603 mg/m 3 , which is higher than the levels reported to cause airway irritation (average 309 mg/m 3 ) based on a human study [ 55 ]. The same study established that one e-cigarette puff results in a glycerol exposure of 348–495 mg/m 3 [ 57 ], which is close to the levels reported to cause airway irritation in rats (662 mg/m 3 ) [ 58 ].

Airway epithelial injury induced by acute vaping of PG and glycerol aerosols (50:50 vol/vol), with or without nicotine, has been reported in two randomised clinical trials in young tobacco smokers [ 32 ]. In vitro, aerosols from glycerol only-containing refills showed cytotoxicity in A549 and human embryonic stem cells, even at a low battery output voltage [ 59 ]. PG was also found to affect early neurodevelopment in a zebrafish model [ 60 ]. Another important issue is that, under heating conditions PG can produce acetaldehyde or formaldehyde (119.2 or 143.7 ng/puff at 20 W, respectively, on average), while glycerol can also generate acrolein (53.0, 1000.0 or 5.9 ng/puff at 20 W, respectively, on average), all carbonyls with a well-documented toxicity [ 61 ]. Although, assuming 15 puffs per e-cigarette unit, carbonyls produced by PG or glycerol heating would be below the maximum levels found in a conventional cigarette combustion (Table 2 ) [ 51 , 62 ]. Nevertheless, further studies are required to properly test the deleterious effects of all these compounds at physiological doses resembling those to which individuals are chronically exposed.

Although PG and glycerol are the major components of e-liquids other components have been detected. When the aerosols of 4 commercially available e-liquids chosen from a top 10 list of “ Best E-Cigarettes of 2014” , were analysed by gas chromatography-mass spectrometry (GC–MS) after heating, numerous compounds were detected, with nearly half of them not previously identified [ 4 ], thus suggesting that the heating process per se generates new compounds of unknown consequence. Of note, the analysis identified formaldehyde, acetaldehyde and acrolein [ 4 ], 3 carbonyl compounds with known high toxicity [ 63 , 64 , 65 , 66 , 67 ]. While no information was given regarding formaldehyde and acetaldehyde concentrations, the authors calculated that one puff could result in an acrolein exposure of 0.003–0.015 μg/mL [ 4 ]. Assuming 40 mL per puff and 15 puffs per e-cigarette unit (according to several manufacturers) [ 4 ], each e-cigarette unit would generate approximately 1.8–9 μg of acrolein, which is less than the levels of acrolein emitted by a conventional tobacco cigarette (18.3–98.2 μg) [ 51 ]. However, given that e-cigarette units of vaping are not well established, users may puff intermittently throughout the whole day. Thus, assuming 400 to 500 puffs per cartridge, users could be exposed to up to 300 μg of acrolein.

In a similar study, acrolein was found in 11 of 12 aerosols tested, with a similar content range (approximately 0.07–4.19 μg per e-cigarette unit) [ 68 ]. In the same study, both formaldehyde and acetaldehyde were detected in all of the aerosols tested, with contents of 0.2–5.61 μg and 0.11–1.36 μg, respectively, per e-cigarette unit [ 68 ]. It is important to point out that the levels of these toxic products in e-cigarette aerosols are significantly lower than those found in CS: 9 times lower for formaldehyde, 450 times lower for acetaldehyde and 15 times lower for acrolein (Table 2 ) [ 62 , 68 ].

Other compounds that have been detected in aerosols include acetamide, a potential human carcinogen [ 5 ], and some aldehydes [ 69 ], although their levels were minimal. Interestingly, the existence of harmful concentrations of diethylene glycol, a known cytotoxic agent, in e-liquid aerosols is contentious with some studies detecting its presence [ 4 , 68 , 70 , 71 , 72 ], and others finding low subtoxic concentrations [ 73 , 74 ]. Similar observations were reported for the content ethylene glycol. In this regard, either it was detected at concentrations that did not exceed the authorised limit [ 73 ], or it was absent from the aerosols produced [ 4 , 71 , 72 ]. Only one study revealed its presence at high concentration in a very low number of samples [ 5 ]. Nevertheless, its presence above 1 mg/g is not allowed by the FDA [ 73 ]. Figure  1 lists the main compounds detected in aerosols derived from humectant heating and their potential damaging effects. It would seem that future studies should analyse the possible toxic effects of humectants and related products at concentrations similar to those that e-cigarette vapers are exposed to reach conclusive results.

Impact of flavouring compounds

The range of e-liquid flavours available to consumers is extensive and is used to attract both current smokers and new e-cigarette users, which is a growing public health concern [ 6 ]. In fact, over 5 million middle- and high-school students were current users of e-cigarettes in 2019 [ 75 ], and appealing flavours have been identified as the primary reason for e-cigarette consumption in 81% of young users [ 76 ]. Since 2016, the FDA regulates the flavours used in the e-cigarette market and has recently published an enforcement policy on unauthorised flavours, including fruit and mint flavours, which are more appealing to young users [ 77 ]. However, the long-term effects of all flavour chemicals used by this industry (which are more than 15,000) remain unknown and they are not usually included in the product label [ 78 ]. Furthermore, there is no safety guarantee since they may harbour potential toxic or irritating properties [ 5 ].

With regards to the multitude of available flavours, some have demonstrated cytotoxicity [ 59 , 79 ]. Bahl et al. evaluated the toxicity of 36 different e-liquids and 29 different flavours on human embryonic stem cells, mouse neural stem cells and human pulmonary fibroblasts using a metabolic activity assay. In general, those e-liquids that were bubblegum-, butterscotch- and caramel-flavoured did not show any overt cytotoxicity even at the highest dose tested. By contrast, those e-liquids with Freedom Smoke Menthol Arctic and Global Smoke Caramel flavours had marked cytotoxic effects on pulmonary fibroblasts and those with Cinnamon Ceylon flavour were the most cytotoxic in all cell lines [ 79 ]. A further study from the same group [ 80 ] revealed that high cytotoxicity is a recurrent feature of cinnamon-flavoured e-liquids. In this line, results from GC–MS and HPLC analyses indicated that cinnamaldehyde (CAD) and 2-methoxycinnamaldehyde, but not dipropylene glycol or vanillin, were mainly responsible for the high cytotoxicity of cinnamon-flavoured e-liquids [ 80 ]. Other flavouring-related compounds that are associated with respiratory complications [ 81 , 82 , 83 ], such as diacetyl, 2,3-pentanedione or acetoin, were found in 47 out of 51 aerosols of flavoured e-liquids tested [ 84 ] . Allen et al . calculated an average of 239 μg of diacetyl per cartridge [ 84 ]. Assuming again 400 puffs per cartridge and 40 mL per puff, is it is possible to estimate an average of 0.015 ppm of diacetyl per puff, which could compromise normal lung function in the long-term [ 85 ].

The cytotoxic and pro-inflammatory effects of different e-cigarette flavouring chemicals were also tested on two human monocytic cell lines—mono mac 6 (MM6) and U937 [ 86 ]. Among the flavouring chemicals tested, CAD was found to be the most toxic and O-vanillin and pentanedione also showed significant cytotoxicity; by contrast, acetoin, diacetyl, maltol, and coumarin did not show any toxicity at the concentrations assayed (10–1000 µM). Of interest, a higher toxicity was evident when combinations of different flavours or mixed equal proportions of e-liquids from 10 differently flavoured e-liquids were tested, suggesting that vaping a single flavour is less toxic than inhaling mixed flavours [ 86 ]. Also, all the tested flavours produced significant levels of ROS in a cell-free ROS production assay. Finally, diacetyl, pentanedione, O-vanillin, maltol, coumarin, and CAD induced significant IL-8 secretion from MM6 and U937 monocytes [ 86 ]. It should be borne in mind, however, that the concentrations assayed were in the supra-physiological range and it is likely that, once inhaled, these concentrations are not reached in the airway space. Indeed, one of the limitations of the study was that human cells are not exposed to e-liquids per se, but rather to the aerosols where the concentrations are lower [ 86 ]. In this line, the maximum concentration tested (1000 µM) would correspond to approximately 80 to 150 ppm, which is far higher than the levels found in aerosols of some of these compounds [ 84 ]. Moreover, on a day-to-day basis, lungs of e-cigarette users are not constantly exposed to these chemicals for 24 h at these concentrations. Similar limitations were found when five of seven flavourings were found to cause cytotoxicity in human bronchial epithelial cells [ 87 ].

Recently, a commonly commercialized crème brûlée -flavoured aerosol was found to contain high concentrations of benzoic acid (86.9 μg/puff), a well-established respiratory irritant [ 88 ]. When human lung epithelial cells (BEAS-2B and H292) were exposed to this aerosol for 1 h, a marked cytotoxicity was observed in BEAS-2B but not in H292 cells, 24 h later. However, increased ROS production was registered in H292 cells [ 88 ].

Therefore, to fully understand the effects of these compounds, it is relevant the cell cultures selected for performing these assays, as well as the use of in vivo models that mimic the real-life situation of chronic e-cigarette vapers to clarify their impact on human health.

The e-cigarette device

While the bulk of studies related to the impact of e-cigarette use on human health has focused on the e-liquid components and the resulting aerosols produced after heating, a few studies have addressed the material of the electronic device and its potential consequences—specifically, the potential presence of metals such as copper, nickel or silver particles in e-liquids and aerosols originating from the filaments and wires and the atomiser [ 89 , 90 , 91 ].

Other important components in the aerosols include silicate particles from the fiberglass wicks or silicone [ 89 , 90 , 91 ]. Many of these products are known to cause abnormalities in respiratory function and respiratory diseases [ 89 , 90 , 91 ], but more in-depth studies are required. Interestingly, the battery output voltage also seems to have an impact on the cytotoxicity of the aerosol vapours, with e-liquids from a higher battery output voltage showing more toxicity to A549 cells [ 30 ].

A recent study compared the acute effects of e-cigarette vapor (with PG/vegetable glycerine plus tobacco flavouring but without nicotine) generated from stainless‐steel atomizer (SS) heating element or from a nickel‐chromium alloy (NC) [ 92 ]. Some rats received a single e-cigarette exposure for 2 h from a NC heating element (60 or 70 W); other rats received a similar exposure of e-cigarette vapor using a SS heating element for the same period of time (60 or 70 W) and, a final group of animals were exposed for 2 h to air. Neither the air‐exposed rats nor those exposed to e-cigarette vapor using SS heating elements developed respiratory distress. In contrast, 80% of the rats exposed to e-cigarette vapor using NC heating units developed clinical acute respiratory distress when a 70‐W power setting was employed. Thus, suggesting that operating units at higher than recommended settings can cause adverse effects. Nevertheless, there is no doubt that the deleterious effects of battery output voltage are not comparable to those exerted by CS extracts [ 30 ] (Figs.  1 and 2 ).

E-cigarettes as a smoking cessation tool

CS contains a large number of substances—about 7000 different constituents in total, with sizes ranging from atoms to particulate matter, and with many hundreds likely responsible for the harmful effects of this habit [ 93 ]. Given that tobacco is being substituted in great part by e-cigarettes with different chemical compositions, manufacturers claim that e -cigarette will not cause lung diseases such as lung cancer, chronic obstructive pulmonary disease, or cardiovascular disorders often associated with conventional cigarette consumption [ 3 , 94 ]. However, the World Health Organisation suggests that e-cigarettes cannot be considered as a viable method to quit smoking, due to a lack of evidence [ 7 , 95 ]. Indeed, the results of studies addressing the use of e-cigarettes as a smoking cessation tool remain controversial [ 96 , 97 , 98 , 99 , 100 ]. Moreover, both FDA and CDC are actively investigating the incidence of severe respiratory symptoms associated with the use of vaping products [ 77 ]. Because many e-liquids contain nicotine, which is well known for its powerful addictive properties [ 41 ], e-cigarette users can easily switch to conventional cigarette smoking, avoiding smoking cessation. Nevertheless, the possibility of vaping nicotine-free e-cigarettes has led to the branding of these devices as smoking cessation tools [ 2 , 6 , 7 ].

In a recently published randomised trial of 886 subjects who were willing to quit smoking [ 100 ], the abstinence rate was found to be twice as high in the e-cigarette group than in the nicotine-replacement group (18.0% vs. 9.9%) after 1 year. Of note, the abstinence rate found in the nicotine-replacement group was lower than what is usually expected with this therapy. Nevertheless, the incidence of throat and mouth irritation was higher in the e-cigarette group than in the nicotine-replacement group (65.3% vs. 51.2%, respectively). Also, the participant adherence to the treatment after 1-year abstinence was significantly higher in the e-cigarette group (80%) than in nicotine-replacement products group (9%) [ 100 ].

On the other hand, it is estimated that COPD could become the third leading cause of death in 2030 [ 101 ]. Given that COPD is generally associated with smoking habits (approximately 15 to 20% of smokers develop COPD) [ 101 ], smoking cessation is imperative among COPD smokers. Published data revealed a clear reduction of conventional cigarette consumption in COPD smokers that switched to e-cigarettes [ 101 ]. Indeed, a significant reduction in exacerbations was observed and, consequently, the ability to perform physical activities was improved when data was compared with those non-vapers COPD smokers. Nevertheless, a longer follow-up of these COPD patients is required to find out whether they have quitted conventional smoking or even vaping, since the final goal under these circumstances is to quit both habits.

Based on the current literature, it seems that several factors have led to the success of e-cigarette use as a smoking cessation tool. First, some e-cigarette flavours positively affect smoking cessation outcomes among smokers [ 102 ]. Second, e-cigarettes have been described to improve smoking cessation rate only among highly-dependent smokers and not among conventional smokers, suggesting that the individual degree of nicotine dependence plays an important role in this process [ 97 ]. Third, the general belief of their relative harmfulness to consumers' health compared with conventional combustible tobacco [ 103 ]. And finally, the exposure to point-of-sale marketing of e-cigarette has also been identified to affect the smoking cessation success [ 96 ].

Implication of e-cigarette consumption in COVID-19 time

Different reports have pointed out that smokers and vapers are more vulnerable to SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) infections or more prone to adverse outcomes if they suffer COVID-19 [ 104 ]. However, while a systematic review indicated that cigarette smoking is probably associated with enhanced damage from COVID-19, a meta-analysis did not, yet the latter had several limitations due to the small sample sizes [ 105 ].

Interestingly, most of these reports linking COVID-19 harmful effects with smoking or vaping, are based on their capability of increasing the expression of angiotensin-converting enzyme 2 (ACE2) in the lung. It is well known that ACE2 is the gate for SARS-CoV-2 entrance to the airways [ 106 ] and it is mainly expressed in type 2 alveolar epithelial cells and alveolar macrophages [ 107 ]. To date, most of the studies in this field indicate that current smokers have higher expression of ACE2 in the airways (reviewed by [ 108 ]) than healthy non-smokers [ 109 , 110 ]. However, while a recent report indicated that e-cigarette vaping also caused nicotine-dependent ACE2 up-regulation [ 42 ], others have revealed that neither acute inhalation of e-cigarette vapour nor e-cigarette users had increased lung ACE2 expression regardless nicotine presence in the e-liquid [ 43 , 110 ].

In regard to these contentions, current knowledge suggests that increased ACE2 expression is not necessarily linked to enhanced susceptibility to SARS-CoV-2 infection and adverse outcome. Indeed, elderly population express lower levels of ACE2 than young people and SARS-CoV-2/ACE2 interaction further decreases ACE2 expression. In fact, most of the deaths provoked by COVID-19 took place in people over 60 years old of age [ 111 ]. Therefore, it is plausible that the increased susceptibility to disease progression and the subsequent fatal outcome in this population is related to poor angiotensin 1-7 (Ang-1-7) generation, the main peptide generated by ACE2, and probably to their inaccessibility to its anti-inflammatory effects. Furthermore, it seems that all the efforts towards increasing ACE2 expression may result in a better resolution of the pneumonic process associated to this pandemic disease.

Nevertheless, additional complications associated to COVID-19 are increased thrombotic events and cytokine storm. In the lungs, e-cigarette consumption has been correlated to toxicity, oxidative stress, and inflammatory response [ 32 , 112 ]. More recently, a study revealed that while the use of nicotine/flavour-containing e-cigarettes led to significant cytokine dysregulation and potential inflammasome activation, none of these effects were detected in non-flavoured and non-nicotine-containing e-cigarettes [ 43 ]. Therefore, taken together these observations, e-cigarette use may still be a potent risk factor for severe COVID-19 development depending on the flavour and nicotine content.

In summary, it seems that either smoking or nicotine vaping may adversely impact on COVID-19 outcome. However, additional follow up studies are required in COVID-19 pandemic to clarify the effect of e-cigarette use on lung and cardiovascular complications derived from SARS-CoV-2 infection.

Conclusions

The harmful effects of CS and their deleterious consequences are both well recognised and widely investigated. However, and based on the studies carried out so far, it seems that e-cigarette consumption is less toxic than tobacco smoking. This does not necessarily mean, however, that e-cigarettes are free from hazardous effects. Indeed, studies investigating their long-term effects on human health are urgently required. In this regard, the main additional studies needed in this field are summarized in Table 3 .

The composition of e-liquids requires stricter regulation, as they can be easily bought online and many incidences of mislabelling have been detected, which can seriously affect consumers’ health. Beyond their unknown long-term effects on human health, the extended list of appealing flavours available seems to attract new “never-smokers”, which is especially worrying among young users. Additionally, there is still a lack of evidence of e-cigarette consumption as a smoking cessation method. Indeed, e-cigarettes containing nicotine may relieve the craving for smoking, but not the conventional cigarette smoking habit.

Interestingly, there is a strong difference of opinion on e-cigarettes between countries. Whereas countries such as Brazil, Uruguay and India have banned the sale of e-cigarettes , others such as the United Kingdom support this device to quit smoking. The increasing number of adolescent users and reported deaths in the United States prompted the government to ban the sale of flavoured e-cigarettes in 2020. The difference in opinion worldwide may be due to different restrictions imposed. For example, while no more than 20 ng/mL of nicotine is allowed in the EU, e-liquids with 59 mg/dL are currently available in the United States. Nevertheless, despite the national restrictions, users can easily access foreign or even counterfeit products online.

In regard to COVID-19 pandemic, the actual literature suggests that nicotine vaping may display adverse outcomes. Therefore, follow up studies are necessary to clarify the impact of e-cigarette consumption on human health in SARS-CoV-2 infection.

In conclusion, e-cigarettes could be a good alternative to conventional tobacco cigarettes, with less side effects; however, a stricter sale control, a proper regulation of the industry including flavour restriction, as well as further toxicological studies, including their chronic effects, are warranted.

Availability of data and materials

Not applicable.

Abbreviations

Angiotensin-converting enzyme 2

Angiotensin 1-7

Bronchoalveolar lavage fluid

Cinnamaldehyde

US Centers for Disease Control and Prevention

Carbon monoxide

Chronic obstructive pulmonary disease

Coronavirus disease 2019

Cigarette smoke

Electronic nicotine dispensing systems

e-cigarette or vaping product use-associated lung injury

Food and Drug Administration

Gas chromatography with a flame ionisation detector

Gas chromatography-mass spectrometry

Granulocyte–macrophage colony-stimulating factor

High performance liquid chromatography

Human umbilical vein endothelial cells

Interleukin

Interferon γ

Liquid chromatography-mass spectrometry

Monocyte chemoattractant protein-1

Matrix metallopeptidase 9

α7 Nicotinic acetylcholine receptor

Nickel‐chromium alloy

Nitric oxide

Propylene glycol

Regulated on activation, normal T cell expressed and secreted

Reactive oxygen species

Severe acute respiratory syndrome coronavirus 2

Stainless‐steel atomizer

Tetrahydrocannabinol

Tumour necrosis factor-α

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Acknowledgements

The authors gratefully acknowledge Dr. Cruz González, Pulmonologist at University Clinic Hospital of Valencia (Valencia, Spain) for her thoughtful suggestions and support.

This work was supported by the Spanish Ministry of Science and Innovation [Grant Number SAF2017-89714-R]; Carlos III Health Institute [Grant Numbers PIE15/00013, PI18/00209]; Generalitat Valenciana [Grant Number PROMETEO/2019/032, Gent T CDEI-04/20-A and AICO/2019/250], and the European Regional Development Fund.

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Marques, P., Piqueras, L. & Sanz, MJ. An updated overview of e-cigarette impact on human health. Respir Res 22 , 151 (2021). https://doi.org/10.1186/s12931-021-01737-5

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Impact of COVID-19 outbreak on the mental health in sports: a review

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Global pandemic, lockdown restrictions, and COVID-19 compulsory social isolation guidelines have raised unprecedented mental health in the sports community. The COVID-19 pandemic is found to affect the mental health of the population. In critical situations, health authorities and sports communities must identify their priorities and make plans to maintain athletes’ health and athletic activities. Several aspects play an important role in prioritization and strategic planning, e.g., physical and mental health, distribution of resources, and short to long-term environmental considerations. To identify the psychological health of sportspeople and athletes due to the outbreak of COVID-19 has been reviewed in this research. This review article also analyzes the impact of COVID-19 on health mental in databases. The COVID-19 outbreak and quarantine would have a serious negative impact on the mental health of athletes. From the accessible sources, 80 research articles were selected and examined for this purpose such as Research Gate, PubMed, Google Scholar, Springer, Scopus, and Web of Science and based on the involvement for this study 14 research articles were accessed. This research has an intention on mental health issues in athletes due to the Pandemic. This report outlines the mental, emotional and behavioural consequences of COVID-19 home confinement. Further, research literature reported that due to the lack of required training, physical activity, practice sessions, and collaboration with teammates and coaching staff are the prime causes of mental health issues in athletes. The discussions also reviewed several pieces of literature which examined the impacts on sports and athletes, impacts on various countries, fundamental issues of mental health and the diagnosis for the sports person and athletes, and the afterlife of the COVID-19 pandemic for them. Because of the compulsory restrictions and guidelines of this COVID-19 eruption, the athletes of different sports and geographical regions are suffering from fewer psychological issues which were identified in this paper. Accordingly, the COVID-19 pandemic appears to negatively affect the mental health of the athletes with the prevalence and levels of anxiety and stress increasing, and depression symptoms remaining unaltered. Addressing and mitigating the negative effect of COVID-19 on the mental health of this population identified from this review.

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Sports psychiatry: mental health and mental disorders in athletes and exercise treatment of mental disorders

Andreas Ströhle

Sport and Transgender People: A Systematic Review of the Literature Relating to Sport Participation and Competitive Sport Policies

Bethany Alice Jones, Jon Arcelus, … Emma Haycraft

Avoid common mistakes on your manuscript.

Introduction

COVID-19 is an arising irresistible illness brought about by the newfound Extreme Intense Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The first patient with COVID-19 was identified in Wuhan, Hubei territory as per the research of WHO [World Health Organization] in 2020. In addition, contamination has spread quickly all over the world which resulted in numerous extreme and lethal clinical cases [ 1 ]. It is identified as a highly transmitted disease which can transmit from one person to another person through the droplets of respiration, hands, nose, mouth etc. and also a high infectiousness disease [ 2 ]. The number of mortalities and grimness around the world due to COVID-19 have raised critical general health and well-being concerns. Additionally, identifying, diagnosing, and treating those who were infected, as well as developing medicines, antibodies, and treatments was focused on by all countries and the World Health Organization to decrease the effect of this pandemic [ 3 ]. Finally, governments were constraining nearly a worldwide quarantine [ 4 ]. As a result, all the people maintained social distancing to overcome this issue. Other countries announced several conditions like no contact between people and also lockdown had been declared [ 5 ]. The refugee crisis has also affected the world of sports affairs.

Due to this pandemic, several individuals get affected which leads to disruption, anxiety, stress, stigma, and xenophobia. In a society or community, the act of an individual affects the agitation of the pandemic which contains the level of severity, degree of flow, and aftereffects [ 6 ]. The complete information about the virus and its effects must be known prevent it. To control the spreading of the virus, regional lockdowns were implemented due to the people-to-people transmission of the SARS-CoV-2. The transmission chain has been broken by employing isolation, social distancing, and closing of educational institutes, workplaces, and entertainment venues by which people stay in their homes [ 7 ]. The social and mental health of people gets greatly affected because of these strict actions throughout the board [ 8 ]. The WHO recommends people stay active and available at home to reduce social relationships during the initial wave of COVID-19 and to prevent the spread of the virus. Throughout the world, after the decrease in the count of COVID-19 cases [ 9 , 10 ], and due to the limit of outbreaks in the initial stage, a survey shows that there is a rise in the COVID-19 cases in the second wave in many regions of the world [ 11 ]. Zhao et al . [ 12 ] say that the second wave of infections will be indicated by the control measures and social distance carried out during the first wave of COVID-19 transmission. Due to this, the athletes faced challenges in doing their regular activities with the supervision of their coach and scientific experts, during the placement period.

Several efforts were carried out to prevent the pandemic situation, but there is no clear information about what will be the next steps followed in the upcoming days. The long-lasting effects of Coronavirus have given great worry to the global environment such as declined economy, venture surety, worldwide market stocks, human well-being, daily groceries supply and medical emergency services. To control the spread of the COVID-19 pandemic, strict actions were followed by the governments like severe lockdowns, restriction of social groups, and organisations like sports events and also unnecessary travel has also been prohibited which greatly affects the sports industry and athletes [ 13 ]. For this reason, the athletes were incapable to regulate their regular training sessions as well not participate in any sports events due to suspensions. Further, Turgut et al. [ 14 ] reported the cancellations and postponements of various global sporting events to follow the global health recommendations and to restrict the spread of the infection.

By considering the risk of transmission and the health problems for both the spectators and the field players, several nations have postponed the local professional football leagues [ 15 ]. Severe economic issues and lack of income were the results of COVID-19 and the elite football clubs also face several problems due to this pandemic [ 16 ]. The final match of the UEFA Champions League and other fewer games were postponed by the Union of European Football Associations (UEFA) to March 2020, and the International Olympic Committee (IOC) and the government of Japan also postponed the Tokyo Olympics of 2020 to July 2021 in which there is no change in the name as 2020 Tokyo Olympics [ 17 ]. Totally of about 57% of the 11,000 athletes who have registered for the postponed Olympic games have already met the requirements, following the International Olympic Committee (IOC) However, the majority of these athletes are now confined as a result of the COVID-19 restriction, which was extended till 2021. Therefore, because of the pandemic these big decisions of cancellation and delaying the tournaments were taken due to which many athletes confronted tight limitations to proceed with their normal preparations or practices. Health authorities prescribe these constraints to avoid the public gathering during matches and events that might work to a quick spread of Coronavirus, bringing about extra tension in the medical services framework [ 18 , 19 , 20 ].

To evade the COVID-19 infection during the lockdown self-isolation, limitations, social disconnection arrangements and an environment of uncertainty created an adverse effect on the populace's mental health [ 21 ] and already available evidence appears to affirm these forecasts [ 22 ]. During the first month of internment, nearly 15.8% and 21.6% of the total population of Sain faced depression, anxiety, and post-traumatic symptoms as per the report of González-Sanguino et al . [ 23 ]. Further, WHO [ 18 , 19 , 20 ] is also concerned about these mental health and psycho-social issues due to this pandemic.

However, to understand these outcomes, there is a need to study the results of the coronavirus pandemic in the sports setting. In that context, Trabelsi et al. [ 24 ] also reported that few coaches, sports psychologists and even psychiatrists found some mental issues in athletes, that may cause adverse consequences in their life. Furthermore, Reardon et al. [ 25 ] identified in a narrative review that elite athletes were suffering from various psychological issues at rates identical to or surpassing the common population due to COVID-19. Moreover, the field specialists cautiously screened and observed the athletes during the Coronavirus pandemic and expressed those athletes needed a mental advisory for adjustments. Similarly, Turgut stated that the new measures of self-segregation from others and quarantine affects exercise, practice routine as well as lifestyle resulting in prompt physical and mental challenges for athletes due to the COVID-19 pandemic. Before the COVID-19 pandemic, elite athletes encountered a lot of stressors during their career, the COVID-19 restrictions seem to have amplified all the stressors with negative consequences on the mental health of athletes. Unfortunately, the present literature does not seem to clarify the possible causes and effects of COVID-19 restrictions on athletes. Subsequently, the present narrative review aims to describe the COVID-19 pandemic lockdown influenced the mental health of elite athletes. Specifically, the primary objective of this review is to identify the common psychological distress and stress responses in elite athletes during the COVID-19 pandemic. Consequently, this study aims to identify factors, either positive or negative, related to psychological distress in elite athletes during the COVID-19 pandemic from various research articles.

Impact of COVID-19 on mental health

Several reasons were identified for this. The people who combat the public health factors (like vaccination) and how they deal with the risk of infections and following losses which was mainly due to the psychological measures. The treatment of any infectious disease like COVID-19 is one of the main problems. The maladaptive behaviours, emotional distress and defensive responses were the results due to the Psychological effects of the pandemic [ 26 ]. The people who were affected psychologically will be harsher. We need to accept that, there will be a low lifespan for the people who were affected mentally and this results in poor physical health in normal cases rather than in other populations [ 27 ]. People who already have mental health or use drug problems are more likely to contract COVID-19, and they may face difficulties getting tested or treated and suffer unfavourable medical or mental impacts as a result of the pandemic.

Secondly, from this study, it is predicted that an increase in anxiety and depression symptoms, with some individuals, eventually developing post-traumatic stress disorder, among those who do not already have these diseases. From the evidence, a suggestion is made that throughout the current pandemic, this risk was not fully recognized in China [ 28 ].

Third, an assumption is made that, the people who work in public health, primary care, emergency services, emergency departments and intensive or critical care may face several psychological disorders. While this risk to healthcare workers has been formally identified by the World Health Organization, more needs to be done to manage anxiety and stress in this population and, in the long run, to help prevent burnout, depression, and post-traumatic stress disorder [ 29 ]. However, physical exercise training generally has health benefits and assists in the prevention of several chronic diseases. Moreover, physical activity improves mental health by reducing anxiety, depression, and negative mood and improving self-esteem. Therefore, the beneficial effects of adapted physical activity, based on personalized and tailor-made exercise, in preventing, treating, and counteracting the consequences of COVID-19 are analysed [ 30 ].

Consequently, it is important to identify some of the unique challenges this population currently faces, and understand where our student-athletes are mentally and physically. This is to ensure their needs are addressed, and the health and well-being of this population are protected. [ 31 ] assessed the impact of the COVID-19 pandemic on Canadian high-performance secondary school student-athletes. Student-athletes should be provided additional mental health support during this maelstrom of changes. In particular, additional mental health support for student-athletes should be anticipated in this maelstrom of changes; specific in-home virtual training during the COVID-19 outbreak should be further strengthened and improved to protect the mental and physical health of the athletes, especially to reduce the risk of anxiety and depression.

Impact of COVID-19 on sports

Throughout the world, the COVID-19 virus has been spread virtually, and to stop the spread of this disease, companies, schools, and colleges have been locked down, and general social life like sports and physical activities has also been hindered. The challenges faced by the athletic industry have been mentioned in the COVID-19 lockdown policy. As a result of the fast transmission of this coronavirus, millions of people have lost their lives, the largest indoor and outdoor sports events have been affected, and without the view of competitions the national and international level sports have been postponed or cancelled or rescheduled or location changes happened [ 32 ]. Sports events have been greatly affected by the COVID-19 virus and there are rescheduled international events like the Olympics which have been discussed earlier.

In overall history, this is the first time the cancellation of Olympic Games due to a medical issue [ 33 ]. The financial loss is not only faced by the country Japan but also the 11,000 Olympic athletes and 4400 Paralympians who participated in several sports events of the Olympics also faced this problem. The Olympics is one of the rare and great opportunities for athletes to establish their talents through participation in competitions in front of the total world. Every participant had worked hard and undergo much training for this. During March and April 2020, football clubs would not be required to release players for national teams, according to a FIFA announcement made on March 13, 2020. Without any response, the players have the opportunity to decline. As per the suggestion of FIFA, all international matches must take place outside of the slots, however, the final choice is based upon the administrators of the competition member associations for friendly matches [ 34 ]. Other sporting events, including the Wimbledon championship, the basketball and football tournaments, the athletics championship, handball and ice hockey, cricket, rugby, skiing, weightlifting, and wrestling were able to modify their schedules or can cancel their competitions altogether. For the top athletes, their professional career gets affected greatly due to this rescheduling of the Olympics and several National and International sports events. Along with the discussion about the performance of the athletes, the effects of COVID-19 on sports events must also be considered. Based on factors like location, opposition, score, number of recovery days, and tactical system, the performance of athletes relies [ 35 ].

Because of this lockdown during the COVID-19 pandemic, throughout the world, there are millions of jobs at risk. Rather than the sports person, the people who were engaged in retail and other services, sports industries along with the sports events and leagues that contain transportation, infrastructure facility, travel, tourism, catering, and media broadcasting in the field of sports were also get affected [ 36 ]. A lot of pressure arises among the athletes and professional players because of this postponement of the competitions. Initially, there is no support from the sponsors if they decide to make them fit in the home itself.

Several educational institutions along with sports education are also get affected because of the COVID-19 lockdown, and those stakeholders the local and national ministries, public and private educational institutions, sports organizations, NGOs and the business community, teachers, scholars, coaches, athletes, parents and some young people were also involved.

Impact of COVID-19 on physical activity

Due to the cancellation of sports events during the COVID-19 lockdown, all the other outdoor activities were also restricted. Furthermore, gyms, stadiums, pools, dance and fitness studios, physiotherapy clinics, and parks were forced to close. These factors encouraged athletes to alter their fitness routine and train at home, where they are frequently not observed by qualified health workers or trained coaches. Several athletes have their gym at home or other pieces of exercise equipment which they can use to practise regularly during a lockdown. Their current level of physical fitness should be maintained, or at the very least not decreased, during the home activity period [ 37 ]. However, most people are unfortunately unable to be actively involved in their regular outside individual or group sporting or physical activities. A high level of physical fitness is required by elite athletes irrespective of the specific type of sport. Generally speaking, elite athletes avoid long periods of rest during and at the end of the competitive season [ 38 ].

The immune system and the anti-viral defences were greatly affected because of continuous exercise every day [ 39 ]. A low regulate exercise is resulted due to the order of stay-at-home by the government and closures of parks, gyms, stadiums, and fitness centres to stop the spread of SARS-CoV-2. Since regular exercise can boost the immune system of a sportsperson and can able to treat several co-morbidities like obesity, diabetes, hypertension, and severe heart diseases that make athletes more prone to infections like COVID-19 so it is considered an unacceptable instruction [ 40 ].

Since they affect several sports damage processes and have the potential to improve repeat intervention and prevention, psychological elements underlying the various stages of sports injury are becoming more and more essential [ 41 ]. Rather than the new concept in history, the confinement scenario resulting from COVID-19 shares several issues with the various stages of sports injury encountered by athletes. The sports activity can be reduced due to some inference, reduction in autonomy, alterations in the sports environment, as a single or group there is a lot of chances to increase their records in the sports field, prohibition of activities that are not related with it, personal and family life changes like earlier retirement because of the alterations in the schedule of sports events. Now there is the existence of deeper problems like abuse of substances, social distance, depressive or anxiety episodes, suicidal thoughts, self-esteem problems, and poor sleep quality. Because a poor perception of the quality of sleep can harm the health of a sportsperson, along with the life of the sportsperson the latter factor is also included [ 42 ]. Long periods of isolation may lead to personal growth and development of the psychological processes of sports exercise, which is under the discussion with the writers. There are many adjustments made by sportspersons because of the existence of restrictions throughout the world since they lack the equipment or appropriate areas to develop their training routines effectively [ 43 ]. Because of the prohibition or postponement of all the local, national, and worldwide contests, this fact has prompted us to investigate how the athletes face this complex situation and their issues. Consequently, during this complicated scenario, particular emphasis should be dedicated to specific exercise interventions tailored for subjects and athletes recovering from COVID-19 [ 44 ]. Studying the psychological effects both good and bad that this situation may have a great interest in the individuals.

For the athletes, both the physical and mental issues get increased due to this continuous COVID-19 lockdown. There arises an unstable life for sports players due to the prohibition and rescheduling of sports events. Professional players or athletes feel stressed because they are pushed to the situation to handle all the problems behind them. The level of worry, stress and anxiety may get increased due to the unstable future [ 45 ].

However, to the researcher’s awareness, how far the mental health of the athletes and professionals get affected due to this pandemic has been examined through several researches and surveys. During the continuous lockdown of COVID-19, athletes and sportspeople have faced a lot of issues like difficulties in sleep, sadness and depression rather than an increase in their physical activities [ 46 ]. To address these mental issues and information and illuminate the sports fraternity as well as the general society about the mental challenges an athlete is facing during this COVID-19 outbreak, this review article's impact of a COVID-19 outbreak on the mental health in sports was taken. To examine the current status of the professional athletes who went for a break during the pandemic period and to measure their mental health several surveys have been carried out. An investigation was also carried out to identify the physical and mental activity of the athletes while they stay at the home.

Methodology

The scoping review was carried out for the criteria and procedures outlined in the available systematic literature data factors and Meta-Analysis (PRISMA) with the Scoping Reviews extension.

The available literature on aerobic exercise intervention on body composition in obese females was considered for the present study. Figure  1 shows the PRISMA flowchart. From the sources like Research Gate, Pub Med, Google Scholar, Springer, Scopus, and Web of Science, a total of 80 research articles were gathered for the study and among that 14 sample papers were selected by making use of keywords like COVID-19, SARS-CoV-2, and athletes. Initially, the selected papers were examined whether they are related to the effect of the pandemic on the sportsperson and to confirm this, their respective reference papers were also examined for the full-text articles. The reviews of the particular research papers were also considered. Some of the measures developed to confirm the eligibility were (1) Population: sports person, professional athletes, players, (2) Intervention: COVID-19 pandemic, (3) Types of Study: a comparative study, randomly controlled trials, clinical trials, review papers, systemic review, and meta-analysis, and (4) Outcomes: an establishment of good and fine result related to the psychological health. Age, injury form, or research design will not be avoided. Studies which are not in English, not publishing results, and are not relevant to the COVID-19 pandemic were removed.

Flow chart of PRISMA

Scope of PRISMA

To provide guidelines for the creation of protocols and for scientific reviews and meta-analyses that evaluate the efficacy of treatments, the PRISMA has been developed. Without the examination of efficacy, the PRISMA undergoes several reviews because of the fewer protocol instructions, writers are recommended to adopt. A protocol has been demonstrated by the research as a document that defines the reasoning, intended purpose, and intended methodology approach of a systematic review before it begins.

The authors who are involved in the development of systematic review procedures for publication, general consumption, or other purposes should PRISMA initially. To identify whether the protocol contains crucial information, it will be useful for the candidates who write review procedures and as a tool for the reviewers. To get a conclusion about a review, the journalists and reviewers make use of PRISMA to identify the correct protocol.

The structure of this document is the same as the previously established journalistic standards, such as the PRISMA Explanation and Elaboration document; it provides thorough justifications and evidence-based justifications for each checklist item. Examples of effective reporting for each checklist item have been discovered which use systematic review and meta-analysis techniques and are provided throughout this document to help the readers to identify in a better way.

During the development of an efficient review protocol, a particular list of items must be taken into account to focus on the PRISMA, and to get a clear view of the planned review process an extra detail will be more helpful in this process. Rather than the customary of the author, there is a need for more words or space in the PRISMA. Transparency and reproducibility will be available by giving more detailed information about that, and hence in the generated systematic report, the details mentioned must be limited by the authors, and if needed the summary of the report will be given and the finished protocol was referred by the readers or PROSPERO record. Following new journal rules aimed at encouraging reproducibility, this review proposes that full explanations of planned scientific details for systematic reviews are acceptable. There are several checklist elements to match how we picture them appearing in a procedure; publishing them in this order may help readers understand what's going on. If the authors feel that changing the order in which the checklist items appear is necessary, they should do so. In their protocol, authors must describe every PRISMA element.

Discussions

These discussions made use of selected articles as described in the above section. After duplicates were removed from the 80 titles and database citations loaded, just 68 remained. After evaluating the titles and abstracts, 54 were found to be appropriate for full-text examination. Of the 54 papers considered eligible, 40 were eliminated because they were unrelated, lacked full texts, or were abstract-only articles. As a result, 14 publications out of 80 were found to meet the meta-analysis’ inclusion criteria.

Original research articles were cross-sectional studies like comparative studies, random controlled trials, clinical trials, review papers, systemic reviews, and meta-analyses. Table 1 represents the effects of the COVID-19 Outbreak on Psychological Health in Sports. The table illustrates the sample of respondents, variables used for the evaluation and outcomes achieved for the respective studies.

The COVID-19 pandemic is a worldwide challenge. Meier et al . [ 61 ] reported administrations of countries and public health organizations take action most effective commendation to restrict contamination is social distancing. Further, various countries opted for mandatory lockdowns and the closing of public areas for maintaining social distancing. A greater level of mental distress was discovered as a result of changing to new protective measures, according to [ 62 ]'s research on the effects of the coronavirus outbreak on public health. Further, due to this outbreak there are severe mental health disorders like increases in fear, anxiety and depression, gambling problems, sleep and eating disorders, psychological rigidity, obsessive–compulsive disorder, family conflicts, fitness concerns, sedentary lifestyle and negative habits, low mood, large intake of alcohol and drugs, self-harm attempts or suicidal behaviour, and rumination [ 13 , 51 , 53 , 54 , 55 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 ] respectively.

Due to the new standards of a pandemic, the athletes have gone through huge changes in their style of living and daily activities, communal relationships, financial-related issues, and loss of goals and satisfaction. In line with these challenges, psychological well-being cannot be isolated from both the physical and mental problems manifestations and related fundamental issues in which the outer injury and recovery may take a long time. Peluso et al . [ 71 ] stated that physical activity has valuable impacts on the control and treatment of various diseases and mental illnesses like depression and anxiety. Further, stress and physical activity effectively affect the factors which influence cardiovascular status [ 72 ]. According to De Matos et al . [ 73 ] the normal problems faced by athletes are physical training, heart diseases, and risk factors. Similarly, during the Coronavirus lockdown, athletes trained less frequently and for shorter periods, which can cause higher depression, anxiety, and stress scores. In addition, [ 74 ] reported that excessively low training load may affect psycho-social engagement among athletes by inducing training-induced physiological and physiological adaptation to aversive preparedness.

Further, McGuine et al . [ 52 ] reported less physical activity and lower quality of life due to school closures and sports cancellations during a pandemic in the USA, and for women players and team sports players’ fewer symptoms like anxiety and depression were faced. Similarly, [ 54 ] also stated that a survey before and after one month of school closure due to the pandemic reported less dissolution of their athletic identity and there is more support from the social environment and the communication between the team members is also increased. Moreover, due to the low quality of sleep and long periods of sleep, they were reported in Spanish handball players due to the decreased training intensity and volume during the pandemic period. Additionally, [ 75 ] mentioned that the numerous physical performance tests of soccer players were get affected in Brazil due to 63 days of quarantine which they conduct during their normal off-season. Furthermore, Haan et al . [ 76 ] reported in their study that Sweden athletes (elite football, ice hockey, and handball players) are concerned about their sport and their careers during this COVID crisis, along with the negative psychological impact of the pandemic.

Furthermore, during this pandemic situation, some players feel lonely and their psychological health gets affected [ 77 ]. Additional factors that have contributed to players' mental suffering include their exclusion from the athletic community, decreased training and activity, a lack of formal coaching, and a lack of social support from fans and the media [ 53 ]. Furthermore, depression, anxiety, and higher athletic identity symptoms were reported in individual and team sports athletes of Turkey and Italy during the lockdown period [ 47 , 73 ] and Uroh and Adewunmi . [ 60 ] also found that single players were more distressed rather than team players during the coronavirus pandemic. Similarly, [ 56 ] stated the negative effect of lockdown on the psychological health and life spheres among youth athletes in Spain. Likewise, individual athletes are more prone to psychological distress than team sports athletes [ 46 , 78 , 79 ]. Individual athletes are at a greater risk because in individual sports athletes are the only responsible person for their success or failure, they cannot get any support from anyone during the competition so they need to work accordingly. Thus, the present circumstance makes individual players more prone to psychological distress in compression to team sports athletes [ 80 , 81 , 82 ]. Additionally, a group of elite and semi-elite athletes from 15 different sports namely soccer, hockey, rugby, cricket, athletics, netball, basketball, endurance running, cycling, track and field, swimming, squash, golf, tennis, and karate in South Africa were examined by Pillay et al. in [ 55 ] to determine the psychological effects of the disease outbreak on their physical, nutritional, and mental health.

Although the outcomes in this study are from various sports, and geographical regions but results were reported the same from every region Athletes are suffering from mental health as well as physical challenges due to the compulsory restrictions and guidelines of this COVID-19 pandemic and during the COVID-19 outbreaks the athletes needed psychosocial services.

COVID-19 impacts on sports and athletes

Several influences were faced by the athletes and players who have long been preparing for the 2020 Tokyo Olympic and Paralympic Games. For some people, no chance is given because of immediate retirement and due to the announcement of a postponement. For instance, British rowing squad member and two-time Olympic medalist Tom Ransley announced his retirement. Eddie Dawkins, who won the silver medal in the Olympics in Rio, recently declared his retirement from the game of track cycling. However, this opportunity is used by others to continue their performance or heal from any injuries they may have experienced the temporal shift in time and rapid modification to optimise their peak. As a consequence, enthusiastic and good attitudes were maintained by the sports players [ 83 ].

Due to the loss of daily, weekly, monthly, and yearly routines, the mental and outer health of the players gets affected. Many athletes lost their normal training routines when the terrible disaster struck in 2011, but the damage was still limited. Athletes carried out their training since many areas of Japan were sufficiently separated from the Fukushima prefecture without the unidentifiable effects of nuclear power plant accidents. The outbreak of COVID-19 has prompted players to stay at home in addition to forcing practically the training centre to be closed. In the Tokyo Olympic and Paralympic Games along with other games, the qualified tournaments get cancelled which was impacted by social distancing measures implemented to prevent the spread of COVID-19. To make it more difficult to achieve a specific goal, these changes have enhanced feelings of doubt, perplexity, and frustration. The athletes work out for a long period due to the impact of practice sessions because there is no way to leave the house and engage in deep and systematic training. Due to this, there may increase in injuries, which in turn could make players feel even more doubt and frustration. Athletes may have increased anxiety due to less communication with their teammates, coaches, and other people.

On the other hand, there is information about athletes who push themselves to a limit as it hurts them and they sometimes feel it necessary to stop [ 84 ]. This type of athlete develops an "exercise dependence prevalence," according to Numanović et al.[ 85 ]research.

The individual athletes felt more stress rather than other team athletes due to this compulsive trend, which is defined by extreme exercise [ 86 ]. Athletes in individual sports are rigorous in their training and intensely focused on their competitive outcomes. The interruption of their preparation due to the limitation in training leads to stress. The roles and responsibilities were divided for the team sports. In comparison with the athletes of individual sports, the team sports participants have more confidence and they can tackle and manage stress easily. As per the frequent discussions with their teammates, the effects of the home lockdown and the confusion around them were seen as less threatening. This condition is acknowledged as a protective factor. As stated earlier, the fitness participants displayed perfection and enjoyment and performs a lot of work. By frequent behaviours like avoiding the issue (avoidance), or acting out of anger and fear they were reacted [ 87 ]. However, the team players may face low stress.

Rather than other types of athletes, the fitness performers show higher values throughout all subscale ratings.

Impacts and actions from various countries

The Health Professionals Council of South Africa has loosened its restrictions on the employment of telehealth to make it more accessible according to the review of Pillay et al. [ 55 ]. This is due to the lockdown and the dangers of COVID-19. Because of travel and financial limitations, just one in four people may contact a sports physician. To know more information about COVID-19, the athletes make use of social media and get knowledge about how effective these channels are at getting important public health messages through to a broad audience. As the healthcare professionals failed to reach the athlete community, there is a need for physicians or other evidence-based channels which were misused for this purpose.

The sports were prohibited at all levels due to COVID-19 outbreaks and the associated quarantine. Because of this situation, the Italian sports community has been subjected to unfavourable psychological pressure, which affects over a long time. Additionally, the Italian sports community is in danger for psychological health due to the psychological effects of COVID-19 outcomes, according to [ 88 ], The players from the youth and amateur levels generated a way for Olympians and professionals who were included in this, along with the supporting staffs, coaches, physical trainers, and managers.

According to [ 89 ], the medical guidelines for COVID-19 treatment in Brazil during the national soccer tournament required RT-qPCR testing of players and coaching staff preceding games and indicated that only asymptomatic players who tested negative be allowed to play.

Even though some teams and players may have less opportunity for testing because of financial inequality. Following this, athletes had a 2.5-fold higher probability of acquiring the disease if a teammate had COVID-19 and were double as likely to be tested for the illness themselves. If the test was conducted by the athlete's team, their chances of being tested will be increased (15-fold).

According to Lundquvist et al. [ 90 ], in France, the quarantine prohibits training in their place, and most of the regional, national, and international tournaments have been cancelled or delayed until further notice. Because of this the anxiety of players increased and their enthusiasm is decreased to return to sports competitions. During the lockdown, athletes had varying options for training depending on their accommodation and the amount of interaction they had with their coaches. To keep the players, motivated, the coaches of various teams scheduled daily workouts using digital tools. In other teams, the athlete's and coaches’ interaction was very rare. A few players questioned the connections and trust with their coaches and their feelings also increased. With the infection of COVID-19, some players and coaches struggled with their symptoms and felt uneasy about their isolation during the crisis. During these times, telephone-based psychological help was also provided.

Because of this lockdown, there is an increase in the negative impact on the physical and mental health of people in India since it reduces physical activity in daily life, as indicated by Jadhav et al. [ 91 ] and the continuous development of COVID-19. The ICC Men's T-20 World Cup editions for 2020 and 2021 were both postponed by one year because of the pandemics during July 2020 declaration by the International Cricket Council. The event was postponed to November 2021 and October 2022, respectively. As per the ICC's declaration on August 8th, the right to host the competition was guaranteed for India in the year 2021 and Australia in the year 2020. The 2021 Women's Cricket World Cup and its semi-final event rescheduled by 1 year as a result of the pandemic.

Based upon the estimation, to improve the country's economy, health, and education Australian sport is funded by $83 billion yearly. A priority on life skills training, ideal social climates, and increased positive results spanning social, personal, and physical sectors have all been recognised as youth sports environments' contributions to children's positive youth development on a worldwide scale. Therefore, [ 92 ] examined how COVID-19 was evaluated by various stakeholders in South Australia's youth sports, including athletes (ages 15 to 18), parents, coaches, and sports administrators.

As the English Football Association (FA) has repeatedly postponed elite men's and women's football matches, the pandemic has put new strains on them. A concern about how much it will be passed out on to elite women's clubs, as more people were already economically insecure. The financial effects of postponed games and reduced television income will be significant in men's football. To put a spotlight on the danger and uncertainty the sport was facing, [ 93 ] examined how the pandemic might affect the development of elite women's football.

The cognitive, affective, and behavioural features of athletes are greatly influenced by perfectionism, which is a significant psychological factor. Through the patterns, it is described as having expectations, perceptions, and evaluations of events, such as "setting excessively high standards, followed by overcritical self-assessment." Perfectionism is associated with a focus on higher goals and more effective performance. Because of this, Lancheva et al. 2022 examined the dominating psychic conditions and perfectionism and their connection to the preferred coping mechanisms during the COVID-19 pandemic among sports students who arrived from Bulgaria and Russia and revealed their specialization based on gender, type of sport, level of qualification, and nationality.

Fundamentals of mental health interviewing and diagnosis of athletes

Without a thorough biopsychosocial clinical assessment, it is impossible to design a management strategy for mental health illnesses and symptoms. In this overview, important details on mental health issues and illnesses in sports that are relevant to this pandemic are addressed. Due to this pandemic, worse mental health conditions and symptoms like anxiety, obsessive–compulsive disorder, PTSD, depression and even suicide attempts among the players [ 94 ], who are frequently young and thus developmental less prepared to deal with the uncertainty that the pandemic has wreaked. The athletes were usually physically active, and due to sudden quit from sports and the migration of much academic education online, some athletes have seen a sudden and significant decrease in physical activity [ 95 ]. The rapid changes might affect mental health because exercise is considered to have both anxiolytic and antidepressant properties [ 96 , 97 ]. Social isolation cancelled events and games and the ensuing uncertainty over how to adjust training schedules, loss of income, loss of training facilities and access to trainers, teammates, and coaches, family infection risks, disruption of daily routines and self-care, anxiety about contracting COVID-19 at sporting events or otherwise, and persistent community distress and additional relevant factors in the mental deterioration health symptoms and disorders in athletes. Because of the changes in sports, professional players get affected mentally (such as those who had planned to retire after 2020 or those who were in their final season of collegiate competition), the pandemic-related sport suspension could mean sport retirement, which could be a particularly difficult transition. Without any willingness, ness if a person gets retired, then there is no plan for this retirement, no support from others and there was a higher level of athlete identity, their mental health may suffer [ 98 ]. Numerous of those unfavourable prognostic variables are probably linked to COVID-19-related retirement from sports.

If a person decides to restart the sport, he may face a lot of stress and anxiety. Due to the return of pandemic training levels, there is an increase in the risk of injury and the play procedures for athletes were returned who previously had COVID-19 that contain a cardiovascular assessment, which has been reported to cause anxiety in certain athletes [ 99 ]. Trust and collaboration during sporting events [ 100 ] get affected because of the tactile communication such as giving high fives or pats on the back which has historically been crucial among teammates, but the athletes must need to get back in later days.

Healthcare professionals may identify new mental health conditions among the players at this time because of the contextual stresses [ 101 ], but they should be careful not to assign pathologies to normal and not to combat the stress which results in distress or dysfunction. Virtual appointments may be used for a variety of purposes. The diagnosis of a fresh case of ADHD, however, is more difficult to determine without a physical examination. The Diagnostic and Statistical Manual of Mental Disorders (DSM-5) states that certain symptoms for such a diagnosis must have been present before the age of 12 [ 102 ], even though were not aware of the player. Stimulants are not authorized when certain diagnostic requirements are met, according to the NCAA, WADA, and some professional sports leagues. According to the NCAA, the usage of ADHD rating scales is one of the major needs for the use of medications [ 103 ] But according to WADA, "there should ideally be a reference in the diagnostic assessment to use allowed diagnostic instruments." There is a challenging test conducted digitally because it can be time-consuming and complex. The athletes should be informed if any changes are happening in the timescales for the identification of disease, and virtual solutions should be investigated if possible.

In the time of the pandemic, the evaluation for suicidal behaviour is essential, and the linked crisis planning influences the management of a cure for the player, particularly for the degree of care advised. Although definitive data are not yet available, some people worry that the pandemic may raise the probability of suicide [ 104 ]. Social isolation, financial struggles, and difficulties to gain standard mental health care during the pandemic are a few factors which will enhance the risk which must be investigated. Firearms sales were used in large numbers in recent days in several countries and providers who are worried about the safety of players in mental health should investigate access to firearms. Guns are linked to an increased risk of suicide even if they are not bought to kill one [ 105 ]. To control the other suicide methods (e.g., excessive medication, and access to high buildings), a suicide risk analysis should be carried out. There is a large number of suicide attempts during the late spring in the Northern Hemisphere and during the effects of COVID-19, it is relatively high, especially during spring and summer sports.

Psychological aspects of return to sport after COVID-19

During the final events of athletes, individual and team training has been hindered which harms the mental health of players during the quarantine and come to a mindset to play again. Athletes are currently dealing with issues like social isolation, career disruption, and restricted access to training environments and instructors, which can harm their general well-being and result in a terrible performance, according to a Simons et al. editorial [ 106 ]. The debate has emerged about the potential that some athletes may have the benefits of training or disadvantage depending on the region due to variations in the degree of confinement around the world. If the athlete is not aware of when to complete the training and competition then he may face tension, anxiety, and sadness. Several surveys say that popular athletes have overcome depression in the baseline similar to the general population. [ 107 ] found that group training greatly increased pain tolerance and may have boosted the types of activity in comparison with taking training alone. During the period of outbreak, the National Alliance on Mental Illness has generated several recommendations namely [ 108 ] (1) a structured work environment, (2) attire and structured breaks which is a normal routine, (3) continuous physical workout with “mindfulness” along with quiet time and deep breathing, (4) safe of self-talk, conversation with other people, nutrition, creating a daily routine for the normal day, (5) available among the friends, family, and colleagues, (6) making use of video tools to connect manually, (7) referring the National Alliance on Mental Health Illness.

During the period of isolation and less exercise among the team, the ideas and guidelines were used which were provided by National Alliance on Mental Health organization and it was taken as a reference.

Throughout the world, there are more effects raised due to this COVID-19 outbreak in the field of sports as well as it also affects the physical activity of sportspersons and other players. Enormous effects of COVID-19 were realized not only in the world of athletics, but also in society, as a result of which businesses, workplaces, social engagements, universities, and educational institutions had to close down quickly. Globally, few longitudinal studies compared mental health before and during COVID-19 and found an increase in anxiety and depression symptoms. However, the majority of significant outdoor and indoor athletic events at the world, regional, and national levels have been cancelled or postponed as a result of COVID-19. The health of all the people around the world gets affected by COVID-19. The present situation requires raising awareness in public, which can be helpful to deal with this calamity. This perspective article provides a detailed overview of the effects of the COVID‐19 outbreak on the mental health of people. An effective plan to safeguard the mental health of this already vulnerable population of athletes is crucial. As sportspeople and athletes are significantly affected by mental disease, this study focuses on mental health, psychological responses, and suffering among them. This study's review includes a selection of articles based on PRISMA meta-analysis. Out of 80 papers found using Research Gate, PubMed, Google Scholar, Springer, Scopus, and Web of Science, 14 articles relevant to the literature were chosen. Furthermore, these selected papers are used in the discussions.

Several risk factors have been identified such as mental and cardiovascular disease in athletes which results in stresses like isolation, a lack of exercise, a low income, and fear of losing their jobs. The stress leads to COVID-19 exposure. To fight the coronavirus outbreak, organizations for occupational therapy and psychosocial stressors and their health will get affected. Many millions of jobs are at stake worldwide as a result of the COVID-19 lockdown, not just for sports professionals but also for individuals in allied retail and athletic services businesses associated with leagues and tournaments. Essentially, the evidence presented in this study supports the hypothesis that the pandemic affects mental health problems in sportsmen. Athletes' mental health concerns are exacerbated by a lack of training, needed physical activity, practise sessions, and teamwork with teammates and coaching staff. The beneficial effects of physical exercise in improving quality of life and well-being have been extensively documented. An adapted physical activity program may represent an important factor to prevent COVID-19 infection, as well as a useful complementary tool to improve the physical and psychological outcomes of COVID-19-affected patients. A suitable exercise program may strengthen the athletes, providing immune protection in the long term and reducing treatment costs. The influence on sports and athletes, the impact on various nations, basic concerns of mental health and diagnosis for sportspeople and athletes, and the COVID-19 pandemic's afterlife for them were all explored in the review. The findings showed that COVID-19 has an impact on elite athletes’ mental health and was linked with stress, anxiety and psychological distress. The magnitude of the impact was associated with athletes’ mood state profile, personality and resilience capacity. Therefore, strongly believe that the findings from this review would help athletes in addressing and mitigating the rise in mental health disorders, which could prove worse than the current pandemic itself. Based on the findings of this study, it was concluded that the athletes of different sports and geographical regions are suffering from mental health issues due to the compulsory restrictions and guidelines of this COVID-19 outbreak.

Future application

Pandemic isolation has created immense pressure on athletes to regulate their training, execute their specific plans, maintain their social networks, to participate in targeted sports events and tournaments, respectively. Further, a finding of this study will support the professionals to prepare or establish specific psychological programmes to motivate and enables athletes to regulate their normal practices during the COVID-19 outbreak.

Relevance for clinical practice

This study was to review the shreds of evidence for the effect of a COVID-19 outbreak on mental health in sports. The findings of the study concluded that the athletes of different sports and geographical regions are suffering from mental health issues due to the compulsory restrictions and guidelines of this COVID-19 outbreak.

Data availability

Data sharing does not apply to this article as no datasets were generated or analysed during the current study.

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Shukla, A., Dogra, D.K., Bhattacharya, D. et al. Impact of COVID-19 outbreak on the mental health in sports: a review. Sport Sci Health 19 , 1043–1057 (2023). https://doi.org/10.1007/s11332-023-01063-x

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Similar DNA changes found in cells of both smokers and e-cigarette users

19 March 2024

E-cigarette users with a limited smoking history experience similar DNA changes to specific cheek cells as smokers, finds a new study led by researchers at UCL and University of Innsbruck.

This study is an incremental step in helping researchers to build a deeper understanding of the long-term effects of e-cigarettes on health. Although it does not show that e-cigarettes cause cancer, studies with long-term follow up are important to assess whether e-cigarettes have harmful effects and, if so, what they are.

The study, published in Cancer Research , analysed the epigenetic effects of tobacco and e-cigarettes on DNA methylation in over 3,500 samples, to investigate the impact on cells that are directly exposed to tobacco (e.g. in the mouth) and those that are not directly exposed (e.g. in blood or cervical cells).

The epigenome refers to an extra layer of information that is superimposed on our genetic material – the DNA. While DNA can be compared to the ‘hardware’ of a computer, epigenetics are comparable to the computer’s ‘software’ and define how, where and when the programs used by the computer are run.

Epigenomes change throughout our lives and can be affected by a variety of genetic or nongenetic factors – including ageing, our lifestyles, exposure to hormones, chemicals and environmental factors, and even stress and psychological trauma. One commonly studied type of epigenetic modification is called DNA methylation.

The researchers found that epithelial cells (cells that typically line organs and are often the cells of origin for cancer) in the mouth showed substantial epigenomic changes in smokers. Importantly, these changes are further elevated in lung cancers or pre-cancers (abnormal cells or tissue that have the potential to develop into cancer), when compared to the normal lung tissue, supporting the idea that the epigenetic changes associated with smoking allow cells to grow more quickly.

The publication also includes new data showing the similar epigenomic changes were likewise observed in the cells of e-cigarette users who had only ever smoked less than 100 tobacco cigarettes in their lives.

First author, Dr Chiara Herzog (UCL EGA Institute for Women’s Health and University of Innsbruck), said: “This is the first study to investigate the impact of smoking and vaping on different kinds of cells – rather than just blood – and we’ve also strived to consider the longer-term health implications of using e-cigarettes.

“We cannot say that e-cigarettes cause cancer based on our study, but we do observe e-cigarette users exhibit some similar epigenetic changes in buccal cells as smokers, and these changes are associated with future lung cancer development in smokers. Further studies will be required to investigate whether these features could be used to individually predict cancer in smokers and e-cigarette users.

“While the scientific consensus is that e-cigarettes are safer than smoking tobacco, we cannot assume they are completely safe to use and it is important to explore their potential long-term risks and links to cancer.

“We hope this study may help form part of a wider discussion into e-cigarette usage – especially in people who have never previously smoked tobacco.”

Through their computational analysis of the samples, the researchers also found that some smoking-related epigenetic changes remain more stable than others after giving up smoking, including smoking-related epigenetic changes in cervical samples – something that has not previously been studied.

Senior author, Professor Martin Widschwendter (UCL EGA Institute for Women’s Health and University of Innsbruck), said: “The epigenome allows us, on one side, to look back. It tells us about how our body responded to a previous environmental exposure like smoking. Likewise exploring the epigenome may also enable us to predict future health and disease. Changes that are observed in lung cancer tissue can also be measured in cheek cells from smokers who have not (yet) developed a cancer.

"Importantly, our research points to the fact that e-cigarette users exhibit the same changes, and these devices might not be as harmless as originally thought. Long-term studies of e-cigarettes are needed. We are grateful for the support the European Commission has provided to obtain these data.”

Tobacco is well known as a modifiable contributor to adverse health outcomes, and it has been estimated to have caused 7.69 million deaths globally in 2019, with numbers expected to increase in the future. The NHS says e-cigarettes are substantially safer than smoking tobacco and smokers are recommended to switch to vaping to improve their health.

The researchers involved in the latest study now hope to further investigate how epigenetic changes related to smoking in cheek swabs could be used for identifying individuals at highest risk of developing cancer and assess the long-term health risks of e-cigarettes. 

This work was supported by funding from the European Union’s Horizon 2020 Research and Innovation programme, The Eve Appeal, and Cancer Research UK.

Dr Ian Walker, Cancer Research UK’s executive director of policy, said: “This study contributes to our understanding of e-cigarettes, but it does not show that e-cigarettes cause cancer. Decades of research has proven the link between smoking and cancer, and studies have so far shown that e-cigarettes are far less harmful than smoking and can help people quit. This paper does however highlight that e-cigarettes are not risk-free, and so we need additional studies to uncover their potential longer-term impacts on human health.

“Smoking tobacco causes 150 cases of cancer every single day in the UK, which is why we look forward to seeing the Government’s age of sale legislation being presented in parliament. Nothing would have a bigger impact on reducing the number of preventable deaths in the UK than ending smoking, and this policy will take us one step closer to a smokefree future.”

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The Landscape of School-Based Mental Health Services

Nirmita Panchal , Cynthia Cox , and Robin Rudowitz Published: Sep 06, 2022

Many children and adolescents are experiencing poor mental and emotional health, which in some cases may be linked to negative impacts from the COVID-19 pandemic and exposure to gun violence , among other factors. In recognition of growing mental health concerns among children, recent policy measures, including the Bipartisan Safer Communities Act and the American Rescue Plan Act , provide pathways to support school-based mental health services for students. These policy actions aim to expand mental health care in schools – a setting that is more easily accessible by children and adolescents.

In this analysis, we explore the landscape of mental health services in schools during the 2021-2022 school year, barriers to offering services, and how recent policies facilitate the expansion of school-based mental health care. We draw upon data from the new 2022 School Pulse Panel , a study by the National Center for Education Statistics and the U.S. Census Bureau that surveys staff of public primary, middle, high, and combined-grade schools monthly on a variety of topics, including school mental health services. 1

School-based mental health services can improve access to care , allow for early identification and treatment of mental health issues, and may be linked to reduced absenteeism and better mental health outcomes. School-based services can also reduce access barriers for underserved populations, including children from low-income households and children of color .

The delivery of mental health services in schools has evolved over time and continues to vary across schools. Some students access in-person mental health services at schools or near campus while others access services through telehealth. Service delivery can range from a single provider (who is not necessarily a licensed mental health professional) to a team of providers, including psychologists, social workers, and academic or guidance counselors. A growing number of schools have also integrated social and emotional learning and other mental health literacy programs into their curriculum.

Despite the growth of school-based mental health services, challenges persist, including mental health provider shortages and inadequate funding.

Landscape of School-Based Mental Health Services

SERVICES OFFERED Most public schools offer mental health services to students, although utilization remains unclear. In the 2021-2022 school year, 96% of public schools reported offering at least one type of mental health service to their students. As shown in Figure 1, the most frequently offered services are:

• Individual-based interventions like one-on-one counseling or therapy (84% of public schools),
• Case management or coordinating mental health services (70%), and
• Referrals for care outside of the school (66%).

Only one-third ( 34% ) of schools provide outreach services, which includes mental health screenings for all students. These universal behavioral health screenings are considered a best practice and allow for schools to better identify all students with needs and tailor services to their specific student population. However, many schools do not offer these screenings often due to a lack of resources or difficulty accessing providers to conduct screenings, burden of collecting and maintaining data, and/or a lack of buy-in from school administrators.

Approximately one out of five schools ( 17% ) reported offering mental health services through telehealth during the 2021-2022 school year. While telehealth became a more widely used pathway to delivering health care during the pandemic, a growing number of schools already began providing care through telehealth prior to the pandemic. The utilization of telehealth in all school-based health care is more common in rural areas – where provider shortages and transportation issues are more prevalent – and can reduce barriers to care for underserved students.

PROVIDER TYPES

Staffing models for school-based mental health care can vary across schools. Sixty-eight percent of public schools have a school or district-employed licensed mental health professional on staff and 51% employ an external mental health provider (Figure 1). While general or academic school counselors can provide mental health services to students as well, they typically focus on short-term and preventive services and are not equipped to offer long-term care. The School Pulse Panel does not include information on the number of mental health providers on staff; however, other research indicates that most schools do not meet the recommended ratios of counselors and/or psychologists to students.

Other school staff, particularly teachers, often play a role in identifying students with mental health needs and linking them to care. However, research prior to the pandemic found that many teachers did not receive adequate training to identify and provide support to students with mental health needs. Since the pandemic began, nearly three out of four schools ( 73% ) have reported providing trainings and professional development to staff in order to help them identify growing mental health concerns among school students. However, data on the impact of these trainings is unavailable and it is unclear what share of schools were providing trainings prior to the pandemic.

School mental health services are supported through multiple sources of funding at the national, state, and local level. As shown in Figure 1, in the 2021-2022 school year, just over half of schools reported receiving funding for mental health services from district or school funds (57%) or federal grants or programs (52%), while smaller shares of schools reported funding from partnerships with organizations (37%) or state programs (32%). At the federal level, many schools receive support through the Department of Education – including grant programs and the Every Student Succeeds Act – and the Department of Health and Human Services ( HHS ). Schools may receive funds through Medicaid in several ways, including reimbursement for medically necessary services that are part of a student’s Individualized Education Plan (IEP),reimbursement for eligible health services for students with Medicaid coverage and for some administrative services. Additionally, many state budgets appropriate funds toward mental health services while fewer states allocate funds directly in their school funding models.

CHANGES DUE TO THE PANDEMIC

In response to growing mental health concerns during the pandemic, 67% of schools reported increasing mental health services offered to students (Figure 2). However, fewer than half of schools (41%) reported hiring new staff to focus on students’ mental health and well-being since the pandemic began (Figure 2). The inability of some schools to staff up despite growing mental health challenges may be due to budget constraints coupled with limited availability of mental health professionals.

In light of the pandemic, 27% of schools added classes for their students on social, emotional, and mental well-being since March 2020 (Figure 2). Additionally, for the 2021-2022 school year, 28% of schools made changes to their academic calendar to address mental health concerns for both staff and students. Examples of these changes include providing additional days off and allocating time to focus on mental wellness during the school day. Several states have introduced or passed measures allowing students excused absences related to mental health.

Barriers to School-Based Mental Health Services

During the 2021-2022 school year, approximately half of schools reported they strongly (12%) or moderately agreed (44%) they could effectively provide mental health services to all students in need. Meanwhile, a third of schools reported they strongly (10%) or moderately disagreed (23%) that they could effectively provide mental health services and 11% neither agreed or disagreed. Among the 88% of schools that did not strongly believe they could effectively provide mental health services to students in need, the most reported limitations involved mental health provider shortages – 61% cited insufficient staff coverage and 57% cited a lack of access to providers (Figure 3). Schools have faced provider shortages for years, but this issue has recently received more attention in light of growing mental health concerns among children. Many schools do not meet recommended ratios for psychologists to students ( 500:1 ) or counselors to students ( 250:1 ). Going into the 2022-2023 school year, 19% of public schools have vacancies for mental health professionals. Among schools with these vacancies, 84% reported it will be somewhat or very difficult to fill these mental health positions.

Among school staff that did not strongly believe they could provide mental health services to all students in need during the 2021-2022 school year, 48% cited inadequate funding as a barrier (Figure 3). Funding challenges for school mental health services have long existed. In order to provide and sustain services, many schools use funding from multiple sources, including at the national, state, and local levels, as previously mentioned. However, this presents several challenges as schools navigate varying specifications of how to utilize funds based on the source and changes to funding streams over time.

How Have Recent Policies Addressed School-Based Mental Health Services?

The American Rescue Plan Act and recent state policies have provided pathways to expand mental health and wellness services in schools. In 2021, the American Rescue Plan Act (ARPA) was passed and designed to provide relief from the continued impacts of the pandemic. A portion of funds from the ARPA ($122.8 billion) were allocated for the Elementary and Secondary School Emergency Relief (ESSER), and many states are using some of these funds to support school-based mental health care. Some ways states and schools are using these funds include growing the school mental health provider workforce (e.g. funding positions for mental health counselors and social workers in schools), partnering with community-based mental health agencies to expand access to care for students, providing trainings for school staff, and providing technical assistance for school mental health programs. However, one study has also found that lower-income schools and schools in rural areas are less likely to use ARPA funds toward school-based mental health services than their counterparts. Some schools (22%) reported using ARPA funds to create new staff positions during the 2021-2022 school year, although a large share of schools did not know (37%) if funds were used for these purposes. Among the schools that did use ARPA funds toward new staffing, 35% reported using a portion of these funds for school mental health professionals (e.g. psychologists and social workers). The ARPA also included funding to support students with disabilities and youth experiencing homelessness. Separately, some states have passed legislation to address growing mental health concerns, including the implementation of suicide prevention programs and mental health screening programs.

The recently passed Bipartisan Safer Communities Act also allocates funds to support school-based mental health services. In response to increasing gun violence and mass shootings, the Bipartisan Safer Communities Act was signed into law in June 2022. This legislation focuses on both gun reform and youth mental health, including provisions to support and expand school-based mental health services, highlighted below.

Despite recent increased attention and resources for school-based mental health services, challenges remain. In May 2022, large shares of public school staff reported that they strongly agree the pandemic has negatively impacted students’ behavioral development (39%) and socioemotional development (45%). It is unclear how schools will adequately address these impacts as they continue to face challenges, including mental health provider shortages , burnout among school staff, disparities by race and ethnicity in access to school services , and long-term sustainability issues. Addressing these challenges and improving access to school-based mental health services may help mitigate rising mental health concerns among youth.

The School Pulse Panel utilizes a random stratified sample of the Common Core of Data , a universe of public schools. This stratified sample includes public and public charter schools, schools with magnet programs, alternative schools, special education schools, and vocational schools. Approximately 2,400 schools were included in the sample. There has been some variation in the number of schools that respond each month. Seven hundred schools responded to the initial survey in January. Approximately 830 schools responded to the April survey – findings from this survey are included in this brief. While school principals are the initial point of contact to complete the survey, they may invite other school and district staff to assist with completion. Published data is weighted and adjusted to account for non-response.

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Title: mm1: methods, analysis & insights from multimodal llm pre-training.

Abstract: In this work, we discuss building performant Multimodal Large Language Models (MLLMs). In particular, we study the importance of various architecture components and data choices. Through careful and comprehensive ablations of the image encoder, the vision language connector, and various pre-training data choices, we identified several crucial design lessons. For example, we demonstrate that for large-scale multimodal pre-training using a careful mix of image-caption, interleaved image-text, and text-only data is crucial for achieving state-of-the-art (SOTA) few-shot results across multiple benchmarks, compared to other published pre-training results. Further, we show that the image encoder together with image resolution and the image token count has substantial impact, while the vision-language connector design is of comparatively negligible importance. By scaling up the presented recipe, we build MM1, a family of multimodal models up to 30B parameters, including both dense models and mixture-of-experts (MoE) variants, that are SOTA in pre-training metrics and achieve competitive performance after supervised fine-tuning on a range of established multimodal benchmarks. Thanks to large-scale pre-training, MM1 enjoys appealing properties such as enhanced in-context learning, and multi-image reasoning, enabling few-shot chain-of-thought prompting.

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Theories Informing eHealth Implementation: Systematic Review and Typology Classification

Milena heinsch.

1 Centre for Brain and Mental Health Priority Research Centre, The University of Newcastle, Callaghan, Australia

2 School of Humanities and Social Science, The University of Newcastle, Callaghan, Australia

Jessica Wyllie

3 Newcastle Business School, The University of Newcastle, Callaghan, Australia

Jamie Carlson

Hannah wells, campbell tickner, frances kay-lambkin, associated data.

PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist.

Key search terms used.

Theory-guided approaches to implementation science have informed translation efforts and the acceptance of eHealth (digital health) interventions in clinical care. However, there is scarce evidence on which theories are best suited to addressing the inherent complexity of eHealth implementation.

The objectives of this systematic review are to identify theories that inform and explain eHealth implementation and to classify these theories using the typology by Sovacool and Hess for theories of sociotechnical change.

An electronic search was conducted in the PsycINFO, MEDLINE, Embase, CINAHL, Scopus, Sociological Source Ultimate, Web of Science, ABI/INFORM, EBSCO, and ProQuest databases in June 2019. Studies were included if they were published between 2009 and June 2019; were written in English; reported on empirical research, regardless of study or publication type; reported on one or more theories in the context of eHealth implementation; and were published in a peer-reviewed journal. A total of 2 reviewers independently assessed the titles, abstracts, and full texts. Theories identified were classified using a typology for theories of sociotechnical change, which was considered a useful tool for ordering and analyzing the diverse theoretical approaches as a basis for future theory building.

Of the 13,101 potentially relevant titles, 119 studies were included. The review identified 36 theories used to explain implementation approaches in eHealth. The most commonly used approaches were the Technology Acceptance Model (TAM) (n=33) and the Unified Theory of Acceptance and Use of Technology (UTAUT) (n=32). These theories were primarily concerned with individual and interpersonal elements of eHealth acceptance. Less common were theories that reflect the various disorderly social processes and structural dimensions of implementation, such as the normalization process theory (n=17) and the structuration theory (n=6).

Conclusions

Theories currently informing the implementation of eHealth interventions predominantly focus on predicting or explaining end-user acceptance. Theoretical perspectives that capture the dense and intricate relationships and structures required to enact sustainable change are less well represented in the eHealth literature. Given the growing acknowledgment of the inherent complexity of eHealth implementation, future research should develop and test models that recognize and reflect the multidimensional, dynamic, and relational nature of this process.

Introduction

In recent years, technological innovation in health care has developed exponentially, and eHealth is now widely viewed as a significant potential contributor to improved quality of care [ 1 , 2 ]. However, despite much policy-level and scholarly discussions of triggering a revolution in health service delivery, problems of implementation and uptake of eHealth among both patients and service providers persist [ 1 , 3 , 4 ].

Poor uptake of eHealth (a term with contested definitions [ 5 ] but, broadly speaking, “health services and information delivered or enhanced through the internet and related technologies” [ 6 ]) is often explained in terms of barriers and facilitators [ 1 ]. In a recent study, Schreiweis et al [ 7 ] identified 77 barriers and 292 facilitators in implementing eHealth services. Similarly, a systematic review by Granja et al [ 8 ] identified 27 factors that determine the success or failure of eHealth interventions. Although studies about barriers and facilitators are important, they tend to fall short of capturing the complexity of the implementation process and the multiple interrelated factors that determine the translation and uptake of eHealth [ 1 , 9 ].

Evidence suggests that theory-informed approaches to implementation science can enhance the translation and acceptance of eHealth into clinical care [ 1 , 10 - 18 ]. Theories offer explanatory frameworks and formal heuristic devices that have the potential to move beyond the basic listing of individual facilitators and barriers to implementation, to capture the dynamic interaction between them [ 1 ]. As Damschroder [ 19 ] notes, theory “enables knowledge to emerge out of seeming chaos,” facilitating exploration of complex relationships and interdependencies between variables that unfold in diverse and changing contexts [ 20 ]. This is of paramount importance in eHealth settings [ 1 , 18 ], which are characterized by a complicated interplay between patients, clinicians, the health care system, and the eHealth technology.

Many theories and models have been articulated to inform and explain eHealth implementation [ 15 ]. Despite this abundance, findings from several reviews show that only a small number of select theories have been used repeatedly across multiple publications and by several authors [ 21 - 24 ]. For example, a recent review by Harst et al [ 23 ] of 24 studies of end-user acceptance of telemedicine found that 2 theories accounted for 20 instances of theory use: the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT). Similarly, a review on the use of theory in eHealth weight management interventions by Willmott [ 24 ] identified 18 studies referencing a theory, of which 16 mentioned either the social cognitive theory or the transtheoretical model.

Theories most commonly used in the literature tend to emphasize individual factors, such as motivation, attitudes, and behavior, rather than the broader social and environmental factors impacting implementation [ 21 , 22 , 25 ]. This is despite evidence highlighting the multilevel nature of technology implementation in health care and the importance of targeting variables at different levels [ 1 , 26 ]. As Glanz and Bishop [ 22 ] noted, social and environmental factors may constrain individuals’ behavior even when they are highly motivated. Therefore, the authors recommend complementing individually oriented theories with theories of social, policy, or organizational change [ 22 ].

One hindrance to this is that the current eHealth implementation literature is fragmented across multiple specialty areas and disciplines, making it difficult to locate the range of theories available [ 27 ]. To improve the selection and application of theory, it is necessary to identify an array of theories, across diverse disciplines, that have the potential to inform eHealth implementation. A further issue is that many theories contain overlapping constructs but use different terms to describe them [ 26 ]. Synthesizing theories according to their similarities would facilitate their selection and application at different levels [ 27 ].

To address these issues, we conducted a systematic review and classification of eHealth implementation theories. The review aims to address the following question: “What theories exist across disciplines that have been used to inform or explain eHealth implementation?” Theories identified by our review were classified using the typology by Sovacool and Hess [ 28 ] for theories of sociotechnical change. This typology provides an accessible and useful framework for organizing and selecting diverse theoretical options that target variables at different levels. Its use also allows the identification of areas where further theoretical development is required.

This systematic review was conducted by members of the review team in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [ 29 ]. A PRISMA checklist is available in Multimedia Appendix 1 [ 29 ]. The authors adopted a flexible approach by continuing to apply the core principles of systematic review methodology but tailoring the PRISMA guidelines to the needs of this review [ 30 ]. As such, formal quality assessment was not conducted for this review, as the perceived validity or trustworthiness of the included studies did not address the overall research question, which sought to identify the existence of theories across a broad and varied body of literature.

Search Strategy

Electronic searches of PsycINFO, MEDLINE, Embase, CINAHL, Scopus, Sociological Source Ultimate, Web of Science, ABI/INOFORM, EBSCO, and ProQuest Databases were conducted by the review team in June 2019 to identify studies that applied one or more theoretical frameworks to inform eHealth implementation. For this review, implementation was defined as “the scientific study of methods to promote the systematic uptake of research findings and other EBPs (evidence-based practices) into routine practice, and, hence, to improve the quality and effectiveness of health services.” [ 31 ]. These databases were chosen because they were deemed to be likely to catalog studies and disciplines relevant to the eHealth context and the specific research question. The search was limited to studies published in the last 10 years (from 2009 to June 2019) and yielded 21,704 abstracts for initial consideration. A full list of key search terms used can be found in Multimedia Appendix 2 . All records were converted into an EndNote library and reduced to 13,101 following deduplication. Papers were then title-checked for relevance to the topic and research questions and aims before further screening by 2 independent reviewers (MH and HW) in accordance with the detailed inclusion and exclusion criteria outlined below.

Eligibility Criteria

Individual studies were included in the review if they were (1) published in the last 10 years (from 2009 to June 2019), (2) published in English, (3) outputs of empirical research or theoretical papers reporting on one or more theories in the context of eHealth implementation (this included all study types and populations), or (4) published in a peer-reviewed journal. Studies were excluded if they were (1) published before 2009, (2) not written in English, (3) studies that did not report on one or more theories applied in the context of eHealth implementation, (4) gray literature not published in a peer-reviewed journal, (5) dissertations, theses, conference proceedings, or abstracts, or (6) any form of literature review. The full eligibility criteria for this review are provided in Textbox 1 .

Eligibility criteria for the review.

Inclusion criteria

• Publication date from 2009 (inclusive) to June 2019
• Australian and international literature in English language
• Papers reporting on one or more theories in the context of eHealth implementation (any study type and population)
• Empirical studies (both quantitative and qualitative)
• Position, discussion, or theoretical papers
• Peer-reviewed articles

Exclusion criteria

• Publication before 2009
• Literature in non-English language
• Papers not reporting on one or more theories in the context of eHealth implementation
• Gray literature or not published in a peer-reviewed journal
• Dissertations or theses or conference proceedings or abstracts
• Literature reviews (narrative, scoping, and systematic)

Identification and Selection of Studies

A total of 2 reviewers (MH and HW) independently applied the predefined inclusion and exclusion criteria to screen for relevant studies from those obtained through database searching. To ensure accuracy, record titles and abstracts were screened manually in EndNote, and documents that did not meet the selection criteria outlined above were excluded by the reviewers. Following 2 rigorous rounds of title and abstract screening, full texts of all potentially eligible studies were examined and further screened by the 2 independent reviewers (MH and HW) using the Covidence web-based software (Veritas Health Innovation Ltd), an effective tool for assisting research teams when performing systematic reviews or meta-analyses [ 32 ]. Articles that failed to meet the selection criteria were excluded and then cross-checked to ensure transparency and accuracy surrounding the reasons given for exclusion. Any conflicts in decision making during the screening phase were resolved via discussion between reviewers or, if needed, with the research coordinator (FKL) until consensus was reached.

Data Extraction and Presentation

As the standardized extraction tool in Covidence did not meet the specific needs of this review, a modified extraction form was developed and piloted by the 2 reviewers (MH and HW) with 10 included studies tabulated and refined accordingly. The modified extraction form was tailored to include characteristics relevant to the research question. The characteristics extracted by the reviewers included (1) name of theory, (2) description, (3) instances of theory use, (4) examples of theory application, and (5) theory type. Instances of theory use refer to the number of occurrences in which a theory was used. As several studies used more than one theory, the total number of theory instances exceeded the number of papers included in the review. Examples of theory application were drawn from the literature to specify how each theory informed eHealth implementation. The reviewers then determined each theory type by drawing on the typology by Sovacool and Hess [ 28 ] for theories of sociotechnical change. This typology categorizes theories according to where they tend to center their analysis. The term center is intended to convey that a theory may involve elements of multiple types but that it approximates one ideal type above all. This typology was considered a useful tool for ordering and analyzing the diverse theoretical approaches identified, as a basis for future theory building [ 33 ].

The typology includes 5 categories: agency, structure, relations, meaning, and norms. Agency-centered theories relate to people’s individual actions, beliefs, and attitudes, and assume that these can be explained without deeper consideration of broader social and systemic elements [ 28 , 34 , 35 ]. In contrast, structural theories propose that people are influenced largely by external forces beyond their control, such as their organizational, political, or macrosocial environments [ 28 , 35 ]. Relational theories attempt to interpret the interactional processes that influence the circulation of knowledge throughout different social networks. They view technology and society as coproduced and coconstructed, with no single dimension creating change by itself [ 28 , 36 ]. Meaning-centered theories focus on language, ideas, symbolism, narratives, rhetorical visions, and other cognitive dimensions that both orient action and are changed by it. Normative theories offer criteria by which to assess the positive or negative impact of technology on society or on a specific group. A sixth category, combined theories, was added to these 5 categories. This included meta-theories that explored a combination of individual, structural, or relational frameworks. All authors (MH, JW, JC, HW, CT, and FKL) reviewed and agreed upon the classification of theories using this typology.

Search Results

The electronic search of key databases resulted in 21,704 potentially eligible articles ( Figure 1 ). This number was reduced to 13,101, following deduplication. Of these, 12,001 papers were excluded based on title screening and application of the eligibility criteria previously outlined. Key reasons for exclusion of papers at title screening included eliminating those that were in non-English language or those that reported on an irrelevant topic to the research question, for example, non-eHealth or theory-related papers. The abstracts of the remaining 1100 papers were then independently screened by reapplying the inclusion and exclusion criteria, and a further 935 papers were excluded. Key reasons for exclusion at abstract screening included nonempirical or gray literature and papers that reported abstracts or protocols only. Following a full-text review of the remaining 165 articles, an additional 46 articles were excluded because of insufficient reporting on or mention of theories related to eHealth implementation. In total, 119 articles met the full, predefined eligibility criteria and were included for data extraction and synthesis of findings. The PRISMA flowchart in Figure 1 details the process of eligibility and study selection.

Flowchart of studies included and excluded from the systematic review.

Theory Summary and Classification

The summary (including theory name, description, instances of theory use, and examples of application to implementation) and classification of all theories used to inform and explain eHealth implementation is provided in Table 1 . In total, 36 distinct theories were identified. Classification of these theories using the typology by Sovacool and Hess [ 28 ] showed that the theories used in the literature were predominantly agency centered (19/36, 53%), followed by relational (7/36, 20%), structural (6/36, 16%), meaning (3/36, 8%), and combined theory types (1/36, 4%). No normative theories were identified.

Summary and classification of eHealth implementation theories.

a TAM: technology acceptance model.

b UTAUT: unified theory of acceptance and use of technology.

c NPT: normalization process theory.

d DOI: diffusion of innovations.

e AST: adaptive structuration theory.

f CFIR: Consolidated Framework for Implementation Research.

g ANT: actor-network theory.

h TPB: theory of planned behavior.

i EHR: electronical health record.

j SCT: social cognitive theory.

k IT: identity theory.

l PMT: protection motivation theory.

m BMHSU: behavioral model of health service utilization.

n ECT: expectation confirmation theory.

o PCT: personal construct theory.

p PAD: pleasure, arousal and dominance.

q JDRM: job demands resource model.

r TTF: Task Technology Fit.

s HIS: hospital information system.

In total, 53% (19/36) of theories were classified as agency centered . Individual theories that occurred most frequently in the literature were the TAM by Davis and Venkatesh [ 76 ] (33 instances), UTAUT by Venkatesh [ 77 ] (32 instances), and Diffusion of Innovations Theory by Rogers [ 78 ] (16 instances). These theories were found to be primarily concerned with the individual and interpersonal elements of eHealth implementation. Although they did, to some extent, appear to consider the influence of organizational and social factors on eHealth adoption, individual attitudes, behaviors, and motivations remained the core focus of theoretical analysis. Theories classified as individual examined the adoption of eHealth either before or soon after the implementation of an intervention. However, they did not emphasize any form of user involvement in the development of an intervention. These theories tended to depict adoption as a temporally discrete and relatively immediate event, rather than as one stage in a larger multistage process. They often focused on what people were going to do soon, a decision they are about to make, or a behavior they need to alter. The diffusion of innovations theory provides an exception, as this theory considers time to be an essential factor influencing adoption [ 79 ].

A total of 20% (7/36) of theories identified in the literature were classified as relational . Of these, the normalization process theory (NPT) by May et al [ 80 ] occurred most frequently in the literature (17 instances), followed by structuration theory (ST) [ 81 ] (6 instances) and actor-network theory (ANT) [ 82 , 83 ] (4 instances). Sociotechnical systems theory, social information processing theory, social worlds theory, and boundary object theory occurred only once each in the literature. Relational theories emphasize social relations and interactions at the human-technology interface. They highlighted the complex networks of social structure and meaning in which people are embedded, proposing that the translation of knowledge is facilitated by processes of circulation both within and across different social worlds. Some relational theories, such as ANT and ST, emphasized the role of nonhuman actors, such as computer software or programs, in transforming and mediating social relationships. These theories tended to view technology and society as coconstructed or coproduced, with no single dimension dictating change by itself. Within these theories, coproduction and implementation were often described as continuous processes, in which eHealth interventions were adapted to better accommodate different end-user settings and needs.

A total of 16% (6/36) of theories were classified as structural . The most common structural theory was institutional theory (IT) [ 84 ] (3 instances). Resource dependence theory, theory of middle managers’ role, contingency theory, task technology fit theory, and technology organization environment theory occurred only once each in the literature. These theories conceptualized structure as including institutional or organizational systems as well as political, cultural, and other macrosocial environments. They often assumed that people are constrained or influenced by external forces frequently beyond their comprehension or control. For example, IT posits that organizational structures and cultural norms drive eHealth implementation, despite strong political influence.

A total of 8% (3/36) of theories were classified as meaning centered : expectation confirmation theory, personal construct theory, and cultural dimension theory. Each of these theories occurred only once in the literature. These theories tended to focus on the cognitive dimensions (expectations, perceptions, and beliefs) that explain people’s willingness to accept the use of new health technologies. Although some meaning-centered theories, such as cultural dimension theory, have considered the influence of cultural values on the adoption and use of eHealth, these theories nonetheless centered their analysis at the individual level and were often used in combination with agency-level theories.

The Consolidated Framework for Implementation Research (CFIR) [ 85 ] was the only theory to be classified as a combined theory type. This theory is a meta-theoretical framework that provides a comprehensive listing of individual, social, and organizational constructs thought to influence eHealth implementation. However, it does not consider how these factors might be interrelated or how changes occur.

Principal Findings

Evidence from a range of disciplines suggests that theory-informed approaches to implementation science are integral to the translation and implementation of eHealth into clinical care [ 1 , 10 - 18 ]. Analysis of the 119 studies included in this review identified 36 distinct theories that inform or explain eHealth implementation. However, only a few selected theories (UTAUT and TAM) were dominant, which is consistent with the findings from previous reviews [ 21 - 24 ]. Although these theories have been empirically proven to explain or predict certain aspects of implementation, Willmott et al [ 24 ] and Davis et al [ 21 ] caution that overreliance on common or favorite theories without direct questioning of their underlying assumptions limits progress in the field.

The typology by Sovacool and Hess [ 28 ] facilitated a closer examination of the assumptions underlying eHealth implementation theories. The findings revealed that the majority of theories were agency centered, emphasizing individual factors rather than the broader social and environmental factors impacting implementation. Although these findings were consistent with previous reviews [ 21 , 22 , 24 , 86 ], the wider net cast for this review provided the needed validation that this trend can be observed across multiple specialty areas and disciplines [ 27 ]. This calls into question whether theories currently being used to inform and explain the eHealth implementation adequately address the multiple and complex factors that influence the implementation process, and highlights the need for more dynamic, multilevel models of eHealth implementation [ 21 , 23 , 87 ].

This review identified a number of theories classified as relational or structural, which, to varying degrees, capture the complexity and multilevel nature of eHealth implementation. The most commonly cited relational theories were NPT, ST, and ANT. These theories recognize the important role of actors, relationships, and networks in mobilizing knowledge and embedding interventions into everyday practice. For ANT, networks are made up of both human and nonhuman actors , and technologies are understood to have agency and the potential to transform human interactions [ 88 , 89 ]. From this perspective, it may be a particularly useful theory for examining the implementation of eHealth technologies and the impacts these technologies have on human behavior. A criticism of ANT is that it has a flat ontology and refuses to consider institutional sources of power and inequality. Here, NPT and ST offer a possible extension, as both theories recognize the inseparable intersection between individual agents and wider social and organizational structures and norms. Structural theories also consider the influence of external forces on individual behavior and decision making. For example, IT, the most commonly used structural theory in this review, posits that an organization’s environment is capable of strongly influencing the development, acceptance, and use of eHealth interventions. This theory is considered particularly relevant for application in eHealth environments, which are highly institutionalized and subject to multiple regulatory forces, high levels of professionalism, and growing network externalities that can influence adoption decisions [ 50 ].

Of particular interest was the lacuna of normative theories identified in this review. Normative theories attempt to answer whether a technology is a net positive or negative for society and individuals [ 28 ]. To do so, they often rely on evaluative criteria determined by ethics, moral studies, political ecology, or social justice. Social justice theory and sustainable development are 2 common examples of normative theories. The absence of normative theories in eHealth implementation studies is emblematic of the broader tendency of implementation science to overlook the importance of contextual factors, such as economic, social, historical, and political forces, that perpetuate inequalities in the delivery of health care services [ 90 ]. This omission is concerning in the context of eHealth, as digital technologies have been found to exacerbate inequalities associated with older age, lower level of educational attainment, and lower socioeconomic status [ 91 ]. Future research should not shy away from normative questions of equity, justice, and sustainability and should find ways to incorporate theoretical approaches that enable exactly that.

When incorporating or combining theories, Sovacool and Hess [ 28 ] highlight the need for careful consideration of the epistemological baggage of different approaches. Combining multiple theoretical approaches may offer a more complete understanding or explanation, yet such combinations may mask contrasting assumptions regarding key issues [ 92 ]. For instance, are people driven primarily by their individual attitudes and motivation or do pervasive organizational cultures and social systems impose norms and values that shape people’s behavior, making individual characteristics relatively unimportant? These challenges may account for the tendency of theories to target variables at the same level. One exception was the CFIR framework, which was the sole theory that provided a menu of constructs at different levels for researchers to choose from. However, although CFIR recognizes the multilevel nature of eHealth implementation, it does not consider the relationship between constructs or how change takes place, leading Nilsen [ 92 ] to contend that it should not be considered a theory at all. Further research is needed to explore how diverse theoretical perspectives can be brought together in ways that capture the dynamic interaction between constructs [ 1 ], while avoiding disconnects and incompatibilities [ 28 ].

Limitations

This study has several limitations. First, papers not published in English were excluded, which may indicate a selection bias. The decision to keep the research question and inclusion criteria for this review broad resulted in a high yield of papers and, to some extent, reduced the specificity of search results. This decision was made to ensure the identification of the full spectrum of theories being used to inform and explain eHealth implementation. Restriction of inclusion criteria in previous systematic reviews [ 24 ] led to the omission of a number of key theories that provide a more comprehensive explanation of the various constituents of the implementation processes. A further limitation is that the protocol for this systematic review was not registered. However, every care was taken to ensure compliance with the core principles of the systematic review methodology. As Mallett [ 30 ] noted, systematic reviews do not constitute a homogenous approach, and researchers may adopt a more flexible approach that better suits their research purpose while continuing to comply with the principles for conducting a systematic review. Finally, the literature search for this review was conducted in June 2019. Given the rapid rate of publication in the field of eHealth, it is likely that recent relevant articles have not been included. As completing an updated search was not feasible for the research team, we suggest that future studies must continue to identify theories used to inform and explain the implementation of eHealth interventions.

This systematic review identified 36 theories that are being used to inform and explain eHealth implementation and classified these theories using the categories adapted from the typology by Sovacool and Hess [ 28 ] for theories of sociotechnical change. The results highlight the dominance of theories that focus mainly on individual readiness to accept health technologies rather than the various disorderly social processes or systemic dimensions of implementation. This calls into question whether theories currently being used to inform and explain eHealth implementation adequately address the multiple and multilevel factors that influence the implementation process. Nonetheless, this review identified a number of theories classified as relational, structural, or combined, which, to varying degrees, capture the complex interactions within a wider organization and policy system. Although less prominent in the literature, these theories may be particularly applicable to the implementation of eHealth in health settings and services.

Acknowledgments

This systematic review is part of the eCliPSE (Electronic Clinical Pathways to Service Excellence) project, which is funded by a National Health and Medical Research Council partnership project grant (APP117181) and Beyond Blue.

Abbreviations

Multimedia appendix 1, multimedia appendix 2.

Conflicts of Interest: None declared.

This paper is in the following e-collection/theme issue:

Published on 25.3.2024 in Vol 26 (2024)

Where Do Oncology Patients Seek and Share Health Information? Survey Study

Authors of this article:

Research Letter

• Eric Freeman 1 , BA   ; 
• Darshilmukesh Patel 2 , BA   ; 
• Folasade Odeniyi 1 , MPH, MBA   ; 
• Mary Pasquinelli 2 , DNP   ; 
• Shikha Jain 2 , MD  

1 College of Medicine, University of Illinois at Chicago, Chicago, IL, United States

2 Department of Medicine, University of Illinois at Chicago, Chicago, IL, United States

Corresponding Author:

Eric Freeman, BA

College of Medicine

University of Illinois at Chicago

1853 West Polk Street

Chicago, IL, 60612

United States

Phone: 1 847 791 0189

Email: [email protected]

Introduction

Social media in health care has many benefits, including the dissemination of health information [ 1 ] and health promotion [ 2 ]. The COVID-19 pandemic has highlighted the benefits of the internet and social media as tools through which individuals can exchange health information. While little is known about oncology patients’ preferences for social media platforms, particularly among minority populations and those in low socioeconomic status communities, some studies have shown its use is linked to the alleviation of patient stress and loneliness, increased feelings of self-efficacy and control of care, and efficient delivery of health information from health practitioners [ 3 ]. The study aims to assess where patients from marginalized communities receive a majority of their health care information by surveying patients in a cancer clinic. This study was conducted at the University of Illinois Chicago, which is a public hospital that mainly serves patients from underresourced communities.

Between March 2021 to June 2021, we administered a 16-item survey ( Multimedia Appendix 1 ) adapted from the National Cancer Institute’s Health Information National Trends Survey (HINTS) [ 4 ] to patients scheduled for an oncology visit at the Outpatient Care Center at UI Health. The survey was administered to 145 patients via email and 161 patients in person. Respondents were asked to identify sources used to self-educate about their diagnosis, preferred information source, social media use and preferences, and demographics. We used chi-square tests to assess associations between categorical variables.

Ethics Approval

This study was approved by the institutional review board at the University of Illinois Chicago and was found to meet the criteria for exemption as defined in the US Department of Health and Human Services Regulations for the Protection of Human Subjects (45 CFR 46.104(d)).

The demographics of our sample can be found in Table 1 . Respondents routinely accessed several forms of health information sources. The top three included their doctor or health care provider (n=274, 89.3%), internet search engines (n=218, 71.2%), and brochures and pamphlets (n=125, 40.7%). However, when directed to choose just one source, 207 (67.4%) chose their doctor or health care provider, while 67 (21.8%) chose internet search engines. The majority of respondents used a smartphone with the internet (n=237, 77.2%), a home desktop or laptop with the internet (n=192, 62.5%), or a tablet with the internet (n=188, 61.2%). However, approximately one-quarter of respondents indicated that they used a mobile phone without internet or a data plan.

We found that the majority of respondents accessed social media in the past year (n=198, 64.7%). Using social media was associated with age ( χ 2 3 =18.7; P <.001) and sex (Fisher P =.001). While respondents primarily used Facebook (n=69, 22.5%), YouTube (n=66, 21.5%), and Instagram (n=25, 8.1%) to receive health information, few shared health information with a medical professional (n=17, 5.5%), and if they did, they primarily used Facebook (n=8, 48.7%).

Principal Findings

Understanding how patients exchange health information is important to ensure access to accurate information and promote engagement with the health care team. We found that a majority of our patients use social media to find health-related information. However, there continues to be an internet access disparity that can limit patients’ ability to improve their health literacy. As social media engagement is linked to positive patient outcomes, using social media interventions can help us improve oncology patients’ illness experience. While both oncology providers and patients are increasingly using social media as a learning and sharing tool [ 5 ], the exact information-seeking behavior of patients with cancer has yet to be fully examined, especially in disadvantaged populations. In the current climate of rampant online medical misinformation, health care workers should find innovative ways to disseminate evidence-based patient-facing information using the platforms most accessed by oncology patients. Our study highlights the need to further explore communication preferences to help develop tailored communication strategies to support underserved patients and their families.

Limitations

Our study has various limitations. This study was a single clinic, single institution study with a relatively small sample size. Additionally, our patient population was older, which could have influenced preferred social media platforms.

Data Availability

The data sets generated or analyzed during this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

None declared.

Social media survey.

• Moorhead SA, Hazlett DE, Harrison L, Carroll JK, Irwin A, Hoving C. A new dimension of health care: systematic review of the uses, benefits, and limitations of social media for health communication. J Med Internet Res. Apr 23, 2013;15(4):e85. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Khatri C, Chapman SJ, Glasbey J, Kelly M, Nepogodiev D, Bhangu A, et al. STARSurg Committee. Social media and internet driven study recruitment: evaluating a new model for promoting collaborator engagement and participation. PLoS One. 2015;10(3):e0118899. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Leist AK. Social media use of older adults: a mini-review. Gerontology. 2013;59(4):378-384. [ CrossRef ] [ Medline ]
• National Cancer Institute. Healthcare Information National Trends Survey. 2018. URL: https://hints.cancer.gov/ [accessed 2023-09-12]
• Watson J. Social media use in cancer care. Semin Oncol Nurs. May 2018;34(2):126-131. [ CrossRef ] [ Medline ]

Abbreviations

Edited by A Mavragani; submitted 21.03.22; peer-reviewed by S El kefi, S Hargreavess, K Na; comments to author 17.11.22; revised version received 16.06.23; accepted 04.07.23; published 25.03.24.

©Eric Freeman, Darshilmukesh Patel, Folasade Odeniyi, Mary Pasquinelli, Shikha Jain. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 25.03.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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From autism to Alzheimer's: A large-scale animal study links brain pH changes to wide-ranging cognitive issues

by Fujita Health University

A global collaborative research group comprising 131 researchers from 105 laboratories across seven countries has published a paper in eLife . The study identifies brain energy metabolism dysfunction leading to altered pH and lactate levels as common hallmarks in numerous animal models of neuropsychiatric and neurodegenerative disorders, such as intellectual disability, autism spectrum disorders, schizophrenia, bipolar disorder, depressive disorders, and Alzheimer's disease.

At the forefront of neuroscience research, the research group sheds light on altered energy metabolism as a key factor in various neuropsychiatric and neurodegenerative disorders. While considered controversial, an elevated lactate level and the resulting decrease in pH is now also proposed as a potential primary component of these diseases.

Unlike previous assumptions associating these changes with external factors like medication, the research group's previous findings suggest that they may be intrinsic to the disorders. This conclusion was drawn from five animal models of schizophrenia/developmental disorders, bipolar disorder , and autism, which are exempt from such confounding factors.

However, research on brain pH and lactate levels in animal models of other neuropsychiatric and neurological disorders has been limited. Until now, it was unclear whether such changes in the brain were a common phenomenon. Additionally, the relationship between alterations in brain pH and lactate levels and specific behavioral abnormalities had not been clearly established.

This study, encompassing 109 strains/conditions of mice, rats, and chicks, including animal models related to neuropsychiatric conditions, reveals that changes in brain pH and lactate levels are a common feature in a diverse range of animal models of diseases, including schizophrenia/developmental disorders, bipolar disorder, autism, as well as models of depression, epilepsy, and Alzheimer's disease. This study's significant insights include:

• Common Phenomenon Across Disorders: About 30% of the 109 types of animal models exhibited significant changes in brain pH and lactate levels, emphasizing the widespread occurrence of energy metabolism changes in the brain across various neuropsychiatric conditions.
• Environmental Factors as a Cause: Models simulating depression through psychological stress , and those induced to develop diabetes or colitis, which have a high comorbidity risk for depression, showed decreased brain pH and increased lactate levels. Various acquired environmental factors could contribute to these changes.
• Cognitive Impairment Link: A comprehensive analysis integrating behavioral test data revealed a predominant association between increased brain lactate levels and impaired working memory, illuminating an aspect of cognitive dysfunction.
• Confirmation in Independent Cohort: These associations, particularly between higher brain lactate levels and poor working memory performance, were validated in an independent cohort of animal models, reinforcing the initial findings.
• Autism Spectrum Complexity: Variable responses were noted in autism models, with some showing increased pH and decreased lactate levels, suggesting subpopulations within the autism spectrum with diverse metabolic patterns.

"This is the first and largest systematic study evaluating brain pH and lactate levels across a range of animal models for neuropsychiatric and neurodegenerative disorders. Our findings may lay the groundwork for new approaches to develop the transdiagnostic characterization of different disorders involving cognitive impairment," states Dr. Hideo Hagihara, the study's lead author.

Professor Tsuyoshi Miyakawa, the corresponding author, explains, "This research could be a stepping stone towards identifying shared therapeutic targets in various neuropsychiatric disorders. Future studies will center on uncovering treatment strategies that are effective across diverse animal models with brain pH changes.

"This could significantly contribute to developing tailored treatments for patient subgroups characterized by specific alterations in brain energy metabolism."

In this paper, titled "Large-scale animal model study uncovers altered brain pH and lactate levels as a transdiagnostic endophenotype of neuropsychiatric disorders involving cognitive impairment ," the mechanistic insights into the reduction in pH and the increase in lactate levels remain elusive. However, it is known that lactate production increases in response to neural hyperactivity to meet the energy demand, and the authors seem to think this might be the underlying reason.

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