research paper on texting and driving

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• Published: 01 March 2016

Texting while driving: the development and validation of the distracted driving survey and risk score among young adults

• Regan W. Bergmark   ORCID: orcid.org/0000-0003-3249-4343 1 , 2 , 3 ,
• Emily Gliklich 1 ,
• Rong Guo 2 , 3 &
• Richard E. Gliklich 1 , 2 , 3  

Injury Epidemiology volume  3 , Article number:  7 ( 2016 ) Cite this article

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Texting while driving and other cell-phone reading and writing activities are high-risk activities associated with motor vehicle collisions and mortality. This paper describes the development and preliminary evaluation of the Distracted Driving Survey (DDS) and score.

Survey questions were developed by a research team using semi-structured interviews, pilot-tested, and evaluated in young drivers for validity and reliability. Questions focused on texting while driving and use of email, social media, and maps on cellular phones with specific questions about the driving speeds at which these activities are performed.

In 228 drivers 18–24 years old, the DDS showed excellent internal consistency (Cronbach’s alpha = 0.93) and correlations with reported 12-month crash rates. The score is reported on a 0–44 scale with 44 being highest risk behaviors. For every 1 unit increase of the DDS score, the odds of reporting a car crash increases 7 %. The survey can be completed in two minutes, or less than five minutes if demographic and background information is included. Text messaging was common; 59.2 and 71.5 % of respondents said they wrote and read text messages, respectively, while driving in the last 30 days.

The DDS is an 11-item scale that measures cell phone-related distracted driving risk and includes reading/viewing and writing subscores. The scale demonstrated strong validity and reliability in drivers age 24 and younger. The DDS may be useful for measuring rates of cell-phone related distracted driving and for evaluating public health interventions focused on reducing such behaviors.

Texting and other cell phone use while driving has emerged as a major contribution to teenage and young adult injury and death in motor vehicle collisions over the past several years (Bingham 2014 ; Wilson and Stimpson 2010 ). Young adults have been found to have higher rates of texting and driving than older drivers (Braitman and McCartt 2010 ; Hoff et al. 2013 ). Motor vehicle collisions are the top cause of death for teens, responsible for 35 % of all deaths of teens 12–19 years old, with high rates of distraction contributing significantly to this percentage (Minino 2010 ). In 2012, more than 3300 people were killed and 421,000 injured in distraction-related crashes in the US, with the worst levels of distraction in the youngest drivers (US Department of Transportation National Highway Traffic Safety Administration 2014 ).

While distracted driving includes any activity that takes eyes or attention away from driving, there has been particular and intense interest on texting and other smartphone-associated distraction as smartphones have become widely available over the past ten years. Multiple studies have examined driving performance while texting or completing other secondary tasks (Yannis et al. 2014 ; Owens et al. 2011 ; Olson et al. 2009 ; Narad et al. 2013 ; McKeever et al. 2013 ; Drews et al. 2009 ; Hickman and Hanowski 2012 ; Leung et al. 2012 ; Long et al. 2012 ). Uniformly, distraction from cell phone use, including texting, dialing or other behaviors, is associated with poorer driving performance (Yannis et al. 2014 ; McKeever et al. 2013 ; Bendak 2014 ; Hosking et al. 2009 ; Irwin et al. 2014 ; Mouloua et al. 2012 ; Rudin-Brown et al. 2013 ; Stavrinos et al. 2013 ). A 2014 meta-analysis of experimental studies found profound effects of texting while driving with poor responsiveness and vehicle control, and higher numbers of crashes (Caird et al. 2014 ). A rigorous case–control study found that among novice drivers, sending and receiving texts was associated with significantly increased risk of a crash or near-crash (O.R. 3.9) (Klauer et al. 2014 ). In commercial vehicles, texting on a cell phone was associated with a much higher risk of a crash or other safety-critical event, such as near-collision or unintentional lane deviation (OR 23.2) (Olson et al. 2009 ). Motor vehicle crash-related death and injury have also been strongly associated with texting (Pakula et al. 2013 ; Issar et al. 2013 ).

Although the dangers of texting and driving are well-established, a focused brief survey on driver-reported texting behavior does not yet exist. Multiple national surveys which include texting while driving as part of a more extensive survey on distracted driving or youth health have found that young drivers have high rates of texting while driving, often in spite of high levels of perceived risk (Hoff et al. 2013 ; Buchanan et al. 2013 ; Cazzulino et al. 2014 ; O’Brien et al. 2010 ; Atchley et al. 2011 ; Harrison 2011 ; Nelson et al. 2009 ). The surveys confirm that young adults are at high risk for distracted driving; in one, 81 % of 348 college students stated that they would respond to an incoming text while driving, and 92 % read texts while driving (Atchley et al. 2011 ). Among several large survey based studies, the National Highway Traffic Safety Administration reported from a 2012 survey that nearly half (49 %) of 21–24 year old drivers had ever sent a text message or email while driving (Tison et al. 2011 -12), and even more alarming, the Centers for Disease Control and Prevention (CDC)’s National Youth Risk Behavior Survey found that nearly as many high school students who drove reported texting in just the past 30 days (41.4 %) ( Kann et al. 2014 ). The problem is not confined to novice drivers. Among US adults ages 18 to 64 years 31 % report reading or sending text messages or emails while driving in prior last 30 days ( Centers for Disease Control and Prevention (CDC) 2013 ).

Given the magnitude of the problem, a very brief questionnaire focused on texting and driving for evaluation of public health measures such as anti-texting while driving laws, cell phone applications and public health campaigns would be useful. The use of self-reported validated surveys is an increasingly common approach to understanding health issues as well as their response to intervention (Guyatt et al. 1993 ; Tarlov et al. 1989 ; Stewart and Ware 1992 ). Current surveys are driving-specific but lengthy and potentially prohibitive for widespread dissemination (Tison et al. 2011 -12, McNally and Bradley 2014 ; Scott-Parker et al. 2012 ; Scott-Parker and Proffitt 2015 ), do not include texting as a survey domain within the realm of distraction (Martinussen, et al, 2013 ), are general health surveys without sufficient information on texting and driving ( Kann et al. 2014 ), or have not been designed or validated to reliably measure and evaluate individual crash risk ( Kann et al. 2014 ). For example, a new survey of reckless driving behavior includes information on multiple driving-related domains of behavior, but administration takes 35 min and the survey does not focus on cell phones (McNally and Bradley 2014 ). Another survey of distraction in youth is similarly comprehensive without a focus on phone use (Scott-Parker et al. 2012 ; Scott-Parker and Proffitt 2015 ). The goal of shorter surveys for evaluation of distracted driving has been well documented and development of the mini Driver Behavior Questionnaire (Mini-DBQ) is an example, although it does not address cell phone related distracted driving (Martinussen et al. 2013 ). However, many interventions target cell phone use specifically rather than distraction broadly. In addition, most surveys do not delve into the specific timing of texting while driving that allows a more precise estimate of the behavior’s prevalence.

The purpose of this study was to develop a reliable self-reported survey for assessing levels of cell phone related distracted driving associated with viewing and typing activities and to validate it in a higher risk population of drivers age 24 years or younger.

Study design and oversight

A literature review and open-ended interviews with experienced and novice drivers were performed to identify the most common domains for item development as well as any existing survey items with validation metrics. The literature review was performed with reviewing terms including “Text*” and “Driv*” reviewing for any studies that included driver-reported outcomes. Initial items were piloted with open-ended responses. Ten novice (18–25 years old) and experienced (30 years old or older with at least 10 years of driving experience) drivers underwent semi-structured interviews about cell phone use while driving to further generate potential survey domains. Text messaging through various applications, map/GPS use, email and social media were prominent themes. “Texting while driving” was interpreted very differently by various participants; some people stated that texting at stop lights or at slow speeds, or reading texts, did not really constitute texting and driving. This finding suggested that a questions that simply asks “do you text and drive?” may be missing a significant proportion of this distracted behavior.

Based on the identified themes, we developed a series of Likert scale and multiple-option items reflecting the most common reading and typing tasks reported on a cell phone (Table  1 ). The format of many of our questions was modeled on the Centers for Disease Control and Prevention National Youth Risk Behavior Survey and after a thorough review of the other surveys described above. The assessed activities included reading or viewing text messages, emails, map directions, internet sites and social messaging boards and typing or writing activities through these same applications. The piloting process revealed that in addition to questions addressing frequency of the activity over the previous 30 days while driving (e.g. every time, most of the time, etc.), it was important to also assess when the activities were performed with respect to vehicular motion or speed (any speed, low speeds, stop and go traffic, etc.) to allow for further risk stratification. Additional items assessed driver attitudes with respect to their perceived level of risk associated with performing these activities. The questionnaire was pre-tested with 30 drivers 18–24 years old and went through multiple iterations. In addition to questions on cell phone reading and writing activities, the questionnaire included demographic information, self-reported “accidents” within the past 12 months of any cause, and potentially high-risk activities such as driving under the influence of alcohol or other substances. Given the colloquial use of the phrase “car accident,” we used the term “car accident” in our survey, but in the results section refer to this number as the crash rate. The question included in the final survey to elicit crash data was, “In the last 12 months, have many car accidents have you been in with you as the driver? (Answers 1, 2, 3, 4, 5 or more).” Based on feedback from the pilot testing, twenty-nine items were selected for testing in the initial questionnaire.

The questionnaire was set up as a web-based survey using standard, HIPAA compliant software. Participants provided informed consent and received a nominal incentive for participating. The study was approved by the Massachusetts Eye and Ear Institutional Review Board.

Participants

Three pools of participants 18–24 years old who had driven in the prior 30 days were recruited: (1) greater Boston metropolitan area were recruited from educational or recreational centers in the greater Boston area with flyers, enrolled through a generic link, and completed a second survey at 14 days for test-retest reliability, after which several questions were eliminated yielding and 11-item questionnaire (2) A panel was used through the software program to recruit participants from two geographic locations, (a) Eastern and (b) Western United States for a larger geographical distribution for further validation. These participants completed the survey a single time.

Item selection: reliability and validity

With the goal of creating a brief and targeted survey, items were selected for inclusion in the total score based on multiple reliability and reliability measures (Table 1 ). Item response distribution was examined prior to analysis. Items with low test-retest reliability in the Boston sample defined as a Spearman correlation of less than 0.4 or a Kappa coefficient below 0.3 were eliminated. Internal consistency was measured with Cronbach’s alpha, examining Cronbach’s alpha for each item and the DDS coefficient with each variable deleted, with any questions with a Cronbach’s alpha under 0.8 eliminated. In addition to face validity, the survey was assessed for criterion-related validity by use of concurrent validity against hypothesized correlates to other assessed variables. We hypothesized a significant correlation to self-reported crashes in the prior 12 months. We additionally postulated that writing related activities would be higher risk than reading or viewing activities alone. Conversely, we hypothesized non-significant correlations with other items (e.g. falling asleep while driving).

Items not focused on cell phone writing and reading behaviors or crash rate also were eliminated from the final survey to allow for brevity. The final survey was then tested in two cohorts of young drivers to confirm internal consistency, time required for survey completion and correlation with crash rate.

Statistical analysis

All data analysis was performed using SAS V9.4 (SAS Institute Inc., Cary, NC). Standard descriptive statistics were reported, mean (SD) for numerical variables, median (min – max) for Likert scale variables and frequency count (%) for categorical variables. The statistical underpinnings of patient-reported outcomes measures and survey design are well established; the reader may reference Fleiss’s Design and Analysis of Clinical Experiments for a detailed discussion of the methods chosen for this study (Fleiss 1999 ).”

An algorithm was created to generate a total Distracted Driving Survey (DDS) score based on the final items selected for the questionnaire where zero represents the lowest possible score. The response for each of the questions included was given a value 1–5 with 1 being the lowest risk answer (ie, no texting and driving) and 5 being the highest risk. For a given subject, the scores for the questions were then summed and reduced by the number of questions such that the lowest score was zero. The final survey, consisting of 11 questions, therefore had a range of possible scores ranging from 0 to 44, with 44 being the highest risk. In addition, two subscores for reading only (DDS-Reading) and writing only (DDS-Writing) related questions were created for further risk stratification based on evidence that writing texts is even more dangerous than reading texts alone (Caird et al. 2014 ). Wilcoxon tests were used for the comparison of DDS score by levels of demographic and behavior variables. In addition, logistic regression was performed to evaluate the effect of DDS score on reported car crashes while adjusting for driving under substance influence.

Study population

There were 228 subjects included in the study (Table 2 ). Of the Boston group, 70 of 79 initial respondents completed the survey at the two-week interval and 14 respondents were additionally excluded for reporting not having driven a motor vehicle in the prior 30 days on one or both surveys. Therefore there were a total of 56 Boston respondents (25 male, 31 female). There were 90 respondents in the Eastern Region and 82 in the Western region.

Of the 228 total respondents, 120 (52.3 %) were female. Participants self-identified as White (63.3 %), Asian (11.4 %), Black/African American (8.0 %) or other (17.3 %). 34 (15.0 %) described themselves as Hispanic. Respondents said their driving was predominantly urban (45.6 %), suburban (44.3 %), or rural (10.1 %). Most (71.5 %) respondents were either in college or had completed some or all of college. Other participants were in or had completed high school (26.3 %), or described their educational status as other (2.2 %).

Item selection: reliability

The survey was first tested in a Boston metropolitan area cohort ( N  = 56) and items were reduced based on Cronbach’s alpha and the Kappa statistic (Tables  3 and 4 ). Eliminated questions asked about use of voice recognition software and riding with a driver who texted, as well as use of specific anti-texting programs, all of which did not meet reliability or validity criteria. To keep the survey brief and focused, questions that were not cell-phone specific were also eliminated (i.e., drowsiness when driving, driving under the influence, seatbelt use) even though these questions were statistically reliable. There were 11 items in the final questionnaire; the Spearman correlation coefficient for test-retest reliability was excellent at 0.82 for the final survey based on the Boston data ( N  = 56) (Tables  3 , 4 and 5 ).

The DDS-Reading or viewing subscore included six items (2–6, 11). The DDS-Writing subscore included four items that asked about specific writing activities including writing texts and emails and at what speeds (7–10). The Spearman coefficient for the DDS-Reading subscore was similar at 0.82 but lower for the DDS-Writing subscore at 0.63 (Table  5 ). Strong agreement was generally observed for the items included in the DDS. In addition, very good agreement was observed for most of the variables used for concurrent validity testing of the DDS including reported crashes in the last 12 months (Kappa = 0.6).

Internal consistency

The 11-item survey with additional demographic questions was then tested in the Eastern and Western US populations. Standardized Cronbach’s alpha for the final 11-item DDS was excellent at 0.92 ( N  = 228) (Table  5 ). The DDS-Reading subscore standardized Cronbach’s alpha was 0.86. The DDS-Writing score standardized Cronbach’s alpha coefficient was 0.85.

Score distribution and association with car crashes

The 11-item questionnaire was then used to calculate the DDS score as described in the methods section with a higher score indicating more risk behaviors. Mean DDS score based on the entire cohort ( N  = 228) was 11.0 points with a standard deviation (SD) of 8.99 and a range of 0 to 44 points. The distribution of scores is shown in Fig.  1 . There was no statistically significant difference of DDS total score by region ( p  = 0.81). The mean scores for were similar for Boston (11.2, standard deviation 7.14), Eastern United States (11.4, standard deviation 9.48), and Western United States (10.5, standard deviation 9.62).

Distribution of the Distracted Driving Survey (DDS) scores. Scores reflect the final 11-item questionnaire, calculated with a range of 0 to 44 with high scores indicating more distraction

Reading and writing scores specific subscores were also calculated and also significantly correlated with crash rate (Table  5 ). Mean writing score was 3.2 (SD 3.48, range 0–16), and mean viewing reading score was 6.57 (SD 5.16, range 0–24).

A higher DDS score indicating higher risk behavior was significantly associated with the self-reported car crashes (Wilcoxon rank sum test, p  = 0.0005). Logistic regression was performed with reported car crashes as the dependent variable and DDS as the independent variable. For every one point increase of the DDS score, the odds of a self-reported car crash increased 7 % (OR 1.07, 95 % confidence interval 1.03 – 1.12, p  = 0.0005). The odds ratio for the DDS-Writing subscore (OR 1.17) was the highest among the scores and subscores. As anticipated, DDS score was not significantly associated with either falling asleep while driving ( p  = 0.11) or driving under the influence ( p  = .09) in the Boston group ( N  = 56), and these questions were eliminated for the Eastern and Western US groups.

In order to better characterize the risk of higher DDS, the DDS-11 score was categorized into < =9, 9–15 and >15 using its median (9 points) and third quartile (15). The odds of car crash for subjects with DDS-11 > 15 is 4.7 times greater than that of subjects with DDS score < =9 (95 % CI 1.8–12.6).

Texting and driving behavior

In this cohort of 228 18–24 year old divers (Table 5 ), we found that 59.2 % reported writing text messages while driving in the prior 30 days. Of the 228 drivers, most wrote text messages never or rarely, while 16 % said they write text messages some of the times they drive and 7.4 % said they write text messages most or every time they drive. When all participants were asked about the speeds at which they write text messages, 9.7 % said they write text messages while driving at any speed and an additional 24.1 % said they write text messages at low speeds or in stop and go traffic, with the remainder writing text messages only at stop lights or not writing text messages while driving at all.

Reading text messages was even more common, with 71.5 % of participants saying they read text messages while driving in the past 30 days – 29.0 % rarely, 27.2 % sometimes, 13.2 % most of the time, and 2.2 % every time they drove. Compared to writing texts, a higher percentage read text messages at any speed (12.7 %) and at low speeds (15.6 %), in stop and go traffic (10.1 %), as well as when stopped at a red light (36.3 %). Reading and writing email and browsing social media were less common. Maps were used on a phone by 74.6 % of respondents in the last 30 days.

In contrast to yes/no answers in other surveys about safety of texting and driving, this study found that only 36.4 % of respondents said it was never safe to text and drive. Drivers reported that it was safe to text and drive never (36.4 %) rarely (27.6 %), sometimes (20.2 %), most of the time (8.8 %) and always (7.0 %).” This is in contrast to yes/no answers in other surveys about texting and driving safety.

The purpose of this study was to create a short validated questionnaire to assess texting while driving and other cell-phone related distracted driving behaviors. The Distracted Driving Survey developed in this study proved to be valid and reliable in a population of 18–24 year old drivers, with excellent internal consistency (Cronbach’s alpha of 0.93). The DDS has excellent internal consistency defined as Cronbach’s alpha =0.9 or greater and strong test retest reliability.(Kline 1999 ) The Mini-DBQ, a valid measure which does not include texting or other cell-phone related distracted driving, is considered a valid measure with Cronbachs alpha of less than 0.6, substantially lower than the DDS (Martinussen et al. 2013 ).

The Distracted Driving Survey score was significantly correlated with self-reported crash rates in the prior 12 months with people in the highest tercile of derived scores (here, those with a score >15) more than 4.7 times as likely to have had a crash than subjects with scores in the lowest tercile of risk (here, those <9). Stepwise logistic regression demonstrated this relationship to have a ‘dose response’, with higher scores incrementally associated with higher crash rates. The odds of a reported crash increased 7 % for every increase of one point of the DDS score (OR 1.07, 95 % confidence interval 1.03 – 1.12, p  = 0.0005). This relationship was further demonstrated to be independent of such factors as driving under the influence of alcohol or other substances, and falling asleep while driving.

The DDS confirmed prior reports of high levels of texting while driving, and further elucidated specific aspects of the behavior including to what extent people read versus write text messages and and what speeds they perform these activities. 59.2 and 71.5 % of respondents said they wrote and read text messages, respectively, while driving in the last 30 days. Respondents were most likely to do these activities while stopped, in stop-and-go traffic or at low speeds although a small percentage said they have read or written text messages while traveling at any speed. Prior studies have shown high rates of texting while driving in spite of high rates of perceived risk. In this study, Likert-scale questions further demonstrated that most respondents actually felt that texting and driving can be safe at least on rare occasions; only 36.4 % of respondents said it was always unsafe to text and drive. These data correspond more directly to the amount of texting and driving reported here including reading or writing texts while stopped or in stop and go traffic.

Texting and other cell phone use while driving is frequently targeted as a public health crisis, but many of these interventions have unclear impact. Since the advent of the Blackberry in 2003 and the first iPhone in 2007, texting and driving has been highlighted in the news and by cell phone carriers, such as with AT&T’s It Can Wait pledge, to which more than 5 million people have committed (AT&T 2014 ). There are multiple smartphone applications and other interventions aimed at reducing texting and driving (Verizon Wireless 2014 ; Lee 2007 ; Moreno 2013 ), and Ford has even created a Do Not Disturb button in select vehicles blocking all incoming calls and texts (Ford 2011 ). Forty-four U.S. states and the District of Columbia ban texting and driving, with Washington State passing the first ban in 2007 (Governors Safety Highway Association 2014 ), and there is a push for even more aggressive laws and enforcement (Catherine Chase 2014 ). Texting bans have been shown to be effective in some studies. Texting bans are associated with reductions in crash-related hospitalizations (Ferdinand et al. 2015 ). Analysis of texting behavior from the U.S. Centers for Disease Control and Prevention 2013 National Youth Risk Behavior Survey showed that text-messaging bans with primary enforcement are associated with reduced texting levels in high school drivers, whereas phone use bans were not (Qiao and Bell 2016 ). Other studies surveying drivers have found a mixed response of whether behavior is altered, with some drivers not altering their behavior (Mathew et al. 2014 ). However, the impact of many of these interventions has not yet been studied or fully understood. While driver reported surveys exist today, in general these instruments have high respondent burden and have not been designed or validated for individual measurement.

We aimed to develop a validated, reliable and brief survey for drivers to report and self-assess their level of risk and distraction to fill gaps in the literature and facilitate standardized measurement of behavior. Initial validation detailed here focused on a population of young drivers most at risk for motor vehicle crashed and deaths. Survey development was carefully undertaken here with semi-structured interviews, pilot testing and testing of young adults in a major metropolitan area as well as in the Western and Eastern United States. Validity and reliability were measured in multiple ways. While there are multiple functions associated with cell phone use that can be distracting to a driver, we focused on typing and reading or viewing activities as those have been both extensively studied and demonstrated to have significant effect sizes in the simulator literature (Caird et al. 2014 ).

The resulting survey is brief and easy to administer. In automated testing, the full research survey required approximately four and a half minutes to complete and completing the 11-item DDS component takes around two minutes. In actual testing, all respondents were able to complete the survey.

This survey provides self-reported data from young US drivers in a relatively small sample size of 228 drivers age 18–24. Participants voluntarily took the survey so it is possible that the type of driver who took the survey may be more attuned to the risks of texting and driving or that there may be some other selection bias. Tradeoffs were made in the comprehensiveness of the questions selected to purposefully construct a brief instrument, with intentional elimination of questions on certain functions of cell phone use and other forms of distraction. For example, this study did not quantify the driving patterns of the respondents in the prior 30 days. Respondents who had not driven in the last 30 days were excluded. Because this study aimed to validate this survey among young people age 18–24, there are college students included who may have more limited driving patterns. Further studies are needed to validate this survey among drivers of all ages. This survey did not aim to quantify the number of texts or viewing time per mile. Further studies could be done to validate this survey against quantitative measures of viewing and reading behavior, which was beyond the scope of this study. However, the high Cronbach’s alpha and other characteristics suggest that the resulting brief instrument is well suited for large population studies that seek to limit respondent burden. Further research will likely lead to refinement in the scoring algorithms used. The performance of the DDS has not yet been studied in older age groups. Strengths of the study include good ethnic representation closely aligned with US census data and an anonymous format conducive to more accurate reporting of these behaviors.

The DDS is intended to be used to assess behavior patterns and risk and to evaluate the impact of public health interventions aimed at reducing texting and other cell phone-related distracted driving behaviors. The DDS score demonstrated strong performance characteristics in this validation study. Further research is needed to evaluate the instrument in larger and more diverse populations and to evaluate its sensitivity to change following interventions. Since a DDS score can be immediately generated at the time the DDS is completed, another area of research is whether the score itself may have value as an intervention.

The Distracted Driving Survey is a brief, reliable and validated measure to assess cell-phone related distraction while driving with a focus on texting and other viewing and writing activities. This survey is designed to provide additional information on frequency of common reading and viewing activities such as texting, email use, maps use, and social media viewing. The data are informative because different anti-distraction interventions target various aspects of cell phone utilization. For example, some anti-texting cell phone applications would not affect maps viewing, email viewing or writing, or social media use and therefore would not impact those behaviors. Further research is required to determine if these trends also hold true for older drivers. Higher DDS scores, indicating more distraction while driving, were associated with an increase in reported crashes in the prior 12 months in a dose–response relationship. Although this finding does not prove causality, the association is concerning and corroborates other studies demonstrating the risks of texting on crash rates on courses and simulators. This study confirmed prior reports of high rates of texting and driving in a young population, with more detailed reports of behavior on writing and reading text messages, the speeds at which these activities are performed, and respondents’ perception of risk. This measure may be used for larger studies to assess distracted driving behavior and to evaluate interventions aimed at reducing cell phone use, including texting, while driving. An improved understanding of the common cell phone functions used by young drivers should be used to inform the interventions aimed at reducing cell phone use while driving.

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Regan W. Bergmark, Emily Gliklich & Richard E. Gliklich

Department of Otolaryngology, Massachusetts Eye and Ear Infirmary, 243 Charles Street, Boston, MA, 02114, USA

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The authors do not have any relevant financial disclosures regarding this research. Research was funded by the Clinical Outcomes Group at Massachusetts Eye and Ear at Harvard Medical School.

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RB, EG and RG conceived of the project and performed the data collection. RG performed statistical analyses with guidance and input from RB and RG, RB and RG wrote the first draft of the paper with subsequent revision from EG and RG. All authors approved of submission.

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Bergmark, R.W., Gliklich, E., Guo, R. et al. Texting while driving: the development and validation of the distracted driving survey and risk score among young adults. Inj. Epidemiol. 3 , 7 (2016). https://doi.org/10.1186/s40621-016-0073-8

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DOI : https://doi.org/10.1186/s40621-016-0073-8

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Texting while driving: A study of 1211 U.S. adults with the Distracted Driving Survey

Affiliations.

• 1 Clinical Outcomes Research Unit, Massachusetts Eye and Ear, United States.
• 2 Department of Otolaryngology, Massachusetts Eye and Ear, 243 Charles Street, Boston, MA 02114, United States; Harvard Medical School, Boston, MA, United States.
• 3 Clinical Outcomes Research Unit, Massachusetts Eye and Ear, United States; Department of Otolaryngology, Massachusetts Eye and Ear, 243 Charles Street, Boston, MA 02114, United States; Harvard Medical School, Boston, MA, United States.
• PMID: 27656355
• PMCID: PMC5030365
• DOI: 10.1016/j.pmedr.2016.09.003

Texting and other cell-phone related distracted driving is estimated to account for thousands of motor vehicle collisions each year but studies examining the specific cell phone reading and writing activities of drivers are limited. The objective of this study was to describe the frequency of cell-phone related distracted driving behaviors. A national, representative, anonymous panel of 1211 United States drivers was recruited in 2015 to complete the Distracted Driving Survey (DDS), an 11-item validated questionnaire examining cell phone reading and writing activities and at what speeds they occur. Higher DDS scores reflect more distraction. DDS scores were analyzed by demographic data and self-reported crash rate. Nearly 60% of respondents reported a cell phone reading or writing activity within the prior 30 days, with reading texts (48%), writing texts (33%) and viewing maps (43%) most frequently reported. Only 4.9% of respondents had enrolled in a program aimed at reducing cell phone related distracted driving. DDS scores were significantly correlated to crash rate (p < 0.0001), with every one point increase associated with an additional 7% risk of a crash (p < 0.0001). DDS scores were inversely correlated to age (p < 0.0001). The DDS demonstrated high internal consistency (Cronbach's alpha = 0.94). High rates of cell phone-related distraction are reported here in a national sample. Distraction is associated with crash rates and occurs across all age groups, but is highest in younger drivers. The DDS can be used to evaluate the impact of public health programs aimed at reducing cell-phone related distracted driving.

Keywords: Accidents; Automobile driving; Cell phones; Text messaging; Traffic.

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Missouri School of Journalism

University of missouri, to keep people from texting and driving, remind them of the deadly consequences — and the choice they have to change their behavior.

University of Missouri research examines strategies for preventing texting and driving as health messages that highlight the deadly consequences but don’t limit a person’s freedom to choose their behavior

Contact: Sara Diedrich, 573-882-3243, [email protected]

Citing the grim statistics alone should be enough to convince people to stop texting and driving.

• More than eight people are killed and 1,161 are injured each day in crashes reported to involve a distracted driver, according to the Center for Disease Control and Prevention (CDC).
• Drivers in their 20s make up 25% of distracted drivers in fatal crashes.

But a new study from the University of Missouri found “death awareness” played a significant role in promoting the adoption of texting-and-driving prevention behaviors. When individuals were primed with reminders of their mortality and the potential consequences of their actions, they were more likely to indicate that they would refrain from texting while driving and adopt safer behaviors behind the wheel.

Additionally, the findings indicated that reactance — or the resistance to being persuaded or controlled — played a paradoxical role in texting-and-driving prevention. When individuals were primed to experience reactance and exhibited initial resistance to prevention messages, researchers found death awareness effectively reduced those individuals’ resistance and positively influenced their behavior.

“When you message people about behaviors that could harm their health, you want to be careful not to induce reactance by using terms that imply they have no choice because that could trigger resistance,” said Zachary Massey, an assistant professor of science communication and strategic communication at the Missouri School of Journalism and co-author on the study. “And if there’s resistance, they might reject the message.”

Massey said crafting prevention messages to warn people about threats to their well-being is tricky — even if the statistics about the health threat appear dire. Convincing drivers — especially young drivers — to stop texting and driving is no different.

Massey’s experimental study examined how young adults respond to different versions of a preventative health message that combines two psychological factors that influence individual behavior — death awareness and reactance.

Reactance is especially profound in teenagers and young adults, who often resist attempts to change their behavior. It can reappear at other stages in life as well.

The experiment employed a comprehensive approach, combining survey data, experimental scenarios and statistical analysis to gain insights into the factors influencing how people respond to texting-and-driving messages. The researchers recruited 208 participants between the ages of 18 and 31 who were randomly assigned to write a short essay either about their death or about experiencing dental pain. Participants then read different versions of texting-and-driving prevention messages adapted from governmental websites. One version of the message threatened participants’ behavioral freedom to choose — “There’s really no choice when it comes to preventing texting and driving: You simply have to do it!” — and the other version supported participants freedom to choose — “You have a choice when it comes to texting and driving: Avoid texting and driving whenever you can!” Participants who wrote about their death and then read the message supporting their behavioral choice showed the strongest indication of changing their texting behavior while driving, providing a possible avenue for practitioners that must communicate deadly consequences of health behaviors to people who may be resistant to those messages.

“This is tricky, tricky ground to work on,” Massey said. “We have to tell people about something they might not want to hear that could have deadly ramifications if they don’t listen to what we’re saying. But if we warn people and threaten their behavior to choose, it could backfire. We have to warn people about deadly threats, but we need to figure out how to frame that information so they will listen.”

“The effects of death awareness and reactance on texting-and-driving prevention” was published in Risk Analysis . Co-author on the study is Elena Bessarabova, assistant professor in the Department of Communication at the University of Oklahoma.

Updated: August 10, 2023

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78 Texting and Driving Essay Topic Ideas & Examples

📃 the texting and driving essay structure, 🏆 best texting and driving topic ideas & essay examples, 📌 most interesting texting and driving topics to write about, 👍 good research topics about texting and driving, ❓ questions about texting and driving.

A texting and driving essay requires a unique approach because it is not merely an academic take on an important subject.

Your paper’s goal is to prevent people from taking a particular course of action, which will harm them, their passengers, and even innocent bystanders.

Thus, from your title to your conclusion, your argument must be easy to apperceive, just as any possible advice that you may give should be easy to follow.

Begin your topic by drafting a texting and driving essay outline and thesis statement, which will later become your structural backbone.

During this process, you need to keep in mind your primary goal; assess each sentence you write with your pre-defined argument and how it may help support your central theme.

For example, you can mention the statistics of road accidents induced by phone-related circumstances and the mechanism behind distracted driving. You can even cite the law on using cell phones and driving.

After you have decided on your main points, do some research on each, amassing a thorough bibliography, which will help convince your readers of your position’s soundness and ethicality.

Book titles are essential but do not forget to search for scientific research that relates to your central theme. Integrating vivid examples from studies on texting and driving may help you sway even the most obstinate of readers.

Furthermore, you can draw inspiration from researchers’ structural choices, especially if you feel like their outline was part of why their argument felt convincing to you personally.

Finally, if you feel like your paper is still lacking something inexplicable, read sample essays online. Doing so will help you see for yourself what techniques do and do not work when convincing a broad audience.

During the body-writing process, remove any ideas that do not relate to your texting and driving essay thesis.

If you are talking about the dangers of texting and driving, then drunk driving is not a useful addition to your essay. To maintain a well-planned essay structure, your writing should be:

• Factual and supported by research;
• Logically interconnected;
• Memorable and expressive;
• With no unrelated topics.

Your texting and driving essay conclusion should bring together all of your points into a single paragraph. In this section, you have to summarize your findings and their implications for your readers explicitly, especially for those who partake in such dangerous practices.

If you feel like your argument is especially compelling, then you may even try to convince your audience to take on the role of spreading information about the dangers of texting and driving themselves. After all, it does affect even pedestrians.

Therefore, appealing to the fact that it may alter the life of any person, who is unfortunate enough to be close to a distracted driver, may be the main idea of your paper.

Finally, there is the matter of choosing your title. Texting and driving essay titles should immediately give your readers an idea of what they will encounter in your work and what kind of knowledge they will gain from it.

Be honest, but do not be afraid to write an attention-catching title. There is nothing academically worse than writing a well-structured and thought-out essay that readers overlook because it lacks a catchy title.

Still not sure how to start? Use IvyPanda to get more inspiring paper samples!

• Texting While Driving Should Be Illegal To begin with, it has been observed from recent studies that have been conducted that majority of American citizens are in complete agreement that texting while one is driving should be banned as it is […]
• Banning Phone Use While Driving Will Save Lives For instance, a driver may receive a phone call or make one, and while tending to the call, takes his mind of the road and increasing the chances of causing an accident.
• Increase in the Use of Mobile Phones and it’s Effects on Young People The purpose of the present paper is to critically evaluate the effects, both positive and negative, of increased use of mobile phones on young people, and how these effects can be mitigated to avoid negative […]
• Effects and Causes of Cell Phone Usage Among Male It will specifically determine the various factors that may cause the use of cell phones among the male students, and how the use can affect the students in the several possible ways ranging from psychological […]
• Dangers of Texting while Driving The research paper will present some statistics to prove that texting while driving is one of the biggest contributors of road accidents in American roads.
• The New Application “Stop Texting and Driving App” The application installed in the driver’s smartphone will disable every function when the vehicle is in motion. The device and the application have more features in order to reduce the rate of having an accident.
• The South Dakota Legislature on Texting and Driving According to the authors of the article, the South Dakota Legislature needs to acknowledge the perils of texting and driving and place a ban on the practice.
• Texting in Modern Society Some people may argue out that texting is time consuming, that is okay because they are right to some extent, but, the effectiveness of their statement is reduced when the benefits of the text are […]
• The Effect of Pets on Driver Distraction The idea is not only to focus on the type of distracter that is dangerous, but also to focus on anything that can lead to any sort of driver distraction.
• Banning Texting while Driving Saves Lives Other nations have limited use of phones, by teenagers, when driving, and a rising number of states and governments have prohibited the exact practice of texting while driving.
• Saving Lives: On the Ban of Texting While Driving To achieve the goals of the objectives proposed above, a comprehensive case study needs to be conducted on the risks of texting while driving and how the prohibition of the act will save lives.
• A Theoretical Analysis of the Act of Cell Phone Texting While Driving The past decade has seen the cell phone become the most common communication gadget in the world, and the US has one of the highest rates of cell phone use.
• An Analysis of the Use of Cell Phones While Driving The first theory is the theory of mass society, and the second theory is the theory of the culture industry. The theory of mass society states that, popular culture is an intrinsic expression of the […]
• Reducing Texting, Drinking And Driving, And Smoking Tobacco
• Texting While Driving Is A Secondary Offense For School Bus
• The Laws and Programs Intended to Prevent Texting While Driving
• Public Service Announcements on Texting and Driving
• Why Texting And Driving Should Be Banned
• Texting While Driving Or Using Any Mobile Device
• Texting And Driving : Using A Cell Phone While Driving
• The Dangers of Texting While Driving to You and the Innocent Bystanders Around You
• The Unsafe Practice Of Teenage Texting And Driving
• The Effects Of Texting While Driving On The World Today
• Comparing and Contrasting Texting and Driving vs Drinking and Driving
• Texting While Driving Is More Dangerous Than Multitasking
• The Solution to Texting and Driving
• Texting While Driving Is Now The Leading Cause Of Death Among Teenagers
• The Risks and Consequences of Texting While Driving
• Physiological Reason Behind Texting and Driving
• Why Is It Important To Talk About Texting and Driving
• Texting While Driving : Should Not Be Created And Enforced For Distracte
• The Issue of Texting While Driving in United States
• The Cause And Effect Of Texting And Driving
• Why Texting While Driving Should Be Illegal
• The Importance of Curbing the Habit of Texting While Driving
• The Dangers of Texting and Driving: Why It’s Against the Law
• What Are the Dangers of Texting While Driving
• Texting While Driving Is A Major Concern Worldwide
• The Problem and Impact of Texting While Driving in the United States
• Dangers Of Texting And Using Cell Phones While Driving
• Investigating The Dangers Of Texting While Driving
• Laws Against Texting While Driving Should Be Made Stronger
• There’s No Surviving If Texting While Driving
• A Study on the Effects of Phone Conversations and Texting on Driving Performance
• Teenage Driving: Texting While Driving
• Texting While Driving as a Serious Issue and Crime
• How We Can Help Stop Texting While Driving
• A Discussion on the Problem of Texting and Driving in America
• The Impact of Texting While Driving to the Society
• Should Drivers Be Banned from Talking and Texting While Driving
• Should Texting While Driving Be Banned in Texas
• Why Is a Mobile Phone While Driving an Important Thing and a Part of Everyday Life?
• How Does Cell Phone Distraction Affect Driving?
• What Do Psychological Studies Say About Using a Mobile Phone While Driving?
• Is Cell Phone Use While Driving a Major Distraction Factor That Causes Accidents?
• What Are the Dangers and Safety Risks of Talking on a Cell Phone While Driving?
• Should Texting and Driving Have More Serious Consequences?
• What Are the Risks and Consequences of Texting While Driving?
• How Do Texting and Driving Affect You Mentally?
• Why Should a Nationwide Ban on the Use of Cell Phones While Driving Be Mandatory?
• Should the Government Ban Drivers from Using Cell Phones And/or Texting While Driving?
• What Laws and Programs Are There to Prevent Texting While Driving?
• Do We Need Laws Prohibiting the Use of Cell Phones While Driving?
• What Are the Opposing Views and Solutions to Distracted Driving?
• Has Texting and Driving Become a Huge Epidemic in Recent Years?
• What Is the Long-Term Benefit of Banning the Use of Portable Wireless Devices While Driving?
• Should Laws Against Texting While Driving Be Strengthened?
• What Common Driving Distractions Lead to Accidents?
• Is Texting and Talking a Fatal Distraction While Driving?
• Why Do Teenagers Use Mobile Phones While Driving?
• Are Cell Phones a Hidden Threat and Why Should Laws Prohibit Their Use While Driving?
• What Are the Main Factors Contributing to the Problem of Distracted Driving?
• How to Prevent People from Texting While Driving?
• Why Should Texting While Driving Be Illegal?
• Is Texting Worse Than Talking While Driving?
• What Are the Pros and Cons of Using a Mobile Phone While Driving?
• How Many Car Accidents Are Caused by Texting?
• What Are the Psychological Factors of Using a Mobile Phone and Driving?
• Chicago (A-D)
• Chicago (N-B)

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Support for distracted driving laws: An analysis of adolescent drivers from the Traffic Safety Culture Index from 2011 to 2017

Caitlin n. pope.

a Graduate Center for Gerontology, Department of Health, Behavior & Society, College of Public Health, University of Kentucky, Lexington, KY 40536, United States

b Center for Injury Research and Policy, Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205, United States

Toni M. Rudisill

c Department of Epidemiology, School of Public Health, West Virginia University, Morgantown, WV 26506, United States

d Center for Injury Research and Policy, Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205, United States

CRediT authorship contribution statement

Introduction:

Adolescent drivers are often the focus of traffic safety legislation as they are at increased risk for crash-related injury and death. However, the degree to which adolescents support distracted driving laws and factors contributing to their support are relatively unknown. Using a large, nationally weighted sample of adolescent drivers in the United States, we assessed if perceived threat from other road users’ engagement in distracted driving, personal engagement in distracted driving behaviors, and the presence of state distracted driving laws was associated with support for distracted driving laws.

The sample included 3565 adolescents (aged 16-18) who participated in the Traffic Safety Culture Index survey from 2011 to 2017. A modified Poisson regression model with robust errors was fit to the weighted data to examine support for distracted driving laws. Models included age, gender, year, state distracted driving laws, personal engagement in distracted driving behavior, and perceived threat from other road users’ engaging in distracted driving.

Approximately 87% of adolescents supported a law against texting and emailing compared to 66% who supported a universal handheld cellphone law. Support for distracted driving legislation was associated with greater perceived threat of other road users engaging in distracted driving while accounting for personal engagement in distracted driving, state distracted driving laws, and developmental covariates.

Discussion:

Greater understanding of the factors behind legislative support is needed. Public health interventions focused on effectively translating the risks of cellphone use while driving and effective policy will further improve the traffic safety culture.

1. Introduction

Distracted driving is a prevalent and risky health behavior that has evolved over time with technology ( Insurance Institute for Highway Safety [IIHS], 2019 ). From manipulating car radios to smartphones, distracted driving presents an increased risk of injury for vehicle occupants of all ages ( Caird & Horrey, 2017 ; Caird, Johnston, Willness, Asbridge, & Steel, 2014a ; Caird, Willness, Steel, & Scialfa, 2008 ; Stavrinos, Pope, Shen, & Schwebel, 2018 ). Although adolescents are not the only offenders of distracted driving in the United States (U.S.) ( National Center for Statistics and Analysis, 2019 ), many factors contribute to their increased vulnerability. Adolescents not only have limited driving experience ( McKnight & McKnight, 2003 ) and possess immature cognitive control mechanisms ( Steinberg, 2005 ), but are regular users of smartphones and consumers of social media ( Pew Research Center, 2018 ). This may increase their likelihood of driving errors and adverse driving outcomes ( Durbin et al., 2014 ; Mayhew, Simpson, & Pak, 2003 ; Pope, Ross, & Stavrinos, 2016 ; Stavrinos et al., 2018 ), making them regular targets for driving restrictions (e.g., young driver laws, graduate driver licensing laws; Lim & Chi, 2013 ; Williams, McCartt, & Sims, 2016 ; Zhu, Chu, & Li, 2009 ; Zhu, Cummings, Chu, Coben, & Li, 2013 ).

Implementation of state-based distracted driving legislation in the U.S. to reduce distracted driving behavior has been inconsistent and has yielded inconclusive evidence regarding effective reduction in distracted driving engagement and related injuries within samples of adolescent drivers ( Lim & Chi, 2013 ; Rudisill, Smith, Chu, & Zhu, 2018 ; Rudisill & Zhu, 2017 ; Zhu, Rudisill, Heeringa, Swedler, & Redelmeier, 2016 ). Whereas many factors may contribute to lack of legislative effectiveness, little is known how drivers perceive distracted driving legislation ( Pope, Mirman, & Stavrinos, 2019 ; Sanbonmatsu, Strayer, Behrends, Ward, & Watson, 2016 ). Perceptions of legislation has been shown to be correlated with health behavior compliance for studies with smoking bans in smoke-free restaurants and bars ( Borland et al., 2006 ) and self-reported rear seat belt usage ( Taylor & Daily, 2019 ). Nonetheless the presence of legislation and how it is perceived does not directly infer adoption of the law or subsequent behavior change, but it may provide further understanding into factors that could facilitate noncompliance and in the presence of legislation inherently undermine legislative effectiveness ( McCartt, Oesch, Williams, & Powell, 2013 ). Additionally, while national surveys have polled drivers of different ages on their degree of support for distracted driving legislation (e.g., AAA’s Traffic Safety Culture Index and the National Highway Safety Administration’s [NHTSA] National Distracted Driving Telephone Survey) little is known about what behavioral factors are associated with support for legislation, especially for groups of vulnerable road users such as adolescent drivers.

Engagement in distracted driving is a complex behavior that involves both automatic and premeditated actions. To better understand variability in drivers’ decisions to engage in this risky health behavior, researchers have utilized constructs of behavioral theory ( Gauld, Lewis, & White, 2014 ; Nemme & White, 2010 ; Shevlin & Goodwin, 2019 ). One such study, Pope et al. (2019) , used a construct of perceived personal threat from the Health Belief Model (i.e., perceived susceptibility and severity of a health problem; Champion & Skinner, 2008 ; Jones et al., 2015 ; Rosenstock, 1974 ), in the context of support for distracted driving legislation. Using a convenience sample of 379 adolescents aged 15-19 surveyed from high school driver’s education classes from the southeast region of the U.S., Pope et al. (2019) found that adolescents were more likely to support legislative restrictions on texting and handheld cellphone use while driving when they perceived other drivers’ engagement in distracted driving as more threatening to their own safety, while accounting for peer’s perceptions and driver age and gender. Whereas this study and others ( Hallett, Lambert, & Regan, 2011 ; Sanbonmatsu et al., 2016 ) have begun to assess the importance or non-importance of support for distracted driving legislation in smaller convenience samples of drivers, much remains unknown on how these associations generalize to the larger samples of adolescent drivers in the context of additional influencing factors at both individual- and environmental-levels ( McLeroy, Steckler, & Bibeau, 1988 ). For example, Sanbonmatsu et al. (2016) found that frequency of cellphone usage negatively correlated with support for distracted driving legislation in a college-aged sample of drivers. Additionally, reports from a nationally representative survey conducted by NHTSA suggested that legislative context also matters. More adults reported supporting a handheld cellphone ban in states that had such ban ( Schroeder, Wilbur, & Peña, 2018 ), when compared to states with no enacted ban. Increasing our knowledge of behavioral constructs, such as perceived threat, in the context of other influencing factors that may contribute to support for distracted driving legislation in larger, more representative samples of vulnerable drivers will help facilitate targeted prevention efforts that when combined with effective law enforcement will help change the U.S. traffic safety culture on distracted driving.

This study aimed to help fill the empirical gap of how threat perceptions are associated with support for distracted driving legislation in the context of other empirically reported covariates (engagement in distracted driving behavior and the presence of state-based distracted driving legislation) using a larger, more generalizable sample of adolescent drivers from the Traffic Safety Culture Index survey across seven consecutive years (2011-2017). The distracted driving legislation of interest included: a) a law against reading, typing, or sending a text message or email while driving, and b) a law against using a handheld cellphone while driving, for all drivers regardless of their age.

1.1. Hypotheses

It was hypothesized that greater perceived threat of other road users’ engagement in distracted driving behaviors would be associated with:

• greater concurrent likelihood of supporting a law against reading, typing, or sending a text message or email while driving, and
• greater concurrent likelihood of supporting a law against using a handheld cellphone while driving, for all drivers regardless of their age.

Additionally, we hypothesized that the associations would hold after accounting for the reported frequency of engagement in distracted driving behavior, the presence of distracted driving laws in the driver’s state of residence, the driver’s age and gender.

2.1. Data sources and study population

Cross-sectional survey data were obtained from the 2011–2017 Traffic Safety Culture Index questionnaires conducted by the AAA Foundation for Traffic Safety. The Traffic Safety Culture Index is an annual survey of attitudes and behaviors with representation from all 50 U.S. states and the District of Columbia (D.C.). Data collection typically begins in late summer lasting approximately three weeks.

A panel consisting of approximately 55,000 individuals were recruited each year using random-digit dialing and addressed-based sampling methods. In order to get national representation, sampling was stratified by U.S. Census Region. For most years, adolescent participants were indirectly recruited from a random sample of adult panel members who indicated being a parent/guardian with at least one individual aged 16–18 residing in the household. For households with multiple residents in that age range, one participant was randomly selected for inclusion in the sample. Data for adolescent participants included a weight, provided by the AAA Foundation for Traffic Safety that incorporates indirect sampling non-response. A full description of the survey methodology from every year is available online ( https://aaafoundation.org/research/ ). The questionnaire was administered electronically after obtaining consent from both the parent/guardian and the participating adolescent. After excluding adolescents who did not report driving in the last 30 days ( n = 1,869), the analytic sample included 3,565 drivers aged 16–18 who had reported driving within the last 30 days. Participant data were obtained without personal identifiers, thus the current study was exempt from institutional review.

To assess the concurrent impact of state distracted driving laws on adolescent drivers’ support, survey data was merged with state law information. Survey start dates were linked to the effective date of a state distracted driving law from 2011 to 2017. Specific details regarding the law type and effective date were obtained by using state-specific legal codes for distracted driving laws ( IHHS & Highway Loss Data Institute [HLDI], 2020 ) to search in the LexisNexis Academic database and state legislative archives ( Statescape,, 2019 ). Laws were independently coded by two researchers, with group discussion and input from a third researcher to resolve disagreements.

2.2. Variables

2.2.1. support for distracted driving legislation.

Two items measured support for distracted driving legislation. Adolescents were prompted to report how strongly they supported or opposed having a law against texting or emailing while driving (“Having a law against reading, typing, or sending a text message or email while driving”) as well as a universal handheld cellphone law (“Having a law against using a handheld cellphone while driving, for all drivers regardless of their age”). Each outcome was a priori dichotomized to provide interpretations consistent with past research on support towards distracted driving legislation ( Pope et al., 2019 ; Sanbonmatsu et al., 2016 ): “support” (combining response choices of “support strongly” and “support somewhat”) and “oppose” (combining response choices of “oppose somewhat” and “oppose strongly”). The term ‘universal’ indicated the law applied to drivers of all ages.

2.2.2. Perceived threat

Two items measured how much threat the adolescent perceived from other road users’ engagement in distracted driving behaviors. Adolescents were prompted to report how much of a threat to your personal safety are drivers (a) “writing text messages or emailing” and (b) “talking on a cellphone while driving”. To account for threat perceptions across two related distracted driving behaviors, threat perceptions on related driving behaviors were combined into an overall score of perceived threat from distracted driving to reduce the number of model covariates. The perceived threat from distracted driving (PTDD) score was calculated by summing responses after assigning a numerical value to the Likert scale for each answer response: 0 = “not a threat at all”, 1 = “minor threat”, 2 = “somewhat serious threat”, and 3 = “very serious threat.” The PTDD score ranged from 0 to 6 (Cronbach α = 0.68), with higher PTDD scores indicating a greater perceptions of threat from other road users’ engagement in distracted driving behaviors.

2.3. Covariates

2.3.1. developmental covariates.

Adolescent age (in years) and gender (0 = female, 1 = male) were reported by the consenting parent/guardian at the beginning of the survey.

2.3.2. Engagement in distracted driving behavior

Three items measured self-reported personal engagement in cellphone-related distraction while driving over the last 30 days. Adolescents were prompted to report how often they engaged in (a) “reading a text message or emailing while driving”, (b) “typing or sending a text message while driving”, and (c) “talking on a cellphone while driving”. A distracted driving behavior (DDB) score was calculated in similar fashion to Pope, Bell, and Stavrinos (2017) to account for the three related distracted driving behaviors and reduce the number of covariates by summing responses after assigning a numerical value to the Likert scale for the frequency of engagement: 0 = “never”, 1 = “just once”, 2 = “rarely”, 3 = “fairly often”, and 4 = “regularly.” The DDB score ranged from 0 to 12 (Cronbach α = 0.85), with higher DDB scores indicating a higher frequency of self-reported engagement in distracted driving behaviors.

2.3.3. State distracted driving legislation

Three types of distracted driving laws were used to measure the presence of a state distracted driving law in the year the survey was administered. Laws were coded as binary variables (0 = “not present”, 1 = “present”) and included (a) universal handheld bans - prohibits any cellphone use while driving for all drivers, (b) universal texting bans - prohibits sending and receiving cellular text messages for all drivers and, (c) young driver bans - prohibits any cellular phone use for novice and young drivers (e.g., > 18 years old, drivers with a permit/intermediate driver license).

2.4. Statistical analysis

Driver demographics and attitudes regarding distracted driving laws were described using raw frequencies and percentages weighted to demographically align the entire sample of responding adolescents to U.S. residents, aged 16–18. Average scores and corresponding 95% confidence intervals (CIs) were calculated for distracted driving behaviors and perceived threat stratified by age, gender, race/ethnicity, and support for laws. The association between support and each item comprising the DDB and PTDD scores was assessed in a correlation matrix with Spearman rank correlation coefficients and Rao-Scott corrected chi-square p -values to address the complex sampling design.

A modified Poisson regression model with robust errors was fit to the weighted data to provide an estimate of relative risk of support for distracted driving legislation. Whereas logistic regressions are commonly used with binary outcomes, they are known to overstate relative risk for common outcomes (>10%; McNutt, Wu, Xue, & Hafner, 2003 ; Spiegelman & Hertzmark, 2005 ). Modified Poisson regression models provide a consistent estimate of relative risk with that of the log-binomial regression model, while the use of robust variance estimation adjusts the error of the estimate ( McNutt et al., 2003 ; Zou, 2004 ). Support for texting and universal handheld laws were dependent variables and independent variables included PTDD scores, adolescent age and gender, year of survey, state distracted driving legislation, and DDB scores. Estimated regression coefficients were exponentiated to provide risk ratios of support for a law against texting or emailing and a universal handheld cellphone law. In addition, two multivariable modified Poisson regression models were fit, and included all individual and environmental covariates. All statistical analyses were performed using SAS, version 9.4 ( SAS Institute Inc, 2015 ).

In the sample of 3,565 adolescent drivers, 16, 17, and 18-year-olds each represented approximately one-third of the sample (30.5%, 36.6%, 32.9%, respectively, see Table 1 ). Male (51.9%) and female (48.1%) respondents were proportionately distributed, and White, non-Hispanic responders were the largest racial/ethnic group with a total of 2,426 respondents (64.7%). Self-reported distracted driving increased with age and ranged from an average DDB score of 1.74 at age 16 (95% CI: 1.58, 1.90) to 3.47 at age 18 (95% CI: 3.26, 3.68). Female adolescent drivers had a higher average PTDD score (5.12, 95% CI: 5.06, 5.17) and lower DDB score (2.35, 95% CI: 2.21, 2.50) than male adolescents (DDB: 2.91, 95% CI: 2.75, 3.06; PTDD: 5.00, 95% CI: 4.95, 5.05). Statistically significant differences in mean PTDD and DBB scores were not observed by racial/ethnic group.

Characteristics of adolescent drivers 16–18 years of age in the Traffic Safety Culture Index, 2011–2017.

Note. CI = confidence interval.

Unweighted frequencies and weighted sample percentages are presented.

Most adolescent drivers expressed support (86.5%) for having a law against reading, typing, or sending a text message or email while driving. Compared to texting, there was less overall support (66.3%) for a universal law against using a handheld cellphone while driving. Adolescents who supported a texting law tended to have lower DDB scores ( M = 2.37, 95% CI: 2.26, 2.48) and higher PTDD scores ( M = 5.16, 95% CI: 5.12, 5.20) compared to those who opposed the legislation (DDB score M = 4.38, 95% CI: 4.04, 4.72; and PTDD score M = 4.38, 95% CI: 4.25, 4.50). Furthermore, this pattern extended to support for a universal handheld cellphone law, with a larger average PTDD score of supporters ( M = 5.27, 95% CI: 5.22, 5.31) compared to those in opposition ( M = 4.64, 95% CI: 4.57, 4.71).

Intercorrelations among the individual items that comprised the DDB and PTDD scores with support for distracted driving legislation were also examined in Table 2 . All items demonstrated statistically significant bivariate associations ( p < .001), except for frequency of calling with perceived threat for other road users’ engagement in texting or emailing while driving. Self-reported distracted driving behaviors were positively correlated with one another and inversely related to support for laws.

Correlation matrix of self-reported engagement in distracted driving and perceived threat of distracted driving for adolescent drivers.

Note. The matrix presents Spearman rank correlation coefficients and Rao-Scott corrected chi-square p -values for the bivariate test of association.

Results from the multivariable model showed perceived threat was associated with support for having a law against texting and emailing while driving (adjusted relative risk [aRR]: 1.08, 95% CI: 1.06, 1.10) as well as a universal handheld cellphone ban (aRR: 1.17, 95% CI: 1.13, 1.21). This suggests that for every 1-unit increase in the PTDD score there was an associated 8% and 17% increase in the likelihood of supporting a law against reading or sending a text message or email while driving and a universal handheld cellphone law, respectively. Regarding covariates, a state universal handheld ban was related to a statistically significant increase in the likelihood of support for a law against handheld cellphone use (aRR: 1.33, 95% CI: 1.26,1.41), however this association was not observed for support of a texting law after adjusting for other factors (aRR: 1.01, 95% CI: 0.98, 1.04). A state universal texting ban was associated with increased support for a texting law (aRR: 1.07, 95% CI: 1.01, 1.13), but marginally related to support for a universal handheld cellphone law (aRR: 0.91, 95% CI: 0.83, 1.00).

Self-reported engagement in distracted driving behaviors was inversely associated with support for a law against reading or sending texts or emails while driving (aRR: 0.98, 95% CI: 0.97, 0.98), as well as support for a universal handheld cellphone law (aRR: 0.96, 95% CI: 0.95, 0.97). This would suggest that for every 1-unit increase in the DDB score there was an associated 2% and 4% decrease in the likelihood of supporting a law against reading or sending a text message or email while driving and a universal handheld cellphone law, respectively. Lastly, no statistically significant associations between adolescent gender, year of the survey, the presence of state young driver bans, and support for either distracted driving law (see Table 3 ). Although 18-year-olds were more likely to support having a law against texting while driving when compared to 16-year-olds (aRR: 1.05, 95% CI: 1.01, 1.10), there was no evidence of an age difference in the support for a universal handheld cellphone law.

Support for distracted driving legislation among adolescent drivers 16–18 years of age in the Traffic Safety Culture Index, 2011–2017.

Note. CI = confidence interval; cRR = crude relative risk; aRR = adjusted relative risk.

Bold results indicate that the 95% confidence interval does not span 1.00 in the multivariable analysis.

4. Discussion

As distracted driving continues to evolve, preventative measures such as legislation must address modifiable factors that promote safe driving behavior. This study extended previous work from smaller convenience-sample studies on support for distracted driving legislation and threat perceptions ( Pope et al., 2019 ; Sanbonmatsu et al., 2016 ) by using a large, national weighted sample of adolescent drivers in the context of other related factors (e.g., developmental covariates, engagement in distracted driving, and state legislation) to produce more generalizable results.

Over the seven-year study period, most adolescent drivers expressed support for having a law against reading, typing, or sending a text message or email while driving, as well as a universal law against using a handheld cell phone while driving. Adolescents who reported supporting universal texting or handheld laws were more likely to report greater perceived threat towards other road users’ engagement in distracted driving. Greater support for these laws was also associated with less engagement in distracted driving behaviors and residing in a state with the respective universal cellphone ban in effect.

Overall, greater perceived threat was associated with greater support for both types of legislation, but a stronger association was found for a universal handheld cellphone law. One potential reason for the stronger association could be due to familiarity with the dangers of texting while driving versus handheld cellphone use. In past years, communications from federal and state platforms as well as the mainstream media have heavily focused on the risk of texting while driving (e.g., NHTSA campaign- U Drive. U Text. U Pay. ), which may overshadow the overall dangers of handheld cellphone use. This might help explain, from a national traffic safety culture perspective, why a disproportionate number of states have adopted texting while driving laws (48 states plus D.C.) compared to those prohibiting handheld cellphone use while driving (25 states plus D.C.; IHHS & HLDI, 2020 ). Although the risks of texting while driving remain embedded in the national safety culture, emphasizing further dangers of handheld cellphone use is also needed. Any cellphone use that occurs behind the wheel has been shown to increase crash risk and decrease driving performance ( Caird, Johnston, Willness, Asbridge, & Steel, 2014b ; Caird et al., 2008 ; Stavrinos et al., 2018 ).

Increasing awareness of the dangers of handheld cellphone use while driving, even before licensure, may increase risk perceptions and translate into greater support for distracted driving legislation. Whereas perceived threat to safety from other’s engagement in distraction was examined in the current study, perceived responsibility and control beliefs regarding one’s behaviors and decisions may also play an important role. For example, adolescents’ risk-taking perceptions ( Carter, Bingham, Zakrajsek, Shope, & Sayer, 2014 ) and their perceived control beliefs ( McDonald & Sommers, 2015 ) have been shown to be associated with engagement in distracted driving behaviors. Unknown, is how these determinants along with other psychosocial and behavioral constructs from health promotion, psychology, or related disciplines may provide a more comprehensive understanding into support for distracted driving legislation.

In addition to perceived risk, other covariates from the literature, such as engagement in distracted driving ( Sanbonmatsu et al., 2016 ) and related-legislation within the state the adolescent resided in ( Schroeder et al., 2018 ), were included to help capture the complex nature of distracted driving. Self-reported engagement in distracted driving was inversely associated with support for cellphone legislation and perceived threat of other drivers’ engagement in distracted driving. These findings support those reported by Sanbonmatsu et al. (2016) , who also found a negative correlation between support for legislation and engagement in personal cellphone use while driving. Importantly, while less frequent engagement or non-engagement in distracted driving was associated with greater support, Sanbonmatsu et al. (2016) also noted a dissonance between engagement and support, as a large number of those who engaged in distracted driving within their sample also reported supporting legislation which restricted cellphone use. While engagement in distracted driving and living in a state with distracted driving legislation helps contextualize distracted driving, it does not holistically capture all relevant individual-level factors. Driving exposure and experience (e.g., amount of supervised practice, driving frequency, driving in diverse environments; McKnight & McKnight, 2003 ; Mirman et al., 2014 ), cognitive control (e.g., development of cognitive processes, developmental disabilities such as Attention-Deficit/Hyperactivity Disorder or Autism Spectrum Disorder; Bishop, Boe, Stavrinos, & Mirman, 2018 ; Pope, Bell, & Stavrinos, 2017 ), and the amount of social media usage; Tian & Robinson, 2017 ) may also play an important role as these factors also influence engagement in distracted driving and overall traffic safety.

Regarding enacted distracted driving legislation in the state where the adolescent resided, findings were similar to those reported in the NHTSA’s National Survey on Distracted Driving Attitudes and Behaviors ( Schroeder et al., 2018 ). We found a context dependent association: adolescents were more likely to support the law if it was present in their specific state. Interestingly, the association was stronger for universal handheld bans when compared to a law against texting and emailing. Previous studies have shown that universal handheld bans reduce both self-reported and observed cellphone use behaviors ( Rudisill, Smith, et al., 2018 ; Rudisill & Zhu, 2017 ; Zhu et al., 2016 ). Although enforcement of this distracted driving legislation can be difficult for police officers ( Rudisill, Baus, & Jarrett, 2018 ), citations for handheld cellphone use surpass that of texting or young driver bans ( Rudisill & Zhu, 2016 ). More research is needed to assess the role of universal handheld bans in combination with other environmental-level factors, such as enforcement, as these factors may influence perceptions and support for handheld bans.

Lastly, with respect to developmental covariates, a statistically significant effect was found between age and support for a law against texting or emailing while driving, while no significant associations were found with gender for either outcome of interest. Although age may impact engagement toward distracted driving across the lifespan ( Pope et al., 2017 ), we found that age played a minor role in explaining support for distracted driving laws in adolescent drivers. Additionally when assessing unadjusted differences, self-reported engagement in distracted driving behaviors increased with every year of age and females reported more perceived threat and less engagement in distracted driving behaviors when compared to males, consistent with other empirical studies ( Morrongiello & Hogg, 2004 ; Rudisill & Zhu, 2017 ; Shevlin & Goodwin, 2019 ; Struckman-Johnson, Gaster, Struckman-Johnson, Johnson, & May-Shinagle, 2015 ).

5. Strengths and limitations

Although the current study was strengthened by the large sample size over multiple years of data, probabilistic sampling methodology used to obtain the data was designed to produce a representative sample of adults, not adolescents, most of whom were indirectly recruited from adult survey panel members. However, as probability of inclusion was known for adult panelists and surveyed adolescents, the weights used in the analysis incorporated these selection probabilities along with a post-stratification adjustment to reflect the population of non-institutionalized U.S. residents. Additionally, results were based on self-reported data of a behavior that is illegal or stigmatizing, which are subject to social desirability bias and could result in underestimates of behavior. Adolescents may have suffered from social desirability in regards to reporting support for legislation. Despite this limitation, self-report still remains a primary way to measure engagement in distracted driving, and is one of the most direct methods for measuring attitudes and perceptions. Although we identified several individual- and environmental-level covariates that are associated with support for distracted driving legislation among adolescent drivers, we could not account for other related covariates such as overconfidence, enforcement, driving exposure, cognitive control, risk-taking perceptions, and control beliefs.

Whereas support for distracted driving legislation was a priori dichotomized to provide interpretations consistent with past research on support towards distracted driving legislation ( Pope et al., 2019 ; Sanbonmatsu et al., 2016 ), dichotomizing a 4-point Likert scale (e.g., combining strongly and somewhat support to support) did not take advantage of the full range of Likert-response data and may have contributed to a loss in measurement information or reduction in power ( MacCallum, Zhang, Preacher, & Rucker, 2002 ). Analyses were reran as an ordinal logistic regression model, but were not used as the proportional odds assumption was violated. Given the goal of the current study was to assess variables of interest in the context of support or opposition, future studies should dig deeper into the concept of legislative support to determine if there is a meaningful difference between varying levels of support and measures of driver behavior. Lastly, while we identified associations we could not test for causal associations between support for laws and engagement in distracted driving because the study was cross-sectional. More research is needed to examine whether support evolves with driving experience or if it directly influences cellphone use while driving.

6. Conclusion

In a nationally representative sample of U.S. adolescent drivers, support for distracted driving legislation was associated with more perceived threat of other road users’ engagement in distracted driving behaviors after accounting for personal engagement in distracted driving behaviors, distracted driving legislation in their state of residence, and developmental covariates. Building off of investigations in previous, smaller convenience samples of drivers this paper provides further evidence that threat perceptions are associated with support for distracted driving legislation using a more generalizable sample of adolescent drivers while accounting for other relevant factors. More public health efforts are needed to increase public awareness of the dangers of handheld cellphone use while driving, as well as the effectiveness of universal handheld bans in reducing distracted driving. Health promotion and behavioral interventions should incorporate perceived threat along with other behavioral determinants into these efforts, while assessing potential differences between reported support and actual compliance with the law. Although support for distracted driving legislation does not guarantee compliance, findings provide insight into how adolescents perceive these preventative measures. By targeting modifiable psychosocial and environmental factors, we can promote better adoption of these polices and enhance the traffic safety culture.

Acknowledgements

The opinions, views, or comments expressed in this paper are those of the authors and do not necessarily represent the official position of funding departments. This work was funded in part by the National Institutes of Health (R01HD074594, 2013-2022). The data were obtained from the AAA Foundation for Traffic Safety. The authors have no conflict of interest to disclose. The data were presented at the 99 th Transportation Research Board annual meeting.

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