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20 striking findings from 2020

The global coronavirus pandemic upended life in the United States and around the world in 2020, disrupting how people work, go to school, attend religious services, socialize with friends and family, and much more. But the pandemic wasn’t the only event that shaped the year. The videotaped killing of George Floyd by police officers in Minneapolis sparked an international outcry and focused new attention on the treatment of racial and ethnic minorities in the U.S. And November’s presidential election appears to have shattered turnout records as around 160 million Americans cast ballots and elected Joe Biden the 46th president.

As 2020 draws to a close, here are 20 striking findings from Pew Research Center’s studies this year, covering the pandemic, race-related tensions, the presidential election and other notable trends that emerged during the year.

Since the very beginning of the U.S. coronavirus outbreak, Democrats have been far more likely than Republicans to see COVID-19 as a “major threat” to public health. In November, Democrats and Democratic-leaning independents were nearly twice as likely as Republicans and GOP leaners (84% vs. 43%) to see the outbreak as a major threat to the health of the U.S. population, even as both sides agreed on the threat it poses to the national economy.

Partisan divisions over the public health threat posed by the virus were far from the only ones when it came to COVID-19: Democrats and Republicans also differed widely on public health strategies ranging from contact tracing to mask wearing .

The pandemic had a dramatic effect on international travel: By April, around nine-tenths of the world’s population (91%) was living in a country with partially or fully closed borders . More than 7 billion people were living in a country with at least some entry restrictions for noncitizens and nonresidents. And that included about 3 billion people, or 39% of the global population, who lived in countries with borders that were completely closed to noncitizens and nonresidents.

For the first time since at least the Great Depression, a majority of young adults in the U.S. were living with their parents this year. Millions of Americans, especially young adults, moved in with family members as the coronavirus spread. In July, 52% of adults ages 18 to 29 were living with one or both parents, up from 47% in February, before the pandemic. The share of young adults living with their parents rose among men and women, in all major racial and ethnic groups and among metropolitan as well as rural residents. Growth was sharpest among the youngest adults – those ages 18 to 24 – as well as among White young adults.

Following the UK’s exit from the European Union, the share of British adults with a favorable view of the EU rose to its highest level on record . The UK formally left the EU in January, concluding a withdrawal process that lasted more than three years. But in Pew Research Center’s first survey in the UK after Brexit, 60% of British adults said they had a positive view of the EU, up from 54% the year before and the highest percentage in surveys dating to 2004. Britons’ views of the EU remained divided along demographic and partisan lines, with younger people, those with a postsecondary education or more and those on the ideological left more likely to express a positive opinion.

International views of China turned much more negative in 2020 , with many people criticizing its handling of COVID-19. The share of adults with an unfavorable opinion of China rose 24 percentage points in Australia, 19 points in the UK and 15 points in Germany, the Netherlands and Sweden, with sizable increases in other countries as well. In all 14 countries surveyed, a majority of adults expressed a negative view of China. And a median of 61% of adults across these nations said China had done a bad job dealing with the coronavirus outbreak.

Around eight-in-ten registered voters in the U.S. (83%) said in the summer that it “ really mattered ” who won this year’s presidential election, the highest share in any presidential election year since at least 2000. Two decades ago, by comparison, just half of registered voters said it “really mattered” who won the contest between George W. Bush and Al Gore.

The election itself underscored voters’ engagement this year: President-elect Joe Biden received more than 81 million votes , while Donald Trump got more than 74 million – the highest and second-highest totals in American history

Trump’s approval rating has been more sharply divided along partisan lines than that of any president in the modern era of polling. Over the course of his presidency through August, an average of 87% of Republicans approved of Trump’s handling of the job, compared with an average of just 6% of Democrats. That 81-point gap between Republicans and Democrats was far larger than the average partisan gaps in approval of Barack Obama (67 points) and George W. Bush (58 points).

Members of the out-of-power party – that is, the party that does not control the White House – have become increasingly critical of U.S. presidents in recent years. The 6% average of Democrats who approved of Trump’s job performance through August was down from an average of 14% of Republicans who approved of Obama and an average of 23% of Democrats who approved of Bush.

Amid widespread economic hardship caused by COVID-19, around four-in-ten U.S. adults said in August that they or someone in their household had been laid off, lost their job or taken a pay cut. The economic shocks of the pandemic affected a broad range of American workers and their families. In the August survey, a quarter of U.S. adults said they or someone in their household had been laid off or lost their job, while around a third (32%) said they or someone in their household had taken a pay cut. All told, 42% of adults reported at least one of these things happening to them or someone in their household. Job losses and pay cuts were especially common among younger adults, Hispanics and those in lower-income families.

More than half of Americans personally know someone who has been hospitalized or died due to COVID-19. In a reflection of the mounting toll the virus has taken, 54% of U.S. adults said in November that they know someone who has been hospitalized or died, up from 39% in August and 15% in April. Around seven-in-ten Black Americans (71%) know someone who has been hospitalized or died from COVID-19, compared with 61% of those who are Hispanic, 49% of those who are White and 48% of those who are Asian.

A large majority of U.S. adults (86%) say there is some kind of lesson or set of lessons for mankind to learn from the coronavirus outbreak, and about a third (35%) say these lessons were sent by God. In open-ended survey responses collected by the Center in the summer, Americans pointed to practical lessons, such as wearing a mask; personal lessons, such as remembering the importance of spending time with family and loved ones; and societal lessons, such as the need for universal health care. Other responses were political in nature, including criticisms of both major parties and concerns about the politicization of the pandemic.

In several countries, the share of people with a favorable view of the U.S. fell in 2020 to its lowest point on record . America’s image abroad declined considerably after Trump took office in 2017, but there was further erosion in 2020 amid widespread criticism of the country’s handling of the coronavirus outbreak. Just 41% of adults in the UK expressed a favorable opinion of the U.S. this year, the lowest percentage on record. And in France and Germany, the share of adults with a positive view of the U.S. fell to levels last seen in March 2003, at the height of tensions over the Iraq War.

Across 13 countries surveyed this summer, a median of just 15% of adults said the U.S. had done a good job responding to the COVID-19 outbreak. That was much lower than the share who gave positive marks to their own country (median of 74%), the World Health Organization (median of 64%), the EU (median of 57%) and China (median of 37%).

Biden and Trump supporters say they fundamentally disagree with each other not just on political priorities, but on core American values . In an October survey, eight-in-ten registered voters who supported Biden (80%) – and a similar share of those who supported Trump (77%) – said they fundamentally disagree with the other side on “core American values and goals.” Only around one-in-five in each group said their differences are limited to politics and policies. In the same survey, 90% of Biden supporters and 89% of Trump supporters said there would be “lasting harm” to the nation if the other candidate won the election.

Across a range of measures, Republicans are far more negative than Democrats in their assessments of the news media. In a February survey, more than half of Republicans and Republican-leaning independents said news organizations don’t care about the people they report on (69%), are not professional (60%), are too critical of America (58%), hurt democracy (56%) and don’t care about how good of a job they do (54%). Democrats and Democratic leaners were far more positive than Republicans on all of these questions. The partisan divide in views of the news media extends to views of specific outlets, too, as a separate Center study found in January .

A small share of highly active Twitter users – most of whom are Democrats – produce the vast majority of tweets from U.S. adults. The most active 10% of users were responsible for 92% of tweets sent between November 2019 and September 2020 by U.S. adults with public-facing accounts. Democrats and Democratic-leaning independents accounted for 69% of these highly active Twitter users, while Republicans and GOP leaners accounted for 26%. 

Only around a quarter of U.S. Hispanics (23%) have heard of the term “Latinx,” and just 3% say they use it to describe themselves. The gender-neutral, pan-ethnic term, which is used to describe the nation’s Hispanic population, has gained traction in recent years among some corporations, local governments, universities and news and entertainment outlets. But relatively few Hispanics are aware of the term and only a small fraction use it to describe themselves. Among Hispanics aware of the term, 65% say “Latinx” should not be used to describe the nation’s Hispanic or Latino population, while 33% say it should.

Around half of Americans (49%) say the Bible should have a great deal or some influence on the laws of the U.S. , including 28% who say it should take precedence when it conflicts with the will of the people. White evangelical Christians are especially likely to hold this view. In a February survey, around nine-in-ten White evangelicals (89%) said the Bible should have a great deal or some influence on America’s laws, and around two-thirds (68%) said they favored the Bible over the will of the people when there is a conflict between the two.

The U.S. Constitution does not mention the Bible, God, Jesus or Christianity, and the First Amendment clarifies that “Congress shall make no law respecting an establishment of religion.” Still, some scholars have argued that the Bible heavily influenced America’s founders.

The Black Lives Matter movement drew widespread public support and online engagement following the police killing of George Floyd in May. In a survey in early June , two-thirds of Americans – including majorities across all major racial and ethnic groups – expressed support for the movement (though support slipped to 55% by September).

Meanwhile, use of the #BlackLivesMatter hashtag surged to record levels on Twitter , with an average of just under 3.7 million daily uses between May 26 – the day after Floyd’s death at the hands of Minneapolis police – and June 7. On May 28, nearly 8.8 million tweets included the hashtag #BlackLivesMatter, making it the busiest single day for the hashtag since Pew Research Center began tracking its use in 2013.

Amid calls to “defund the police,” only a quarter of Americans said in June that they favor a reduction in spending on policing in their area. The largest share of Americans (42%) preferred spending on policing in their area to stay about the same, while 31% said spending on police should be increased. Support for police spending cuts was higher – but still short of a majority – among adults under 30, Black adults and those who identify as Democratic or lean to the Democratic Party.

A growing share of Americans have heard of the group of conspiracy theories known as QAnon, and a substantial portion of Republicans who are aware of it say it is a good thing for the country. The share of U.S. adults who have heard or read at least a little about QAnon rose from 23% in February to 47% in September. Democrats and Democratic-leaning independents who are aware of QAnon overwhelmingly see it as a very or somewhat bad thing for the country (90% say this), but Republicans and GOP leaners are more divided. Half of Republicans who are aware of QAnon say it is a very or somewhat bad thing for the nation, while 41% say it is a very or somewhat good thing.

In a year in which big tech companies faced growing scrutiny, nine-in-ten Republicans – and around six-in-ten Democrats (59%) – said it’s likely that social media sites intentionally censor political viewpoints . Overall, around three-quarters of U.S. adults (73%) said in June that it’s very or somewhat likely that social media sites censor political viewpoints they find objectionable. In late May, Twitter began labeling tweets by Trump as misleading, prompting the president and some of his supporters to accuse social media platforms of censoring conservative voices.

Read the other posts in our striking findings series:

• 19 striking findings from 2019
• 18 striking findings from 2018
• 17 striking findings from 2017
• 16 striking findings from 2016
• 15 striking findings from 2015

John Gramlich is an associate director at Pew Research Center .

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ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of  The Pew Charitable Trusts .

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Evidence-Based Research Series-Paper 1: What Evidence-Based Research is and why is it important?

Affiliations.

• 1 Johns Hopkins Evidence-based Practice Center, Division of General Internal Medicine, Department of Medicine, Johns Hopkins University, Baltimore, MD, USA.
• 2 Digital Content Services, Operations, Elsevier Ltd., 125 London Wall, London, EC2Y 5AS, UK.
• 3 School of Nursing, McMaster University, Health Sciences Centre, Room 2J20, 1280 Main Street West, Hamilton, Ontario, Canada, L8S 4K1; Section for Evidence-Based Practice, Western Norway University of Applied Sciences, Inndalsveien 28, Bergen, P.O.Box 7030 N-5020 Bergen, Norway.
• 4 Department of Sport Science and Clinical Biomechanics, University of Southern Denmark, Campusvej 55, 5230, Odense M, Denmark; Department of Physiotherapy and Occupational Therapy, University Hospital of Copenhagen, Herlev & Gentofte, Kildegaardsvej 28, 2900, Hellerup, Denmark.
• 5 Musculoskeletal Statistics Unit, the Parker Institute, Bispebjerg and Frederiksberg Hospital, Copenhagen, Nordre Fasanvej 57, 2000, Copenhagen F, Denmark; Department of Clinical Research, Research Unit of Rheumatology, University of Southern Denmark, Odense University Hospital, Denmark.
• 6 Section for Evidence-Based Practice, Western Norway University of Applied Sciences, Inndalsveien 28, Bergen, P.O.Box 7030 N-5020 Bergen, Norway. Electronic address: [email protected].
• PMID: 32979491
• DOI: 10.1016/j.jclinepi.2020.07.020

Objectives: There is considerable actual and potential waste in research. Evidence-based research ensures worthwhile and valuable research. The aim of this series, which this article introduces, is to describe the evidence-based research approach.

Study design and setting: In this first article of a three-article series, we introduce the evidence-based research approach. Evidence-based research is the use of prior research in a systematic and transparent way to inform a new study so that it is answering questions that matter in a valid, efficient, and accessible manner.

Results: We describe evidence-based research and provide an overview of the approach of systematically and transparently using previous research before starting a new study to justify and design the new study (article #2 in series) and-on study completion-place its results in the context with what is already known (article #3 in series).

Conclusion: This series introduces evidence-based research as an approach to minimize unnecessary and irrelevant clinical health research that is unscientific, wasteful, and unethical.

Keywords: Clinical health research; Clinical trials; Evidence synthesis; Evidence-based research; Medical ethics; Research ethics; Systematic review.

Copyright © 2020 Elsevier Inc. All rights reserved.

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Impact of COVID-19 pandemic on mental health in the general population: A systematic review

Jiaqi xiong.

a Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON

Orly Lipsitz

c Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Ontario

Flora Nasri

Leanna m.w. lui, hartej gill, david chen-li, michelle iacobucci.

e Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore

f Institute for Health Innovation and Technology (iHealthtech), National University of Singapore, Singapore

Amna Majeed

Roger s. mcintyre.

b Department of Psychiatry, University of Toronto, Toronto, Ontario

d Brain and Cognition Discovery Foundation, Toronto, ON

Associated Data

As a major virus outbreak in the 21st century, the Coronavirus disease 2019 (COVID-19) pandemic has led to unprecedented hazards to mental health globally. While psychological support is being provided to patients and healthcare workers, the general public's mental health requires significant attention as well. This systematic review aims to synthesize extant literature that reports on the effects of COVID-19 on psychological outcomes of the general population and its associated risk factors.

A systematic search was conducted on PubMed, Embase, Medline, Web of Science, and Scopus from inception to 17 May 2020 following the PRISMA guidelines. A manual search on Google Scholar was performed to identify additional relevant studies. Articles were selected based on the predetermined eligibility criteria.

Results: Relatively high rates of symptoms of anxiety (6.33% to 50.9%), depression (14.6% to 48.3%), post-traumatic stress disorder (7% to 53.8%), psychological distress (34.43% to 38%), and stress (8.1% to 81.9%) are reported in the general population during the COVID-19 pandemic in China, Spain, Italy, Iran, the US, Turkey, Nepal, and Denmark. Risk factors associated with distress measures include female gender, younger age group (≤40 years), presence of chronic/psychiatric illnesses, unemployment, student status, and frequent exposure to social media/news concerning COVID-19.

Limitations

A significant degree of heterogeneity was noted across studies.

Conclusions

The COVID-19 pandemic is associated with highly significant levels of psychological distress that, in many cases, would meet the threshold for clinical relevance. Mitigating the hazardous effects of COVID-19 on mental health is an international public health priority.

1. Introduction

In December 2019, a cluster of atypical cases of pneumonia was reported in Wuhan, China, which was later designated as Coronavirus disease 2019 (COVID-19) by the World Health Organization (WHO) on 11 Feb 2020 ( Anand et al., 2020 ). The causative virus, SARS-CoV-2, was identified as a novel strain of coronaviruses that shares 79% genetic similarity with SARS-CoV from the 2003 SARS outbreak ( Anand et al., 2020 ). On 11 Mar 2020, the WHO declared the outbreak a global pandemic ( Anand et al., 2020 ).

The rapidly evolving situation has drastically altered people's lives, as well as multiple aspects of the global, public, and private economy. Declines in tourism, aviation, agriculture, and the finance industry owing to the COVID-19 outbreak are reported as massive reductions in both supply and demand aspects of the economy were mandated by governments internationally ( Nicola et al., 2020 ). The uncertainties and fears associated with the virus outbreak, along with mass lockdowns and economic recession are predicted to lead to increases in suicide as well as mental disorders associated with suicide. For example, McIntyre and Lee (2020b) have reported a projected increase in suicide from 418 to 2114 in Canadian suicide cases associated with joblessness. The foregoing result (i.e., rising trajectory of suicide) was also reported in the USA, Pakistan, India, France, Germany, and Italy ( Mamun and Ullah, 2020 ; Thakur and Jain, 2020 ). Separate lines of research have also reported an increase in psychological distress in the general population, persons with pre-existing mental disorders, as well as in healthcare workers ( Hao et al., 2020 ; Tan et al., 2020 ; Wang et al., 2020b ). Taken together, there is an urgent call for more attention given to public mental health and policies to assist people through this challenging time.

The objective of this systematic review is to summarize extant literature that reported on the prevalence of symptoms of depression, anxiety, PTSD, and other forms of psychological distress in the general population during the COVID-19 pandemic. An additional objective was to identify factors that are associated with psychological distress.

Methods and results were formated based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines ( Moher et al., 2010 ).

2.1. Search strategy

A systematic search following the PRISMA 2009 flow diagram ( Fig. 1 ) was conducted on PubMed, Medline, Embase, Scopus, and Web of Science from inception to 17 May 2020. A manual search on Google Scholar was performed to identify additional relevant studies. The search terms that were used were: (COVID-19 OR SARS-CoV-2 OR Severe acute respiratory syndrome coronavirus 2 OR 2019nCoV OR HCoV-19) AND (Mental health OR Psychological health OR Depression OR Anxiety OR PTSD OR PTSS OR Post-traumatic stress disorder OR Post-traumatic stress symptoms) AND (General population OR general public OR Public OR community). An example of search procedure was included as a supplementary file.

Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) study selection flow diagram. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

2.2. Study selection and eligibility criteria

Titles and abstracts of each publication were screened for relevance. Full-text articles were accessed for eligibility after the initial screening. Studies were eligible for inclusion if they: 1) followed cross-sectional study design; 2) assessed the mental health status of the general population/public during the COVID-19 pandemic and its associated risk factors; 3) utilized standardized and validated scales for measurement. Studies were excluded if they: 1) were not written in English or Chinese; 2) focused on particular subgroups of the population (e.g., healthcare workers, college students, or pregnant women); 3) were not peer-reviewed; 4) did not have full-text availability.

2.3. Data extraction

A data extraction form was used to include relevant data: (1) Lead author and year of publication, (2) Country/region of the population studied, (3) Study design, (4) Sample size, (5) Sample characteristics, (6) Assessment tools, (7) Prevalence of symptoms of depression/anxiety/ PTSD/psychological distress/stress, (8) Associated risk factors.

2.4 Quality appraisal

The Newcastle-Ottawa Scale (NOS) adapted for cross-sectional studies was used for study quality appraisal, which was modified accordingly from the scale used in Epstein et al. (2018) . The scale consists of three dimensions: Selection, Comparability, and Outcome. There are seven categories in total, which assess the representativeness of the sample, sample size justification, comparability between respondents and non-respondents, ascertainments of exposure, comparability based on study design or analysis, assessment of the outcome, and appropriateness of statistical analysis. A list of specific questions was attached as a supplementary file. A total of nine stars can be awarded if the study meets certain criteria, with a maximum of four stars assigned for the selection dimension, a maximum of two stars assigned for the comparability dimension, and a maximum of three stars assigned for the outcome dimension.

3.1. Search results

In total, 648 publications were identified. Of those, 264 were removed after initial screening due to duplication. 343 articles were excluded based on the screening of titles and abstracts. 41 full-text articles were assessed for eligibility. There were 12 articles excluded for studying specific subgroups of the population, five articles excluded for not having a standardized/ appropriate measure, three articles excluded for being review papers, and two articles excluded for being duplicates. Following the full-text screening, 19 studies met the inclusion criteria.

3.2. Study characteristics

Study characteristics and primary study findings are summarized in Table 1 . The sample size of the 19 studies ranged from 263 to 52,730 participants, with a total of 93,569 participants. A majority of study participants were over 18 years old. Female participants ( n  = 60,006) made up 64.1% of the total sample. All studies followed a cross-sectional study design. The 19 studies were conducted in eight different countries, including China ( n  = 10), Spain ( n  = 2), Italy ( n  = 2), Iran ( n  = 1), the US ( n  = 1), Turkey ( n  = 1), Nepal ( n  = 1), and Denmark ( n  = 1). The primary outcomes chosen in the included studies varied across studies. Twelve studies included measures of depressive symptoms while eleven studies included measures of anxiety. Symptoms of PTSD/psychological impact of events were evaluated in four studies while three studies assessed psychological distress. It was additionally observed that four studies contained general measures of stress. Three studies did not explicitly report the overall prevalence rates of symptoms; notwithstanding the associated risk factors were identified and discussed.

Summary of study sample characteristics, study design, assessment tools used, prevalence rates and associated risk factors.

3.3. Quality appraisal

The result of the study quality appraisal is presented in Table 2 . The overall quality of the included studies is moderate, with total stars awarded varying from four to eight. There were two studies with four stars, two studies with five stars, seven studies with six stars, seven studies with seven stars, and one study with eight stars.

Results of study quality appraisal of the included studies.

3.4. Measurement tools

A variety of scales were used in the studies ( n  = 19) for assessing different adverse psychological outcomes. The Beck Depression Inventory-II (BDI-II), Patient Health Questionnaire-9/2 (PHQ-9/2), Self-rating Depression Scales (SDS), The World Health Organization-Five Well-Being Index (WHO-5), and Center for Epidemiologic Studies Depression Scale (CES-D) were used for measuring depressive symptoms. The Beck Anxiety Inventory (BAI), Generalized Anxiety Disorder 7/2-item (GAD-7/2), and Self-rating Anxiety Scale (SAS) were used to evaluate symptoms of anxiety. The Depression, Anxiety, and Stress Scale- 21 items (DASS-21) was used for the evaluation of depression, anxiety, and stress symptoms. The Hospital Anxiety and Depression Scale (HADS) was used for assessing anxiety and depressive symptoms. Psychological distress was measured by The Peritraumatic Distress Inventory (CPDI) and the Kessler Psychological Distress Scale (K6/10). Symptoms of PTSD were assessed by The Impact of Event Scale-(Revised) (IES(-R)), PTSD Checklist (PCL-(C)-2/5). Chinese Perceived Stress Scale (CPSS-10) was used in one study to evaluate symptoms of stress.

3.5. Symptoms of depression and associated risk factors

Symptoms of depression were assessed in 12 out of the 19 studies ( Ahmed et al., 2020 ; Gao et al., 2020 ; González-Sanguino et al., 2020 ; Huang and Zhao, 2020 ; Lei et al., 2020 ; Mazza et al., 2020 ; Olagoke et al., 2020 ; Ozamiz-Etxebarria et al., 2020 ; Özdin and S.B. Özdin, 2020 ; Sønderskov et al., 2020 ; Wang et al., 2020a ; Wang et al., 2020b ). The prevalence of depressive symptoms ranged from 14.6% to 48.3%. Although the reported rates are higher than previously estimated one-year prevalence (3.6% and 7.2%) of depression among the population prior to the pandemic ( Huang et al., 2019 ; Lim et al., 2018 ), it is important to note that presence of depressive symptoms does not reflect a clinical diagnosis of depression.

Many risk factors were identified to be associated with symptoms of depression amongst the COVID-19 pandemic. Females were reported as are generally more likely to develop depressive symptoms when compared to their male counterparts ( Lei et al., 2020 ; Mazza et al., 2020 ; Sønderskov et al., 2020 ; Wang et al., 2020a ). Participants from the younger age group (≤40 years) presented with more depressive symptoms ( Ahmed et al., 2020 ; Gao et al., 2020 ; Huang and Zhao, 2020 ; Lei et al., 2020 ; Olagoke et al., 2020 ; Ozamiz-Etxebarria et al., 2020 ;). Student status was also found to be a significant risk factor for developing more depressive symptoms as compared to other occupational statuses (i.e. employment or retirement) ( González et al., 2020 ; Lei et al., 2020 ; Olagoke et al., 2020 ). Four studies also identified lower education levels as an associated factor with greater depressive symptoms ( Gao et al., 2020 ; Mazza et al., 2020 ; Olagoke et al., 2020 ; Wang et al., 2020a ). A single study by Wang et al., 2020b reported that people with higher education and professional jobs exhibited more depressive symptoms in comparison to less educated individuals and those in service or enterprise industries.

Other predictive factors for symptoms of depression included living in urban areas, poor self-rated health, high loneliness, being divorced/widowed, being single, lower household income, quarantine status, worry about being infected, property damage, unemployment, not having a child, a past history of mental stress or medical problems, having an acquaintance infected with COVID-19, perceived risks of unemployment, exposure to COVID-19 related news, higher perceived vulnerability, lower self-efficacy to protect themselves, the presence of chronic diseases, and the presence of specific physical symptoms ( Gao et al., 2020 ; González-Sanguino et al., 2020 ; Lei et al., 2020 ; Mazza et al., 2020 ; Olagoke et al., 2020 ; Ozamiz-Etxebarria et al., 2020 ; Özdin and Özdin, 2020 ; Wang et al., 2020a ).

3.6. Symptoms of anxiety and associated risk factors

Anxiety symptoms were assessed in 11 out of the 19 studies, with a noticeable variation in the prevalence of anxiety symptoms ranging from 6.33% to 50.9% ( Ahmed et al., 2020 ; Gao et al., 2020 ; González-Sanguino et al., 2020 ; Huang and Zhao, 2020 ; Lei et al., 2020 ; Mazza et al., 2020 ; Moghanibashi-Mansourieh, 2020 ; Ozamiz-Etxebarria et al., 2020 ; Özdin and Özdin, 2020 ; Wang et al., 2020a ; Wang et al., 2020b ).

Anxiety is often comorbid with depression ( Choi et al., 2020 ). Some predictive factors for depressive symptoms also apply to symptoms of anxiety, including a younger age group (≤40 years), lower education levels, poor self-rated health, high loneliness, female gender, divorced/widowed status, quarantine status, worry about being infected, property damage, history of mental health issue/medical problems, presence of chronic illness, living in urban areas, and the presence of specific physical symptoms ( Ahmed et al., 2020 ; Gao et al., 2020 ; González-Sanguino et al., 2020 ; Huang and Zhao, 2020 ; Lei et al., 2020 ; Mazza et al., 2020 ;  Moghanibashi-Mansourieh, 2020 ; Ozamiz-Etxebarria et al., 2020 ; Ozamiz-Etxebarria et al., 2020 ; Wang et al., 2020a ; Wang et al., 2020b ).

Additionally, social media exposure or frequent exposure to news/information concerning COVID-19 was positively associated with symptoms of anxiety ( Gao et al., 2020 ; Moghanibashi-Mansourieh, 2020 ). With respect to marital status, one study reported that married participants had higher levels of anxiety when compared to unmarried participants ( Gao et al., 2020 ). On the other hand, Lei et al. (2020) found that divorced/widowed participants developed more anxiety symptoms than single or married individuals. A prolonged period of quarantine was also correlated with higher risks of anxiety symptoms. Intuitively, contact history with COVID-positive patients or objects may lead to more anxiety symptoms, which is noted in one study ( Moghanibashi-Mansourieh, 2020 ).

3.7. Symptoms of PTSD/ psychological distress/stress and associated risk factors

With respect to PTSD symptoms, similar prevalence rates were reported by Zhang and Ma (2020) and N. Liu et al. (2020) at 7.6% and 7%, respectively. Despite using the same measurement scale as Zhang and Ma (2020) (i.e., IES), Wang et al. (2020a) noted a remarkably different result, with 53.8% of the participants reporting moderate-to-severe psychological impact. González et al. ( González-Sanguino et al., 2020 ) noted 15.8% of participants with PTSD symptoms. Three out of the four studies that measured the traumatic effects of COVID-19 reported that the female gender was more susceptible to develop symptoms of PTSD. In contrast, the research conducted by Zhang and Ma (2020) found no significant difference in IES scores between females and males. Other risk factors included loneliness, individuals currently residing in Wuhan or those who have been to Wuhan in the past several weeks (the hardest-hit city in China), individuals with higher susceptibility to the virus, poor sleep quality, student status, poor self-rated health, and the presence of specific physical symptoms. Besides sex, Zhang and Ma (2020) found that age, BMI, and education levels are also not correlated with IES-scores.

Non-specific psychological distress was also assessed in three studies. One study reported a prevalence rate of symptoms of psychological distress at 38% ( Moccia et al., 2020 ), while another study from Qiu et al. (2020) reported a prevalence of 34.43%. The study from Wang et al. (2020) did not explicitly state the prevalence rates, but the associated risk factors for higher psychological distress symptoms were reported (i.e., younger age groups and female gender are more likely to develop psychological distress) ( Qiu et al., 2020 ; Wang et al., 2020 ). Other predictive factors included being migrant workers, profound regional severity of the outbreak, unmarried status, the history of visiting Wuhan in the past month, higher self-perceived impacts of the epidemic ( Qiu et al., 2020 ; Wang et al., 2020 ). Interestingly, researchers have identified personality traits to be predictive of psychological distresses. For example, persons with negative coping styles, cyclothymic, depressive, and anxious temperaments exhibit greater susceptibility to psychological outcomes ( Wang et al., 2020 ; Moccia et al., 2020 ).

The intensity of overall stress was evaluated and reported in four studies. The prevalence of overall stress was variably reported between 8.1% to over 81.9% ( Wang et al., 2020a ; Samadarshi et al., 2020 ; Mazza et al., 2020 ). Females and the younger age group are often associated with higher stress levels as compared to males and the elderly. Other predictive factors of higher stress levels include student status, a higher number of lockdown days, unemployment, having to go out to work, having an acquaintance infected with the virus, presence of chronic illnesses, poor self-rated health, and presence of specific physical symptoms ( Wang et al., 2020a ; Samadarshi et al., 2020 ; Mazza et al., 2020 ).

3.8. A separate analysis of negative psychological outcomes

Out of the nineteen included studies, five studies appeared to be more representative of the general population based on the results of study quality appraisal ( Table 1 ). A separate analysis was conducted for a more generalizable conclusion. According to the results of these studies, the rates of negative psychological outcomes were moderate but higher than usual, with anxiety symptoms ranging from 6.33% to 18.7%, depressive symptoms ranging from 14.6% to 32.8%, stress symptoms being 27.2%, and symptoms of PTSD being approximately 7% ( Lei et al., 2020 ; Liu et al., 2020 ; Mazza et al., 2020 ; Wang et al., 2020b ; Zhang et al., 2020 ). In these studies, female gender, younger age group (≤40 years), and student population were repetitively reported to exhibit more adverse psychiatric symptoms.

3.9. Protective factors against symptoms of mental disorders

In addition to associated risk factors, a few studies also identified factors that protect individuals against symptoms of psychological illnesses during the pandemic. Timely dissemination of updated and accurate COVID-19 related health information from authorities was found to be associated with lower levels of anxiety, stress, and depressive symptoms in the general public ( Wang et al., 2020a ). Additionally, actively carrying out precautionary measures that lower the risk of infection, such as frequent handwashing, mask-wearing, and less contact with people also predicted lower psychological distress levels during the pandemic ( Wang et al., 2020a ). Some personality traits were shown to correlate with positive psychological outcomes. Individuals with positive coping styles, secure and avoidant attachment styles usually presented fewer symptoms of anxiety and stress ( Wang et al., 2020 ; Moccia et al., 2020 ). ( Zhang et al. 2020 ) also found that participants with more social support and time to rest during the pandemic exhibited lower stress levels.

4. Discussion

Our review explored the mental health status of the general population and its predictive factors amid the COVID-19 pandemic. Generally, there is a higher prevalence of symptoms of adverse psychiatric outcomes among the public when compared to the prevalence before the pandemic ( Huang et al., 2019 ; Lim et al., 2018 ). Variations in prevalence rates across studies were noticed, which could have resulted from various measurement scales, differential reporting patterns, and possibly international/cultural differences. For example, some studies reported any participants with scores above the cut-off point (mild-to-severe symptoms), while others only included participants with moderate-to-severe symptoms ( Moghanibashi-Mansourieh, 2020 ; Wang et al., 2020a ). Regional differences existed with respect to the general public's psychological health during a massive disease outbreak due to varying degrees of outbreak severity, national economy, government preparedness, availability of medical supplies/ facilities, and proper dissemination of COVID-related information. Additionally, the stage of the outbreak in each region also affected the psychological responses of the public. Symptoms of adverse psychological outcomes were more commonly seen at the beginning of the outbreak when individuals were challenged by mandatory quarantine, unexpected unemployment, and uncertainty associated with the outbreak ( Ho et al., 2020 ). When evaluating the psychological impacts incurred by the coronavirus outbreak, the duration of psychiatric symptoms should also be taken into consideration since acute psychological responses to stressful or traumatic events are sometimes protective and of evolutionary importance ( Yaribeygi et al., 2017 ; Brosschot et al., 2016 ; Gilbert, 2006 ). Being anxious and stressed about the outbreak mobilizes people and forces them to implement preventative measures to protect themselves. Follow-up studies after the pandemic may be needed to assess the long-term psychological impacts of the COVID-19 pandemic.

4.1. Populations with greater susceptibility

Several predictive factors were identified from the studies. For example, females tended to be more vulnerable to develop the symptoms of various forms of mental disorders during the pandemic, including depression, anxiety, PTSD, and stress, as reported in our included studies ( Ahmed et al., 2020 ; Gao et al., 2020 ; Lei et al., 2020 ). Greater psychological distress arose in women partially because they represent a higher percentage of the workforce that may be negatively affected by COVID-19, such as retail, service industry, and healthcare. In addition to the disproportionate effects that disruption in the employment sector has had on women, several lines of research also indicate that women exhibit differential neurobiological responses when exposed to stressors, perhaps providing the basis for the overall higher rate of select mental disorders in women ( Goel et al., 2014 ; Eid et al., 2019 ).

Individuals under 40 years old also exhibited more adverse psychological symptoms during the pandemic ( Ahmed et al., 2020 ; Gao et al., 2020 ; Huang and Zhao, 2020 ). This finding may in part be due to their caregiving role in families (i.e., especially women), who provide financial and emotional support to children or the elderly. Job loss and unpredictability caused by the COVID-19 pandemic among this age group could be particularly stressful. Also, a large proportion of individuals under 40 years old consists of students who may also experience more emotional distress due to school closures, cancelation of social events, lower study efficiency with remote online courses, and postponements of exams ( Cao et al., 2020 ). This is consistent with our findings that student status was associated with higher levels of depressive symptoms and PTSD symptoms during the COVID-19 outbreak ( Lei et al., 2020 ; Olagoke et al., 2020 , Wang et al., 2020a ; Samadarshi et al., 2020 ).

People with chronic diseases and a history of medical/ psychiatric illnesses showed more symptoms of anxiety and stress ( Mazza et al., 2020 ; Ozamiz-Etxebarria et al., 2020 ; Özdin and Özdin, 2020 ). The anxiety and distress of chronic disease sufferers towards the coronavirus infection partly stem from their compromised immunity caused by pre-existing conditions, which renders them susceptible to the infection and a higher risk of mortality, such as those with systemic lupus erythematosus ( Sawalha et al., 2020 ). Several reports also suggested that a substantially higher death rate was noted in patients with diabetes, hypertension and other coronary heart diseases, yet the exact causes remain unknown ( Guo et al., 2020 ; Emami et al., 2020 ), leaving those with these common chronic conditions in fear and uncertainty. Additionally, another practical aspect of concern for patients with pre-existing conditions would be postponement and inaccessibility to medical services and treatment as a result of the COVID-19 pandemic. For example, as a rapidly growing number of COVID-19 patients were utilizing hospital and medical resources, primary, secondary, and tertiary prevention of other diseases may have unintentionally been affected. Individuals with a history of mental disorders or current diagnoses of psychiatric illnesses are also generally more sensitive to external stressors, such as social isolation associated with the pandemic ( Ho et al., 2020 ).

4.2. COVID-19 related psychological stressors

Several studies identified frequent exposure to social media/news relating to COVID-19 as a cause of anxiety and stress symptoms ( Gao et al., 2020 ; Moghanibashi-Mansourieh, 2020 ). Frequent social media use exposes oneself to potential fake news/reports/disinformation and the possibility for amplified anxiety. With the unpredictable situation and a lot of unknowns about the novel coronavirus, misinformation and fake news are being easily spread via social media platforms ( Erku et al., 2020 ), creating unnecessary fears and anxiety. Sadness and anxious feelings could also arise when constantly seeing members of the community suffering from the pandemic via social media platforms or news reports ( Li et al., 2020 ).

Reports also suggested that poor economic status, lower education level, and unemployment are significant risk factors for developing symptoms of mental disorders, especially depressive symptoms during the pandemic period ( Gao et al., 2020 ; Lei et al., 2020 ; Mazza et al., 2020 ; Olagoke et al., 2020 ;). The coronavirus outbreak has led to strictly imposed stay-home-order and a decrease in demands for services and goods ( Nicola et al., 2020 ), which has adversely influenced local businesses and industries worldwide. Surges in unemployment rates were noted in many countries ( Statistics Canada, 2020 ; Statista, 2020 ). A decrease in quality of life and uncertainty as a result of financial hardship can put individuals into greater risks for developing adverse psychological symptoms ( Ng et al., 2013 ).

4.3. Efforts to reduce symptoms of mental disorders

4.3.1. policymaking.

The associated risk and protective factors shed light on policy enactment in an attempt to relieve the psychological impacts of the COVID-19 pandemic on the general public. Firstly, more attention and assistance should be prioritized to the aforementioned vulnerable groups of the population, such as the female gender, people from age group ≤40, college students, and those suffering from chronic/psychiatric illnesses. Secondly, governments must ensure the proper and timely dissemination of COVID-19 related information. For example, validation of news/reports concerning the pandemic is essential to prevent panic from rumours and false information. Information about preventative measures should also be continuously updated by health authorities to reassure those who are afraid of being infected ( Tran, et al., 2020a ). Thirdly, easily accessible mental health services are critical during the period of prolonged quarantine, especially for those who are in urgent need of psychological support and individuals who reside in rural areas ( Tran et al., 2020b ). Since in-person health services are limited and delayed as a result of COVID-19 pandemic, remote mental health services can be delivered in the form of online consultation and hotlines ( Liu et al., 2020 ; Pisciotta et al., 2019 ). Last but not least, monetary support (e.g. beneficial funds, wage subsidy) and new employment opportunities could be provided to people who are experiencing financial hardship or loss of jobs owing to the pandemic. Government intervention in the form of financial provisions, housing support, access to psychiatric first aid, and encouragement at the individual level of healthy lifestyle behavior has been shown effective in alleviating suicide cases associated with economic recession ( McIntyre and Lee, 2020a ). For instance, declines in suicide incidence were observed to be associated with government expenses in Japan during the 2008 economic depression ( McIntyre and Lee, 2020a ).

4.3.2. Individual efforts

Individuals can also take initiatives to relieve their symptoms of psychological distress. For instance, exercising regularly and maintaining a healthy diet pattern have been demonstrated to effectively ease and prevent symptoms of depression or stress ( Carek et al., 2011 ; Molendijk et al., 2018 ; Lassale et al., 2019 ). With respect to pandemic-induced symptoms of anxiety, it is also recommended to distract oneself from checking COVID-19 related news to avoid potential false reports and contagious negativity. It is also essential to obtain COVID-19 related information from authorized news agencies and organizations and to seek medical advice only from properly trained healthcare professionals. Keeping in touch with friends and family by phone calls or video calls during quarantine can ease the distress from social isolation ( Hwang et al., 2020 ).

4.4. Strengths

Our paper is the first systematic review that examines and summarizes existing literature with relevance to the psychological health of the general population during the COVID-19 outbreak and highlights important associated risk factors to provide suggestions for addressing the mental health crisis amid the global pandemic.

4.5. Limitations

Certain limitations apply to this review. Firstly, the description of the study findings was qualitative and narrative. A more objective systematic review could not be conducted to examine the prevalence of each psychological outcome due to a high heterogeneity across studies in the assessment tools used and primary outcomes measured. Secondly, all included studies followed a cross-sectional study design and, as such, causal inferences could not be made. Additionally, all studies were conducted via online questionnaires independently by the study participants, which raises two concerns: 1] Individual responses in self-assessment vary in objectivity when supervision from a professional psychiatrist/ interviewer is absent, 2] People with poor internet accessibility were likely not included in the study, creating a selection bias in the population studied. Another concern is the over-representation of females in most studies. Selection bias and over-representation of particular groups indicate that most studies may not be representative of the true population. Importantly, studies in inclusion were conducted in a limited number of countries. Thus generalizations of mental health among the general population at a global level should be made cautiously.

5. Conclusion

This systematic review examined the psychological status of the general public during the COVID-19 pandemic and stressed the associated risk factors. A high prevalence of adverse psychiatric symptoms was reported in most studies. The COVID-19 pandemic represents an unprecedented threat to mental health in high, middle, and low-income countries. In addition to flattening the curve of viral transmission, priority needs to be given to the prevention of mental disorders (e.g. major depressive disorder, PTSD, as well as suicide). A combination of government policy that integrates viral risk mitigation with provisions to alleviate hazards to mental health is urgently needed.

Authorship contribution statement

JX contributed to the overall design, article selection , review, and manuscript preparation. LL and JX contributed to study quality appraisal. All other authors contributed to review, editing, and submission.

Declaration of Competing Interest

Acknowledgements.

RSM has received research grant support from the Stanley Medical Research Institute and the Canadian Institutes of Health Research/Global Alliance for Chronic Diseases/National Natural Science Foundation of China and speaker/consultation fees from Lundbeck, Janssen, Shire, Purdue, Pfizer, Otsuka, Allergan, Takeda, Neurocrine, Sunovion, and Minerva.

Supplementary material associated with this article can be found, in the online version, at doi: 10.1016/j.jad.2020.08.001 .

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Nih research matters.

December 22, 2020

2020 Research Highlights — Promising Medical Findings

Results with potential for enhancing human health.

With NIH support, scientists across the United States and around the world conduct wide-ranging research to discover ways to enhance health, lengthen life, and reduce illness and disability. Groundbreaking NIH-funded research often receives top scientific honors. In 2020, these honors included one of NIH’s own scientists and another NIH-supported scientist who received Nobel Prizes . Here’s just a small sample of the NIH-supported research accomplishments in 2020.

Full 2020 NIH Research Highlights List

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New approaches to COVID-19

As the global pandemic unfolded, researchers worked at unprecedented speed to develop new treatments and vaccines. Scientists  studied antibodies from the blood of people who recovered from COVID-19 and identified potent, diverse ones that neutralize SARS-CoV-2 . Some antibody treatments have now been given emergency use authorization by the FDA, with many others in development . However, such antibodies—called monoclonal antibodies—are difficult to produce and must be given intravenously. NIH-researchers have been pursuing other approaches, including using antibodies from llamas , which are only about a quarter of the size of a typical human antibody and could be delivered directly to the lungs using an inhaler. Computer-designed “miniproteins” and other antiviral compounds are also under investigation.

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Universal mosquito vaccine tested

Most mosquito bites are harmless. But some mosquitoes carry pathogens, like bacteria and viruses, that can be deadly. A small trial showed that a vaccine against mosquito saliva—designed to provide broad protection against mosquito-borne diseases—is safe and causes a strong immune response in healthy volunteers. More studies are needed to test its effectiveness against specific diseases.

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Machine learning detects early signs of osteoarthritis

Osteoarthritis is the most common type of arthritis. It results when cartilage, the tissue that cushions the ends of the bones, breaks down. People with osteoarthritis can have joint pain, stiffness, and swelling. Some develop serious pain and disability from the disease. Using artificial intelligence and MRI scans, scientists identified signs of osteoarthritis three years before diagnosis. The results suggest a way to identify people who may benefit from early interventions.

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Advances in restoring vision

Several common eye diseases, such as age-related macular degeneration and retinitis pigmentosa, damage the retina, the light-sensitive tissue in the eye. They can eventually lead to vision loss. Two studies looked at ways to restore vision in mouse models. Researchers reprogrammed skin cells into light-sensing eye cells that restored sight in mice. The technique may lead to new approaches for modeling and treating eye diseases. Other scientists restored vision in blind mice by using gene therapy to add a novel light-sensing protein to cells in the retina. The therapy will soon be tested in people.

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Blood protein signatures change across lifespan

The bloodstream touches all the tissues of the body. Because of the constant flow of proteins through the body, some blood tests measure specific proteins to help diagnose diseases. Researchers determined that the levels of nearly 400 proteins in the blood can be used to determine people’s age and relative health. More research is needed to understand if these protein signatures could help identify people at greater risk of age-related diseases.

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Understanding HIV’s molecular mechanisms

More than a million people nationwide are living with HIV, the virus that causes AIDS. HIV attacks the immune system by destroying immune cells vital for fighting infection. Researchers uncovered key steps in HIV replication by reconstituting and watching events unfold outside the cell. The system may be useful for future studies of these early stages in the HIV life cycle. In other work, experimental treatments in animal models of HIV led to the viruses emerging from their hiding places inside certain cells—a first step needed to make HIV vulnerable to the immune system.

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Test distinguishes Parkinson’s disease from related condition

A protein called alpha-synuclein plays a major role in Parkinson’s disease as well as other brain disorders. Early symptoms of Parkinson’s disease and another disease involving alpha-synuclein, multiple system atrophy, can be similar. Researchers created a test using cerebrospinal fluid that can distinguish between these two diseases with 95% accuracy. The results have implications for the early diagnosis and treatment of these conditions and may help in the development of new targeted therapies.

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Understanding allergic reactions to skin care products

Personal care products like makeup, skin cream, and fragrances commonly cause rashes called allergic contact dermatitis. It’s not well understood how chemical compounds in personal care products trigger such allergic reactions. Scientists gained new insight into how personal care products may cause immune responses that lead to allergic responses in some people. Understanding how compounds in these products trigger immune reactions could lead to new ways to prevent or treat allergic contact dermatitis.

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ROLAND GRAD, MD, MSc, AND MARK H. EBELL, MD, MS

Am Fam Physician. 2021;104(1):41-48

Related letter: Should Muscle Relaxants Be Used as Adjuvants in Patients With Acute Low Back Pain?

Published online June 9, 2021.

Author disclosure: Dr. Grad has no relevant financial affiliations. Dr. Ebell is cofounder and editor-in-chief of Essential Evidence Plus; see Editor's Note.

This article summarizes the top 20 research studies of 2020 identified as POEMs (patient-oriented evidence that matters), including the two most highly rated guidelines of the year on gout and chronic obstructive pulmonary disease (COPD). Regarding COVID-19, handwashing and social distancing through stay-at-home orders or quarantine measures are effective at slowing the spread of illness. Use of proper face masks (not gaiters or bandanas) is also effective at preventing transmission. This is important because the virus can infect others during the presymptomatic phase. Aspirin can no longer be recommended for the primary prevention of cardiovascular disease. Human papillomavirus vaccination is strongly associated with reduced risk of invasive cervical cancer, especially in women who were vaccinated before 17 years of age. When a woman who is postmenopausal has a screening bone mineral density test, rechecking the test after three years does not help to identify those who will have a fragility fracture. A higher daily step count is associated with lower all-cause mortality. After one year of follow-up, physical therapy is preferred to glucocorticoid injections for osteoarthritis of the knee; acetaminophen is ineffective for acute low back pain or pain due to knee or hip osteoarthritis; and adding a muscle relaxant to ibuprofen does not improve functional outcomes or pain in people reporting moderate to severe back pain one week after starting treatment. Although short-term antibiotics and steroids are effective in treating acute exacerbations of COPD, not much else is. Successful communication with patients seeking an antibiotic for a flulike illness can be achieved with combinations of messaging, including information on antibiotic resistance and the self-limiting nature of the illness. A new prediction rule effectively identifies patients with a history of penicillin allergy who have a low likelihood of positive findings on allergy testing. Low-value screening tests in asymptomatic, low-risk patients often lead to further testing, diagnostic procedures, or referrals. A new tool helps determine the amount of change needed to signify a real difference between two laboratory values in the same person over time. Finally, a pillar of our specialty, continuity of care, is associated with decreased all-cause mortality.

Annually for 22 years, a team of clinicians has systematically reviewed English-language medical journals to identify original research most likely to change and improve primary care practice. The team includes experts in family medicine, pharmacology, hospital medicine, and women's health. 1 , 2

The goal of this process is to identify POEMs (patient-oriented evidence that matters). A POEM must report at least one patient-oriented outcome, such as improvement in symptoms, morbidity, or mortality. It should also be free of important methodologic bias, making the results valid and trustworthy. Finally, if applied in practice, the results would change what some physicians do by prompting them to adopt a new practice or discontinue an old one that has been shown to be ineffective or harmful. Adopting POEMs in clinical practice should improve patient outcomes. Of more than 20,000 research studies published in 2020 in the journals reviewed by the POEMs team, 306 met criteria for validity, relevance, and practice change. These POEMs are emailed daily to subscribers of Essential Evidence Plus (Wiley-Blackwell, Inc.).

The Canadian Medical Association purchases a POEMs subscription for its members, many of whom receive the daily POEM. As these physicians read a POEM, they can rate it using a validated questionnaire. This process is called the Information Assessment Method ( https://www.mcgill.ca/iam ). POEM ratings address the domains of clinical relevance, cognitive impact, use of this information in practice, and expected health benefits if that POEM is applied to a specific patient. 3 , 4 In 2020, each of the 306 daily POEMs was rated by an average of 1,230 physicians.

In this article, we present the 20 most clinically relevant POEMs as rated by Canadian Medical Association members in 2020. This is the 10th installment of our annual series ( https://www.aafp.org/afp/toppoems ). As we write this article, the pandemic rolls on. However, beyond COVID-19, our patients continue to face the usual (and unusual) health problems of everyday life. Thus, we summarize the clinical question and bottom-line answer for research studies identified as a top 20 POEM, organized by topic and followed by a brief discussion. This set of 20 POEMs includes the two most relevant practice guidelines of the year. The full POEMs are available online at https://www.aafp.org/afp/poems2020 .

The year 2020 saw the emergence of SARS-CoV-2, a novel coronavirus that causes the COVID-19 illness, and 54 POEMs addressed the epidemiology, diagnosis, treatment, and prognosis of patients with COVID-19. Not surprisingly, the four most highly rated POEMs of the year and five in the top 20 provided evidence regarding preventive measures ( Table 1 ) . 5 – 10 These were published early in the pandemic and provided important evidence for family physicians to share with their patients.

The most highly rated POEM was originally published in 2015 and reissued in 2020 after the content was updated for COVID-19. The study was a large pragmatic trial in the United Kingdom that randomized participants to a brief online hand-washing intervention or usual care. Those who completed the intervention had fewer respiratory tract infections over the next four months (51% vs. 59%; P < .001; number needed to treat = 12). 5

The next COVID-19 POEM was a Cochrane review. 6 The authors reviewed the results of modeling studies of COVID-19 and previous coronavirus pandemics, as well as four observational studies. They concluded that quarantine measures are effective in slowing the spread of infection, especially when implemented early and in conjunction with other public health measures.

The third POEM summarized the results of two ecologic studies. The first compared COVID-19 incidence rates in eight Iowa counties that did not have stay-at-home orders with seven neighboring Illinois counties that had such orders in place. 7 The second compared a Georgia county that implemented stay-at-home orders approximately two weeks before the rest of Georgia with seven surrounding counties. 8 In both studies, earlier implementation of stay-at-home orders was associated with a lower incidence of COVID-19.

Another POEM summarized the results of one of the earliest studies to evaluate the effectiveness of different kinds of masks for preventing the spread of respiratory droplets. 9 Using laser technology, 14 types of masks were evaluated. Gaiters and bandanas were essentially useless, polypropylene and surgical masks were much better, and N95 masks were best.

An important factor in the rapid spread of COVID-19 was transmission by asymptomatic people. One of the first publications to report this was described in the next POEM. Chinese researchers studying infector-infectee transmission pairs found that the peak of infectiousness occurs about one day before symptom onset, and that 44% of secondary cases occur during the presymptomatic period. 10 Another study (not among the top 20 POEMs) reviewed cohorts in which the entire group was tested for COVID-19 during an outbreak and found that the rates of asymptomatic or presymptomatic infection are 75% or higher in young adult populations and approximately 40% overall. 11

Prevention and Screening

POEMs addressing prevention topics are summarized in Table 2 . 12 – 15 The first was a meta-analysis of randomized trials comparing aspirin with placebo as primary prevention. It compared four studies that recruited patients since 2005, with older studies that largely recruited patients in the 1980s and 1990s. The newer studies no longer found that patients taking aspirin for primary prevention have significant reductions in cancer incidence or mortality, cardiovascular mortality, or nonfatal myocardial infarction. Any benefits were countered by harms, such as increased gastrointestinal bleeding. 12 Perhaps we are doing a better job of screening for cancer and preventing cardiovascular events through use of statins and antihypertensives, lessening the need for aspirin in prevention.

We often tell our patients to walk more, but does walking affect mortality? The next POEM used data from a national sample of U.S. residents whose daily steps were measured between 2003 and 2006. 13 All-cause mortality was 77 per 1,000 person-years for those with less than 4,000 steps per day; 21 per 1,000 for those with 4,000 to 7,999 steps per day; 7 per 1,000 for those with 8,000 to 11,999 steps per day; and 4.8 per 1,000 for those with at least 12,000 steps per day. Step intensity was not significantly associated with mortality after controlling for total daily steps. Bottom line? Higher step counts are associated with lower all-cause mortality, suggesting our patients should keep walking. 13

When a woman who is postmenopausal has a screening bone mineral density test, the question arises about whether to repeat the test and, if so, how often? The third POEM in this group is a cohort study from the Women's Health Initiative, which showed that information gained from a second test three years after the first does not add predictive value beyond the first test result. Because bone density changes little over time, one bone mineral density test at around 65 years of age is likely to be sufficient for the purpose of screening to prevent a fragility fracture. 14

Studies have shown that human papillomavirus (HPV) vaccination can reduce the likelihood of precancerous abnormalities identified on a Papanicolaou (Pap) test. The next POEM is a Swedish study that used data from a national health registry to compare the risk of invasive cervical cancer in 527,871 vaccinated women vs. 1,145,112 unvaccinated women. 15 After adjusting for differences between groups, the incidence rate ratio (IRR; the ratio of the incidence of cancer in vaccinated people to that in unvaccinated people) for invasive cervical cancer was 0.37 (95% CI, 0.21 to 0.57). For women who were vaccinated before 17 years of age, the IRR was only 0.12 (95% CI, 0.00 to 0.34), whereas for those vaccinated between 17 and 30 years of age, the IRR was 0.47 (95% CI, 0.27 to 0.75). HPV vaccination is strongly associated with a lower risk of invasive cervical cancer, especially when given early.

Musculoskeletal

Three POEMs addressing musculoskeletal topics are summarized in Table 3 . 16 – 19 The first is a study that randomized adults with knee osteoarthritis to up to three corticosteroid injections or up to eight physical therapy sessions in the first six weeks, with additional sessions as needed. 16 The rapid and large improvement in the first month for both groups is somewhat surprising, with relatively little further improvement the rest of the year. This suggests regression to the mean may have contributed to the observed improvement (i.e., patients were identified when their arthritis was flaring up and would have improved no matter what). Also, the open-label design may have contributed to a Hawthorne effect (alteration of behavior by the participants of a study who know they are being observed) for those in the physical therapy group and a placebo effect for those in the injection group. A Cochrane review concluded that glucocorticoid injections are effective, although primarily in the two to four weeks following injection. 17 Overall, the physical therapy group did better at one year, with continued improvement, whereas the injection group plateaued after the first month. 16

The next POEM is a systematic review of randomized controlled trials of acetaminophen compared with placebo for pain relief in adults. 18 It showed that acetaminophen is more effective than placebo in providing some pain relief in patients with acute migraine and might be more beneficial than placebo (which also works well) for tension headaches. A single dose of acetaminophen is about twice as likely as placebo to reduce postpartum perineal pain, and it may be effective (but not as effective as other treatments) for acute renal colic. Importantly, acetaminophen is ineffective for patients with acute low back pain or pain due to knee or hip osteoarthritis.

The last POEM in this category addresses a drug class often prescribed for back pain. In people with moderate to severe low back pain who are taking a nonsteroidal anti-inflammatory drug, such as ibuprofen (up to 600 mg three times per day), does the addition of a muscle relaxant improve function or reduce pain? No. 19 This finding reminds us of an earlier randomized controlled trial from the same author that was a top POEM of 2015. At that time, we advised not adding cyclobenzaprine to naproxen for patients with acute low back pain. 20

Respiratory

Two top POEMs on respiratory topics are summarized in Table 4 . 21 , 22 One is a meta-analysis of randomized controlled trials of treatment in patients with an exacerbation of chronic obstructive pulmonary disease. 21 This shows that antibiotics and corticosteroids are effective in treating acute exacerbations in outpatient and inpatient settings, regardless of the severity of the exacerbation. Current research does not provide good guidance on which antibiotic is best or on the optimal dose or duration of corticosteroid treatment. Notably, studies performed in critically ill patients were not included in this analysis.

The other POEM addresses the tricky issue of communicating with patients who appear to be seeking an antibiotic for a flulike illness. It is a randomized trial conducted online in the United Kingdom showing that patients receiving the combination of a fear-based message about increased antibiotic resistance and an empowering message are less likely to visit a physician for their next respiratory tract infection (45.1% to 46.1% vs. 29.2%; P < .001) and less likely to request an antibiotic (52.5% to 54.7% vs. 42.3%; P < .001) than those receiving only fear-based messaging. 22

Miscellaneous

Four top POEMs do not fall easily into a single category ( Table 5 ) . 23 – 26 The first is about a new clinical prediction rule to identify patients who report a penicillin allergy but are unlikely to have a true allergy if tested. The FAST rule is as follows—five years or less since the reaction: 2 points; anaphylaxis, angioedema, or severe cutaneous reaction: 2 points; treatment required for reaction: 1 point. Patients with a score of 0 have a less than 1% likelihood of a positive result on allergy testing, and those with a score of 1 or 2 have a 5% likelihood. 23

The COVID-19 pandemic upended medical practice, leading to large reductions in the use of health care services. For example, the Canadian Urological Association recommended the cessation of prostate-specific antigen screening for prostate cancer until the resolution of the pandemic. 27 In this context, the next POEM reminds us to avoid low-value screening tests in asymptomatic, low-risk patients because they often lead to further testing, diagnostic procedures, or referrals. 24 Specifically, screening tests in low-risk patients, such as chest radiography and electrocardiography in adults as part of an annual health examination and Pap tests in those younger than 21 years or older than 69 years, should be avoided.

How many patients still ask for annual blood tests despite physicians counseling them on the low yield for doing this? The next POEM was the top non–COVID-related POEM of 2020 for clinical relevance. 25 This POEM describes a new online tool ( https://www.bmj.com/content/368/bmj.m149 ) to illustrate the variability in a single laboratory value (such as the A1C test) and the amount of change needed to signify a real difference between two values in the same person over time. For example, given the variability inherent in total cholesterol measurements, it is difficult to detect a clinically important change within a one-year period.

Our final miscellaneous POEM is a systematic review of the value of a pillar of family medicine—personal continuity of care. This is defined by an ongoing relationship with a physician that builds the physician-patient relationship and promotes the healing power of interactions. This core principle of family medicine stands up to scientific scrutiny by demonstrating an associated reduction in mortality. 26

Practice Guidelines

POEMs sometimes summarize high-impact clinical practice guidelines. Key messages from the two highest rated guidelines, addressing gout and chronic obstructive pulmonary disease, are summarized in Table 6 . 28 , 29

The full text of the POEMs discussed in this article is available at https://www.aafp.org/afp/poems2020 .

A list of top POEMs from previous years is available at https://www.aafp.org/afp/toppoems .

Editor's Note: This article was cowritten by Dr. Mark Ebell, deputy editor for evidence-based medicine for AFP and cofounder and editor-in-chief of Essential Evidence Plus, published by Wiley-Blackwell, Inc. Because of Dr. Ebell's dual roles and ties to Essential Evidence Plus, the concept for this article was independently reviewed and approved by a group of AFP 's medical editors. In addition, the article underwent peer review and editing by three of AFP 's medical editors. Dr. Ebell was not involved in the editorial decision-making process.—Sumi Sexton, MD, Editor-in-Chief

The authors thank Wiley-Blackwell, Inc., for giving permission to excerpt the POEMs; Drs. Allen Shaughnessy, Henry Barry, David Slawson, Nita Kulkarni, and Linda Speer for selecting and writing the original POEMs; the academic family medicine fellows and faculty of the University of Missouri–Columbia for their work as peer reviewers; Joulé, Inc., for supporting the POEMs CME program in Canada; Pierre Pluye, PhD, for codeveloping the Information Assessment Method; and Maria Vlasak for her assistance with copyediting the POEMs.

Shaughnessy AF, Slawson DC, Bennett JH. Becoming an information master: a guidebook to the medical information jungle. J Fam Pract. 1994;39(5):489-499.

Ebell MH, Barry HC, Slawson DC, et al. Finding POEMs in the medical literature. J Fam Pract. 1999;48(5):350-355.

Grad RM, Pluye P, Mercer J, et al. Impact of research-based synopses delivered as daily e-mail: a prospective observational study. J Am Med Inform Assoc. 2008;15(2):240-245.

Pluye P, Grad RM, Johnson-Lafleur J, et al. Evaluation of email alerts in practice: part 2. Validation of the information assessment method. J Eval Clin Pract. 2010;16(6):1236-1243.

Little P, Stuart B, Hobbs FDR, et al. An internet-delivered handwashing intervention to modify influenza-like illness and respiratory infection transmission (PRIMIT): a primary care randomised trial [published correction appears in Lancet . 2015;386(10004):1630]. Lancet. 2015;386(10004):1631-1639.

Nussbaumer-Streit B, Mayr V, Dobrescu AI, et al. Quarantine alone or in combination with other public health measures to control COVID-19: a rapid review. Cochrane Database Syst Rev. 2020(4):CD013574.

Lyu W, Wehby GL. Comparison of estimated rates of coronavirus disease 2019 (COVID-19) in border counties in Iowa without a stay-at-home order and border counties in Illinois with a stay-at-home order. JAMA Netw Open. 2020;3(5):e2011102.

Ebell MH, Bagwell-Adams G. Mandatory social distancing associated with increased doubling time: an example using hyperlocal data. Am J Prev Med. 2020;59(1):140-142.

Fischer EP, Fischer MC, Grass D, et al. Low-cost measurement of face mask efficacy for filtering expelled droplets during speech. Sci Adv. 2020;6(36):eabd3083.

He X, Lau EHY, Wu P, et al. Temporal dynamics in viral shedding and transmissibility of COVID-19 [published correction appears in Nat Med . 2020;26(9):1491–1493]. Nat Med. 2020;26(5):672-675.

Oran DP, Topol EJ. Prevalence of asymptomatic SARSCoV-2 infection: a narrative review. Ann Intern Med. 2020;173(5):362-367.

Moriarty F, Ebell MH. A comparison of contemporary versus older studies of aspirin for primary prevention. Fam Pract. 2020;37(3):290-296.

Saint-Maurice PF, Troiano RP, Bassett DR, et al. Association of daily step count and step intensity with mortality among US adults. JAMA. 2020;323(12):1151-1160.

Crandall CJ, Larson J, Wright NC, et al. Serial bone density measurement and incident fracture risk discrimination in postmenopausal women. JAMA Intern Med. 2020;180(9):1232-1240.

Lei J, Ploner A, Elfström KM, et al. HPV vaccination and the risk of invasive cervical cancer. N Engl J Med. 2020;383(14):1340-1348.

Deyle GD, Allen CS, Allison SC, et al. Physical therapy versus glucocorticoid injection for osteoarthritis of the knee. N Engl J Med. 2020;382(15):1420-1429.

Jüni P, Hari R, Rutjes AWS, et al. Intra-articular corticosteroid for knee osteoarthritis. Cochrane Database Syst Rev. 2015(10):CD005328.

Saragiotto BT, Abdel Shaheed C, Maher CG. Paracetamol for pain in adults. BMJ. 2019;367:l6693.

Friedman BW, Irizarry E, Solorzano C, et al. A randomized, placebo-controlled trial of ibuprofen plus metaxalone, tizanidine, or baclofen for acute low back pain. Ann Emerg Med. 2019;74(4):512-520.

Friedman BW, Dym AA, Davitt M, et al. Naproxen with cyclobenzaprine, oxycodone/acetaminophen, or placebo for treating acute low back pain: a randomized clinical trial. JAMA. 2015;314(15):1572-1580.

Dobler CC, Morrow AS, Beuschel B, et al. Pharmacologic therapies in patients with exacerbation of chronic obstructive pulmonary disease: a systematic review with meta-analysis. Ann Intern Med. 2020;172(6):413-422.

Roope LSJ, Tonkin-Crine S, Herd N, et al. Reducing expectations for antibiotics in primary care: a randomised experiment to test the response to fear-based messages about antimicrobial resistance. BMC Med. 2020;18(1):110.

Trubiano JA, Vogrin S, Chua KYL, et al. Development and validation of a penicillin allergy clinical decision rule. JAMA Intern Med. 2020;180(5):745-752.

Bouck Z, Calzavara AJ, Ivers NM, et al. Association of low-value testing with subsequent health care use and clinical outcomes among low-risk primary care outpatients undergoing an annual health examination. JAMA Intern Med. 2020;180(7):973-983.

McCormack JP, Holmes DT. Your results may vary: the imprecision of medical measurements. BMJ. 2020;368:m149.

Baker R, Freeman GK, Haggerty JL, et al. Primary medical care continuity and patient mortality: a systematic review. Br J Gen Pract. 2020;70(698):e600-e611.

Kokorovic A, So AI, Hotte SJ, et al. A Canadian framework for managing prostate cancer during the COVID-19 pandemic: recommendations from the Canadian Urologic Oncology Group and the Canadian Urological Association. Can Urol Assoc J. 2020;14(6):163-168.

FitzGerald JD, Dalbeth N, Mikuls T, et al. 2020 American College of Rheumatology Guideline for the Management of Gout [published correction appears in Arthritis Rheumatol . 2021;73(3):413]. Arthritis Rheumatol. 2020;72(6):879-895.

Nici L, Mammen MJ, Charbek E, et al. Pharmacologic management of chronic obstructive pulmonary disease. An official American Thoracic Society Clinical Practice Guideline [published correction appears in Am J Respir Crit Care Med. 2020;202(6):910]. Am J Respir Crit Care Med. 2020;201(9):e56-e69.

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ORIGINAL RESEARCH article

Impact of weekly physical activity on stress response: an experimental study.

• 1 Department of Physical Education, Sport and Human Movement, Autonomous University of Madrid, Madrid, Spain
• 2 Didactic and Behavioral Analysis in Sport Research Group, Faculty of Sport Sciences, University of Extremadura, Cáceres, Spain
• 3 Sport of Studies Center, Rey Juan Carlos University, Madrid, Spain

The aim of this research is focused on analyzing the alteration of the psychophysiological and cognitive response to an objective computerized stress test (Determination Test - DT-, Vienna test System ® ), when the behavioral response is controlled. The sample used was sports science students (N = 22), with a mean age of 22.82 (M age = 22.82; SD years = 3.67; M PhysicalActivity hours/Week = 7.77; SD hours / week = 3.32) A quasi-experimental design was used in which the response of each participant to the DT test was evaluated. The variable “number of hours of physical activity per week” and the variable “level of behavioral response to stress” were controlled. Before and after this test, the following parameters were measured: activation and central fatigue (Critical Flicker Fusion Threshold (CFF Critical flicker fusion ascending and Critical flicker fusion descending; DC potential), and perceived exertion (Central Rating of Perceived Exertion and Peripheral Rating of Perceived Exertion). Significant differences were found in all of the measures indicated. The usefulness of this protocol and the measures used to analyze the stress response capacity of the study subjects are discussed.

Introduction

The analysis of psychophysiological fatigue is considered very important in different contexts ( Lohani et al., 2019 ). In this sense, the consideration of the study of humans’s response to external and internal loads ( Wijesuriya et al., 2007 ; Wilson et al., 2007 ) has become one of the most important research topics. The external loads exerted on the individual are added to their skills and coping strategies, resulting in a level of tolerance and adaptation to each situation ( Folkman and Lazarus, 1988 ). Along the last decades, distinctions are often made between physical and mental fatigue role, indicating clear methodologies for the analysis of physiological fatigue, but with clear limitations in the study of central fatigue, because this is measurable only indirectly, which emphasizes the importance of developing new central fatigue analysis procedures ( Bittner et al., 2000 ).

Throughout the decades of research on this topic, different strategies have been used to evaluate the adaptation to these external and internal loads ( Lazarus, 1990 ; Amann, 2011 ). Thus, for example, a multitude of self-reports and standardized tests have been used ( Britner et al., 2003 ), to which physiological and biological measures have been added ( Arza et al., 2019 ). However, relatively low attention is usually given to the Central Nervous System (CNS)-related mechanisms, which play a major role on the development of fatigue ( Tarvainen et al., 2014 ), but are rarely monitored in the sport and physical activity field ( Valenzuela et al., 2020 ). Most of the studies related to central fatigue to date have focused on the effect it has on performing strenuous physical tasks ( Amann and Dempsey, 2008 ), although over the last few years there has been a notable increase in interest in studying the role of central fatigue in explaining human performance ( Inzlicht and Marcora, 2016 ). In this sense, the psychobiological model based on motivational intensity theory has gained special strength ( Gendolla and Richter, 2010 ). This model emphasizes that perception of effort and potential motivation are the central determinants of task engagement. Both variables are taken into consideration in our research, controlling the involvement in the task (motivation), by applying a computerized test, and analyzing the perception of both central and peripheral effort as detailed in the methodological section.

Two of these measures, which focus the methodological attention of this research due to its great potential in the study of this topic, are the Critical Flicker Fusion Threshold (CFFT), evaluated using one Flicker Fusion instrument ( Vicente-Rodríguez et al., 2020 ), and the DC Potential, evaluated using the OmegaWave technology. The neuro-physiological basis of flicker perception is complex but well established ( Görtelmeyer and Zimmermann, 1982 ). In particular, flickering light directly influences cortical activity. The CFFT was measured using two red light- emitting diodes in binocular foveal fixation. The continuous psychophysical method of limits was employed to determine CFFT ( Woodworth and Schlosberg, 1954 ). The utility of CFFT in sport has been focused on the relationship of arousal level with CNS ( Görtelmeyer and Zimmermann, 1982 ). Increase in CFFT suggests an increase in cortical arousal and sensory sensitivity. By contrast, a decrease of CFFT suggests a reduction in the efficiency of the system to process information ( Li et al., 2004 ; Clemente and Díaz, 2019 ). On the other hand, for the evaluation of the brain’s direct current (DC) potentials -slow potentials that reflect alterations in brain excitability- OmegaWave technology has gained strength in recent years ( Naranjo-Orellana et al., 2020 ; Valenzuela et al., 2020 ). This device not only measures the Heart Rate Variability (HRV) but it also simultaneously a brainwave signal (DC potential) in order to complement the information obtained from HRV to assess the athlete’s functional state ( Naranjo-Orellana et al., 2020 ). DC potentials—frequency ranges between 0 and 0.5 Hz, are correlated with different brain processes, such as take consciousness during decision making ( Guggisberg and Mottaz, 2013 ) high alertness states ( Bachmann, 1984 ), arousal state ( Haider et al., 1981 ), or attention ( Rösler et al., 1997 ).

To date, most studies conducted in the evaluation of central fatigue have shown that the greatest disturbances are produced by tasks that require efforts at maximum speed that involve a large amount of force ( Davranche and Pichon, 2005 ; Clemente and Díaz, 2019 ). However, there are very few studies that have analyzed central fatigue through controlled analysis of a task that primarily involves central fatigue ( Fuentes et al., 2019 ). In this sense, the aim is to apply a computerized test (DT, Vienna Test System), that allows evaluating people’s tolerance to stress and central fatigue by applying a standardized protocol, in physical activity practitioners. The knowledge in this field is really limited, for this reason we developed the present research with the aim of studying the modifications in CFFT and DC potentials in a sample group of regular physical activity. The first hypothesis establishes that the computerized stress task increases the participants’ perception of central fatigue, while keeping the perception of peripheral fatigue stable. As a consequence, the second hypothesis establishes that differences will be found in the “post” situation in the CFFT measures and in the central physiological indicators, which would indicate a relationship between the subjective and objective measures of central fatigue.

Materials and Methods

This study followed a quasi-experimental design ( Montero and León, 2007 ) and it received the approval of the University Ethical Commission in compliance with the Helsinki Declaration. All subjects were informed about the procedure and gave their written consent to participate. This study was carried out complying with the Standards for Ethics in Sport and Exercise Science Research ( Harriss et al., 2019 ).

Participants

The participants included 22 individuals from Madrid (Spain), 18 of whom were male and 4 females. These participants were aged between 18 and 36 years ( M years = 22.82, SD years = 3.67). All of the participants regularly engaged in physical activity, between 4 and 14 h per week ( M hours / week = 7.77, SD hours / week = 3.32). The inclusion criteria was that they performed physical activity at least 3 times a week and 150 min of moderate/vigorous physical activity. The exclusion criteria was not correctly performing the proposed measurements. Four participants were excluded from the study for not completing the measurements correctly. Intentional sampling methods were used ( Montero and León, 2007 ). Due to the impossibility of continuing with the data collection due to the Alert State decreed by the Spanish Government as a result of COVID-19, the sample had to be closed with the participants who had passed all the tests before March 2020.

Instrumentation and Study Variables

The number of hours of physical activity per week and the scores obtained on the DT test were used as controlled variables. This allows us to know that the differences found are not due to the ability to respond to stress, or to the weekly amount of physical exercise performed. Therefore, only the subjects in which there were no statistically significant differences in their weekly level of physical exercise, nor in the scores obtained in the DT test, were used.

To carry out this research, three measurement systems have been used: OmegaWave device, Flicker Fusion Unit (Vienna Test System), and the Determination Test (Vienna Test System). OmegaWave is a device that assesses the physiological readiness of athletes by examining the autonomic balance through HRV and brain‘s energy balance via DC potential ( Gómez-Oliva et al., 2019 ), Elastic chest band MEDITRACE (dominant hand and forehead). Coach + application (OmegaWave Ltd, Espoo, Finland) was used on Ipad mini 2 32GB. The Vienna Test System is an instrument for computerized psychological assessments that allows the objective evaluation of different psychological parameters. The Determination Test (DT Vienna test system) ( Whiteside, 2002 ; Whiteside et al., 2003 ) was used to determine neuropsychological fatigue. The test studied the attentional capacity, reactive stress tolerance, reaction speed among continuously, and quickly changing acoustic and visual stimuli. The test is simple, the difficulty of the task lies in the different modality of the arriving stimuli and their speed. This way we measure those cognitive abilities of the people involved that are needed for the distinction of colors and sounds, the perception of the characteristics of stimuli, their memorization, and finally, the selection of the adequate answer. The stimuli coming during the test are not predictable. Instead, the subjects need to react to them randomly ( Schuhfried, 2013 ). We study four key variables: the average value of reaction speed (sec), the number of correct answers (raw score), which reflects the ability of the respondent to precisely and quickly select the adequate answer even under pressure. Furthermore, we also examine the number of incorrect answers (raw score) which can show us how likely the respondent is to get confused under stress and pressure; finally, the high number of missed answers (raw score) reveals that the respondent is not capable of maintaining his/her attention under stress and is prone to giving up these situations ( Neuwirth and Benesch, 2012 ). The duration of this test was 6 min.

Before and after the stress test the following parameters were analyzed in this order:

Parameters analyzed through OmegaWave Coach + device ® (OmegaWave Ltd, Espoo, Finland):

– Hear Rate Variability (HRV). Square root of the mean of the squares of successive RR interval differences (RMSSD), Standard deviation of all normal to normal RR intervals (SDNN), and Standard deviation of successive squares of intervals RR (SDSD). OmegaWave is a device that assesses the physiological readiness of athletes by examining autonomic balance through HRV and brain‘s metabolic state via DC potential ( Ilyukhina and Zabolotskikh, 2020 ). Elastic chest band MEDITRACE (dominant hand and forehead). Coach + application (Omegawave Ltd., Espoo, Finland) was used on Ipad mini 2 32GB. For calculating HRV it be used the Root Mean Square of the Successive Differences score (RMSSD) ( Ilyukhina et al., 1982 ). It was used before and after the stress test.

– DC potential dynamics. DC Potential represent changes in the brain’s metabolic balance in response to increased exercise intensity or psychological challenges and are linked to cognitive and mental load ( Wagshul et al., 2011 ; Ilyukhina, 2015 ).

– CNS System Readiness ( Ilyukhina, 1986 ). It’s indicated by a floating grade from 1.0 to 7.0, where 7.0 is the optimal state. This index represents the state of the brain’s energy level and is composed of three factors (in order of significance): stabilization point of DC potential (mV), stabilization time (reduces system readiness state of 1.0–7.0, if not optimal), and curve shape (reduces system readiness state of 1.0–7.0, if not optimal).

– Stabilization point of DC Potential (mV) ( Ilyukhina et al., 1982 ; Ilyukhina, 2013 ): The first priority in DC analysis is the stabilization point of DC Potential. In the literature, especially by Ilyukhina, this point is defined as Level of Operational Rest. In 1982, the combined work of Ilyukhina and Sychev was published which outlined quantitative parameters of LOR for the assessment of the healthy human’s adaptation and compensatory−adaptive abilities to physical and mental loads in sports.

– Stabilization time ( Ilyukhina and Zabolotskikh, 1997 ). The second priority of analysis is to look at the stabilization time. measured in minutes. The spontaneous relaxation speed represents neuroreflex reactivity (neural control of baroreflex arch) of cardiovascular and respiratory systems. This measure associated with psycho-emotional dynamic and stability. Normal stabilization time occurs within 2 min and represents optimal balance within stress-regulation systems.

– Curve Shape: The curve shape is composed of two elements: Difference between measurement start mV and end mV values ( Table 1 ). The optimal shape of the curve should show a smooth transition from a higher initial value (active wakefulness) to a lower stabilization value (operational rest DC potential form represents the dynamic interaction within stress-regulation systems). DC potential form can indicate the level of CNS activation balance.

Parameters analyzed though Flicker Fusion unit (Vienna Test System ® ):

– Critical flicker fusion ascending (Hz) (CFFA) and Critical flicker fusion descending (Hz) (CFFD). Cortical arousal was measured using the critical flicker fusion threshold (Hz) (CFFT) in a viewing chamber (Vienna Test System ® ), following the procedure of previous studies ( Clemente et al., 2016 ). An increase in CFFT suggests an increase in cortical arousal and information processing; a decrease in CFFT values below the baseline reflects a reduction in the efficiency of information processing and central nervous system fatigue ( Whiteside, 2002 ). It was used before and after the stress test.

Parameters analyzed though DT test (Vienna Test System ® ):

– We study four key variables: the average value of reaction speed (msec), the number of correct answers (raw score), which reflects the ability of the respondent to precisely and quickly select the adequate answer even under pressure. Furthermore, we also examine the number of incorrect answers (raw score) which can show us how likely the athlete is to get confused under stress and pressure; finally, the high number of missed answers (raw score) reveals that the respondent is not capable of maintaining his/her attention under stress and is prone to giving up these situations ( Neuwirth and Benesch, 2012 ). The duration of this test was 6 min without instructions.

Parameters analyzed by self-report instruments:

– Central Rating of Perceived Exertion (RPEC) and Peripheral Rating of Perceived Exertion (RPEP). The Rating of Perceived Exertion ( Borg, 1998 ), was used as a measure of central (cardiorespiratory) and peripheral (local-muscular, metabolic) exertion before and after the stress test ( Bolgar et al., 2010 ; Cárdenas et al., 2017 ). The RPE is a 15 point category-ratio; the odd numbered categories have verbal anchors. Beginning at 6, “no exertion at all,” and goes up to 20, “maximal exertion.” Before testing, subjects were instructed on the use of the RPE scale ( Noble and Robertson, 1996 ). We use the scale with the clear differentiation between central as peripheral perceived exertion following the recommendations of the medical staff and under the guideline of Borg ( Borg, 1982 ), for applied studies.

Table 1. Simplified curve change mV reduction algorithm.

The participants were contacted and informed about the measurement protocol and of the date and time of the data collection. All of the measurements were collected during the same day. The total data collection time per participant was approximately 45 min. The order of measurements was the following: CFFT, DC Potential, RPE, DT test, RPE, CFFT, and DC Potential.

Data were analyzed using the Statistical Package for the Social Sciences (SPSS) version 21 (SPSS Inc., Chicago, Ill., United States). Means and SDs were calculated using traditional statistical techniques. Normality was tested with the Shapiro-Wilk test. As the distributions were not adjusted to the normal, non-parametric tests were used. A Wilcoxon sign ranges test for intragroup comparisons were conducted to analyze differences between pre and post-test. A Rho Spearman coefficient was used to know the correlations between variables. The Effect Size was tested using the formula = Z/ N for non-parametric tests ( Tomczak and Tomcak, 2014 ). Following the considerations of Cohen (1988) , the effect size is considered small when the value is inferior to 0.10, medium when it varies between 0.10 and 0.30 and high when it is superior to 0.50. The significance level was set at p < 0.05.

Descriptive Analysis, Normality Test According N, Wilcoxon Test, and Effect Sizes

Firstly, the normality tests were realized with the Shapiro-Wilk test. It was determined that most of the variables were not normal, due to which non-parametric statistical tests were applied. In relation to the descriptive analyzes of the study variables, shown in Table 2 , after applying the stressor via the DT test, worse values were obtained in all the variables measured. This reflects the alterations in the central response evaluated. Regarding the Wilcoxon rank test that was used to analyze whether there were differences between the scores obtained before and after applying the stressor (DT test), significant differences were found in the variables OverallDc ( p < 0.05), Flicker ascending ( p < 0.01), Flicker descending ( p < 0.01), Central RPE ( p < 0.01) and Physical RPE ( p < 0.01), while not finding significant differences in the rest of the variables ( Table 2 ).

Table 2. Descriptive analysis of the measured variables.

Correlation Analysis

A Spearman bivariate correlation analysis was performed. Spearman’s Rho coefficient was used, since the distribution was non-parametric. Note that significant correlations were found ( Table 3 ) entre OverallDC con DCSSatabilizationLevel ( p = 0.000; r = 0.791 ∗∗ ); OWCNS ( p = 0.005; r = 0.581 ∗∗ ); OWDCC ( p = 0.013; r = 0.522 ∗ ); Flicker Descending ( p = 0.044; r = 0.432 ∗ ). DCSStabilizationLevel con OWCNS ( p = 0.000; r = 0.766 ∗∗ ); Flicker Descending ( p = 0.049; r = 0.424 ∗ ). DCSStabilizationTime con OWCNS ( p = 0.005; r = 0.572 ∗ ); OWDCC ( p = 0.046; r = 0.430 ∗ ); Flicker Ascending ( p = 0.006; r = 0.563 ∗∗ ). OWCNS correlated with Flicker Ascending ( p = 0.018; r = 0.499 ∗ ), and SDSD with Flicker Descending score ( p = 0.046; r = −0.430 ∗ ).

Table 3. Rho Spearman coefficient.

The objective of the present research was to study the modification of DC potentials and the CFFT scores after the computerized stress test (DT). The analysis of the subjective cognitive responses about fatigue after DT test reveals significant differences in the participants, both at a physical and central level. As regards the first hypothesis, it is partially fulfilled. There are significant differences in central perceived fatigue, with a very high effect size, which supports the hypothesis and emphasizes the usefulness of the established research protocol. However, significant differences also appear in peripheral perceived fatigue, which is beyond the initial approaches. This result is of special interest because it allows to consider the relationship between both types of perceived fatigue ( Bittner et al., 2000 ; Clemente et al., 2016 ). These results, taking into account that the participants did the test sitting down, emphasize the effect achieved through the protocol used to generate stress in them, without significant differences in the performance achieved in the task. Previous research carried out with the DT test already points in this same direction ( Ong, 2015 ). The differences found in the perception of physical fatigue even without previous movement are interesting. Similar results are found in studies carried out in contexts such as chess ( Fuentes et al., 2019 ), where central fatigue due to the demands of each game also leads to physical fatigue of the players. This fact seems relevant insofar as the studies should incorporate measures of both dimensions to be able to explain a higher percentage of variance of the results found.

As regards the second hypothesis, the decrease of CFFD values indicates that it has a negative effect generating central fatigue and an alteration in cortical activation ( Li et al., 2004 ; Clemente, 2016 ). These results confirm the alterations in cortical activation found in physiological efforts of high intensity and of short duration, such as sprints at maximum speed ( Clemente et al., 2011 ). This same trend is also observed in research focused on generating a high level of stress in soldiers, which emphasizes the usefulness of using the DT test to create stress in the participants ( Clemente et al., 2016 ). In line with the ideas defended by Clemente (2016) , decreased in CFFD scores seem to be linked to high sympathetic autonomous nervous system activation, which could also affect higher cognitive functions, such as executive processes (i.e., making complex decisions, memory, and attention processes) ( Shields et al., 2016 ). These same considerations can also be made with respect to the significant differences found in CFFA scores. Higher scores are found after the stress test, which implies that the participants have needed more time to respond to the flicker task as consequence of central fatigue ( Fuentes et al., 2019 ; Lohani et al., 2019 ).

Regarding the results obtained in the Overall DC scores, the significant differences show a pattern of alteration as a consequence of the stress test. As Naranjo-Orellana et al. (2020) point out, the OW test obtains good reliability and validity values using the heart rate variability as a measure in conjunction with the DC Potential (stabilitation DC, stabilitation time, and curve shape). Changes in the DC potentials have been reported to be reflective of performance in different brain processes ( Haider et al., 1981 ; Valenzuela et al., 2020 ). The lower scores obtained after the stress test could indicate, as with the CFF scores, an increase in central fatigue detected by the OmegaWave system ( Valenzuela et al., 2020 ). This result, in any case, needs to be analyzed in detail in future research.

Therefore, monitoring the DC potentials and the CFF scores could be useful to control the cognitive load of the different tasks that having a high mental demand.

Due to the exceptional circumstances of data collection in the present study, some of the study limitations were the sample size and the small number of women who participated in it. Future research works should expand the sample power, as well as determine its effect in a sedentary sample.

To conclude, this is the first study that has jointly analyzed the scores obtained in the analysis of low-frequency brain waves (DC potentials), together with those obtained in the Flicker test. In this sense, although the performance in a specific task seems similar, the demand it has for the person must be evaluated, being useful the use of research protocols similar to the ones we have used. The results open a new field where both measurements could be interesting and useful to assess the cognitive demands of persons.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.

Ethics Statement

The studies involving human participants were reviewed and approved by the University Ethical Commission in compliance with the Helsinki Declaration. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

RV: conceptualization, investigation, resources, writing—review and editing, and project administration. RV, ML-R, and RJ-C: methodology, data curation, writing—original draft preparation, visualization, supervision, and formal analysis. ML-R and RJ-C: software and validation.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords : central fatigue, omega wave, cognitive response, psychophysiology, stress

Citation: de la Vega R, Jiménez-Castuera R and Leyton-Román M (2021) Impact of Weekly Physical Activity on Stress Response: An Experimental Study. Front. Psychol. 11:608217. doi: 10.3389/fpsyg.2020.608217

Received: 19 September 2020; Accepted: 04 December 2020; Published: 12 January 2021.

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Copyright © 2021 de la Vega, Jiménez-Castuera and Leyton-Román. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Marta Leyton-Román, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Prevalence of Cardiovascular-Kidney-Metabolic Syndrome Stages in US Adults, 2011-2020

• 1 Brigham and Women’s Hospital Heart and Vascular Center, Harvard Medical School, Boston, Massachusetts

Cardiovascular, kidney, and metabolic (CKM) diseases are pathophysiologically interrelated, 1 have affected more than 25% of US adults between 2015-2020, 2 and were the leading causes of death in 2021. 3 In 2023, the American Heart Association introduced a novel staging construct, termed CKM syndrome , 1 to enhance multidisciplinary approaches to prevention, risk stratification, and management of these disorders. Based on risk factors and established disease, the stages range from 0 (no risk factors) to 4 (established cardiovascular disease [CVD]).

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Aggarwal R , Ostrominski JW , Vaduganathan M. Prevalence of Cardiovascular-Kidney-Metabolic Syndrome Stages in US Adults, 2011-2020. JAMA. Published online May 08, 2024. doi:10.1001/jama.2024.6892

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The health benefits and business potential of digital therapeutics

Around the world, the burden of chronic disease is increasing at a rapid pace. Unfortunately, most of these conditions are irreversible and need to be managed through lifelong medication use. However, many patients struggle with adhering to prescribed medications and implementing the behavioral and lifestyle changes that are needed to manage their diseases and stabilize their conditions. Often, physicians and other healthcare providers have little ability to monitor the extent to which patients are following their recommendations and maintaining treatment regimens. As a result, disease burdens at a population level are higher than they should be.

These challenges have created a need for compre­hensive disease management solutions that are best enabled by digital technologies. In 2021, global digital health funding grew 79 percent over the previous year to reach $57.2 billion. 1 State of digital health 2021 report , CB Insights, January 20, 2022. Much attention and funding have flowed toward digital therapeutics , which can include multiple points of intervention along the patient journey, including monitoring, medication adherence, behavioral engagement, person­alized coaching, and real-time custom health recommendations. Within digital health, funding for digital therapeutics (including solutions for mental health) has grown at an even faster pace—up 134 percent from the prior year to reach $8.9 billion in 2021. 2 Heather Landi, “Global digital health funding skyrockets to $57.2B with record cash for mental health, telehealth,” Fierce Healthcare,January 21, 2022.

The impact potential here is significant, both in terms of clinical outcomes and economic benefits for stakeholders and societies. For example, research has shown that digital disease management can drive a 45 percent reduction in the three-month rate of major adverse cardiovascular events (MACEs) and a 50 percent reduction in the 30-day readmission rates for patients after acute myocardial infarction (AMI). 3 Jerilyn K. Allen et al., “Digital health intervention in acute myocardial infarction,” Circulation: Cardiovascular Quality and Outcomes , July 15, 2021, Volume 14, Issue 7; Pawel Buszman et al., “Managed care after acute myocardial infarction (MC-AMI) reduces total mortality in 12-month follow-up—results from Poland’s National Health Fund Program of Comprehensive Post-MI Care—A population-wide analysis,” Journal of Clinical Medicine , 2020, Volume 9, Issue 10. Similarly, it can help lower hemoglobin A1c (HbA1c) levels by one percentage point among patients with type 2 diabetes. 4 Marcy K. Abner et al., “A novel intervention including individualized nutritional recommendations reduces hemoglobin A1c level, medication use, and weight in type 2 diabetes,” JMIR Diabetes , 2017, Volume 2, Issue 1. These data points illustrate the extent to which digital disease manage­ment can help save lives while also keeping patients healthier, which reduces costs for many stake­holders, including the patients themselves.

Research has shown that digital disease management can drive a 45 percent reduction in the three-month rate of major adverse cardiovascular events (MACEs) and a 50 percent reduction in the 30-day readmission rates for patients.

Many players are trying to disrupt the disease management space and develop new innovative models to manage chronic diseases. New-age start-ups bring radical, unconstrained perspectives, while incumbents contribute a much more detailed understanding of the challenges and various stake­holders. Ultimately, both start-ups and incumbents have critical roles to play in disrupting the space and scaling up solutions.

Digital therapeutics can play an important role in chronic-disease management

The burden of chronic diseases has been increasing globally and is expected to continue. Chronic diseases (such as cardiovascular disease, cancer, diabetes, and respiratory disease) were causes or contributing factors in 75 percent of worldwide deaths in 2010 and 79 percent in 2020. By 2030, experts predict that chronic diseases will contribute to as much as 84 percent of total global mortality (exhibit).

Poor monitoring of and adherence to prescribed medications undermine the management of chronic diseases. According to a 2021 global study, compliance among patients with type 2 diabetes ranges from 69 to 79 percent. 5 Diagnosis-related groups (DRG) treatment data: compliance (medication possession ratio) among patients with type 2 diabetes ranges between 69 to 79 percent for top-20 type 2 diabetes drugs; compliance rates for cancers according to a study on 52,450 patients was 37 percent. Patients were found to be most compliant in the 50- to 59-year-old range (49 percent compliant), with decreased compliance at the extremes of age. See Joseph Blansfield et al., “Analyzing the impact of compliance with national guidelines for pancreatic cancer care using the National Cancer Database,” Journal of Gastrointestinal Surgery , August 2018, Volume 22, Issue 8; Nathan Levitan, “Industry Voices—Here’s how AI is impacting the delivery of cancer care right now,” Fierce Healthcare, June 28, 2019.

Of course, chronic diseases need to be managed not only by medication but also with regular monitoring and lifestyle changes. Hence, providers need better end-to-end solutions that proactively and comprehensively monitor patient health, as well as encourage behavioral changes to improve adherence to prescribed medications, diet, and lifestyles.

Digital technologies can play an important role in improving disease management by tackling these challenges. The potential for digital therapeutics to have a big impact is evidenced by the fact that almost two-thirds of the global population now has internet access.

Research has shown that digital solutions for disease management can drive better outcomes for patients living with chronic diseases. Examples include the following:

• A study of ten thousand patients by the Poland National Health Fund showed a 45 percent reduction in three-month MACE rate and a 40 percent reduction in 12-month mortality rate achieved through managed care after AMI. The study involved cardiac rehabilitation with physician guidance, counseling sessions on lifestyle modification, education on the associated risk factors, therapy, and in-person relaxation sessions. 6 “Managed care after acute myocardial infarction,” 2021.
• A study by the Mayo Clinic in partnership with Healarium showed a reduction in three-month rehospitalizations and emergency department visits of 40 percent for patients following AMI, a weight reduction of 4.0 kilograms, and a 10.8-millimeter reduction in systolic blood pressure. The study involved tracking of vitals, diet, and physical activity, setting reminders and goals, information on current health status, and educational courses for patients. 7 Thomas G. Allison et al., “Digital health intervention as an adjunct to cardiac rehabilitation reduces cardiovascular risk factors and rehospitalizations, Journal of Cardiovascular Translational Research , 2015, Volume 8, Issue 5.
• A US study of more than one thousand patients by Johns Hopkins and Corrie Health showed a 50 percent reduction in the 30-day readmission rate in patients following AMI attained through digital-health-based interventions. The study involved continuous monitoring of vitals with the help of connected devices; educational content on procedures, risk factors, and lifestyle modifications; medication management through reminders and tracking adherence; connection with the care team; mood tracking; and the ability to check the side effects of medication. 8 “Digital health intervention in acute myocardial infarction,” 2021.
• A one percentage-point reduction in HbA1c levels was shown in patients with type 2 diabetes who participated in an online patient community as part of Virta Health’s ten-week nonrandomized parallel arm study with 262 outpatients. The patients were given individualized nutritional recommendations through dedicated health coaches, continuous glucose monitoring kits, and online counseling with doctors. 9 “A novel intervention including individualized nutritional recommendations reduces hemoglobin A1c level,” 2017.

Eight key elements of impactful digital therapeutics solutions

Strong digital therapeutics solutions typically contain most or all of the following eight elements:

• Regular monitoring, measurement, and feedback through connected medical devices . Devices such as smart inhalers for respiratory conditions or continuous glucose monitors for diabetes can provide patients with nudges and alerts for out-of-range readings. For example, Boston-based Biofourmis applies digital therapeutics through the continuous monitoring of connected medical devices. The company offers a doctor-prescribed digital platform approved by the US Food and Drug Administration for patients suffering from chronic heart conditions. Its unique wearable devices offer specialty chronic heart care management, including automated medication management combined with a multidisciplinary remote clinical-care team. In 2022, the company was valued at $1.3 billion.
• Keeping payers and providers in the loop. When patients grant access to their vital statistics, insurance companies, caregivers, and employers can reward them for progress in stabilizing or improving chronic health conditions. For example, Livongo, a program from Teladoc Health, allows patients with diabetes to monitor their condition regularly and send alerts via Bluetooth to an app on their own and their caregiver’s phones if readings exceed normal ranges. Over time, patients enrolled with Livongo have achieved a 0.8 percentage-point drop in HbA1c for diabetics, a 10.0-millimeter hemoglobin drop in blood pressure for patients with hypertension, a 1.8-point drop in body mass index, and a 7.0 percent drop in weight. Livongo allows payers and providers to identify and reward good behavior, as well as deter or penalize poor adherence to health plans prescribed by providers.
• Personalized coaching and support . Patients can connect with specific coaches to obtain a personalized diet and exercise plan tailored to their chronic illnesses. This can be very effective from a therapeutic standpoint. A meta-analysis of digital health interventions on blood pressure management showed that digital counseling alongside antihypertensive medical therapy reduced systolic blood pressure by 50 percent relative to controls. 10 Ella Huszti et al., “Advancing digital health interventions as a clinically applied science for blood pressure reduction: A systematic review and meta-analysis,” Canadian Journal of Cardiology , May 2020, Volume 36, Issue 5. For example, Hinge Health has built a $6.2 billion business that offers wearable sensors combined with personalized exercise therapy and one-on-one health coaching.
• Gamified behavioral modification. Digital therapeutics solutions can include gamified challenges and incentives to track and drive adherence to prescribed diets, lifestyle practices, and medications. For example, Discovery, a South African health insurance company, encourages its members to make healthier choices through its Vitality behavioral change program that combines data analytics with rewards and incentives for healthier lifestyle choices.
• Building a thriving community . An active virtual patient community can drive adherence by challenging and motivating patients to live up to their own health goals. For instance, one study of seven thousand patients with amyotrophic lateral sclerosis (ALS), multiple sclerosis, Parkinson’s disease, HIV, fibromyalgia, or mood disorders found that nearly 60 percent thought the PatientsLikeMe health network helped give them a better understanding of the side effects of medications. The study also found that nearly a quarter of patients with mood disorders needed less inpatient care thanks to their use of the PatientsLikeMe site. 11 “PatientsLikeMe,” Agency for Healthcare Research and Quality, accessed January 2023.
• Health mall. A recent McKinsey survey found that 90 percent of healthcare leaders believe that patients interacting with digital health ecosystems want an integrated journey rather than point experiences or solutions. 12 Stefan Biesdorf, Ulrike Deetjen, and Basel Kayyali, “ Digital health ecosystems: Voices of key healthcare leaders ,” McKinsey, October 12, 2021. Healthcare companies can meet this desire for integration by offering digital health malls that include access to prescribed medications, health supplements, wellness products, and diagnostic tests at the click of a button.
• Patient education . Digital education materials can give patients and their family members information on disease conditions, treatment options, diet, and healthy lifestyle choices. For instance, the Midday app launched by Mayo Clinic and digital health start-up Lisa Health provides support, including educational content, to women experiencing menopause. 13 Tia R. Ford, “Lisa Health launches Midday, an app leveraging AI to personalize the menopause journey, in collaboration with Mayo Clinic,” Mayo Clinic, July 19, 2022.
• Advanced analytics to predict and prevent health events . Organizations are working now to build data algorithms that could identify and predict triggers for healthcare events. They could suggest when to take preventative action or where lifestyle and behavioral changes might forestall adverse events.

How incumbents can thrive in the digital therapeutics space

Digital therapeutics have tremendous potential to reduce disease burdens, deliver better clinical outcomes, help providers make more informed treatment decisions, and improve patients’ lives by offering better ways to manage chronic health conditions. Digital therapeutics also offer incumbents access to new sections of the healthcare value chain and a way to play in the much larger end-to-end healthcare market. Given these opportunities, healthcare and pharma incumbents may wish to explore ways to compete and win in this space.

Incumbents have certain inherent advantages in building digital therapeutics offerings. They already have direct access to patients, plus deep knowledge of the pain points in the disease management journey. They also fully understand the disease science that needs to be integrated into the digital health offering.

Still, incumbents also have some work to do to be competitive in digital therapeutics. To successfully launch and scale an offering, they may need to recruit or upskill employees with skills in product development, design, technology, medicine, data science, and strategic partnerships. Incumbents should plan to spend from three to five years building their digital capabilities and inculcating their new digital workforce with the culture, vision, mission, and values to compete successfully against nimble start-ups.

Incumbents that move quickly still have an opportunity to gain a first-mover advantage in the growing digital therapeutics sector, where promising start-ups can receive multibillion-dollar valuations. By developing their own digital therapeutics offerings, incumbents may also find themselves in a stronger position to protect their core businesses from being disrupted by others.

Chirag Adatia is a partner in McKinsey’s Gurugram office, where Samarth Shah is a consultant. Ralf Dreischmeier  is a senior partner in the London office.  Kirtika Sharma is a partner in the Mumbai office.

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Managing boundaries for well-being: a study of work-nonwork balance crafting during the COVID-19 pandemic

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• Sophie E. Brogle 1 ,
• Philipp Kerksieck   ORCID: orcid.org/0000-0001-5327-3668 1 ,
• Georg F. Bauer   ORCID: orcid.org/0000-0003-0232-8141 1 &
• Anja I. Morstatt   ORCID: orcid.org/0000-0001-6223-9939 1  

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In the wake of the COVID-19 pandemic, the boundaries between the work and nonwork domain have rapidly blurred, presenting employees with new challenges and potentially heightening interference of the work with the nonwork domain (work-home interference, WHI) and vice versa (home-work interference, HWI). To counteract these interferences, employees can apply work-nonwork balance crafting (WNBC), referring to proactive efforts for balancing both life domains by targeting the permeability of the boundary between them. Employees may focus their crafting on the boundary toward the work domain (WNBC-work) or the non-work domain (WNBC-nonwork), e.g., shielding each domain from negative spillover from the other. This study aims to investigate the longitudinal associations of WNBC with health outcomes during the COVID-19 pandemic. We hypothesized that individuals who exhibited more such crafting behaviors at the onset of the pandemic would experience higher mental well-being and work engagement at later points in the COVID-19 pandemic, mediated by lower WHI and HWI. We surveyed N  = 2,171 German-speaking employees from Germany, Austria, and Switzerland at three time points during 2020. Structural equation model results indicate that only WNBC-work is longitudinally negatively associated with reduced HWI and positively associated with mental well-being and work engagement. Further, in our sample, only HWI is longitudinally associated with lower mental well-being. No indirect effect reaches significance. Our results underline the importance of WNBC in the work domain in the early phases of the COVID-19 pandemic. Future research should explore the nature of the direct links between WNBC and mental well-being and work engagement and consider alternative mediating processes, such as gain spirals. Lastly, our study underscores that supporting employees in crafting boundaries for well-being can be crucial, particularly during times of crisis.

Avoid common mistakes on your manuscript.

Introduction

The world was hit by the COVID-19 pandemic in March 2020, and governments worldwide aimed to contain the virus and prevent its spreading by inaugurating various measures, e.g., school closures, remote work, nationwide lockdowns (Hale et al., 2021 ; Rudolph et al., 2021 ). Since then, life for many employees has changed drastically, and the boundaries between work and nonwork have blurred increasingly (Cho, 2020 ; Vaziri et al., 2020 ). For many, their life domains clashed literally, as they were forced to work remotely, through which private and professional life occurred in the same physical space (Kaltiainen & Hakanen, 2023 ; Kniffin et al., 2021 ). For others, e.g., health workers or supermarket staff, a drastic increase in workload and increasingly straining mental and physical work conditions might have interfered with their capacities to deal with nonwork demands (Benfante et al., 2020 ). Such life domain conflicts are associated with adverse health consequences such as exhaustion (Reinke & Gerlach, 2022 ), but also reduced work engagement (Karatepe & Karadas, 2016 ) and impaired mental health (Yucel & Fan, 2019 ). For the time of the pandemic, a large study in the UK covering only a time frame until May 2020 indicated that mental health was negatively affected in this early phase (O’Connor et al., 2021 ). Thus, more knowledge on how life domain conflicts and impaired health unfolded throughout the pandemic is needed. Further, employees might have experienced the pandemic differentially, likely depending on their proactive and agentic efforts to deal with the pandemic (Demerouti & Bakker, 2022 ). Thus, the present study aims to examine how employees’ proactive crafting of the boundaries between work and non-work life domains is related to employee health during the COVID-19 crisis. We hypothesized that exhibiting more WNBC at the start of the pandemic would be associated with higher mental well-being and work engagement 8 months later, mediated by lower WHI and HWI.

Proactive behaviors, e.g., job crafting (Tims et al., 2012 ; Tims & Bakker, 2010 ; Wrzesniewski & Dutton, 2001 ), have been shown to play a relevant role in changing situations, e.g., organizational changes, and in maintaining work engagement (Petrou et al., 2018 ). Further, job crafting has been identified as a buffer to life domain conflicts (Lyu & Fan, 2022 ). However, the single life domain focus for crafting lacks breadth in understanding proactivity. The concept of job crafting has recently been transferred to the off-job domain (see literature on off-job crafting; Kujanpää et al., 2022 ) and further to the idea that the boundaries between life domains and, therefore, a balance between them can also be actively crafted (work nonwork balance crafting; WNBC) (Kerksieck et al., 2022 ). Due to the above-described shifting and blurring of work-nonwork boundaries during the COVID-19 crisis, WNBC, as a targeted, proactive strategy, is well suited to study how employees dealt with the clashing of life domains and how this is related to mental well-being and work engagement of employees during the first year of the global health crisis. Recent research indicates that there may be associations between crafting in different life domains, and that crafting is not limited to specific life domains but can also span across life domains (de Bloom et al., 2020 ; Demerouti et al., 2020 ). Consequently, WNBC itself focuses on the proactive crafting of said boundary and not, as other types of crafting, on crafting specific life domain characteristics with a focus on a single domain. We used a cross-lagged panel model approach with three waves, covering a period of 8 months during 2020. For the analysis, a structural equation model was specified, and the hypotheses were tested using path estimates. Our research model is depicted in Fig.  1 .

Our study makes two main contributions to research on the COVID-19 pandemic and crafting in general. First, our study captures a significant part of the first pandemic year and can shed light on long-term associations of proactive behaviors early in the pandemic. The beginning of the pandemic was a difficult time for most people. In many cases, workers faced a completely new situation, e.g., when affected by home office regulations (Tušl et al., 2021 ). However, by forming new routines and adapting behaviors to the novel needs caused by the pandemic and lockdown measures, people had a chance to influence their future experience of the pandemic (Chankasingh et al., 2022 ). By reviewing how WNBC at the pandemic onset is longitudinally linked with life domain conflicts and well-being, we provide knowledge on how to design early interventions supporting employees in their own crafting during future emergencies. For policymakers as well as organizations, this can increase future crisis preparedness.

Second, we add knowledge to WNBC research and, more specifically, whether life domain conflicts mediate between WNBC and well-being in both life domains, more specifically with mental well-being and work engagement. Previously, it was already demonstrated that WNBC is longitudinally linked with family role and job performance, job and life satisfaction, and work engagement (Kerksieck et al., 2022 ). However, the mechanisms still need to be clarified. Further, we closely examine domain-specific associations, which aids in understanding which domain focus might be more relevant in maintaining well-being over time. Those insights are highly significant for a more informed crisis management and can help organizations in supporting their employees to use bottom-up self-management crafting strategies to their benefit. This is important, since research on life-role interference during the pandemic has indicated the urgent need for proactive adjustments of roles employees fulfill at work and at home (Syrek et al., 2022 ). Furthermore, this knowledge about the role of WNBC will become ever more important even outside of the pandemic context, as the future of work will increasingly require the ability to deal with demands from the continuously intertwined work and nonwork domains (Caringal-Go et al., 2022 ; Mäkikangas et al., 2024 ; Rudolph et al., 2021 ).

Work-nonwork balance crafting as a buffer to life domain conflicts

Crafting generally describes self-initiated behaviors people undertake to shape or mold certain aspects of their lives according to their individual needs or preferences (Wrzesniewski & Dutton, 2001 ). Specifically, WNBC (Kerksieck et al., 2022 ) builds on this conceptualization and refers to proactive behaviors aiming to craft the boundaries between work and nonwork life according to individual needs (de Bloom et al., 2020 ). Other forms of crafting, e.g., job crafting (Tims et al., 2012 ; Wrzesniewski & Dutton, 2001 ) or off-job crafting (Kujanpää et al., 2022 ) show negative associations with stress experience (Ingusci et al., 2021 ) and burnout (Pijpker et al., 2022 ), among others.

WNBC efforts can focus on different life domains, resp. dimensions. For example, if someone tries hard to make time to take care of their remotely schooled children (due to the lockdown) while being strongly demanded by their work tasks, they focus on their private life and proactively ensure that they can meet their family obligations. Employees might be proactively setting clear time boundaries for work-related requests. In this way, the caring responsibilities in the nonwork life domain are prioritized. Work-related demands are dealt with after caring duties have been completed. In this way, crafting efforts are directed towards the employee’s nonwork life domain (WNBC-nonwork). On the other hand, employees might also proactively regulate a bad mood due to non-work reasons, e.g., increasingly demanding and stressful caring responsibilities due to COVID-19 measures, so that their work life is not affected. Their crafting efforts are then focused on the work life domain (WNBC-work). These two behaviors – protecting the work domain from the nonwork domain and vice versa – are not mutually exclusive and can take place simultaneously. Additionally, WNBC can occur as physical, relational, or cognitive crafting (Kerksieck et al., 2022 ). While theories on segmentation and integration are already well studied (Ashforth et al., 2000 ), they lack a nuanced perspective of how the boundary between life domains is created. WNBC suggests an active process whereby employees build their boundary from both sides: When guarding the work domain, they may prevent spillover from the non-work domain to the work domain, while still allowing a spillover from work to non-work, and vice versa. Therefore, WNBC offers a more comprehensive perspective that also allows for the prioritization of one domain. To summarize, WNBC refers to proactive behaviors aimed at balancing life domains – or managing life domain conflicts – and can be focused on the work or the nonwork domain.

Intensified life domain conflicts as consequence of pandemic working conditions

At the beginning of the pandemic, forced teleworking, increasing strain at work, and, for many, also increased care duties rapidly aggravated life domain conflicts. Both work-home interference (WHI) and home-work interference (HWI) (Kopelman et al., 1983 ) are indicators of a disturbed life domain balance, resulting in, i.e., life domain conflicts. When experiencing high WHI, an individual might not be able to fully enjoy the company of their family and friends because they worry about their work, which might be more demanding than usual due to the changed work form and workplace uncertainty during the pandemic. Whereas when experiencing high HWI, an individual might have issues focusing on work because they are preoccupied with worries about one of their family members being ill or struggling during the pandemic. Although the two constructs are conceptually separated due to the different directions of influence, they are related and can co-occur (Frone et al., 1992 ).

During the pivotal pandemic phase when first containment measures were put in place, we assume that the adoption and extent of WNBC practices varied among individuals and that WNBC potentially acted as a buffer against the escalation of life domain conflicts. Especially the beginning of the pandemic has been a crucial phase that set how employees experienced the subsequent course of the pandemic (Chankasingh et al., 2022 ). When crafting for the boundaries between the work and nonwork life, potentially a work-nonwork balance is created (Gravador & Teng-Calleja, 2018 ) and accompanied by lower conflicts between the two domains (both WHI and HWI), depending on the domain focus of the crafting.

Relationships between WNBC and life domain conflicts

We assume a life domain congruence in the association between WNBC and the life domain conflicts, such that WNBC with a focus on the nonwork domain is negatively associated with WHI, and WNBC with a focus on the work domain is negatively associated with HWI. Previous research found that high work demands, e.g., a high workload, predict WHI and high demands in the private domain predict HWI (Demerouti et al., 2004 ). In general, employees employing WNBC use different behaviors to prevent the potential for conflict between their work and private life domains. Employees focusing on WNBC in the nonwork domain, e.g., strategically distribute their work hours, thereby creating pockets of dedicated quality time with their family or partner. By orchestrating such intentional compartmentalization, WNBC minimizes the permeation of work-related stressors into the nonwork domain. Similarly, employees focus WNBC on the work domain, e.g., craft clashing domains, by actively planning their working days and fitting small time pockets to complete necessary private chores while ensuring that their work receives sufficient attention. Further, WNBC also captures employees’ behaviors to prevent negative affect spillover in the work or the nonwork domain to protect their resources and maintain their functioning in both life domains. This should in the long run reduce life domain conflicts as well.

Drawing a specific lens on the unfolding of the pandemic, we suggest that individuals who embraced WNBC strategies during the initial stages of the crisis were able to mitigate the aggravation of life domain conflicts. This proactive stance towards WNBC might have acted as a buffer against the rising challenges imposed by the pandemic. Consequently, as the pandemic unfolded, individuals who had actively engaged in higher levels of WNBC might have been better positioned to navigate subsequent challenges, leading to a diminished prevalence of life domain conflicts in their ongoing experiences compared to those who had engaged in comparatively less WNBC practices.

Based on the above literature review, we derive the following hypotheses:

H1.1: WNBC-nonwork at the pandemic onset (t1) is negatively associated with WHI in the middle of 2020 (t2).

H1.2: WNBC-work at the pandemic onset (t1) is negatively associated with HWI in the middle of 2020 (t2).

Life domain conflicts and impaired long-term mental well-being and work engagement

Experiencing life domain conflicts impairs health and well-being both in the short and long run (e.g., Karatepe & Karadas, 2016 ; Yucel & Fan, 2019 ). According to the Work-Home Resources Model (ten Brummelhuis & Bakker, 2012 ), chronic demands or life domain conflicts lead to a person having to constantly invest resources, which can lead to a depletion process over time. This continuous resource depletion (termed “loss spiral” in the Conservation of Resources Theory (COR; Hobfoll, 1989 ) is associated with negative consequences (e.g., Brosschot et al., 2006 ), including reduced work engagement (Halbesleben, 2010 ; Xanthopoulou et al., 2009 ).

Thus, we suggest that also during the COVID-19 pandemic, both types of life domain conflicts are negatively associated with subsequent mental well-being and work engagement, the health indicators of our study. Whether in pre-pandemic times or since the onset of the pandemic, whenever employees experience life domain conflicts, they need to invest resources to maintain their usual level of performance and to deal with the demands of their work and nonwork life, which further drains their resources and impairs their health.

First, we study how life domain conflicts are linked with mental well-being. The WHO defines mental well-being as a state ‘which allows individuals to realize their abilities, cope with the normal stresses of life, work productively and fruitfully, and make a contribution to their community’ (World Health Organization, 2001 , p. 1). It captures both aspects of satisfaction, positive affect, and psychological functioning (Ryan & Deci, 2001 ). As life domain conflicts have been linked with higher stress (Chapman et al., 1994 ), lower life satisfaction (Adams et al., 1996 ), and reduced general well-being (Grant-Vallone & Donaldson, 2001 ), we suggest that they predict reduced mental well-being as well.

H2.1: (a) HWI and (b) WHI in the middle of 2020 (t2) are negatively related to mental well-being at the end of 2020 (t3).

Second, we study how life domain conflicts are linked with work engagement. Work engagement can be defined as a positive, fulfilling state in which employees strive to actively use their personal resources to accomplish the work tasks at hand and perform well in the process (Schaufeli & Greenglass, 2001 ). Previous research has shown that life domain conflicts in both directions are negatively associated with work engagement (Karatepe & Karadas, 2016 ), both before and during the pandemic (Galanti et al., 2021 ). When private life interferes with work, e.g., due to additional care duties for stay-at-home children who had remote classes during the pandemic (Rieth & Hagemann, 2021 ), employees might have a more challenging time focusing on work, thereby limiting the experience of work engagement. However, work might also interfere with private life, e.g., due to forced teleworking, which might call employees to invest additional resources to maintain performance, increasing exhaustion in the long run. Accordingly, Kaltiainen and Hakaken ( 2023 ) report an indirect link between increased telework due to the pandemic and decreased work engagement via WHI. To conclude, we suggest that both types of life domain conflicts are associated with lower work engagement:

H2.2: (a) HWI and (b) WHI in the middle of 2020 (t2) are negatively related to work engagement at the end of 2020 (t3).

WNBC and subsequent mental well-being and work engagement

Finally, in this study, we also aim to study the long-term association between WNBC and both mental well-being and work engagement via reduced life domain conflicts. This adds depth to our understanding of how proactive behaviors early in the pandemic could be connected to long-term health and well-being. Notably, the direct link between WNBC and heightened work engagement has been substantiated in existing literature (Kerksieck et al., 2022 ). Our study examines a potential mechanism explaining this association by reviewing the life domain conflicts as mediators. Therefore, we link WNBC as a proactive and preventive strategy to the loss spiral between life domain conflicts and health and well-being. By its proactive nature, WNBC might preempt and neutralize potential conflicts that may arise in the context of the COVID-19 pandemic’s unique demands. In doing so, WNBC may counter the loss spiral and prevent the gradual depletion of personal resources occasioned by unaddressed life domain conflicts. In turn, WNBC potentially aids in preserving and potentially augmenting precious personal resources and maintaining health and well-being.

To conclude, we suggest the following hypotheses:

H3.1: WNBC-nonwork at the pandemic onset (t1) is indirectly positively related to (a) work engagement and (b) mental well-being at the end of 2020 (t3), mediated by WHI in the middle of 2020 (t2).

H3.2: WNBC-work at the pandemic onset (t1) is indirectly positively related to (a) work engagement and (b) mental well-being at the end of 2020 (t3), mediated by HWI in the middle of 2020 (t2).

Data collection and sample

For our study, we refer to three time points from a more extensive longitudinal panel data collection. The three survey waves cover the period from April 2020 to December 2020 (see Fig.  1 ). Participants were recruited via the market research provider Bilendi (formerly respondi; www.bilendi.de ). Inclusion criteria were a weekly working time of at least 20 h per week and being employed. The age ranged from 17 to 66 years ( M  = 46.9, SD  = 11.23), with 45.78% of the sample identifying as female. Participants came from Germany, Austria, or Switzerland. Sample sizes were 2,130 (wave 1), 1,633 (wave 2), and 1,178 (wave 3). In total, adjusted data points from 2,171 participants are available between waves 1 and 3, whereby participants who only took part in one wave were also included.

Research model. Note . H3.1 and H3.2 are mediation hypotheses whose sub-paths are shown in the model. For the constructs at t2 and t3, stability was controlled by including an autoregressive path from the previous wave, though not shown in the model. The nationwide Swiss lockdown due to the COVID-19 pandemic began on March 16 th , 2020 (Lockdown, 2021 )

Dropout analyses of those participants who only participated in wave 1 and not in waves 2 and 3 were performed (dropout: N  = 497). Mean differences in age ( M continuers = 47.71 years vs. M dropout = 44.02 years; t (764.15) = 6.1501, p  < .001) and life domain conflicts (WHI: M continuers = 1.85 vs. M dropout = 1.92; t (778.56) = -2.482, p  = .013; HWI: M continuers = 1.55 vs. M dropout = 1.62; t (778.32) = -2.684, p  = .007) were significant, indicating that the participants who dropped out after wave 1 were slightly younger and experienced higher life domain conflicts than the people who kept on participating in wave 2 and/or 3. In both cases, the group means were close to each other. There was no significant difference for gender and WNBC.

All measures were presented in German. In Table  1 , descriptive statistics and correlations between measures are presented.

• Work-nonwork balance crafting

WNBC was measured at wave 1 with 16 items of the Work-Nonwork Balance Crafting Scale (Kerksieck et al., 2022 ). The construct consists of two factors – crafting in the work and the nonwork domain. In the original scale, both factors contain all three crafting behaviors established (physical, relational, and cognitive/emotional). Example items are: ‘ If I must get personal chores done during working time, I make sure that my work won’t be negatively affected. ’ (WNBC-work) and ‘ I try hard to meet my private obligations, even if I’m demanded strongly by my work. ’ (WNBC-nonwork). The response format corresponds to a 5-point Likert scale (1 = ‘strongly disagree’ to 5 = ‘strongly agree’). Thus, a higher score indicated that respondents craft a less permeable boundary toward the focused domain, e.g., the work domain. In such a case, employees inhibit a spillover from, for example, negative emotions experienced outside of work to the work domain. The items were asked concerning the last four weeks. The original WNBC construct as a two-factor solution had a poor model fit (χ 2 (76) = 2055.731, p  < .001, CFI = 0.669, TLI = 0.602, RMSEA = 0.111, SRMR = 0.092). We suggest that the scale in its complete form is not fully applicable to participants’ situations at the onset of the pandemic. Therefore, the scale was shortened via an iterative, theory-based process based on the consensus of two raters (First and last author), and all authors agreed to the shortened version. We removed items referring to, e.g., physical crafting, vacations, and work goals, as during the pandemic, many employees worked from home, vacations were not possible, and we argue that work goals might not have been a priority in such uncertain times. For example, Kossek et al. ( 2021 ) identified in a qualitative study on women working in STEM jobs that they experienced substantial role demands associated with work and nonwork role sacrifice during the pandemic. Thus, adapting the original WNBC scale to represent the challenging and unique time more adequately at the onset of the pandemic resulted in a shortened scale of 8 items, four mirroring items for each of the two factors (see Appendix Table 3 ). Since each of the items that belong to a different crafting domain (e.g., work) share variance through a common crafting dimension (e.g., relational), four residual covariances for each of the mirroring items were added, resulting in a satisfactory model fit (χ 2 (15) = 99.859, p  < .001, CFI = 0.970, TLI = 0.944, RMSEA = 0.052, SRMR = 0.029). In allowing such residual covariances, we follow the approach taken by (Kerksieck et al., 2022 ) for the initial scale.

Home-work-interaction and work-home-interaction

HWI and WHI (Kopelman et al., 1983 ) were assessed at wave 1 (stability control) and wave 2 (mediators in research model) with four and eight items from the Survey Work-Home Interaction – NijmeGen (Geurts et al., 2005 ). Items were anchored on a 4-point Likert scale (0 = ‘never’ to 3 = ‘always’). An example item for HWI is: ‘ How often does it occur that problems with your spouse/family/friends affect your job performance? ’ and for WHI, an example item is: ‘ How often does it occur that you are irritable at home because your work is demanding? ’. The higher the score, the more respondents experienced an interference between the work and home life domains.

Mental well-being and work engagement

Mental well-being was assessed at wave 2 (stability control) and wave 3 (outcome in research model) with seven items from the Warwick-Edinburgh Mental Well-Being Scale (WEMWBS; Tennant et al., 2007 ). The items refer to the last two weeks. An example item is: ‘ I felt optimistic about the future ’. The 5-point Likert scale ranges from 1 = ‘none of the time’ to 5 = ‘all of the time’, whereby a higher score indicated more mental well-being.

Work engagement was assessed at wave 2 (stability control) and wave 3 (outcome in research model) with nine items from the Utrecht Work Engagement Scale (UWES-9; Schaufeli et al., 2006 ). These include the three dimensions of vitality, dedication, and absorbedness. An example item is: ‘ At my work, I feel bursting with energy. ’ (dedication). The 7-point Likert scale ranges from 0 = ‘never’ to 6 = ‘always’, whereby a higher score reflects more work engagement.

Data analysis strategy

The data was prepared and analyzed with the open-source statistical program R Project (R Core Team, 2020 ) and the R package lavaan (Rosseel, 2012 ). First, the data was processed and checked to see if any cases needed to be excluded, e.g., due to unemployment at the time of the survey. Because four constructs were included in the model from two points, measurement invariance was analyzed for these constructs using confirmatory factor analyses (CFA) and following the common recommendations for longitudinal analyses (Putnick & Bornstein, 2016 ). As a minimum requirement, metric measurement invariance (equal factor structure and factor loadings) referring to equal factor loadings across time should hold in longitudinal models (Putnick & Bornstein, 2016 ). Model fit indices were accepted if close to recommended cut-off criteria, e.g., Root Mean Square Error of Approximation (RMSEA < 0.06) or Comparative Fit-Index (CFI ≥ 0.95) (Hu & Bentler, 1999 ). Furthermore, model comparison tests between the proposed measurement models against one-factor models were conducted for each wave. In the next step, the structural equation model was specified, and the hypotheses were tested using path estimates. Hypotheses 3.1a, 3.1b, 3.2a, and 3.2b, which predict a mediation effect, were also tested. Bootstrapping with 2,000 bootstraps was used as the calculation method, which, according to Hayes and Scharkow ( 2013 ), is the most suitable procedure for indirect effects. For missing values, the full information maximum likelihood method was used (Arbuckle et al., 1996 ). After the results of the hypothesized associations, post hoc analyses were conducted to investigate hypotheses that did not turn out as expected.

Before specifying the full research model, we examined whether assumptions of measurement invariance hold for those constructs for which we control for stability in the model. For WHI together with HWI, the model with metric measurement invariance indicated a good model fit (χ 2 (244) = 1238.667, p  < .001, CFI = 0.955, TLI = 0.949, RMSEA = 0.044, SRMR = 0.040). For mental well-being and work engagement, the models with residual measurement invariance (additionally equal item intercepts and residual variances) fit the data well and superior to less strict assumptions (mental well-being: χ 2 (75) = 831.135, p  < .001, CFI = 0.940, TLI = 0.927, RMSEA = 0.078, SRMR = 0.046; work engagement: χ 2 (151) = 705.050, p  < .001, CFI = 0.982, TLI = 0.982, RMSEA = 0.047, SRMR = 0.020). Our final model reaches a good fit as well (χ 2 (1955) = 9421.085, p  < .001, CFI = 0.897, TLI = 0.894, RMSEA = 0.042, SRMR = 0.076).

The direct and indirect effects of the research model are presented in Table  2 . First, we review the associations from WNBC to the life domain conflicts. The link between WNBC-work and HWI is significant ( b = -0.047, SE  = 0.021, p  = .024), but the link between WNBC-nonwork and WHI is not significant at a = 0.05 ( b  = 0.018, SE  = 0.025, p  = .465). Therefore, our results support H1.2, but not H1.1. Next, we review the associations of HWI and WHI with work engagement and mental well-being. Only the link between HWI and mental well-being is significant ( b = -0.154, SE  = 0.066, p  = .018), but all other hypothesized links are not significant (WHI to mental well-being: b  = 0.002, SE  = 0.045, p  = .959; HWI to work engagement: b = -0.181, SE  = 0.113, p  = .109; WHI to work engagement: b = -0.096, SE  = 0.083, p  = .250). Therefore, our results support H2.1a, but not H2.1b, H2.2a, or H2.2b. Lastly, we specified indirect effects from WNBC-work and -nonwork to work engagement and mental well-being via life domain conflicts. We obtained these results by bootstrapping our research model (no. of bootstraps = 2000). No significant indirect effects emerged as all 95% confidence intervals include zero (see Table  2 for full results). Therefore, our results do not support the hypotheses H3.1a, H3.1b, H3.2a, or H3.2b.

Interestingly, the autoregressive paths of both WHI ( b  = 0.824, SE  = 0.019, p  < .001) and HWI ( b  = 0.797, SE  = 0.023, p  < .001) were relatively stable between the two waves, as were the autoregressive paths of mental well-being ( b  = 0.766, SE  = 0.028, p  < .001) and work engagement ( b  = 0.784, SE  = 0.022, p  < .001). The discussion examines how this high stability, especially of life domain conflicts, could help explain the results.

Furthermore, although no hypotheses were formulated for the direct association between the WNBC facets and the outcomes, the paths were also specified in the structural equation model. Direct paths between WNBC-work at wave 1, mental well-being at wave 3, and work engagement at wave 3, resp. WNBC-nonwork at wave 1, mental well-being at wave 3, and work engagement at wave 3 were specified. Results show a significant positive association between WNBC-work and work engagement ( b  = 0.371, SE  = 0.148, p  = .012) and WNBC-work and mental well-being ( b  = 0.145, SE  = 0.061, p  = .018). No significant associations were found for WNBC-nonwork and work engagement ( b = -0.037, SE  = 0.124, p  = .763), nor WNBC-nonwork and mental well-being ( b = -0.013, SE  = 0.059, p  = .828).

We aimed to explore the role of WNBC during the COVID-19 pandemic and its longitudinal associations with mental well-being and work engagement via WHI and HWI. For our study, we referred to survey data that captured a significant part of 2020, where the onset of the pandemic and two lockdowns had major implications for the lives of many employees. Therefore, our study provides valuable information on how behavior early in the pandemic is associated with long-term health and well-being. This section discusses our main findings concerning their theoretical and practical contributions.

WNBC and life domain conflicts during the COVID-19 pandemic

We hypothesized that WNBC-work is negatively associated with HWI and WNBC-nonwork with WHI, but our results only support the first relationship (support for H1.2, but not H1.1). Considering the unique context of the pandemic, mandatory work reductions or increased workload were previously found to be associated with a perceived negative impact of the pandemic on work-life (Tušl et al., 2021 ), which might have overshadowed and impaired individual efforts. This shows that to address WHI, individual efforts were not enough, and employees were in high need of support from organizations, e.g., through more flexibility. On the other hand, individual efforts to protect the work domain prevented HWI, highlighting that employees can effectively support themselves in this area.

Further, we discuss two additional aspects that might have played a role in these findings. First, we note the relatively high stability of the life domain conflicts that emerged in our analyses. WHI and HWI turned out to be more stable than expected between wave 1 and wave 2, meaning that people’s perceived life domain conflicts between April and June/July hardly changed. This leaves little room for individual efforts such as crafting to explain remaining variance. Considering shorter timeframes to investigate the association between WNBC and life domain conflicts might be useful. Potentially, individual crafting has a more nuanced effect on life domain conflicts on a shorter, weekly to monthly basis, whereas, in more extended periods, more stable trends occur. Indeed, previous research points to short-term fluctuations in life domain conflicts, predicted by, e.g., daily workload (Ilies et al., 2007 ), but higher mean stability over mid- to long-term time frames (Smith et al., 2022 ). Similar stabilities have been reported when comparing pre-pandemic and pandemic levels of work-family conflict (Bernhardt et al., 2023 ; Reimann et al., 2022 ). Thus, we suggest that future research on the association of WNBC and life domain conflicts also considers shorter time frames.

Within our study, we used a shortened 8-item version of the WNBC-scale that better fits the context of the pandemic than the original full scale. During the iterative, theory-driven, and two-person consensus-based process, aspects of the initial scale were dropped, e.g., the aspect of crafting a physical boundary. The original scale incorporated physical WNBC as, e.g., an earlier or later start to work if needed due to obligations outside of work (Kerksieck et al., 2022 ). During the pandemic, employees may have adopted other crafting strategies, such as going grocery shopping during working hours to avoid big crowds and, therefore, limit the spread of covid viruses. Furthermore, some of the items that were omitted for the shortened scale included strategies that were thought to be deprioritized due to the crisis. For example, one item describes a cognitive/emotional strategy in which employees temporarily emphasize their work (e.g., work more before vacations to get things done; Kerksieck et al., 2022 ). Since the pandemic and its lockdown measures caused a shift in focus and impeded travelling plans, vacations were not a well-suited example during this time of crisis. In modern working times, such behaviors and possible scenarios should also be considered and captured by a revised WNBC scale.

The shortened WNBC scale focuses mainly on cognitive crafting. Therefore, we can also interpret our findings regarding the specific aspects of crafting retained in the scale: To prevent HWI, aspects of cognitive crafting are quite important. However, more action-oriented or social crafting strategies, such as communicating with others, might play a more vital role in preventing WHI. As these aspects are not reflected in our shortened scale, this might explain the absence of a link between WNBC-nonwork and WHI. However, communicating with others to secure boundaries between work and nonwork was reported as the least often used strategy by parents working from home in a study by Allen et al. ( 2021 ). Considering that the pandemic likely had lasting impacts on the accelerated new work movement, a revised balance crafting scale could be helpful to represent better the post-pandemic work context (Kniffin et al., 2021 ; Rudolph et al., 2021 ). Lastly, future use of our shortened scale would provide more information on its validity and reliability measures (Clark & Watson, 2016 ).

Life domain conflicts, work engagement, and mental well-being

Partly in line with previous findings (e.g., Karatepe & Karadas, 2016 ), we only found a significant association between HWI and mental well-being (supporting H2.1a, but not H2.1b, H2.2a, or H2.2b). Therefore, employees who experienced high levels of HWI in the middle of 2020 reported lower mental well-being at the end of 2020, highlighting them as an at-risk group in need of further support. For those employees whose life outside of work was affecting their work life, organizational support might be highly relevant, e.g., by providing appropriate flexibility and autonomy or reviewing projects and workload (Kossek et al., 2021 ). However, work engagement was not longitudinally associated with HWI, and neither was WHI with the health outcomes we reviewed. The unique context of the pandemic needs to be considered and might provide possible explanations. Research highlighted that compared to pre-pandemic states, life domain conflicts, and other stressors increased (Reimann et al., 2022 ). The WHO reported that from the onset of the pandemic onwards, the prevalence of mental health issues (e.g., anxiety) rose, yet at the same time also the awareness of mental health issues (World Health Organization, 2022 ). In milder cases, the open discussion of risk factors such as life domain conflicts and stressors might have equipped employees with more coping tools (Pfefferbaum & North, 2020 ), therefore limiting the effect of life domain conflicts on work engagement and mental well-being. Accordingly, research also found a growing resilience in mental health of the population in response to the pandemic (Daly & Robinson, 2021 ).

WNBC and long-term health and well-being during the pandemic

Lastly, we assumed a longitudinal link between WNBC, work engagement, and mental well-being via life domain conflicts. Our results do not support such an indirect effect (H3.1a, H3.1b, H3.2a, H3.2b), but we found direct significant links between WNBC-work and both outcomes. Considering the complex, differentiated associations between WNBC with the life domain conflicts and the life domain conflicts with the health outcomes, it is unsurprising that we did not find an indirect effect. The significant relationship between WNBC-work and work engagement over eight months is in line with a previous finding, which showed the same association for a three-month period (Kerksieck et al., 2022 ). Thus, we corroborate the longitudinal association, but the effect is not mediated by life domain conflicts, as was assumed in the hypotheses of this study. It is unknown whether this finding might be caused by a lagged or a cumulative effect or if interpersonal differences in traits, such as identification with one’s job, are responsible for this association between eight months. Identification with one’s job could explain why those employees who emphasized protecting their work domain also report higher work engagement, as for both the proactive behavior and high work engagement, a high priority of work might be a driver. Future research should corroborate this finding using different time lags and investigate potential third-variable explanations.

Further, as we could not shed light on a longitudinal mechanism explaining the association between WNBC and health outcomes, we suggest that future research dives into alternative mediating processes, reviewing gain spirals instead of a loss spiral, as we did in our research. Like the loss spiral, the gain spiral can be derived from the COR theory (Hobfoll, 1989 ). It describes a process where an initial resource gain enables further resource gains, leading to an upward spiral (Hobfoll, 2002 ). If, for example, a person can save time by working from home (because they do not have to commute), they can then use that time to gain other resources, such as energetic resources through recovery. Tims et al. ( 2015 ) have found that through job crafting, employees can improve their well-being (e.g., more work engagement, more job satisfaction, less burnout) through increased social job resources. Therefore, this relationship might apply to WNBC because persons who actively craft their boundaries between the work and the nonwork domains can improve their outcomes through increased resources (e.g., measured by work-home enrichment instead of interference; Kopelman et al., 1983 ). Thus, looking at WNBC and its association with the gain spiral instead of the loss spiral could be a promising next step to understanding the WNBC construct from a resource perspective.

Strengths and limitations

The research of this paper contributes to the existing literature in three main ways: First, it extends the literature by looking at a relatively new crafting construct that still offers a high research potential. We demonstrate here that a shortened version of the WNBC provides a good fit with the unique context of the pandemic, yet also that the structure of WNBC could be subject to revision in future research. Second, our study shows that WNBC partly played a role in reducing life domain conflicts at the beginning of the pandemic, which points to a strategy that could be taken up by early interventions in future times of crisis, as well as the limits of individual proactive behaviors. For the work domain, individual efforts should be coupled with organizational support to reduce life domain conflicts. Third, our study adds knowledge on the longitudinal association between life domain conflicts and health outcomes, showing that contrary to earlier findings, only HWI was associated with lower mental well-being in our study. This opens a field for new research in which the unique context of the pandemic and specific developments could be reviewed. For example, the overall more open conversation about struggles might have aided in buffering the effects of life domain conflicts on health (Bu et al., 2021 ).

Besides these strengths, the study has several limitations. The first limitation that needs to be addressed is the inability to use the full scale for WNBC due to its low fit. As outlined earlier, we attempted to increase fit in terms of both content validity to the pandemic context and statistical fit. Our shortened scale highlights those generalizable parts of WNBC that also fit the unique context of the COVID-19 pandemic, but we note that other parts still need to be included. To address this, further research is required to corroborate the shortened scale or revise the original scale to fit newer developments in working life.

Second, our study solely relies on data collected in 2020, and our findings cannot be lightly generalized to other non-pandemic periods or populations. Contrary to previous research, we did not find a link between WHI and studied health outcomes. We discussed earlier that this could be due to the unique context of the pandemic, in which the increased collective awareness about the daily struggles employees faced might have also increased awareness and individual ways of coping. To shed more light on this assumption, these findings should be corroborated in other (milder) times of crisis, e.g., financial crises or severe organizational changes, and outside of exceptional circumstances. Further, our sample consists solely of German-speaking employees from Germany, Austria, and Switzerland. Our findings may not be generalizable to other cultural contexts, e.g., more feminine cultures such as Finland (Hofstede, 2016 ). For the original WNBC scale, a cross-cultural validation has been conducted, which also showed a longitudinal correlation between WNBC-nonwork and work engagement in Finland, likely due to the restoration and spillover of relevant resources (Kerksieck et al., 2022 ). In similar fashion, future research should investigate further cultural differences in the associations between WNBC, life domain conflicts, and employee well-being.

Lastly, we note that in the dropout analysis, a significant difference was found for life domain conflicts, meaning that out of all participants participating in wave 1, those that did not participate in waves 2 and 3 reported significantly more life domain conflicts, likely because they had less time or energy for study participation. However, the remaining sample should still be representative, and severely biased results are not expected because of this dropout, especially since missing values were treated with the reliable full information maximum likelihood method in which the parameters are estimated using the available data in the sample (Newman, 2014 ).

Practical implications

We can derive two main practical implications from our findings, primarily concerning political and organizational institutions. First, we have found that WNBC focusing on the work domain is longitudinally associated with lower HWI, higher work engagement, and mental well-being. In times of crisis, organizations can support their employees in crafting by disseminating information about how proactive crafting can aid them in maintaining their own well-being. On top of that, organizations should enable employees to use these strategies for their own benefit. In any case, employees can proactively enact crafting efforts to improve their work and nonwork lives. Taking advantage of this bottom-up self-management strategy is what makes crafting a convincing concept in theory and practice since its early outlines (Wrzesniewski & Dutton, 2001 ) to a concept with obvious relevance during the COVID-19 pandemic and for the future of work (Bakker et al., 2023 ; Tims et al., 2022 ).

Second, our results also point to the limits of individual crafting. WNBC might not have enough force to reduce WHI, which could instead be addressed through support in organizing work and granting flexibility and autonomy. Other forms of crafting interventions, e.g., targeted toward job crafting (van den Heuvel et al., 2015 ; van Wingerden et al., 2017 ) or off-job/needs crafting (Kosenkranius et al., 2023 ; Laporte et al., 2022 ), as well as targeted organizational support for employees, should be considered to complement pandemic or crisis mitigation plans.

We aimed to study the role of WNBC for long-term health and well-being during the COVID-19 pandemic. Using a shortened version of the WNBC scale to fit the context of the pandemic, our results point to complex associations between WNBC and life domain conflicts, work engagement, and mental well-being. We found a direct positive association between WNBC-work and work engagement and mental well-being and a negative association between WNBC-work and HWI, yet no indirect association between WNBC-work and WNBC-nonwork with work engagement and mental well-being via life domain conflicts. We conclude that engaging in WNBC early in times of crisis is associated with better long-term health. This urges companies to take responsibility and support their workers by providing a crafting-friendly environment and helping reduce their life domain conflicts. Our findings are not only relevant during future crises but also to support employees in the future of work.

Data availability

The data that support the findings of this study are available from the authors upon reasonable request. Study participants were asked to give consent to use the data for research and within research publication, but not for open public access.

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Brogle, S.E., Kerksieck, P., Bauer, G.F. et al. Managing boundaries for well-being: a study of work-nonwork balance crafting during the COVID-19 pandemic. Curr Psychol (2024). https://doi.org/10.1007/s12144-024-06118-x

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The spread of infectious diseases was further promoted due to busy cities, increased travel, and climate change, which led to outbreaks, epidemics, and even pandemics. The world experienced the severity of the 125 nm virus called the coronavirus disease 2019 (COVID-19), a pandemic declared by the World Health Organization (WHO) in 2019. Many investigations revealed a strong correlation between humidity and temperature relative to the kinetics of the virus’s spread into the hosts. This study aimed to solve the riddle of the correlation between environmental factors and COVID-19 by applying RepOrting standards for Systematic Evidence Syntheses (ROSES) with the designed research question. Five temperature and humidity-related themes were deduced via the review processes, namely 1) The link between solar activity and pandemic outbreaks, 2) Regional area, 3) Climate and weather, 4) Relationship between temperature and humidity, and 5) the Governmental disinfection actions and guidelines. A significant relationship between solar activities and pandemic outbreaks was reported throughout the review of past studies. The grand solar minima (1450-1830) and solar minima (1975-2020) coincided with the global pandemic. Meanwhile, the cooler, lower humidity, and low wind movement environment reported higher severity of cases. Moreover, COVID-19 confirmed cases and death cases were higher in countries located within the Northern Hemisphere. The Blackbox of COVID-19 was revealed through the work conducted in this paper that the virus thrives in cooler and low-humidity environments, with emphasis on potential treatments and government measures relative to temperature and humidity.

• The coronavirus disease 2019 (COIVD-19) is spreading faster in low temperatures and humid area.

• Weather and climate serve as environmental drivers in propagating COVID-19.

• Solar radiation influences the spreading of COVID-19.

• The correlation between weather and population as the factor in spreading of COVID-19.

Graphical abstract

Introduction

The revolution and rotation of the Earth and the Sun supply heat and create differential heating on earth. The movements and the 23.5° inclination of the Earth [ 1 ] separate the oblate-ellipsoid-shaped earth into northern and southern hemispheres. Consequently, the division results in various climatic zones at different latitudes and dissimilar local temperatures (see Fig.  1 ) and affects the seasons and length of a day and night in a particular region [ 2 ]. Global differential heating and climate variability occur due to varying solar radiation received by each region [ 3 ]. According to Trenberth and Fasullo [ 4 ] and Hauschild et al. [ 5 ] the new perspective on the issue of climate change can be affected relative to the changes in solar radiation patterns. Since the study by Trenberth and Fasullo [ 4 ] focused on climate model changes from 1950 to 2100, it was found that the role of changing clouds and trapped sunlight can lead to an opening of the aperture for solar radiation.

The annual average temperature data for 2021 in the northern and southern hemispheres ( Source: meteoblue.com ). Note: The black circles mark countries with high Coronavirus disease 2019 (COVID-19) infections

Furthermore, the heat from sunlight is essential to humans; several organisms could not survive without it. Conversely, the spread of any disease-carrying virus tends to increase with less sunlight exposure [ 6 ]. Historically, disease outbreaks that led to epidemic and pandemic eruptions were correlated to atmospheric changes. Pandemic diseases, such as the flu (1918), Asian flu (1956–1958), Hong Kong flu (1968), and recently, the coronavirus disease 2019 (COVID-19) (2019), recorded over a million death toll each during the winter season or minimum temperature conditions [ 7 ]. The total number of COVID-19 cases is illustrated in Fig.  2 .

A graphical representation of the total number of COVID-19 cases across various periods between 2020 and 2021. ( Source : www.worldometers.info ). Note: The black circles indicate countries with high numbers COVID-19-infections

In several previous outbreaks, investigations revealed a significant association between temperature and humidity with a particular focus on the transmission dynamics of the infection from the virus into the hosts [ 8 , 9 , 10 ]. Moreover, disease outbreaks tended to heighten in cold temperatures and low humidity [ 11 ]. Optimal temperature and sufficient relative humidity during evaporation are necessary for cloud formation, resulting in the precipitated liquid falling to the ground as rain, snow, or hail due to the activity of solar radiation balancing [ 4 ].

Consequently, the radiation balancing processes in the atmosphere are directly linked to the living beings on the earth, including plants and animals, and as well as viruses and bacterias. According to Carvalho et al. [ 12 ]‘s study, the survival rate of the Coronaviridae Family can decrease during summer seasons. Nevertheless, numerous diseases were also developed from specific viruses, such as influenza, malaria, and rubella, and in November 2019, a severe health threat originated from a 125 nm size of coronavirus, had resulted in numerous deaths worldwide.

Transmission and symptoms of COVID-19

The COVID-19, or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is an infectious disease caused by a newly discovered pathogenic virus from the coronavirus family, the novel coronavirus (2019-nCoV) [ 13 ]. The first case was recorded in Wuhan, China, in December 2019 [ 14 ]. The pathogenic virus is transmitted among humans when they breathe in air contaminated with droplets and tiny airborne particles containing the virus [ 14 , 15 , 16 , 17 , 18 ].

According to the World Health Organization (WHO), the most common symptoms of COVID-19 infection include fever, dry cough, and tiredness. Nevertheless, older people and individuals with underlying health problems (lung and heart problems, high blood pressure, diabetes, or cancer) are at higher risk of becoming seriously ill and developing difficulty breathing [ 19 ]. The COVID-19 was initially only predominant in China but rapidly spread to other countries globally. The remarkably swift acceleration of the number of infections and mortality forced WHO to declare COVID-19 a global public health emergency on the 30th of January 2020, which was later declared as a pandemic on the 11th of March 2020 [ 20 ].

Since no vaccine was available then, WHO introduced the COVID-19 preventative measures to reduce the chances of virus transmission. The guideline for individual preventative included practising hand and respiratory hygiene by regularly cleaning hands with soap and water or alcohol-based sanitisers, wear a facemask and always maintaining at least a one-meter physical distance [ 21 ]. Nevertheless, the worldwide transmission of COVID-19 has resulted in fear and forced numerous countries to impose restrictions rules, such as lockdown, travel bans, closed country borders, restrictions on shipping activities, and movement limitations, to diminish the spread of COVID-19 [ 22 ].

According to WHO, by the 2nd of December 2020, 63,379,338 confirmed cases and 1,476,676 mortalities were recorded globally. On the 3rd of December 2021, 263,655,612 confirmed cases and deaths were recorded, reflecting increased COVID-19 infections compared to the previous year. The American and European regions documented the highest COVID-19 patients with 97,341,769 and 88,248,591 cases, respectively (see Fig. 2 ), followed by Southeast Asia with 44,607,287, Eastern Mediterranean accounted 16,822,791, Western Pacific recorded 6,322,034, and Africa reported the lowest number of cases at 6,322,034 [ 19 ].

Recently, an increasing number of studies are investigating the association between environmental factors (temperature and humidity) and the viability, transmission, and survival of the coronavirus [ 23 , 24 , 25 , 26 ]. The results primarily demonstrated that temperature was more significantly associated with the transmission of COVID-19 [ 27 , 28 , 29 ] and its survival period on the surfaces of objects [ 30 ]. Consequently, the disease was predominant in countries with low temperature and humidity [ 31 ], which was also proven by Diao et al. [ 32 ]‘s study demonstrating higher rates of COVID-19 transmission in China, England, Germany, and Japan.

A comprehensive systematic literature review (SLR) is still lacking despite numerous research on environmental factors linked to coronavirus. Accordingly, this article aimed to fill the gap in understanding and identifying the correlation between environmental factors and COVID-19 by analysing existing reports. Systematically reviewing existing literature is essential to contribute to the body of knowledge and provide beneficial information for public health policymakers.

Methodology

The present study reviewed the protocols, formulation of research questions, selection of studies, appraisal of quality, and data abstraction and analysis.

The protocol review

The present SLR was performed according to the reporting standards for systematic evidence syntheses (ROSES) and followed or adapted the guidelines as closely as possible. Thus, in this study, a systematic literature review was guided by the ROSES review protocol (Fig.  3 ). Compared to preferred reporting items for systematic review and meta-analysis (PRISMA), ROSES is a review protocol specifically designed for a systematic review in the conservation or environment management fields [ 33 ]. Compared to PRISMA, ROSES offers several advantages, as it is tailored to environmental systematic review, which reduces emphasis on quantitative synthesis (e.g. meta-analysis etc.) that is only reliable when used with appropriate data [ 34 ].

The flow diagram guide by ROSES protocol and Thematical Analysis

The current SLR started by determining the appropriate research questions, followed by the selection criteria, including the review, specifically on the keywords employed and the selection of journals database. Subsequently, the appraisal quality process and data abstraction and analysis were conducted.

Formulation of research questions

The entire process of this SLR was guided by the specific research questions, while sources to be reviewed and data abstraction and analysis were in line with the determined research question [ 35 , 36 ]. In the present article, a total of five research questions were formed, namely:

What the link between solar activity and COVID-19 pandemic outbreaks?

Which regions were more prone to COVID-19?

What were the temporal and spatial variabilities of high temperature and humidity during the spread of COVID-19?

What is the relationship between temperature and humidity in propagating COVID-19?

How did the government’s disinfection actions and guidelines can be reducing the spread of COVID-19?

Systematic searching strategies

Selection of studies.

In this stage of the study, the appropriate keywords to be employed in the searching process were determined. After referring to existing literature, six main keywords were chosen for the searching process, namely COVID-19, coronavirus, temperature, humidity, solar radiation and population density. The current study also utilised the boolean operators (OR, AND, AND NOT) and phrase searching.

Scopus was employed as the main database during the searching process, in line with the suggestion by Gusenbauer and Haddaway [ 37 ], who noted the strength of the database in terms of quality control and search and filtering functions. Furthermore, Google Scholar was selected as the supporting database. Although Halevi et al. [ 38 ] expressed concerns about its quality, Haddaway et al. [ 39 ] reported that due to its quantity, Google Scholar was suitable as a supporting database in SLR studies.

In the first stage of the search, 2550 articles were retrieved, which were then screened. The suitable criteria were also determined to control the quality of the articles reviewed [ 40 ]. The criteria are: any documents published between 2000 to 2022, documents that consist previously determined keywords, published in English, and any environment-related studies that focused on COVID-19. Based on these criteria, 2372 articles were excluded and 178 articles were proceeded to the next step namely eligibility. In the eligibility process, the title and the abstract of the articles were examined to ensure its relevancy to the SLR and in this process a total of 120 articles were excluded and only 58 articles were processed in the next stage.

Appraisal of the quality

The study ensured the rigor of the chosen articles based on best evidence synthesis. In the process, predefined inclusion criteria for the review were appraised by the systematic review team based on previously established guidelines and the studies were then judged as being scientifically admissible or not [ 40 ]. Hence, by controlling the quality based on the best evidence synthesis, the present SLR controls its quality by including articles that are in line with the inclusion criteria. It means that any article published within the timeline (in the year 2000 and above), composed of predetermined keywords, in English medium, and environment-related investigations focusing on COVID-19 are included in the review. Based on this process, all 58 articles fulfilled all the inclusion criteria and are considered of good quality and included in the review.

Data abstraction and analysis

The data abstraction process in this study was performed based on five research questions (please refer to 2.2, formulation of research questions). The data that was able to answer the questions were abstracted and placed in a table to ease the data analysis process. The primary data analysis technique employed in the current study was qualitative and relied on thematic analysis.

The thematic technique is a descriptive method that combines data flexibly with other information evaluation methods [ 41 ], aiming to identify the patterns in studies. Any similarities and relationships within the abstracted data emerge as patterns. Subsequently, suitable themes and sub-themes would be developed based on obtained patterns [ 42 ]. Following the thematic process, five themes were selected in this study.

Background of the selected articles

The current study selected 58 articles for the SLR. Five themes were developed based on the thematic analysis from the predetermined research questions: the link between solar activity and pandemic outbreaks, regional area, climate and weather, the relationship between temperature and humidity, and government disinfection action guidelines. Among the articles retrieved between 2000 and 2022; two were published in 2010, one in 2011, four in 2013, three in 2014, two in 2015, six in 2016 and 2017, respectively, one in 2018, six in 2019, twelve in 2020, eight in 2021, and seven in 2022.

Temperature- and humidity-related themes

The link between solar activity and pandemic outbreaks.

Numerous scientists have investigated the relationship between solar activities and pandemic outbreaks over the years ([ 43 ]; A [ 27 , 44 , 45 ].). Nuclear fusions from solar activities have resulted in minimum and maximum solar sunspots. Maximum solar activities are characterised by a high number of sunspots and elevated solar flare frequency and coronal mass injections. Minimum solar sunspot occurrences are identified by low interplanetary magnetic field values entering the earth [ 1 ].

A diminished magnetic field was suggested to be conducive for viruses and bacteria to mutate, hence the onset of pandemics. Nonetheless, Hoyle and Wickramasinghe [ 46 ] reported that the link between solar activity and pandemic outbreaks is only speculative. The literature noted that the data recorded between 1930 and 1970 demonstrated that virus transmissions and pandemic occurrences were coincidental. Moreover, no pandemic cases were reported in 1979, when minimum solar activity was recorded [ 47 ].

Chandra Wickramasinghe et al. [ 48 ] suggested a significant relationship between pandemic outbreaks and solar activities as several grand solar minima, including Sporer (1450–1550 AD), Mounder (1650–1700 AD), and Dalton (1800–1830) minimums, were recorded coinciding with global pandemics of diseases, such as smallpox, the English sweat, plague, and cholera pandemics. Furthermore, since the Dalton minimum, which recorded minimum sunspots, studies from 2002 to 2015 have documented the reappearance of previous pandemics. For example, influenza subtype H1N1 1918/1919 episodically returned in 2009, especially in India, China, and other Asian countries. Zika virus, which first appeared in 1950, flared and became endemic in 2015, transmitted sporadically, specifically in African countries. Similarly, SARS-CoV was first recorded in China in 2002 and emerged as an outbreak, MERS-CoV, in middle east countries a decade later, in 2012.

In 2020, the World Data Centre Sunspot Index and Long-term Solar Observations ( http://sidc.be ) confirmed that a new solar activity was initiated in December 2019, during which a novel coronavirus pandemic also occurred, and present a same as the previous hypothesis. Nevertheless, a higher number of pandemic outbreaks were documented during low minimum solar activities, including Ebola (1976), H5N1 (Nipah) (1967–1968), H1N1 (2009), and COVID-19 (2019–current). Furthermore, Wickramasinghe and Qu [ 49 ] reported that since 1918 or 1919, more devastating and recurrent pandemics tend to occur, particularly after a century. Consequently, within 100 years, a sudden surge of influenza was recorded, and novel influenza was hypothesised to emerge.

Figure  4 demonstrates that low minimum solar activity significantly reduced before 2020, hence substantiating the claim that pandemic events are closely related to solar activities. Moreover, numerous studies (i.e. [ 43 ], Chandra [ 46 , 47 , 48 ]) reported that during solar minimums, new viruses could penetrate the surfaces of the earth and high solar radiation would result in lower infection rates, supporting the hypothesis mentioned above.

The number of sunspots in the last 13 years. Note : The yellow curve indicates the daily sunspot number and the 2010–2021 delineated curve illustrates the minimum solar activity recorded (source: http://sidc.be/silso )

Regional area

In early December 2019, Wuhan, China, was reported as the centre of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak [ 50 ]. Chinese health authorities immediately investigated and controlled the spread of the disease. Nevertheless, by late January 2020, the WHO announced that COVID-19 was a global public health emergency. The upgrade was due to the rapid rise in confirmed cases, which were no longer limited to Wuhan [ 28 ]. The disease had spread to 24 other countries, which were mainly in the northern hemisphere, particularly the European and Western Pacific regions, such as France, United Kingdom, Spain, South Korea, Japan, Malaysia, and Indonesia [ 51 , 52 ]. The migration or movement of humans was the leading agent in the spread of COVID-19, resulting in an almost worldwide COVID-19 pandemic [ 53 ].

The first hotspots of the epidemic outspread introduced by the Asian and Western Pacific regions possessed similar winter climates with an average temperature and humidity rate of 5–11 °C and 47–79%. Consequently, several publications reviewed in the current study associated the COVID-19 outbreak with regional climates (i.e. [ 1 , 29 , 54 , 55 ]) instead of its close connection to China. This review also discussed the effects of a range of specific climatological variables on the transmission and epidemiology of COVID-19 in regional climatic conditions.

America and Europe documented the highest COVID-19 cases, outnumbering the number reported in Asia [ 19 ] and on the 2nd of December 2020, the United States of America (USA) reported the highest number of confirmed COVID-19 infections, with over 13,234,551 cases and 264,808 mortalities (Da S [ 56 ].). The cases in the USA began emerging in March 2020 and peaked in late November 2020, during the wintertime in the northern hemisphere (December to March) [ 53 ]. Figure  5 demonstrates the evolution of the COVID-19 pandemic in several country which represent comparison two phase of summer and one phase of winter. Most of these countries tend to increase of COVID cases close to winter season. Then, it can be worsening on phase two of summer due to do not under control of human movement although the normal trend it is presenting during winter phase.

The evolution of the COVID-19 pandemic from the 15th of February 2020 to the 2nd of December 2020 ( Source: https://www.worldometers.info/coronavirus )

The coronavirus spread aggressively across the European region, which recorded the second highest COVID-19 confirmed cases after America. At the end of 2020, WHO reported 19,071,275 Covid-19 cases in the area, where France documented 2,183,275 cases, the European country with the highest number of confirmed cases, followed by the United Kingdom (1,629,661 cases) and Spain (1,652,801 cases) [ 19 ]. Europe is also located in the northern hemisphere and possesses a temperate climate.

The spatial and temporal transmission patterns of coronavirus infection in the European region were similar to America and the Eastern Mediterranean, where the winter season increased COVID-19 cases. Typically, winter in Europe occurs at the beginning of October and ends in March. Hardy et al. [ 57 ] also stated that temperature commonly drops below freezing (approximately − 1 °C) when snow accumulates between December to mid-March, resulting in an extreme environment. Figure 5 indicates that COVID-19 cases peaked in October when the temperature became colder [ 21 ]. Similarly, the cases were the highest in the middle of the year in Australia and South Asian countries, such as India, that experience winter and monsoon, respectively, during the period.

In African regions, the outbreak of COVID-19 escalated rapidly from June to October before falling from October to March, as summer in South Africa generally occurs from November to March, while winter from June to August. Nevertheless, heavy rainfall generally transpires during summer, hence the warm and humid conditions in South Africa and Namibia during summer, while the opposite happens during winter (cold and dry). Consequently, the outbreak in the region recorded an increasing trend during winter and subsided during the summer, supporting the report by Gunthe et al. [ 58 ]. Novel coronavirus disease presents unique and grave challenges in Africa, as it has for the rest of the world. However, the infrastructure and resources have limitations for Africa countries facing COVID-19 pandemic and the threat of other diseases [ 59 ].

Conclusively, seasonal and regional climate patterns were associated with COVID-19 outbreaks globally. According to Kraemer et al. [ 60 ], they used real-time mobility data in Wuhan and early measurement presented a positive correlation between human mobility and spread of COVID-19 cases. However, after the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases were still indicative of local chains of transmission outside of Wuhan.

Climate and weather

The term “weather” represents the changes in the environment that occur daily and in a short period, while “climate” is defined as atmospheric changes happening over a long time (over 3 months) in specific regions. Consequently, different locations would experience varying climates. Numerous reports suggested climate and weather variabilities as the main drivers that sped or slowed the transmission of SARS-CoV-2 worldwide [ 44 , 61 , 62 , 63 ].

From a meteorological perspective, a favourable environment has led to the continued existence of the COVID-19 virus in the atmosphere [ 64 ]. Studies demonstrated that various meteorological conditions, such as the rate of relative humidity (i.e. [ 28 ]), precipitation (i.e. [ 65 ]), temperature (i.e. [ 66 ]), and wind speed factors (i.e. [ 54 ]), were the crucial components that contributed to the dynamic response of the pandemic, influencing either the mitigation or exacerbation of novel coronavirus transmission. In other words, the environment was considered the medium for spreading the disease when other health considerations were put aside. Consequently, new opinions, knowledge, and findings are published and shared to increase awareness, thus encouraging preventive measures within the public.

The coronavirus could survive in temperatures under 30 °C with a relative humidity of less than 80% [ 67 ], suggesting that high temperatures and lower relative humidity contributed to the elicitation of COVID-19 cases [ 18 , 51 , 58 , 68 ]. Lagtayi et al. [ 7 ] highlighted temperature as a critical factor, evidently from the increased transmission rate of MERS-Cov in African states with a warm and dry climate. Similarly, the highest COVID-19 cases were recorded in dry temperate regions, especially in western Europe (France and Spain), China, and the USA, while the countries nearer to the equator were less affected. Nevertheless, the temperature factor relative to viral infections depends on the protein available in the viruses. According to Chen and Shakhnovich [ 69 ], there is a good correlation between decreasing temperature and the growth of proteins in virus. Consequently, preventive measures that take advantage of conducive environments for specific viruses are challenging.

Precipitation also correlates with influenza [ 43 ]. A report demonstrated that regions with at least 150 mm of monthly precipitation threshold level experienced fewer cases than regions with lower precipitation rates. According to Martins et al. [ 70 ], influenza and COVID-19 can be affected by climate, where virus can be spread through the respiratory especially during rainfall season. The daily spread of Covid-19 cases in tropical countries, which receive high precipitation levels, are far less than in temperate countries [ 27 ]. Likewise, high cases of COVID-19 were reported during the monsoon season (mid-year) in India during which high rainfall is recorded [ 71 ]. Moreover, the majority of the population in these regions has lower vitamin D levels, which may contribute to weakened immune responses during certain seasons [ 27 ].

Rainfall increases the relative atmospheric humidity, which is unfavourable to the coronaviruses as its transmission requires dry and cold weather. Moreover, several reports hypothesised that rain could wash away viruses on object surfaces, which is still questioned. Most people prefer staying home on rainy days, allowing less transmission or close contact. Conversely, [ 72 ] exhibited that precipitation did not significantly impact COVID-19 infectiousness in Oslo, Norway due the location in northern hemisphere which are during winter season presenting so cold.

Coşkun et al. [ 54 ] and Wu et al. [ 29 ] claimed that wind could strongly correlate with the rate of COVID-19 transmission. Atmospheric instability (turbulent occurrences) leads to increased wind speed and reduces the dispersion of particulate matter (PM 2.5 and PM 10 ) in the environment and among humans. An investigation performed in 55 cities in Italy during the COVID-19 outbreak proved that the areas with low wind movement (stable atmospheric conditions) possessed a higher correlation coefficient and exceeded the threshold value of the safe level of PM 2.5 and PM 10 . Resultantly, more individuals were recorded infected with the disease in the regions. As mentioned in Martins et al. [ 70 ] the COVID-19 can be affected by climate and the virus can be spread through respiratory which is the virus moving in the wind movement.

The relationship between temperature and humidity

Climatic parameters, such as temperature and humidity, were investigated as the crucial factors in the epidemiology of the respiratory virus survival and transmission of COVID-19 ([ 61 ]; S [ 73 , 74 ].). The rising number of confirmed cases indicated the strong transmission ability of COVID-19 and was related to meteorological parameters. Furthermore, several studies found that the disease transmission was associated with the temperature and humidity of the environment [ 55 , 64 , 68 , 75 ], while other investigations have examined and reviewed environmental factors that could influence the epidemiological aspects of Covid-19.

Generally, increased COVID-19 cases and deaths corresponded with temperature, humidity, and viral transmission and mortality. Various studies reported that colder and dryer environments favoured COVID-19 epidemiologically [ 45 , 76 , 77 ]. As example tropical region, the observations indicated that the summer (middle of year) and rainy seasons (end of the year) could effectively diminish the transmission and mortality from COVID-19. High precipitation statistically increases relative air humidity, which is unfavourable for the survival of coronavirus, which prefers dry and cold conditions [ 32 , 34 , 78 , 79 ]. Consequently, warmer conditions could reduce COVID-19 transmission. A 1 °C increase in the temperature recorded a decrease in confirmed cases by 8% increase [ 45 ].

Several reports established that the minimum, maximum, and average temperature and humidity correlated with COVID-19 occurrence and mortality [ 55 , 80 , 81 ]. The lowest and highest temperatures of 24 and 27.3 °C and a humidity between 76 and 91% were conducive to spreading the virulence agents. The propagation of the disease peaked at the average temperature of 26 °C and humidity of 55% before gradually decreasing with elevated temperature and humidity [ 78 ].

Researchers are still divided on the effects of temperature and humidity on coronavirus transmission. Xu et al. [ 26 ] confirmed that COVID-19 cases gradually increased with higher temperature and lower humidity, indicating that the virus was actively transmitted in warm and dry conditions. Nevertheless, several reports stated that the spread of COVID-19 was negatively correlated with temperature and humidity [ 10 , 29 , 63 ]. The conflicting findings require further investigation. Moreover, other factors, such as population density, elderly population, cultural aspects, and health interventions, might potentially influence the epidemiology of the disease and necessitate research.

Governmental disinfection actions and guidelines

The COVID-19 is a severe health threat that is still spreading worldwide. The epidemiology of the SAR-CoV-2 virus might be affected by several factors, including meteorological conditions (temperature and humidity), population density, and healthcare quality, that permit it to spread rapidly [ 16 , 17 ]. Nevertheless, in 2020, no effective pharmaceutical interventions or vaccines were available for the diagnosis, treatment, and epidemic prevention against COVID-19 [ 73 , 82 ]. Consequently, after 2020 the governments globally have designed and executed non-pharmacological public health measures, such as lockdown, travel bans, social distancing, quarantine, public place closure, and public health actions, to curb the spread of COVID-19 infections and several studies have reported on the effects of these plans [ 13 , 83 ].

The COVID-19 is mainly spread via respiratory droplets from an infected person’s mouth or nose to another in close contact [ 84 ]. Accordingly, WHO and most governments worldwide have recommended wearing facemasks in public areas to curb the transmission of COVID-19. The facemasks would prevent individuals from breathing COVID-19-contaminated air [ 85 ]. Furthermore, the masks could hinder the transmission of the virus from an infected person as the exhaled air is trapped in droplets collected on the masks, suspending it in the atmosphere for longer. The WHO also recommended adopting a proper hand hygiene routine to prevent transmission and employing protective equipment, such as gloves and body covers, especially for health workers [ 86 ].

Besides wearing protective equipment, social distancing was also employed to control the Covid-19 outbreak [ 74 , 87 ]. Social distancing hinders the human-to-human transmission of the coronavirus in the form of droplets from the mouth and nose, as evidenced by the report from Sun and Zhai [ 88 ]. Conversely, Nair & Selvaraj [ 89 ] demonstrated that social distancing was less effective in communities and cultures where gatherings are the norm. Nonetheless, the issue could be addressed by educating the public and implementing social distancing policies, such as working from home and any form of plague treatment.

Infected persons, individuals who had contact with confirmed or suspected COVID-19 patients, and persons living in areas with high transmission rates were recommended to undergo quarantine by WHO. The quarantine could be implemented voluntarily or legally enforced by authorities and applicable to individuals, groups, or communities (community containment) [ 90 ]. A person under mandatory quarantine must stay in a place for a recommended 14-day period, based on the estimated incubation period of the SARS-CoV-2 [ 19 , 91 ]. According to Stasi et al. [ 92 ], 14-days period for mandatory quarantine it is presenting a clinical improvement after they found 5-day group and 10-day group can be decrease number of patient whose getting effect of COVID-19 from 64 to 54% respectively. This also proven by Ahmadi et al. [ 43 ] and Foad et al. [ 93 ], quarantining could reduce the transmission of COVID-19.

Lockdown and travel bans, especially in China, the centre of the coronavirus outbreak, reduced the infection rate and the correlation of domestic air traffic with COVID-19 cases [ 17 ]. The observations were supported by Sun & Zhai [ 88 ] and Sun et al. [ 94 ], who noted that travel restrictions diminished the number of COVID-19 reports by 75.70% compared to baseline scenarios without restrictions. Furthermore, example in Malaysia, lockdowns improved the air quality of polluted areas especially in primarily at main cities [ 95 ]. As additional, Martins et al. [ 70 ] measure the Human Development Index (HDI) with the specific of socio-economic variables as income, education and health. In their study, the income and education levels are the main relevant factors that affect the socio-economic.

A mandatory lockdown is an area under movement control as a preventive measure to stop the coronavirus from spreading to other areas. Numerous governments worldwide enforced the policy to restrict public movements outside their homes during the pandemic. Resultantly, human-to-human transmission of the virus was effectively reduced. The lockdown and movement control order were also suggested for individuals aged 80 and above or with low or compromised immunities, as these groups possess a higher risk of contracting the disease [ 44 ].

Governments still enforced movement orders even after the introduction of vaccines by Pfizer, Moderna, and Sinovac, as the vaccines only protect high-risk individuals from the worst effects of COVID-19. Consequently, in most countries, after receiving the first vaccine dose, individuals were allowed to resume life as normal but were still required to follow the standard operating procedures (SOP) outlined by the government.

The government attempted to balance preventing COVID-19 spread and recovering economic activities, for example, local businesses, maritime traders, shipping activities, oil and gas production and economic trades [ 22 , 96 ]. Nonetheless, the COVID-19 cases demonstrated an increasing trend during the summer due to the higher number of people travelling and on vacation, primarily to alleviate stress from lockdowns. Several new variants were discovered, including the Delta and Omicron strains, which spread in countries such as the USA and the United Kingdom. The high number of COVID-19 cases prompted the WHO to suggest booster doses to ensure full protection.

As mentioned in this manuscript, the COVID-19 still uncertain for any kind factors that can be affected on spreading of this virus. However, regarding many sources of COVID-19 study, the further assessment on this factor need to be continue to be sure, that we ready to facing probably in 10 years projection of solar minimum phase can be held in same situation for another pandemic.

The sun has an eleven-year cycle known as the solar cycle, related to its magnetic field, which controls the activities on its surface through sunspots. When the magnetic fields are active, numerous sunspots are formed on its surface, hence the sun produces more radiation energy emitted to the earth. The condition is termed solar maximum (see Fig.  6 , denoted by the yellow boxes). Alternatively, as the magnetic field of the sun weakens, the number of sunspots decreases, resulting in less radiation energy being emitted to the earth. The phenomenon is known as the solar minimum (see Fig. 6 , represented by the blue boxes).

The emergence and recurrence of pandemics every 5 years in relation to solar activities ( Source: www.swpc.noaa.gov/ ). Note: The yellow boxes indicate the solar maximum, while the blue boxes represent the solar minimum

The magnetic field of the sun protects the earth from cosmic or galactic cosmic rays emitted by supernova explosions, stars, and gamma-ray bursts [ 97 ]. Nevertheless, galactic cosmic rays could still reach the earth during the solar minimum, the least solar radiation energy period. In the 20th and early 21st centuries, several outbreaks of viral diseases that affected the respiratory system (pneumonia or influenza), namely the Spanish (1918–1919), Asian (1957–1958) and Hong Kong (1968) flu, were documented. Interestingly, the diseases that claimed numerous lives worldwide occurred at the peak of the solar maximum.

Figure  6 illustrates the correlation between the number of sunspots and disease outbreaks from 1975 to 2021, including COVID-19, that began to escalate in December 2019. Under the solar minimum conditions, the spread of Ebola (1976), H5N1 (1997–1998), H1N1 (2009), and COVID-19 (2019-2020) were documented, while the solar maximum phenomenon recorded SARS (2002) and H7N9 (2012–2013) or MERS outbreaks. Nonetheless, solar activity through the production of solar sunspots began to decline since the 22nd solar cycle. Accordingly, further studies are necessary to investigate the influence such solar variations could impart or not on pandemic development.

Despite the findings mentioned above, the sun and cosmic radiations could influence the distribution or outspread of disease-spreading viruses. The rays could kill the viruses via DNA destruction or influence their genetic mutations, which encourage growth and viral evolution. Nevertheless, the connection between radiation and the evolutionary process requires further study by specialists in the field it is become true or not.

The spread of viral diseases transpires naturally in our surroundings and occurs unnoticed by humans. According to records, the spread of pandemic diseases, including the Black Death (fourteenth century) and the Spanish flu (1919), was significantly influenced by the decline and peak of solar activities. Furthermore, in the past 20 years, various diseases related to the influenza virus have been recorded. According to the pattern observed, if all diseases were related to the solar cycle (solar maximum and minimum), the viral diseases would reoccur every 5 to 6 years since they first appeared between 1995 and 2020. Accordingly, the next pandemic might occur around 2024 or 2025 and need to have a proper study for prove these statements. Nonetheless, the activities on the surface of the sun have been weakening since the 23rd solar cycle and it can be proven later after the proper study can be make it.

The beginning of the COVID-19 spread, only several countries with the same winter climate with an average temperature of 5–11 °C and an average humidity rate of 47–79% located at latitudes 30–50 N reported cases. The areas included Wuhan distribution centres in China, the United Kingdom, France, Spain, South Korea, Japan, and the USA (see Fig.  5 ). Other than biological aspects, the higher number of confirmed cases recorded in colder environments was due to the human body secreting less lymphoproliferative hormone, leading to decreased immunogenicity effects and increased risk of infection [ 24 ]. Consequently, the virus could attack and rapidly infect humans during the period [ 1 , 54 ].

The lymphoproliferative response is a protective immune response that plays a vital role in protecting and eradicating infections and diseases. On the other hand, staying in warm conditions or being exposed to more sunlight would lower the risks of infection. According to Asyary and Veruswati [ 98 ], sunlight triggers vitamin D, which increases immunity and increases the recovery rates of infected individuals.

Researchers believe that viruses could survive in the environment for up to 3 to 4 years or even longer. The survival rate of the microorganisms is relatively high, which is related to their biological structures, adaptability on any surfaces, and transmission medium to spread diseases. Viruses possess simple protein structures, namely the spike, membrane, and envelope protein; therefore, when they enter living organisms (such as through the respiratory system), the viruses are easily transmitted.

Once they have entered a host, the viruses duplicate exponentially and swarm the lungs. Subsequently, after the targeted organs, such as the lungs, are invaded, the viruses attack the immune system and create confusion in protective cells to destroy healthy cells. The situation is still considered safe in younger and healthy individuals as their immune systems could differentiate and counter-attack the viruses, curing them. Nonetheless, in elders and individuals with several chronic diseases, most of their protective cells are dead, hence their immune system is forced to work hard to overcome the infection. Pneumonia and death tend to occur when the situation is overwhelming [ 85 ]. Consequently, the viruses are harmful to humans as they could multiply in a short period, enter the blood, and overrun the body.

The coronavirus could attach to surfaces without a host, including door knobs and steel and plastic materials. The microorganisms could survive alone, but virologists have yet to determine how long. If someone touches any surface with the virus, the individual would then be infected. The situation would worsen if the infected person contacted numerous people and became a super spreader. A super spreader does not exhibit any symptoms and continuously transmits the virus without realising it. An infected individual transmits the coronavirus via droplets from coughs or sneezes. Nevertheless, scientists have yet to determine if coronavirus is spread via airborne or droplets, hence requiring thorough evaluation [ 99 ].

The COVID-19 virus mutates over time, and it can be changing any times. Mutations alter the behaviour and genetic structure of the virus, resulting in a new strain. Numerous research have been conducted to procure vaccines and anti-viral medications, but mutations have led to evolutionary disadvantages. The novel strains are more infectious than the original ones. As of November 2020, approximately six new coronavirus strains have been detected, each displaying different transmission behaviours [ 100 ].

Recent studies demonstrated that the mutated viruses exhibit little variability, allowing scientists to produce viable vaccines [ 71 ]. Furthermore, different types of vaccines are manufactured by different countries, which could be advantageous. Currently, most countries also recommend booster doses to attain extra protection after receiving the mandatory two vaccine doses. In same time, the social and physical interactions between humans also necessitate to be aware.

The COVID-19 virus is primarily transmitted through droplets produced by an infected person. Accordingly, physical distancing, a one-metre minimum distance between individuals [ 19 ], and following the SOP might prevent or avoid spreading the disease. Moreover, self-quarantine, school closures, working from home, cancelling large events, limiting gatherings, and avoiding spending long periods in crowded places are essential strategies in enforcing physical distancing at a community level. The policies are essential precautions that could reduce the further spreading of coronavirus and break the chain of transmission.

Government support also need to control the spread of COVID-19 with the strict SOP. The SOP enforcement in public places would enhance adherence to the new practice among the public and the community, aiding in curbing disease transmission. Practising limited meetings and social gatherings, avoiding crowded places, workplace distancing, preventing non-necessary travels of high-risk family members, especially those with chronic disease, and adhering to the recommended SOP could reduce coronavirus outbreaks. Nonetheless, individual awareness is also necessary to achieve COVID-19 spread prevention.

Many researchers are focused on identifying the primary drivers of pandemic outbreaks. Seasonal, temperature, and humidity differences significantly impacted COVID-19 growth rate variations. It is crucial to highlight the potential link between the recurrence of pandemics every 5 years and solar activities, which can influence temperature and humidity variations. Notable variations in COVID-19 mortality rates were observed between northern and southern hemisphere countries, with the former having higher rates. One hypothesis suggests that populations in the northern hemisphere may receive insufficient sunlight to maintain optimal vitamin D levels during winter, possibly leading to higher mortality rates.

The first COVID-19 case was detected in Wuhan, China, which is in the northern hemisphere. The number of cases rapidly propagated in December during the winter season. At the time, the temperature in Wuhan was recorded at 13–18 °C. Accordingly, one theory proposes that the survival and transmission of the coronavirus were due to meteorological conditions, namely temperatures between 13 and 18 °C and 50–80% humidity.

Daily rainfall directly impacts humidity levels. The coronavirus exhibited superior survival rates in cold and dry conditions. Furthermore, transmissible gastroenteritis (TGEV) suspensions and possibly other coronaviruses remain viable longer in their airborne states, which are more reliably collected in low relative humidity than in high humidity. Consequently, summer rains would effectively reduce COVID-19 transmission in southern hemisphere regions.

In southern hemisphere regions, the summer seasons are accompanied by a high average temperature at the end and beginning of the year. Countries with temperatures exceeding 24 °C reported fewer infections. As temperatures rise from winter to summer, virus transmission is expected to decline. Nonetheless, the activities and transmission of the virus were expected to decrease during winter to summer transitions, when the countries would be warmer. The peak intensity of infections strongly depends on the level of seasonal transmissions.

Social distancing plays a critical role in preventing the overload of healthcare systems. Many respiratory pathogens, including those causing mild common cold-like syndromes, show seasonal fluctuations, often peaking in winter. This trend can be attributed to increased indoor crowding, school reopening, and climatic changes during autumn.

The spread of COVID-19 to neighbouring regions can be attributed to population interactions. Migration patterns, such as the movement from northern to southern regions during the warmer months, have significant epidemiological impacts. This trend mirrors the behavior of influenza pandemics where minor outbreaks in spring or summer are often followed by major waves in autumn or winter.

Availability of data and materials

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Abbreviations

Novel coronavirus

Coronavirus disease 2019

Deoxyribonucleic acid

Swine influenza

Influenza A virus subtype H5N1

Asian Lineage Avian Influenza A(H7N9) Virus

Middle East respiratory syndrome

Middle East respiratory syndrome Coronavirus

Particulate matter

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

RepOrting standards for Systematic Evidence Syntheses

Severe Acute Respiratory Syndrome

Severe Acute Respiratory Syndrome Coronavirus

Syndrome coronavirus 2

Systematic literature review

Standard operating procedure

Transmissible gastroenteritis Virus

United States of America

World Health Organization

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Menhat, M., Ariffin, E.H., Dong, W.S. et al. Rain, rain, go away, come again another day: do climate variations enhance the spread of COVID-19?. Global Health 20 , 43 (2024). https://doi.org/10.1186/s12992-024-01044-w

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Accepted : 22 April 2024

Published : 14 May 2024

DOI : https://doi.org/10.1186/s12992-024-01044-w

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Globalization and Health

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U.S. Tightens Rules on Risky Virus Research

A long-awaited new policy broadens the type of regulated viruses, bacteria, fungi and toxins, including those that could threaten crops and livestock.

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By Carl Zimmer and Benjamin Mueller

The White House has unveiled tighter rules for research on potentially dangerous microbes and toxins, in an effort to stave off laboratory accidents that could unleash a pandemic.

The new policy, published Monday evening, arrives after years of deliberations by an expert panel and a charged public debate over whether Covid arose from an animal market or a laboratory in China.

A number of researchers worried that the government had been too lax about lab safety in the past, with some even calling for the creation of an independent agency to make decisions about risky experiments that could allow viruses, bacteria or fungi to spread quickly between people or become more deadly. But others warned against creating restrictive rules that would stifle valuable research without making people safer.

The debate grew sharper during the pandemic, as politicians raised questions about the origin of Covid. Those who suggested it came from a lab raised concerns about studies that tweaked pathogens to make them more dangerous — sometimes known as “gain of function” research.

The new policy, which applies to research funded by the federal government, strengthens the government’s oversight by replacing a short list of dangerous pathogens with broad categories into which more pathogens might fall. The policy pays attention not only to human pathogens, but also those that could threaten crops and livestock. And it provides more details about the kinds of experiments that would draw the attention of government regulators.

The rules will take effect in a year, giving government agencies and departments time to update their guidance to meet the new requirements.

“It’s a big and important step forward,” said Dr. Tom Inglesby, the director of the Johns Hopkins Center for Health Security and a longtime proponent of stricter safety regulations. “I think this policy is what any reasonable member of the public would expect is in place in terms of oversight of the world’s most transmissible and lethal organisms.”

Still, the policy does not embrace the most aggressive proposals made by lab safety proponents, such as creating an independent regulatory agency. It also makes exemptions for certain types of research, including disease surveillance and vaccine development. And some parts of the policy are recommendations rather than government-enforced requirements.

“It’s a moderate shift in policy, with a number of more significant signals about how the White House expects the issue to be treated moving forward,” said Nicholas Evans, an ethicist at University of Massachusetts Lowell.

Experts have been waiting for the policy for more than a year. Still, some said they were surprised that it came out at such a politically fraught moment . “I wasn’t expecting anything, especially in an election year,” Dr. Evans said. “I’m pleasantly surprised.”

Under the new policy, scientists who want to carry out experiments will need to run their proposals past their universities or research institutions, which will to determine if the work poses a risk. Potentially dangerous proposals will then be reviewed by government agencies. The most scrutiny will go to experiments that could result in the most dangerous outcomes, such as those tweaking pathogens that could start a pandemic.

In a guidance document , the White House provided examples of research that would be expected to come under such scrutiny. In one case, they envisioned scientists trying to understand the evolutionary steps a pathogen needed to transmit more easily between humans. The researchers might try to produce a transmissible strain to study, for example, by repeatedly infecting human cells in petri dishes, allowing the pathogens to evolve more efficient ways to enter the cells.

Scientists who do not follow the new policy could become ineligible for federal funding for their work. Their entire institution may have its support for life science research cut off as well.

One of the weaknesses of existing policies is that they only apply to funding given out by the federal government. But for years , the National Institutes of Health and other government agencies have struggled with stagnant funding, leading some researchers to turn instead to private sources. In recent years, for example, crypto titans have poured money into pandemic prevention research.

The new policy does not give the government direct regulation of privately funded research. But it does say that research institutions that receive any federal money for life-science research should apply a similar oversight to scientists doing research with support from outside the government.

“This effectively limits them, as the N.I.H. does a lot of work everywhere in the world,” Dr. Evans said.

The new policy takes into account the advances in biotechnology that could lead to new risks. When pathogens become extinct, for example, they can be resurrected by recreating their genomes. Research on extinct pathogens will draw the highest levels of scrutiny.

Dr. Evans also noted that the new rules emphasize the risk that lab research can have on plants and animals. In the 20th century, the United States and Russia both carried out extensive research on crop-destroying pathogens such as wheat-killing fungi as part of their biological weapons programs. “It’s significant as a signal the White House is sending,” Dr. Evans said.

Marc Lipsitch, an epidemiologist at Harvard and a longtime critic of the government’s policy, gave the new one a grade of A minus. “I think it’s a lot clearer and more specific in many ways than the old guidance,” he said. But he was disappointed that the government will not provide detailed information to the public about the risky research it evaluates. “The transparency is far from transparent,” he said.

Scientists who have warned of the dangers of impeding useful virus research were also largely optimistic about the new rules.

Gigi Gronvall, a biosafety specialist at the Johns Hopkins Bloomberg School of Public Health, said the policy’s success would depend on how federal health officials interpreted it, but applauded the way it recognized the value of research needed during a crisis, such as the current bird flu outbreak .

“I was cautiously optimistic in reading through it,” she said of the policy. “It seems like the orientation is for it to be thoughtfully implemented so it doesn’t have a chilling effect on needed research.”

Anice Lowen, an influenza virologist at Emory University, said the expanded scope of the new policy was “reasonable.” She said, for instance, that the decision not to create an entirely new review body helped to alleviate concerns about how unwieldy the process might become.

Still, she said, ambiguities in the instructions for assessing risks in certain experiments made it difficult to know how different university and health officials would police them.

“I think there will be more reviews carried out, and more research will be slowed down because of it,” she said.

Carl Zimmer covers news about science for The Times and writes the Origins column . More about Carl Zimmer

Benjamin Mueller reports on health and medicine. He was previously a U.K. correspondent in London and a police reporter in New York. More about Benjamin Mueller

• Open access
• Published: 15 May 2024

Long COVID symptoms after 8-month recovery: persistent static lung hyperinflation associated with small airway dysfunction

• Po-Chun Lo 1 , 3 ,
• Jia-Yih Feng 1 , 2 , 4 ,
• Yi-Han Hsiao 1 , 2 ,
• Kang-Cheng Su 1 , 2 ,
• Kun-Ta Chou 1 , 2 ,
• Yuh-Min Chen 1 , 2 ,
• Hsin-Kuo Ko 1 , 2 &
• Diahn-Warng Perng 1 , 2 , 4  

Respiratory Research volume  25 , Article number:  209 ( 2024 ) Cite this article

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Limited research has investigated the relationship between small airway dysfunction (SAD) and static lung hyperinflation (SLH) in patients with post-acute sequelae of COVID-19 (PASC) especially dyspnea and fatigue.

64 patients with PASC were enrolled between July 2020 and December 2022 in a prospective observational cohort. Pulmonary function tests, impulse oscillometry (IOS), and symptom questionnaires were performed two, five and eight months after acute infection. Multivariable logistic regression models were used to test the association between SLH and patient-reported outcomes.

SLH prevalence was 53.1% (34/64), irrespective of COVID-19 severity. IOS parameters and circulating CD4/CD8 T-cell ratio were significantly correlated with residual volume to total lung capacity ratio (RV/TLC). Serum CD8 + T cell count was negatively correlated with forced expiratory volume in the first second (FEV 1 ) and forced vital capacity (FVC) with statistical significance. Of the patients who had SLH at baseline, 57% continued to have persistent SLH after eight months of recovery, with these patients tending to be older and having dyspnea and fatigue. Post-COVID dyspnea was significantly associated with SLH and IOS parameters R5-R20, and AX with adjusted odds ratios 12.4, 12.8 and 7.6 respectively. SLH was also significantly associated with fatigue.

SAD and a decreased serum CD4/CD8 ratio were associated with SLH in patients with PASC. SLH may persist after recovery from infection in a substantial proportion of patients. SAD and dysregulated T-cell immune response correlated with SLH may contribute to the development of dyspnea and fatigue in patients with PASC.

Post-acute sequelae of COVID-19 (PASC), also known as long COVID, is a syndrome that affects multiple organs after a SARS-CoV-2 infection. The World Health Organization defines PASC as involving symptoms that are present three months after SARS-CoV-2 infection, that have a duration of at least two months, and that cannot be explained by an alternative diagnosis [ 1 ]. Subramanian et al. analyzed a UK primary care database and reported that 62 symptoms were significantly associated with SARS-CoV-2 infection at 12 weeks in non-hospitalized adults [ 2 ]. On the basis of conservative models, the estimated global proportion of PASC was approximately 6.2–12.7% [ 3 , 4 ]. Notably, dyspnea, fatigue, and neurocognitive impairment are the most common symptoms reported in patients with PASC [ 5 , 6 , 7 ], and several systematic reviews have indicated that approximately 18–41% of the patients had breathlessness after COVID-19 [ 8 , 9 ]. Even patients with mild COVID-19 had an increased risk of persistent dyspnea and weakness at one-year follow-up [ 10 ]. Post-COVID-19 breathlessness significantly impairs recovery and reduces working capacity [ 6 ].

The mechanisms underlying post-COVID-19 breathlessness are not fully understood [ 8 , 11 ]. Post-COVID-19 breathlessness has been correlated with pulmonary function abnormalities [ 12 ], including restrictive ventilatory impairment and decreased lung diffusing capacity [ 5 , 12 , 13 ] in patients with SARS-CoV-2 infection 4–6 months after discharge from the hospital [ 14 ]. Cho et al. also identified small airway dysfunction (SAD), air-trapping, and static lung hyperinflation (SLH) [ 15 ] in patients with PASC; moreover, 88.1% of patients with PASC had abnormal impulse oscillometry (IOS) parameters [ 16 ]. IOS uses an oscillation technique in an effort-independent manner to measure airway resistance and reactance. We previously reported that respiratory reactance at 5 Hz (X5) can predict SLH in severe asthma with high sensitivity and specificity [ 17 ]. Clinically, dyspnea in patients with PASC is associated with abnormal lung function. However, whether SAD and SLH are associated with respiratory symptoms in PASC remains unclear.

We hypothesized that SARS-CoV-2 infection leads to SAD and contributes to SLH, which is associated with respiratory symptoms in patients with PASC. In this prospective observational cohort study, we determined the interrelationship between small airway function, SLH, and respiratory symptoms in patients with PASC eight months after SARS-CoV-2 infection.

Study design and participant selection

This single-center, prospective, observational cohort study was approved by the Institutional Review Board of Taipei Veterans General Hospital (VGHTPE-IRB No. 2020-07-011CC). We enrolled consenting adults aged over 20 years with COVID-19 confirmed through polymerase chain reaction who remained symptomatic for two months between July 2020 and December 2022. Eligible participants were referred to the post-acute COVID-19 clinic at Taipei Veterans General Hospital. We excluded participants with a history of chronic lung disease, including chronic obstructive pulmonary disease, asthma, bronchiectasis, lung cancer, pulmonary fibrosis, pulmonary tuberculosis, or any neuromuscular or spinal disease that affected lung function and small airway function.

We collected demographic information and conducted the following examinations at the first (two months after diagnosis), second (five months after diagnosis) and third (eight months after diagnosis) follow-up visits (visits 1, 2, and 3 respectively): (1) symptom assessment and administration of St. George’s Respiratory Questionnaire (SGRQ); (2) administration of spirometry, lung volume, diffusing capacity of the lung for carbon monoxide (DLCO), 6-min walk test (6MWT), and IOS; and (3) analysis of serum and plasma samples for CD4 + and CD8 + T-cell counts. The data collected at visit 1 were considered as baseline data for analysis.

Symptom assessment

At each visit, the participants with PASC were asked to complete a structured questionnaire, which collected data related to the following symptoms: fatigue, cough, dyspnea, phlegm, anosmia, dysgeusia, sleep disturbance, alopecia, myalgia, upper respiratory symptoms, nausea, headache, diarrhea, lightheadedness, palpitations, chest pain, brain fog, and skin rash.

Pulmonary function tests

Pulmonary function tests were performed according to the manufacturer’s recommendations and the American Thoracic Society and the European Respiratory Society (ERS) guidelines [ 18 ] using the Spiro Medics system 2130 (SensorMedics, Anaheim, CA, USA). SLH was defined as a residual volume to total lung capacity ratio (RV/TLC) ≥ 40% [ 19 ]. Impulse oscillometry (Jaeger MS-IOS, Germany) was performed before spirometry, and measurements were performed in triplicate according to the ERS technical standards [ 20 ]. IOS have better sensitivity than forced expiratory flow between 25% and 75% (FEF25%-75%) to detect SAD [ 21 ]. The cutoffs for SAD were the difference between resistance at 5 and 20 Hz (R5-R20) greater than 0.07 kPa/(L/s), the area under the reactance curve between 5 Hz and the resonant frequency (AX) greater than 0.44 kPa/L, X5 less than − 0.12 kPa/(L/s), or resonant frequency (Fres) greater than 14.14 Hz [ 21 ]. Exercise-induced hypoxemia (EIH) was defined as a drop of 4% in SpO2 during exercise [ 22 ].

Continuous variables are reported as median and interquartile range (IQR) or mean with standard error measures. Categorical variables are reported as counts and percentages. Data were tested for normality using the Shapiro-Wilk before analysis. We compared the data between the ambulatory, hospitalized, and intensive care unit (ICU) groups by using the Kruskal–Wallis test for nonnormally distributed variables and the chi-square test for categorical variables. Comparisons between the SLH and non-SLH groups were performed using the Mann–Whitney U test for continuous variables and Fisher’s exact test for categorical variables. Variables were compared between visits 1, 2, and 3 by using paired-samples t-tests and McNemar’s test. Spearman’s rank correlation test was used to determine associations between the RV/TLC ratio and clinical variables. Univariable and multivariable logistic regression analyses were used to test associations between symptoms, the presence of SLH, and clinical variables. Clinically meaningful variables and those with p  < 0.15 in the univariable logistic regression were subsequently analyzed in the multivariable model. R5-R20, AX, Fres, and X5 were highly correlated and were found to have multicollinearity [ 23 ]. Therefore, a separate multivariate logistic regression model was used for each IOS parameter for predicting long COVID symptoms, and these were subsequently adjusted for age, sex, smoking, COVID-19 severity, and body mass index (BMI). Seventeen participants missed their visits during the follow-up period, and we applied listwise deletion in the logistic regression model for the association between SLH and long COVID symptoms at visit 3. P  values < 0.05 were taken as statistically significant. All data were analyzed using SPSS for Windows (version 25.0, IBM, Armonk, NY, USA) and plotted using OriginPro 2022 (OriginLab Corporation, version SR1, Northampton, MA, USA) and GraphPad Prism (version 9; La Jolla, CA, USA).

Baseline characteristics of the study population

We included 64 participants with PASC (mean age, 56.7 years; 28 men) who were ambulatory ( n  = 40), hospitalized ( n  = 14), or admitted to the ICU ( n  = 10; Fig.  1 ). Their demographic and baseline clinical characteristics are presented in Table  1 . The mean age and median BMI were not significantly different between the groups. SLH prevalence was 53.1% (34/64), irrespective of the COVID-19 severity. The PASC patients exhibited 10.9% (7/64) with FEV1% predicted < 80%, 87.5% (56/64) with DLCO%pred < 80% and no airway obstruction (FEV1/FVC < 0.7) (Table  2 ). The percentage of abnormality of IOS parameters meeting our SAD cutoff were 43.8% in R5-R20, 56.3% in AX, 54.7% in X5, and 57.8% in Fres (Table  2 ). The clinical symptoms of the patients with PASC are summarized in Supplementary Table A , with the most commonly reported being fatigue (57.8%), followed by brain fog (50.0%) and respiratory symptoms, including mucus secretion (46.9%), cough (43.8%), and dyspnea (43.8%).

Study flowchart

A total of 64 participants with PASC were enrolled in this prospective cohort study. We stratified the participants based on their acute phase severity into ambulatory ( n  = 40), hospitalized ( n  = 14), and ICU ( n  = 10) groups. Participants were followed up at visits 1 (2 months after diagnosis), 2 (5 months after diagnosis) and 3 (8 months after diagnosis). 17 participants missed their visits during the follow-up period. COPD = chronic obstructive pulmonary disease, ICU = intensive care unit, PASC = post-acute sequelae of COVID-19, PFT = pulmonary function test, TB = tuberculosis

Clinical features of patients with PASC with and without SLH

The patients with PASC and SLH were older and had lower BMI, and RV/TLC ratio than those without SLH (Table  3 ). However, FEV1 (% pred), FVC (% pred), total lung capacity (TLC % pred), residual volume (RV % pred), EIH and COVID-19 severity were not significantly different between the groups. Compared to patients without SLH, those with SLH had significantly higher values of the IOS parameters R5-R20, AX, and Fres, lower values of CD4/CD8 T cell ratio and higher prevalence of fatigue and dyspnea. The percentage of SAD was significantly higher in the SLH group compared to the non-SLH group.

Correlation between RV/TLC, IOS parameters, and serum T-cell counts

All IOS parameters (R5-R20, X5, AX, and Fres) and DLCO were significantly correlated with RV/TLC (Fig.  2 ). Serum T-cell counts and CD4/CD8 ratios are presented in Table  2 . The decrease in CD4 + and increase in CD8 + T-cell counts reduced the CD4/CD8 ratio in the SLH group, which was significantly correlated with RV/TLC (Fig.  2 ). Furthermore, CD8 + T-cell counts, but not CD4 + T-cell counts, were negatively correlated with FEV1 (% pred, p  = 0.006) and FVC (% pred, p  = 0.011).

Correlations between RV/TLC, impulse oscillometry parameters, and serum T-cell count

Correlations are presented using a scatter plot and marginal histogram. ( A ) Resonant frequency (Fres), ( B ) difference between resistance at 5 Hz and 20 Hz (R5-R20), ( C ) reactance at 5 Hz (X5), ( D ) area under the reactance curve between 5 Hz and resonant frequency (AX), ( E ) DLCO%, ( F ) CD4 + T-cell count, ( G ) CD8 + T-cell count, and ( H ) CD4/CD8 T-cell count. DLCO = diffusing capacity of the lung for carbon monoxide, RV = residual volume, TLC = total lung capacity

Dynamic changes of lung function and long COVID symptoms

Six months (V3) after visit 1, the lung function parameters of FEV1, FVC, TLC, RV/TLC, DLCO % pred and SGRQ scores significantly improved (Table  4 ). However, FEF25%-75%, IOS parameters (R5-R20, X5, AX, and Fres), percentage of abnormality in IOS parameters and the prevalence of dyspnea, cough and mucin production did not significantly change between visits. However, the prevalence of SLH and fatigue showed a significant decrease between PASC and 6 months after PASC (53.1% vs. 37.0%, p  = 0.003; 57.8% vs. 40.4%, p  = 0.019, respectively). To explore the transition of SLH in patients with PASC between visits, a Sankey diagram is presented in Fig.  3 , illustrating 37% of the patients (17/46) were SLH. Among the patients with SLH, 57% (16/28) had persistence of SLH, and 43% (12/28) experienced recovery. 6% of patients without SLH (1/18) developed lung hyperinflation between visits. Patients with persistent SLH were characterized by older age, a higher prevalence of dyspnea and fatigue, R5-R20 > 0.07 kPa/(L/S), and a lower serum B cell count at visit 1 (supplementary Table B ).

Sankey diagrams for SLH transition

Flows are color-coded as the following state: light blue flows have persistent SLH at the visit 3, light green flows have a recovery to non-SLH at the visit 3, yellow flows revert to SLH at the visit 3, and blue flows have non-SLH at the visit 3. The numbers in the flow are numbers of participants. SAD = small airway dysfunction, SLH = static lung hyperinflation, V = visit

Relationship between clinical variables, dyspnea, and fatigue at visits 2 and 3

The results of the multivariable logistic regression for associations between clinical variables, dyspnea, and fatigue at visits 2 and 3 are presented in Fig.  4 . SLH was the most crucial factor for dyspnea development at both visits (adjusted odds ratio [aOR]: 9.73; 95% confidence interval [CI]: 1.87–50.65; p  = 0.007 at visit 2 and aOR: 12.36; 95% CI: 1.34-114.32; p  = 0.027 at visit 3) (Fig.  4 A, C) and fatigue (aOR: 11.59; 95% CI: 2.23–60.41; p  = 0.004 at visit 2 and aOR: 5.94; 95% CI: 1.01–35.07; p  = 0.049 at visit 3) (Fig.  4 B, D).

SLH predicts dyspnea and fatigue 5 and 8 months after recovery

The logistic regression analysis at 5 and 8 months post recovery is displayed in a forest plot with adjusted odds ratios (aORs) and 95% confidence intervals (CIs) for ( A ) dyspnea visit 2, ( C ) dyspnea at visit 3, ( B ) fatigue at visit 2, and ( D ) fatigue at visit 3. The cutoffs for R5-R20 and AX was 0.07 kPa/(L/s) and 0.44 kPa/L, respectively. Orange labels indicate the significant predictors of symptoms based on the 95% CI of the regression model. AX = area under reactance curve between 5 Hz and resonant frequency, DLCO = diffusing capacity of the lung for carbon monoxide, FEF25%-75% = forced expiratory flow at 25% to 75% of FVC, FEV1 = forced expiratory volume in 1 s, FVC = forced vital capacity, SGRQ = St George’s Respiratory Questionnaire, SLH = static lung hyperinflation, V = visit, X5 = reactance at 5 Hz

At visit 3, both R5-R20 greater than 0.07 kPa/(L/s) and AX greater than 0.44 kPa/L showed significant associations with dyspnea after adjusting for age, sex, severity of infection, history of smoking, and BMI (aOR for R5-R20: 12.84, 95% CI: 1.31-125.71, p  = 0.028; aOR for AX: 7.60, 95% CI: 1.14–50.48, p  = 0.036) (Fig.  4 A, C). Furthermore, older age was also significantly associated with dyspnea, but lung function parameters or EIH did not predict the development of dyspnea. Detailed analysis of clinical variables associated with dyspnea and fatigue using logistic regression is presented in Supplementary Tables C1 – C4 . 22% (14/64) patients received bronchodilator treatment during the cohort study. We categorized PASC patients into those with and without the usage of bronchodilators and compared pulmonary function, IOS parameters, and symptoms between visits. Improvement in FEV1%pred and FEV1/FVC was observed in both groups. However, IOS parameters, the percentage of abnormality of IOS parameters, or dyspnea did not improve in either group (see supplement Table D ). Univariable logistic regression analyses were further employed to assess the associations between long COVID dyspnea and fatigue, and the usage of bronchodilators. The insignificant result indicated that the usage of bronchodilators does not impact long COVID dyspnea and fatigue (details are provided in supplement Table C1 – 4 ).

In this prospective observational study, we demonstrated that long COVID symptoms—dyspnea and fatigue—are independently associated with SLH, which is correlated with small airway resistance and reactance (R5-R20, X5, AX, and Fres) measured using the IOS. In addition, peripheral airways resistance and reactance, measured by R5-R20 and AX, respectively are associated with post-COVID dyspnea. SLH persistence significantly increased the risk of dyspnea and fatigue after COVID-19. The CD4/CD8 ratio was significantly negatively correlated with RV/TLC. The results of our study indicated that SAD and dysregulated T-cell immune response to SARS-CoV-2 infection are independently associated with SLH, significantly contributing to the development of dyspnea and fatigue in patients with PASC.

COVID-19 patients who develop PASC have been reported to have a high prevalence of SAD and air trapping (58%), as confirmed by using CT images [ 15 ]. In patients with severe asthma, we previously reported that SAD is associated with SLH and that airway reactance X5 predicts SLH development with a high probability [ 17 ]. In patients with chronic obstructive pulmonary disease, reactance, Fres, and AX were better than resistance parameters in stratifying degree of air trapping and were significantly correlated with RV/TLC [ 24 , 25 ]. IOS is more sensitive than spirometry in detecting SAD in asthma patients without fixed airway obstruction [ 26 ]. Additionally, IOS demonstrates greater sensitivity in detecting SAD compared to FEF25%-75% in symptomatic patients with preserved pulmonary function [ 21 ]. In this study, 53.1% of patients with PASC had SLH, with a higher prevalence of dyspnea (76.5%), fatigue (61.8%), and SAD (65.6%). We found that SAD, defined by FEF25–75% less than 65%, was observed in 12.5% of our study participants, compared to 65.6% defined by IOS parameters. The result may suggest that IOS may be more sensitive in detecting SAD compared to using FEF25–75% in PASC patients with preserved lung function. Further, SLH was significantly correlated with SAD in patients with PASC. Our results and those of previous studies exploring the relationship between SAD and SLH development in airway and infectious lung diseases provide a new focus on SAD management.

The etiologies and mechanisms underlying the development of PASC remain unknown. Previous studies have reported inconsistent results regarding the association of post-COVID breathlessness with impaired lung function and a reduction of DLCO [ 8 ]. In patients with PASC, lung function and DLCO recovered between 6 months and 1 year, but dyspnea persisted [ 27 ]. In our study, impaired lung function and reduction of DLCO were not found to be significantly associated with post-COVID dyspnea. We have also demonstrated a significant improvement in lung function and DLCO after an 8-month recovery period. In survivors of COVID-19, quantitative analysis of expiratory chest CT indicated the presence of air-trapping, which was highly correlated with the RV/TLC ratio, and air-trapping persisted for more than 200 days after diagnosis [ 15 ]. Here, we observed that IOS parameter, AX, was only moderately correlated with the RV/TLC ratio, and was not significantly correlated with dyspnea at visit 2. Our data indicated that SLH was the major contributor and independent predictor of clinical symptoms, dyspnea, and fatigue in patients with PASC. We also demonstrated a significant association between SAD measured by R5-R20 and AX and post-COVID breathlessness. Despite a significant improvement in lung function, small airways did not show recovery after 8 months. Our findings reveal a possible new mechanism for post-COVID breathlessness. Air-trapping in smokers with preserved lung function was associated with higher all-cause mortality and adverse respiratory outcomes, leading to increased hospital admissions in the future [ 28 ]. Patients with SAD were more likely to report respiratory symptoms such as dyspnea, cough, and chronic phlegm, even if they had never smoked [ 21 , 29 ]. Additionally, SAD in patients with COPD or asthma was found to be associated with more exacerbations, poorer disease control, and increased dyspnea [ 23 , 30 , 31 ]. Some patients recovered from lung hyperinflation between two and eight months after COVID-19 infection, whereas a small proportion of those without SLH developed lung hyperinflation. The reason for the progressive deterioration of SAD leading to SLH in patients with PASC remains unclear. SARS-CoV can damage alveolar pneumocytes and form fibrogranulation tissue in small airways and airspaces [ 32 ]. Similar to SARS-CoV, SARS-CoV-2 uses angiotensin-converting enzyme 2 for host cell entry [ 33 ], and this protein is more abundant in pulmonary alveoli and small airways than in large airways, as indicated in a study using single-cell analysis data [ 34 , 35 ] Mycoplasma pneumoniae , Haemophilus influenzae , influenza virus, and rhinovirus can target small airways and trigger progressive bronchiolitis [ 36 , 37 ]. Thus, the persistent SAD in patients with PASC may be attributed to a direct SARS-CoV-2 infection over the small airways. Whether SARS-CoV-2 can target the small airways and whether the persistent dysregulated immune reaction leads to SAD warrant further investigation.

A low CD4/CD8 ratio (< 1.5) may reflect immune senescence and may be associated with various disease entities [ 38 ]. Zhang et al. reported that the CD4/CD8 ratio was not significantly different between patients with critical and mild COVID-19 [ 39 ]. This phenomenon was also observed in the current study. The relative proportions of circulating CD4 + T-cell counts decreased significantly after SARS-CoV-2 infection, while the circulating CD8 + T-cell counts increased [ 40 ]. The interaction between alveolar macrophages and T cells may drive persistent alveolar inflammation [ 41 ]. Pulmonary DNA vaccination can induce CD8 + T-cell generation to mediate protective antiviral immunity [ 42 ]. Further, dysregulated respiratory CD8 + T-cell responses have been associated with the development of lung function impairment after acute COVID-19 [ 43 , 44 ]. Our data indicated that a decreased serum CD4/CD8 ratio was independently associated with SLH. Taken together, a decreased CD4/CD8 ratio because of a reduced CD4 + T-cell count and increased CD8 + T-cell count may cause SAD and, eventually, SLH. Further research is warranted to explore the relationship between dysregulated CD4/CD8 immune response, SAD, and SLH development in patients with PASC.

This study has several limitations. First, lung function parameters were not obtained from participants prior to SARS-COV-2 infection. To minimize possible confounders such as SAD and lung function abnormalities, participants with chronic lung disease or lung cancer were excluded. Second, participants were enrolled primarily during the pandemic of the delta variant of COVID-19, and most participants did not receive the vaccine for COVID-19. Antiviral agents were all used in our participants during the acute infection phase (2 months before data collection at visit 1). Due to the small sample size, we were unable to properly group participants with different antiviral agent, which prevented us from conducting an analysis on their effects. It is worth noting that a recently published randomized controlled trial (RCT) did not demonstrate long-term benefits for remdesivir in patients hospitalized due to COVID-19 [ 45 ]. The effectiveness of Nirmatrelvir–Ritonavir against long COVID remains inconclusive [ 46 , 47 , 48 ]. Molnupiravir treatment reduces the risk of long COVID in high-risk patients but does not decrease the risk of dyspnea or cough [ 49 ]. Thus, we could not evaluate the effects of vaccine immunization, anti-viral agent and subsequent immune reactions on the small airways of participants during the study period. Third, the number of patients in each group was relatively small. Therefore, the generalizability of our findings may be limited. Fourth, The Global Lung Function Initiative (GLI) reference equations for static lung volume indicate that the RV/TLC ratio increases with age in individuals over 40 years old [ 50 ]. Currently, in Taiwan, we do not have the upper limit of normal (ULN) for RV/TLC ratios, and the GLI reference equations are designed to fit individuals of European ancestry. Using the absolute RV/TLC ratio rather than the ULN may lead to misclassification of patients. Finally, the follow-up period was insufficient to determine when, if at all, the SAD would return to normal and whether managing lung hyperinflation could improve clinical symptoms, dyspnea, and fatigue in patients with PASC. Prospective studies with longer follow-up periods and treatment of SLH are urgently needed.

In conclusion, SAD measured using IOS and a decreased serum CD4/CD8 ratio correlated with SLH development in patients with PASC. SLH was common among patients with PASC, irrespective of COVID-19 severity, and was independently associated with the long COVID symptoms of dyspnea and fatigue. Additionally, SAD was significantly associated with post-COVID breathlessness. These findings suggest that SLH may persist in some patients even after recovering from acute SARS-CoV-2 infection. Therefore, future studies should focus on exploring the underlying mechanisms of SAD in patients with PASC to develop targeted therapeutic interventions. Furthermore, considering the persistence of SAD in association with SLH, long-term assessments of patients with PASC are warranted to better understand and manage their respiratory health.

Data availability

We cannot share individual-level data owing to data protection rules.

Abbreviations

Adjusted odds ratio

Area under reactance curve between 5 Hz and resonant frequency

Body mass index

Confidence interval

Diffusing capacity of the lung for carbon monoxide

Forced expiratory volume in the first second

Resonant frequency

Forced vital capacity

Impulse oscillometry

• Post-acute sequelae of COVID-19

Residual volume to total lung capacity ratio

R20 Difference between resistance at 5 and 20 Hz

• Small airway dysfunction

CoV-2 Severe acute respiratory syndrome coronavirus 2

St George’s Respiratory Questionnaire

• Static lung hyperinflation

Respiratory reactance at 5 Hz

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Acknowledgements

The authors thank all the healthcare workers of the post-acute COVID-19 clinics at Taipei Veterans General Hospital for their valuable contribution to patient care.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Jia-Yih Feng, Yi-Han Hsiao, Kang-Cheng Su, Kun-Ta Chou, Yuh-Min Chen, Hsin-Kuo Ko & Diahn-Warng Perng

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Conception and design: All authors contributed to the design of the study. P.C.L., H.K.K., and D.W.P. acquired the data. All authors directed the analyses, which P.C.L. conducted with supervision from D.W.P. P.C.L., H.K.K., and D.W.P. drafted the manuscript. All the authors contributed to the discussion and interpretation of the results, which secured the intellectual content of the manuscript. All authors have approved the final version of the manuscript for submission.

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Lo, PC., Feng, JY., Hsiao, YH. et al. Long COVID symptoms after 8-month recovery: persistent static lung hyperinflation associated with small airway dysfunction. Respir Res 25 , 209 (2024). https://doi.org/10.1186/s12931-024-02830-1

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Today, the European Commission published a study aimed at improving access to and reuse of research results, including publications and data for scientific purposes. This marks a significant step under the European Research Area Policy Agenda 2022-2024 on an EU copyright and data legislative and regulatory framework fit for research. 

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• Published: 18 June 2020

Advances in depression research: second special issue, 2020, with highlights on biological mechanisms, clinical features, co-morbidity, genetics, imaging, and treatment

• Julio Licinio 1 &
• Ma-Li Wong 1  

Molecular Psychiatry volume  25 ,  pages 1356–1360 ( 2020 ) Cite this article

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The current speed of progress in depression research is simply remarkable. We have therefore been able to create a second special issue of Molecular Psychiatry , 2020, focused on depression, with highlights on mechanisms, genetics, clinical features, co-morbidity, imaging, and treatment. We are also very proud to present in this issue a seminal paper by Chottekalapanda et al., which represents some of the last work conducted by the late Nobel Laureate Paul Greengard [ 1 ]. This brings to four the number of papers co-authored by Paul Greengard and published in our two 2020 depression special issues [ 1 , 2 , 3 , 4 ].

The research content of this special depression issue starts with Chottekalapanda et al.’s outstanding contribution aimed at determining whether neuroadaptive processes induced by antidepressants are modulated by the regulation of specific gene expression programs [ 1 ]. That team identified a transcriptional program regulated by activator protein-1 (AP-1) complex, formed by c-Fos and c-Jun that is selectively activated prior to the onset of the chronic SSRI response. The AP-1 transcriptional program modulated the expression of key neuronal remodeling genes, including S100a10 (p11), linking neuronal plasticity to the antidepressant response. Moreover, they found that AP-1 function is required for the antidepressant effect in vivo. Furthermore, they demonstrated how neurochemical pathways of BDNF and FGF2, through the MAPK, PI3K, and JNK cascades, regulate AP-1 function to mediate the beneficial effects of the antidepressant response. This newly identified molecular network provides “a new avenue that could be used to accelerate or potentiate antidepressant responses by triggering neuroplasticity.”

A superb paper by Schouten et al. showed that oscillations of glucocorticoid hormones (GC) preserve a population of adult hippocampal neural stem cells in the aging brain [ 5 ]. Moreover, major depressive disorder (MDD) is characterized by alterations in GC-related rhythms [ 6 , 7 ]. GC regulate neural stem/precursor cells (NSPC) proliferation [ 8 , 9 ]. The adrenals secrete GC in ultradian pulses that result in a circadian rhythm. GC oscillations control cell cycle progression and induce specific genome-wide DNA methylation profiles. Schouten et al. studied primary hippocampal NSPC cultures and showed that GC oscillations induced lasting changes in the methylation state of a group of gene promoters associated with cell cycle regulation and the canonical Wnt signaling pathway. Furthermore, in a mouse model of accelerated aging, they showed that disruption of GC oscillations induced lasting changes in dendritic complexity, spine numbers and morphology of newborn granule neurons. Their results indicate that GC oscillations preserve a population of GR-expressing NSPC during aging, preventing their activation possibly by epigenetic programming through methylation of specific gene promoters. These important observations suggest a novel mechanism mediated by GC that controls NSPC proliferation and preserves a dormant NSPC pool, possibly contributing to neuroplasticity reserve in the aging brain.

MDD has a critical interface with addiction and suicide, which is of immense clinical and research importance [ 10 ]. Peciña et al. have reviewed a growing body of research indicating that the endogenous opioid system is directly involved in the regulation of mood and is dysregulated in MDD [ 11 ]. Halikere et al. provide evidence that addiction associated N40D mu-opioid receptor variant modulates synaptic function in human neurons [ 12 ].

Two papers by Amare et al. and Coleman et al. examine different genetic substrates for MDD, identifying novel depression-related loci as well as studying the interface with trauma [ 13 , 14 ].

The dissection of MDD clinical phenotypes, including their interface with other illnesses is a topic of several articles in this special issue. Belvederi Murri et al. examined the symptom network structure of depressive symptoms in late-life in a large European population in the 19 country Survey of Health, Ageing, and Retirement in Europe (SHARE) (mean age 74 years, 59% females, n  = 8557) [ 15 ]. They showed that the highest values of centrality were in the symptoms of death wishes, depressed mood, loss of interest, and pessimism. Another article focused on a specific feature of MDD, namely changes in appetite. Simmons et al. aimed at explaining why some individuals lose their appetite when they become depressed, while others eat more, and brought together data on neuroimaging, salivary cortisol, and blood markers of inflammation and metabolism [ 16 ]. Depressed participants experiencing decreased appetite had higher cortisol levels than other subjects, and their cortisol values correlated inversely with the ventral striatal response to food cues. In contrast, depressed participants experiencing increased appetite exhibited marked immunometabolic dysregulation, with higher insulin, insulin resistance, leptin, c-reactive protein (CRP), interleukin 1 receptor antagonist (IL-1RA), and IL-6, and lower ghrelin than subjects in other groups, and the magnitude of their insulin resistance correlated positively with the insula response to food cues. Their findings support the existence of pathophysiologically distinct depression subtypes for which the direction of appetite change may be an easily measured behavioral marker.

Mulugeta et al. studied the association between major depressive disorder and multiple disease outcomes in the UK Biobank ( n  = 337,536) [ 17 ]. They performed hypothesis-free phenome-wide association analyses between MDD genetic risk score (GRS) and 925 disease outcomes. MDD was associated with several inflammatory and hemorrhagic gastrointestinal diseases, and intestinal E. coli infections. MDD was also associated with disorders of lipid metabolism and ischemic heart disease. Their results indicated a causal link between MDD and a broad range of diseases, suggesting a notable burden of co-morbidity. The authors concluded that “early detection and management of MDD is important, and treatment strategies should be selected to also minimize the risk of related co-morbidities.” Further information on the shared mechanisms between coronary heart disease and depression in the UK Biobank ( n  = 367,703) was explored by Khandaker et al. [ 18 ]. They showed that family history of heart disease was associated with a 20% increase in depression risk; however, a genetic risk score that is strongly associated with CHD risk was not associated with depression. Their data indicate that comorbidity between depression and CHD arises largely from shared environmental factors.

In a systematic review and meta-analysis of cohort studies, Wang et al. examined the interface of depression and anxiety in relation to cancer incidence and mortality [ 19 ]. Their analyses suggest that depression and anxiety may have an etiologic role and prognostic impact on cancer, although there is potential reverse causality.

Several papers in this issue examine imaging in MDD, either to unravel the underlying disease processes or to identify imaging biomarkers of treatment response. Let us first look at the studies focused on elucidating brain circuitry alterations in MDD. Arterial spin labeling (ASL) was used by Cooper et al. to measure cerebral blood flow (CBF; perfusion) in order to discover and replicate alterations in CBF in MDD [ 20 ]. Their analyses revealed reduced relative CBF (rCBF) in the right parahippocampus, thalamus, fusiform, and middle temporal gyri, as well as the left and right insula, for those with MDD. They also revealed increased rCBF in MDD in both the left and the right inferior parietal lobule, including the supramarginal and angular gyri. According to the authors, “these results (1) provide reliable evidence for ASL in detecting differences in perfusion for multiple brain regions thought to be important in MDD, and (2) highlight the potential role of using perfusion as a biosignature of MDD.” Further data on imaging in MDD was provided by a coordinated analysis across 20 international cohorts in the ENIGMA MDD working group. In that paper, van Velzen et al. showed that in a coordinated and harmonized multisite diffusion tensor imaging study there were subtle, but widespread differences in white matter microstructure in adult MDD, which may suggest structural disconnectivity [ 21 ].

Four articles in this special issue examine imaging biomarkers of treatment response. Greenberg et al. studied reward-related ventral striatal activity and differential response to sertraline versus placebo in depressed using functional magnetic resonance imaging while performing a reward task [ 22 ]. They found that ventral striatum (VS) dynamic response to reward expectancy (expected outcome value) and prediction error (difference between expected and actual outcome), likely reflecting serotonergic and dopaminergic deficits, was associated with better response to sertraline than placebo. Their conclusion was that treatment measures of reward-related VS activity may serve as objective neural markers to advance efforts to personalize interventions by guiding individual-level choice of antidepressant treatment. Utilizing whole-brain functional connectivity analysis to identify neural signatures of remission following antidepressant treatment, and to identify connectomic predictors of treatment response, Korgaonkar et al. showed that intrinsic connectomes are a predictive biomarker of remission in major depressive disorder [ 23 ]. Based on their results that team proposed that increased functional connectivity within and between large-scale intrinsic brain networks may characterize acute recovery with antidepressants in depression. Repple et al. created connectome matrices via a combination of T1-weighted magnetic resonance imaging (MRI) and tractography methods based on diffusion-weighted imaging severity of current depression and remission status in 464 MDD patients and 432 healthy controls [ 24 ]. Reduced global fractional anisotropy (FA) was observed specifically in acute depressed patients compared to fully remitted patients and healthy controls. Within the MDD patients, FA in a subnetwork including frontal, temporal, insular, and parietal nodes was negatively associated with symptom intensity, an effect remaining when correcting for lifetime disease severity. Their findings provide new evidence of MDD to be associated with structural, yet dynamic, state-dependent connectome alterations, which covary with current disease severity and remission status after a depressive episode. The effects of electroconvulsive therapy (ECT), the most effective treatment for depression, on the dentate gyrus (DG) were studied by Nuninga et al. through an optimized MRI scan at 7-tesla field strength, allowing sensitive investigation of hippocampal subfields [ 25 , 26 ]. They documented a large and significant increase in DG volume after ECT, while other hippocampal subfields were unaffected. Furthermore, an increase in DG volume was related to a decrease in depression scores, and baseline DG volume predicted clinical response. These findings suggest that the volume change of the DG is related to the antidepressant properties of ECT, possibly reflecting neurogenesis.

Three articles report new directions for antidepressant therapeutics. Papakostas et al. presented the results of a promising phase 2, double-blind, placebo-controlled study of NSI-189 phosphate, a novel neurogenic compound, in MDD patients [ 27 ]. As the endogenous opioid system is thought to play an important role in the regulation of mood, Fava et al. studied the buprenorphine/samidorphan combination as an investigational opioid system modulator for adjunctive treatment of MDD in two phase 3, randomized, double-blind, placebo-controlled studies that utilized the same sequential parallel-comparison design [ 28 ]. One of the studies achieved the primary endpoint, namely change from baseline in Montgomery–Åsberg Depression Rating Scale (MADRS)-10 at week 5 versus placebo) and the other study did not achieve the primary endpoint. However, the pooled analysis of the two studies demonstrated consistently greater reduction in the MADRS-10 scores from baseline versus placebo at multiple timepoints, including end of treatment. These data provide cautious optimism and support further controlled trials for this potential new treatment option for patients with MDD who have an inadequate response to currently available antidepressants. Fava et al. also report the results of a double-blind, placebo-controlled, dose-ranging trial of intravenous (IV) ketamine as adjunctive therapy in treatment-resistant depression, using four doses of ketamine and a control [ 29 , 30 ]. They show that there was evidence for the efficacy of the two higher doses of IV ketamine and no clear or consistent evidence for clinically meaningful efficacy of the two lower doses studied.

Overall, in this issue, immense progress in depression research is provided by outstanding studies that highlight advances in our understanding of MDD biology, clinical features, co-morbidity, genetics, brain imaging (including imaging biomarkers), and treatment. Building on the groundbreaking articles from our previous 2020 special issues on stress and behavior [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 ] and on depression [ 2 , 3 , 4 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 ], we are proud that the stunning progress presented here found its home in our pages. From inception in 1996, we have aimed at making Molecular Psychiatry promote the integration of molecular medicine and clinical psychiatry [ 63 ]. It is particularly rewarding to see that goal achieved so spectacularly in this second 2020 special issue on MDD, a disorder of gene-environment interactions that represents a pressing public health challenge, with an ever increasing impact on society [ 64 , 65 , 66 ]. We are privileged to have in these two 2020 depression special issues four remarkable papers from Paul Greengard’s teams that provide substantial new data on the mechanisms of antidepressant action [ 1 , 2 , 3 , 4 ]. Such profound advances in basic science are needed to facilitate and guide future translational efforts needed to advance therapeutics [ 67 , 68 ].

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Licinio, J., Wong, ML. Advances in depression research: second special issue, 2020, with highlights on biological mechanisms, clinical features, co-morbidity, genetics, imaging, and treatment. Mol Psychiatry 25 , 1356–1360 (2020). https://doi.org/10.1038/s41380-020-0798-1

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• Published: 18 May 2024

Correlates of intention to screen for cervical cancer among adult women in Kyotera District, Central Uganda: a community based cross-sectional study

• Richard Kabanda 1 ,
• Arthur Kiconco 2 , 3 ,
• Anguzu Ronald 1 ,
• Kirsten M. M. Beyer 1 &
• Steven A. John 4  

BMC Women's Health volume  24 , Article number:  296 ( 2024 ) Cite this article

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Introduction

Cervical cancer continues to pose a major public health challenge in low-income countries. Cervical cancer screening programs enable early detection and effectively reduce the incidence of cervical cancer as well as late-stage diagnosis and mortality. However, screening uptake remains suboptimal in Uganda. This study assessed correlates of intention to screen for cervical cancer among women in the Kyotera district of Central Uganda.

We analyzed cross-sectional data collected to determine the effectiveness of community audio towers (CATs) as a modality of health communication to support cervical cancer prevention. Women ( n  = 430) aged 21–60 years without a prior history of cervical cancer screening were surveyed about demographics, sources of health information and cervical cancer screening intentions in 2020. We used generalized linear modelling with modified Poisson regression and backwards variable elimination to identify adjusted prevalence ratios and 95% confidence intervals (CI) to determine factors associated with intention to screen for cervical cancer.

Half (50.2%) of the participants had intentions to screen for cervical cancer within twelve months and 26.5% had moderate knowledge about cervical cancer. Nearly half (46.0%) considered themselves at risk of cervical cancer. Compared to residents who primarily received their health information from social media and radio, participants who received health information primarily from CATs (aPR:0.64, 95% CI:0.52–0.80, p  < 0.001) and TV (aPR:0.52, 95% CI:0.34–0.82, p  = 0.005) had a lower prevalence of intention to screen for cervical cancer. The prevalence of intentions to screen for cervical cancer in twelve months was higher among those resided in town councils (aPR:1.44, 95% CI:1.12–1.86, p  = 0.004) compared to rural areas, and higher among those who considered themselves to be at risk of cervical cancer (aPR:1.74, 95% CI:1.28–2.36, p  < 0.001) compared to those who did not.

Conclusions

We found suboptimal prevalence of intentions to screen for cervical cancer among women in central Uganda. Additional research and implementation projects are needed to increase cervical cancer screening. Targeting risk perceptions and behavioral approaches to increase intentions could be effective in future intervention work. Based on urban-rural differences, additional work is needed to support equitable sharing of information to support cancer prevention messaging; CATs and TV may best help reach those with lower intentions to screen based on our research.

Peer Review reports

Cervical cancer continues to pose a major global public health burden, with over 340,000 deaths annually [ 1 ]; projections estimate this number increasing to 400,000 annual deaths by 2030 [ 2 ]. Cervical cancer is the fourth most common cancer among women globally [ 3 ], with an estimated 604,127 new cases of cervical cancer in 2020 [ 1 ] and an anticipated increase to 700,000 by 2030 [ 4 ]. Cervical cancer is among the common human papillomavirus (HPV)-related diseases, with nearly all cases of cervical cancer attributable to HPV infection; specifically, HPV types 16 and 18 are known to cause 70% of cervical cancers and precancerous cervical lesions [ 5 , 6 , 7 ].

There are significant socioeconomic disparities in cervical cancer incidence rates, with national rates increasing as the Human Development Index (HDI) decreases; the poor, especially in low- and middle-income countries (LMICs), shoulder the largest disease burden [ 1 ]. The highest cervical cancer incidence occurs in Africa, followed by Latin America, Asia, and Melanesia. Within sub-Saharan Africa, the 2020 age-adjusted incidence rate for cervical cancer was highest in eastern Africa, estimated at 40 cases per 100,000 women-years [ 1 ]. The 2023 age-adjusted incidence and mortality rates for Uganda were 56.2 and 41.4 respectively [ 8 ]. In the same year, the annual estimates indicated that 6,959 women were diagnosed with cervical cancer and 4,607 died from the disease—making it the first most frequent cancer among women in Uganda [ 8 ].

Despite disparities in cervical cancer incidence rates, resources for prevention, diagnosis and treatment are limited in most LMICs [ 9 , 10 ]. Although preventable and curable if identified at an early stage, cervical cancer remains a top cancer killer of women in low-resource settings [ 11 ]. The HIV/AIDS epidemic is also believed to exasperate high rates of cervical cancer incidence and mortality, as the risk of development, progression, and recurrence of HPV-induced cervical precursor lesions and cervical cancer are higher among women living with HIV (WLHIV) [ 12 , 13 , 14 , 15 , 16 ]. Despite reductions in HIV new infections in Uganda, the HIV prevalence remains high at 7.2% among women compared to 4.3% among men [ 17 ].

The World Health Organization (WHO) Global Cervical Cancer Elimination Initiative (GCCEI) aims to reduce incidence below a threshold of 4 cases per 100,000 women-years in every country [ 2 ]. Cervical cancer is the number one cause of cancer-related deaths among women in Uganda [ 18 ], and the WHO estimates approximately 3,915 Ugandan women were diagnosed with cervical cancer and 2,160 died from the disease in 2014 [ 19 ]. In Uganda, cervical cancer screening guidelines recommend visual inspection of the cervix with acetic acid (VIA) annually for women living with HIV and every 3 years for those HIV-negative [ 20 ].

Cervical cancer screening programs enable the detection of cervical lesions before they become cancerous, which can effectively reduce the incidence of cervical cancer by 75–90% [ 21 , 22 ]. Screening also results in earlier detection of cancer, improving prognosis among those diagnosed and treated. As such, population-based cervical cancer screening programs are effective in reducing cervical cancer mortality [ 23 , 24 ]. Despite these statistics, only a small percentage (estimated at 19%) of women have been screened for cervical cancer in LMICs, compared to 63% in high-income countries [ 25 ]. In Uganda, it is estimated that the percentage of women who had ever screened for cervical cancer ranged from 9 to 10% and only about 7.5% had screened in the last 5 years in 2023 [ 8 ].

Further, researchers have previously attributed low cervical cancer screening uptake to a number of key factors, including limited resources required for successful screening programs [ 25 , 26 ], cervical cancer knowledge gaps [ 27 , 28 , 29 , 30 ], fear of positive diagnosis [ 31 ], and lower risk perception and negative attitudes [ 32 ]. The SARS-CoV-2 (i.e., COVID-19) pandemic is also believed to have led to delays in diagnosis and treatment due closures of health facilities, disruptions in access due to loss of insurance as people were laid off from work, and fear of COVID-19 exposure by those eligible for screening and care [ 33 ]. Most cervical cancer prevention programs aimed at increasing screening uptake usually focus on modifiable contextual factors such as knowledge, women’s intentions, and service availability, among others. However, few studies have assessed correlates of intention to screen for cervical cancer. As such, we assessed correlates of intention to screen for cervical cancer among adult women in Kyotera District, Central Uganda.

Study design

This was a cross-sectional analytical study based on secondary analysis of data collected at baseline for a study to determine the efficacy of community audio towers (CATs) as a health communication channel used in the prevention of cervical cancer in rural communities in Uganda [ 34 ]. The primary study was carried out between March and June 2020. It compared the use of CATs to disseminate messages on cervical cancer versus other health communication channels and cervical cancer screening among women aged 21 to 60 years. This analysis focused on data collected at baseline, prior to the use of the CATs for dissemination of cervical cancer-prevention messaging.

Study setting

This study was carried out in Kyotera district, located in the south-central region, southwest of Kampala Capital City in Uganda. Kyotera District headquarters are approximately 182 km from Kampala and forty-seven kilometers from Masaka City. Kyotera District was created from Rakai District in the year 2015 by an Act of Parliament but started operating as an independent district and local government on July 1, 2017, with two counties of Kakuuto and Kyotera. The district is primarily rural and borders with Kalangala, Masaka, Rakai, and Lwengo districts in Uganda and the Missenyi district in the south, which is in the Kagera region of Republic of Tanzania.

Kyotera District was part of Rakai where the first case of HIV/AIDS in Uganda was discovered at the Uganda-Tanzania border of Mutukula [ 35 ]. The district is known for its high HIV prevalence, currently standing at 11.1% [ 36 ]. There is a known link between HIV/AIDS and cancers, including cancer of the cervix, which shares similar risk factors. Although there are no disaggregated data showing the district prevalence of cervical cancer, the prevalence of cancer of the cervix is likely high in Kyotera.

Inclusion and exclusion criteria

The study population consisted of women aged 21–60 years living in Kyotera district. To be eligible, participants were required to: (1) be aged 21–60 years; (2) have lived in Kyotera for at least 3 months; and (3) have direct access to information as narrow-casted from CATs. Participants were excluded if (1) they had previously screened for cervical cancer in the past three years or one year for those LHIV; and (2) intended to relocate in the proceeding 16 weeks at the time of the survey. All participants consented to participate in this study.

Sample size and sampling

Sample size.

The sample size for the parent study was 480 participants. Each cluster (village) had sixty participants, and eight clusters were included. Fifty (50) of the participants had screened in the previous three years and were excluded from this sample; thus, the final sample for analysis was 430 participants.

Sampling and recruitment procedures

The initial process of sampling was based on the composition of Kyotera district in terms of counties. Kyotera has two counties, and each of these forms a health subdistrict. Recruitment of participants from clustered villages was done by systematic sampling from a list of households registered by community health workers to have the targeted age group. Where households had more than one eligible participant, only one was sampled and the lottery method was used to select one.

Study variables

Dependent variable.

The dependent variable for this study was intention to screen for cervical cancer. This was measured using three questions: (i) If never screened for cervical cancer, would you like to be screened? With responses: ‘Yes,’ ‘No’ or ‘I do not know.’ ‘No’ and ‘I do not know’ were merged as No; (ii) If yes above, when do you intend to have the screening done? With responses: in three months, six months, one year, not sure and never; (iii) Where would you like to go for the screening? With responses: nearby government hospital, private health facility, regional referral hospital, or any other. A previous study in an area closer to the study area measured intention using two questions of whether one intended to go for screening and when [ 32 ], but we added a third question of where they intended to go for the screening. Those who responded ‘yes’ in the first question, intention to go in either three or six months or one year and indicated where they intended to go for screening were considered to have intentions to screen.

Independent variables

We measured cervical cancer knowledge using a 20-item scale consisting of four constructs: risk factors (six items); signs and symptoms (eight items); eligibility for screening (5 items); and routine cervical cancer screening recommendations (one item). A previous study in Eastern Uganda considered all women who scored above the average for 20-point possible answers to be more knowledgeable, while those who scored below the average were considered to have less knowledge [ 37 ]. For this study, we considered those who scored in the 75th percentile to be knowledgeable and those whose scores fell below the 75th percentile to be less knowledgeable. The other independent variables considered were age (categorized into 20–29, 30–39, 40–49 and 50–59); marital status (single, cohabiting/married, commercial Sex Worker/divorced/widowed); work status (employed, student/not-working); regular income (yes and no); highest level of education (A-level+, O-level, PLE, none); residence (rural area, town board, town council); family cancer history (don’t know, no, and yes); common source of health information (social media/FM radio, TV, CATs, health worker, and any other); perceived self-risk for cervical cancer (yes, no, I do not know); fear of getting diagnosed with cancer (yes, no, I do not know, refused to answer); and fear of the cervical cancer screening procedure (yes, no, I do not know, refused to answer).

Statistical analysis

Descriptive statistics were reported using frequency distributions of the participant characteristics at individual level. Bivariate and multivariable analyses were conducted using generalized linear modelling with modified Poisson regression; prevalence ratios (PR) instead of odds ratios were used because of the high prevalence of intention to screen for cervical cancer [ 38 , 39 ]. We built our final analytic model using backwards elimination, where only variables with a p -value ≤ 0.2 were considered for the adjustment stage to determine the factors independently associated with intention to screen for cervical cancer. Collinearity between independent variables was assessed using pairwise correlation analysis. Data were analysed using Stata/SE 17, and statistical significance was considered at p  < 0.05; 95% confidence intervals are reported.

Baseline characteristics of the participants

Half of the participants had intentions to be screened for cervical cancer. In addition, half the participants were aged 20–29 years, and nearly three-quarters (73%) were married or cohabiting, as shown in Table  1 below. There was almost an equal distribution in residence for rural, town board and town council. Three-quarters (76%) of the participants reported no family history of cervical cancer, and 46% considered themselves at risk of cervical cancer. Half (52.5%) of the participants had full-time jobs, 62% had a regular source of income, and only 12.8% had more than A-level (high school equivalent) education.

CATs were mentioned as the main source of health information for nearly half (49.8%); more than half (64%) also feared being diagnosed with cervical cancer, while 40.9% feared the screening procedure. Approximately one-quarter (26.5%) had moderate knowledge about cervical cancer.

Correlates of intention to screen for cervical cancer among adult women

At the bivariate level, knowledge of cervical cancer, residence, common source of health information and perceived risk of getting cervical cancer were associated with intention to screen for cervical cancer. After adjusting for potential confounders, only participants’ residence, common source of health information and perceived risk of getting cervical cancer were independently associated with intention to screen for cervical cancer. Participants who resided in the town council were 44% more likely to have intentions to screen for cervical cancer compared to those who lived in the rural areas (see Table  2 ).

Compared to participants who mentioned FM radio or social media as their main source of health information, those who mentioned television were 48% less likely to have intentions to screen for cervical cancer, while those who mentioned CATs/health workers were 36% less likely to have intentions to screen for cervical cancer. Participants who perceived themselves to be at risk of cervical cancer were 74% more likely to have intentions to screen compared to those who did not. The full results are presented in Table  2 .

This study assessed the correlates of intention to screen for cervical cancer in Kyotera district, Central Uganda. We found 50.2% of the participants had intentions of being screened for cervical cancer. This prevalence was slightly lower than the 63% reported in a neighbouring district of Masaka in 2013 [ 32 ] and the 61% reported in rural Indonesia in 2016 [ 40 ]. However, it is higher than the prevalence of 45.3% in Ethiopia in 2017 [ 41 ]. Given the length of time since these prior estimates, we classify the prevalence of intention to screen in our study as suboptimal; we would anticipate increasing rates of screening over time and that there is a gap in cervical cancer related health promotion. As such, additional efforts are needed to promote cervical cancer screening in this area.

Participants who resided in the town council were 44% more likely to have intentions to screen for cervical cancer compared to those who lived in rural areas. This could be attributed to geographic proximity to health services located in urban areas, as well as the potential differences in income between urban and rural areas. Women who lived in urban and semiurban areas in Eastern Uganda were four times and two times more likely to have high knowledge about cervical cancer than their rural counterparts, respectively [ 37 ]. Our findings and others indicate there is a disparity in intentions to screen, which likely translates to differences in screening uptake. More equitable approaches to service delivery are warranted, including increased funding to support health education and cervical cancer screening promotion.

CATs were mentioned as the main source of health information for 49.8% of participants, compared to only 8.4% reported health workers as their main source of health information. The proportion that reported health workers was lower than the 15.1% reported from health facilities in Eastern Uganda [ 37 ]. Another study conducted in a neighbouring district had found that women who had discussions on cervical cancer with health care providers reported more intentions to screen for cervical cancer [ 32 ]. Even among women in Thailand, having received a recommendation from health care providers was associated with decisions to attend cervical cancer screening [ 42 ]. As such, integrating cervical cancer screening into health workers education packages and disseminating information via CATs may be effective health communication delivery mechanisms, where available. This may be feasible in Kyotera since a previous study found that the majority of the health workers believed CATs were accessible and easier to communicate on health issues; however, fewer than 20% used them [ 43 ].

We found only 46% of the sample considered themselves at risk of cervical cancer compared to 76.0% who perceived themselves to be at risk of cervical cancer in another study in Eastern Uganda, as reported in 2017 [ 44 ]. Risk perceptions were identified to be particularly important since those who perceived themselves to be at risk of cervical cancer were 74% more likely to have intentions to screen compared to those who did not; these findings align with prior reports in a neighbouring district [ 32 ]. Relatedly, a family history of cervical cancer was not associated with higher intentions of screening in this study, but it was reported to be associated among women in rural areas of Indonesia [ 40 ]; further research is needed to identify potential differences between family history and the impact on cancer screening. Multiple approaches for conducting effective Health education should be strengthened including use of print and interpersonal communication, as this could to help increase risk perception, increase intentions for screening, and ultimately aid in increasing uptake of cervical cancer screening.

Although being knowledgeable about cervical cancer was not associated with intentions to screen for cervical cancer after adjustment, it is important to note that only 26.5% of the participants had moderate knowledge (> 75th percentile) about cervical cancer. Increasing knowledge about cervical cancer is a critical area for further intervention given its importance in decision-making; yet it is likely this factor significant at the bivariate level was no longer significant after adjustment because of potential correlation with other social determinants of health (e.g., rurality). Knowledge has been reported to be associated with intention to undergo Pap smear testing in rural areas of Indonesia [ 40 ]. Therefore, improving knowledge about cervical cancer literacy could improve screening uptake.

Over half of the participants feared being diagnosed with cervical cancer, while 40.9% feared the screening procedure. Although these fears were not associated with intentions to screen for cervical cancer after adjustment, they could remain potential barriers to screening. These findings are inconsistent with previous studies conducting in neighbouring districts, Thailand, and Ethiopia [ 32 , 42 , 45 ]. Additional qualitative research could help identify nuance in these reports and is recommended; decreasing barriers to screening and managing a diagnosis are important to support patients in cancer prevention.

Strengths and limitations

We applied approaches to maximize the validity of the findings of this study. First, we assessed the outcome variable with more than a single question to only consider those who indicated the intention as well as when and where they would go for screening as those with intention to minimize social desirability bias. The district-wide sampling and the rich distribution of participants by age are other strengths of this study and are key to representativeness and thus generalizability of the study findings across the district and similar contexts. Despite these, some limitations are acknowledged. First, there could have been some people who still indicated intentions without actual intentions. In addition, the inherent limitations of cross-sectional study design including recall and difficulties with self-reporting on other variables other than intention cannot miss acknowledgement.

In this study, we found only half of adult women sampled in the Kyotera district, Central Uganda, had intentions for cervical cancer screening, and only 46% considered themselves at risk of cervical cancer. Urban residence, risk perception, and CATs as a source of health information were associated with higher intentions to screen for cervical cancer. The urban-rural difference calls for equity in cervical cancer health education and service delivery. In addition to other communication channels, targeting health information sharing via CATS and interactive TV educational messages may help reach those with lower intentions to screen.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

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Richard Kabanda, Anguzu Ronald & Kirsten M. M. Beyer

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RK conceptualized the study and collected data; AK contributed to study conceptualization, analyzed data and drafted the manuscript; AR, KMMB, SAJ, RK and AK all reviewed the manuscript.

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This study was approved by the Institutional Review Board (IRB) of Mildmay Uganda (#REC 0801–2020) and the Uganda National Council of Science and Technology (UNCST) (SS 5233). Administrative clearance was also sought from the Ministry of Health and Kyotera District Local Government. Participants provided written informed consent before being interviewed. Confidentiality and privacy were maintained throughout the research process.

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Kabanda, R., Kiconco, A., Ronald, A. et al. Correlates of intention to screen for cervical cancer among adult women in Kyotera District, Central Uganda: a community based cross-sectional study. BMC Women's Health 24 , 296 (2024). https://doi.org/10.1186/s12905-024-03129-5

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Received : 05 September 2023

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DOI : https://doi.org/10.1186/s12905-024-03129-5

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