research paper on texting and driving

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Texting while driving: A discrete choice experiment

Anne m. foreman.

a National Institute for Occupational Safety and Health, United States

Jonathan E. Friedel

Yusuke hayashi.

b Pennsylvania State University, Hazelton, United States of America

Oliver Wirth

CRediT authorship contribution statement

Texting while driving is one of the most dangerous types of distracted driving and contributes to a large number of transportation incidents and fatalities each year. Drivers text while driving despite being aware of the risks. Although some factors related to the decision to text while driving have been elucidated, more remains to be investigated in order to better predict and prevent texting while driving. To study decision making involved in reading a text message while driving, we conducted a discrete choice experiment with 345 adult participants recruited from Amazon’s Mechanical Turk. Participants were presented with multiple choice sets, each involving two different scenarios, and asked to choose the scenario in which they would be more likely to text while driving. The attributes of the scenarios were the relationship to the text-message sender, the road conditions, and the importance of the message. The attributes varied systematically across the choice sets. Participants were more likely to read a text message while driving if the sender of the message was a significant other, the message was perceived to be very important, and the participant was driving on rural roads. Discrete choice experiments offer a promising approach to studying decision making in drivers and other populations because they allow for an analysis of multiple factors simultaneously and the trade-offs among different choices.

1. Introduction

Distracted driving, or engaging in secondary tasks while driving, results in significant loss of life and monetary damages. In 2018, distracted driving resulted in 2841 deaths in the United States {National Highway Traffic Safety Administration, 2020 #2936}. Districted driving accounted for $39.7 billion or 16 % of all economic costs from motor vehicle crashes in 2010 ( Blincoe et al., 2015 ). Distracted driving can involve three types of distraction: visual, manual, or cognitive ( National Highway Traffic Safety Administration, 2017 ). Common distractions include reaching for an object (visual and manual), eating (manual), or talking to a passenger (cognitive). In 2017, 14 % of fatal crashes caused by distracted driving involved cell phone use ( National Center for Statistics and Analysis, 2019 ). Cell phone use while driving has been found to be just as dangerous as driving under the influence of alcohol. A driving simulations study comparing drivers talking on a phone and drivers with a blood alcohol concentration of 0.08 % found that the distracted drivers suffered performance deficits that were just as pro-found as the drivers under in the influence of alcohol ( Strayer et al., 2006 ).

One of the most pernicious forms of distracted driving is texting while driving (TWD) because it involves visual, manual, and cognitive distractions ( Alosco et al., 2012 ). During a simulated driving task, 66 % of drivers exhibited lane excursions while texting ( Rumschlag et al., 2015 ), and in another simulation study, TWD led to five times more crashes than driving without texting ( Bendak, 2015 ). A study examining the effects of texting on the simulated driving performance of young drivers found that in the TWD condition, drivers spent up to 400 % more time not looking at the road compared to conditions in which they were not texting ( Hosking et al., 2009 ). A meta-analysis of driving simulation studies concluded that reading and typing text messages while driving diverts attention away from the road, increases response time to hazards, and increases the risk of crashing ( Caird et al., 2014 ). Despite the risks, TWD remains prevalent. In a large naturalistic study, 23 % of drivers were observed using their phones and 9 % of drivers were observed TWD ( Kruger et al., 2018 ). Several surveys have also indicated a similar pattern. A survey of U.S. drivers found that for 30 days prior to the survey 48 % and 33 % reported reading or writing texts while driving, respectively ( Gliklich et al., 2016 ). In an online survey of drivers aged 18–64 years old, 31 % reported that they had read or sent text or e-mail messages while driving in the last 30 days ( Naumann and Dellinger, 2013 ).

Drivers text while driving despite being aware of the risks. College students reported TWD with relative frequency despite also agreeing that it is dangerous and should be illegal ( Harrison, 2011 ) and that it is just as dangerous as driving while legally intoxicated ( Terry and Terry, 2016 ). In another study surveying young drivers, the majority of respondents reported that initiating, replying to, and reading a text while driving were more dangerous than talking on a cell phone. The respondents in that study also rated reading a text while driving as a dangerous behavior (a mean score of 4.63 on a scale from 1 to 7), yet 92 % of the young drivers reported reading a text while driving ( Atchley et al., 2011 ). Research also suggests that drivers are more likely to text while driving farther from their destination ( Hayashi et al., 2018 , 2016 ), while stopped at a red light ( Bernstein and Bernstein, 2015 ), or while driving at slower speeds ( Oviedo-Trespalacios et al., 2017 ). Although some of these factors related to the decision to text while driving have been elucidated, more remains to be investigated in order to better predict and prevent texting while driving.

Behavioral economics has been one tool that has been used more recently to quantify some of the factors that affect the decision to text while driving. Behavioral economics has been defined as “the application of economic concepts and approaches to the molar study of individuals’ choices and decisions” ( Bickel et al., 2014 , p. 643). The behavioral economic process of discounting has been used to conceptualize TWD; discounting refers to the process by which delayed or probabilistic outcome loses its value as a function of the delay to or probability of the receipt of the outcome, respectively. Individuals who more steeply discount delayed outcomes are more likely to engage in text messaging while driving ( Hayashi et al., 2015 ). Additionally, discounting can quantify the likelihood with which people are willing to wait to respond to a text message when driving ( Hayashi et al., 2016 ). A similar study examined how the likelihood of a car crash affected the likelihood of waiting to respond to a text message ( Hayashi et al., 2018 ).

1.1. Discrete choice experiments

Although the decision to text while driving likely involves numerous factors, previous studies have only examined some of the factors, usually in isolation of other factors, or studies have compared multiple quantitative outcomes (e.g., distance and probability of a car crash). One method for examining choice behavior that is frequently used in other fields but has not been as broadly applied with safety-related choices is discrete choice experiments (DCEs). With a DCE, one can easily arrange choices among options that differ according to both qualitative and quantitative factors. The approach is also well-suited to choice contexts that include multiple factors that are inextricable or may potentially interact with one another. The goal of the present study was to assess the effects of multiple factors simultaneously on the likelihood of TWD by using a DCE.

DCEs are a behavioral economic approach to systematically assess individual preferences among products or services, and they have been widely implemented in marketing ( Chandukala et al., 2008 ), health economics ( de Bekker-Grob et al., 2012 ), and environmental valuation ( Hoyos, 2010 ), among other fields. For example, a participant in a DCE might be asked to choose between two different products (e.g., cell phones that differ in screen size, storage amount, and price) or two different services (e.g., diabetes treatment programs that differ in length, content, and cost). In a DCE, two or more alternatives presented to the participant are termed a “choice set”, and the characteristics or features of each alternative are termed “attributes”. A typical DCE is comprised of multiple choice sets in which the attributes of the alternatives are systematically varied. Analysis of the participants’ choice patterns across the choice sets can reveal the influence of the different attributes on choice. Additionally, DCEs can be used to understand any trade-offs among the attributes that affect preferences. Previous studies investigating texting while driving have not evaluated multiple qualitative variables (e.g., weather, road conditions, etc.) simultaneously, and the DCE is an appropriate methodology with which to do so.

DCEs were first proposed by McFadden (1974) and are rooted in the random utility theory ( Thurstone, 1927 ) of behavior. As it relates to DCEs, the random utility theory proposes that each choice alternative being considered has a latent “utility” and individuals will always choose the choice alternative that has the greatest utility. As utility is a latent construct, it is not directly observable, but it can be derived by studying choice patterns ( J. J. Louviere, 2001 ). Discrete choice experiments are designed to systematically vary the attributes associated with choice sets to determine the utility derived (or lost) by each attribute. The derived measures of utility consist of two components: systematic utility and random utility ( J. J. Louviere et al., 2010 ). The systematic component consists of the attributes of the alternatives and the characteristics of the individual. The random component consists of the factors responsible for the preference that cannot be identified (for example, if they went unmeasured) and measurement error that is an inherent part of any measurement procedure ( J. Louviere et al., 2000 ). The basic axiom of random utility theory is:

where U ni is the latent, unobservable utility that person n associates with choice alternative i , V ni is the systematic, explainable component of utility that individual n associates with choice alternative i , and ε ni is the random component.

1.2. Study objectives

The present study applied the behavioral economic framework of DCEs to study decision making involved in reading a text message while driving. Participants were presented with multiple choice sets, each involving two different scenarios, and asked to choose the scenario in which they would be more likely to text while driving. The attributes of the scenarios were varied systematically across the choice sets. To select the attributes to include, we conducted reviews of the relevant literature and consulted with subject matter experts. Previous research has found that individuals are more likely to respond to a text immediately rather than waiting to reply when the text sender is closer to them in social distance (e.g., a significant other) in both driving ( Foreman et al., 2019 ) and non-driving contexts ( Atchley and Warden, 2012 ). Other factors, such as the perceived importance of a phone call ( Nelson et al., 2009 ) and road conditions ( Atchley et al., 2011 ) have been found to be a strong predictor of talking on the phone while driving. Both middle-aged adults ( Engelberg et al., 2015 ) and younger adults ( Schroeder and Sims, 2014 ) report TWD at high rates while stopped at red lights. Therefore, we selected relationship to the text message sender, the perceived importance of the message, and road conditions as the attributes for the DCE.

In addition, we compared a sample of drivers who did and did not drive for work to assess whether this factor interacts with the aforementioned factors on the decision to text while driving. Motor vehicle crashes are the leading cause of workplace fatalities ( U.S. Department of Labor, 2019 ), and driver distraction has been shown to increase the likelihood of motor vehicle crashes among commercial large-truck drivers ( Peng and Boyle, 2012 ; Zhu et al., 2011 ) and increase the likelihood that a crash will be fatal ( Bunn et al., 2005 ). Employees who drive for organizations with stronger safety climates and greater management support for safe driving have reported lower rates of distracted driving ( Wills et al., 2006 ) and distracted-related crashes ( Swedler et al., 2015 ). It is conceivable that those who drive for work within an organization that emphasizes safe driving may engage in safer driving practices outside of work. Therefore, it would be important to evaluate whether there were any differences in choice behavior between those who did and did not drive for work in our sample.

The main objective of the present study was to assess the most important factors that influence drivers’ decisions to read a text while driving. Based on the existing evidence, we hypothesized that the relationship to the sender would have the greatest utility in the selection of choice scenarios, followed by the perceived importance of the message and the road conditions. The design and implementation of a DCE to study TWD allowed for a simultaneous assessment of multiple categorical attributes that may affect the decision to read a text while driving in contrast to previous studies in which small numbers of continuous variables were examined independently (e.g., distance to destination and probability of a crash). A secondary objective was to conduct an exploratory analysis of the choices of participants who report driving for work and assess whether their choices differed from those of participants who did not drive for work.

2.1. Participants

Participants ( N = 345) were recruited from Amazon’s Mechanical Turk (MTurk), an online crowdsourcing platform in which individuals are compensated for completing short tasks or surveys (termed human intelligence tasks; HIT). Although a sample size of 100 participants is typically sufficient for DCEs ( Pearmain and Kroes, 1990 ), we wanted ensure sufficient power for evaluating interaction effects. Participants were eligible for the HIT of this study if they lived in the United States, were over 18 years of age, possessed a U.S. driver’s license, and owned or possessed a cell phone capable of sending or receiving text messages. Participants self-reported on each of the eligibility criteria. Individuals who drive for work also had to be employed outside of completing tasks on MTurk and drove a vehicle for work other than commuting. The survey HIT was only available to those who consistently completed HITs with a high degree of accuracy (i.e., 95 % of previously completed HITs accepted as satisfactory). The survey also included questions that were attention checks (see below), and data from participants who did not pass attention checks or who did not answer all of the questions were dropped from the analysis ( n = 20). Participants were compensated $1.00 for successful completion of the survey. The research was conducted with approval from Pennsylvania State University’s Institutional Review Board.

2.2. Materials

The survey was hosted on Qualtrics (Qualtrics XM, Provo, UT). Various sections of the survey, described below, were presented to participants in a pseudorandom order, as determined by Qualtrics’s randomization function. The survey questions contained within each section were presented in a fixed order.

The survey consisted of demographic questions (age, gender, race, ethnicity, and education), a brief assessment of the respondent’s driving habits, and the DCE. Other items that were included in the survey but are not relevant for the present study were the Distracted Driving Survey ( Bergmark et al., 2016 ), the Barratt Impulsiveness Scale (BIS) ( Patton et al., 1995 ) and an eight-item delay discounting questionnaire ( Gray et al., 2014 ). The respondents were asked if they were employed outside of completing HITs on MTurk and whether they drove for work, not including commuting to and from home.

2.3. Discrete choice experiment

Another section of the survey consisted of a DCE. The DCE was constructed based on a well-cited guidance document (e.g., Johnson et al., 2013 ). As an important first step, we conducted a review of the relevant literature related to TWD and consulted subject matter experts to develop a list of potential attributes and levels. The list of potential attributes and levels was then refined to the final three attributes and associated levels (see Table 1 ).

The attributes, levels, and definitions.

Considering the small number of attributes and levels, it was not possible to include all of the attributes in each choice profile while still keeping the total number choice sets to a minimum number necessary for the analysis. One consideration for determining the array of choice sets and the arrangement of those choice sets (i.e., choice alternatives in each question) is that including pairwise comparisons of every permutation of attributes and levels is often impractical, and, therefore, researchers must rely on a limited array of profiles in the final DCE. For example, with our attributes and levels, if all possible combinations of attributes and levels in Table 1 were compared in a two-alternative design, then there would be 36 (4 * 3 * 3) possible profiles and 630 (36 * (36−1) / 2) possible combinations of two-alternative choice questions. There are several techniques to determine a limited array of profiles across choice sets that will lead to sufficient information for later statistical analysis ( Johnson et al., 2013 ; Street et al., 2008 ). We elected to use a Bayesian D-optimal design constructed by JMP software (SAS Institute, Cary, North Carolina, USA). The final design resulted in 2 blocks of 12 choice sets with 2 choice profiles per set; all three attributes were permitted to vary within a choice set. Participants were randomly assigned to see only one of the two blocks of choice sets. A screenshot of one question from the DCE is shown in Fig. 1 as an example.

A screenshot of a DCE question presented to participants during the survey.

2.4. Data analysis

The analyses for the DCE portion of the survey was conducted with NLogit (Econometric Software, Inc., Plainview, NY). Sociodemographic characteristics that were included in the DCE analysis were modeled as non-random categorical parameters which were dummy coded. The levels for each attribute were also dummy coded. We first conducted analyses using the multinomial logit model based on the following equation: V = β 0 + β 1 RELATIONSHIP + β 2 ROAD + β 3 IMPORTANCE + ε, where V is the utility of a given driving scenario, β 0 is a constant reflecting a right- or left-side bias in scenario selection, and β 1 , β 2 , and β 3 are coefficients indicating the relative importance of each of the attributes. To account for the presence of preference heterogeneity across participants, we then estimated a mixed-logit model that included random effects as well as fixed effects because the random effects can often account for potential variation in relative preferences across participants ( Hauber et al., 2016 ). This is in contrast to the multinomial logit model, which assumes homogeneous preferences across participants ( Train, 2009 ). A mixed-logit model is similar to a mixed-effects regression in that some coefficients are fixed and others are random. The mixed-logit estimation relies on boot-strapped estimators. For our estimation, we specified 2000 Halton draws, indicating that the obtained choice data were a panel (i.e., multiple choices by each participant), and that there was unobserved preference heterogeneity. Initially, the mixed-logit models were estimated with random effects for each attribute and level. To determine the most parsimonious model, if the distribution of a random parameter was not statistically significant (at p < .05), then the specification of the parameter was reverted to a fixed effect. The final model (reported below) included N fixed effects and M random effects. We calculated marginal effects for each level based on the final mixed model ( Hensher et al., 2005 ).

The demographic characteristics of the respondents are shown in Table 2 . Approximately half of the respondents were male (51.1 %), white (83.1 %), non-Hispanic (92.6 %), and had at least some college education (91.4 %). Approximately 50 % of the sample drove for work.

Demographic Characteristics of the Sample.

The parameter estimates for the multinomial and mixed-logit models are in Table 3 . In the mixed logit model, all attribute parameters were initially specified as random linear parameters with normal distributions. In the initial mixed logit, only two of the random parameters (Very Important and Moderately Important) had significant standard deviations, indicating that there was significant variance in those parameters from the mean and thus a fixed parameter was not appropriate. Therefore, the attributes with significant standard deviations were retained as random parameters and attributes with non-significant standard deviations were reverted to fixed parameters. To assess the differences in choices between those who do and do not drive for work, the Drive for Work variable was included in the mixed-model as an interaction term. It is also important to note that the reported regression coefficients relate the attribute levels to the utility associated with responding to a text message that has that attribute. A positive regression coefficient indicates that utility is gained by responding to a text message with that feature and, all else being equal, on average a person is more likely to respond to a text message that has that feature. A negative regression coefficient indicates that utility is lost by responding to a text message with that feature and, all else being equal, on average a person is less likely to respond to a text message that has that feature. A significant coefficient ( p < .05) indicates that the attribute level had a significant effect on the decision to text while driving relative to the base case level, and the sign of the coefficient indicates the direction of the effect.

Beta coefficients and 95 % confidence intervals for the multinomial logit model (left) and mixed-logit model (right).

In the multinomial logit model, all of the regression coefficients were significant. With regard to the effects of the sender on the decision to read a text message while driving relative to the reference case (Casual Friend), Significant Other (β: 1.24, 95 % CI 1.08–1.39) had the greatest impact on utility, followed by Family Member (β: 1.21, 95 % CI 1.05–1.37) and Boss (β: 0.58, 95 % CI 0.43 to 0.72). In terms of the importance of the text message, in comparison to the reference case (Not Important), Very Important (β: 2.03, 95 % CI 1.86–2.20) messages had the greatest influence on utility followed by Moderately Important (β: 1.05, 95 % CI: 0.89–1.20) messages. In terms of the road condition, in comparison to the reference case (Rural), Highway (β: 0.40, 95 % CI 0.53 to 0.27) roads had the greatest influence on utility followed by City (β: 0.19, 95 % CI: 0.33 to 0.07) roads.

The pattern of results obtained with the mixed-logit model were similar to the pattern of results obtained with the multinomial logit model. In the mixed logit model, the coefficients for Moderately Important and Very Important were random effects indicating that there was preference heterogeneity across participants for the strength of the importance of the message in the decision to read a text while driving. This preference heterogeneity indicates that the pattern of results for the multinomial logit model, particularly the coefficients associated with Moderately Important and Very Important text messages, are not accurate.

For the Driving Status by attribute level interactions with the mixed-logit model, there were several significant interactions. In terms of sender, relative to the reference case (Not Drive for Work × Casual Friend), Drive for Work × Family Member (β: 0.86 95 % CI: 1.33 to 0.39) and Drive for Work × Significant Other (β: 0.75, 95 % CI: 1.28 to 0.22) had significantly less of an impact on the utility of a scenario. In terms of importance of the message, relative to the reference case (Not Drive for Work × Not Important), Drive for Work x Very Important (β: 0.75, 95 % CI: 1.28 to 0.22) and Drive for Work × Moderately Important (β: 0.75, 95 % CI: 1.28 to 0.22) also had significantly less of an influence on utility. Additionally, relative to the reference case (Not Drive for Work × Rural), Drive for Work × Highway (β: 0.51, 95 % CI: 0.08 to 0.93) and Drive for Work × City (β: 0.56, 95 % CI: 0.07–1.05) roads had significantly more of an impact on the utility of a texting scenario. The Drive for Work × Boss interaction coefficient was not statistically significant.

The marginal effect of each attribute level on the decision to read a text while driving—expressed as the change in the choice probability—is shown in Fig. 2 . These marginal probabilities indicate how each attribute level affects the likelihood of reading a text message relative to the basal level of reading a text message while driving. For example, a participant was 27 % more likely to read a text message if it was very important relative to their baseline likelihood of reading a text message. Thus, these marginal effects represent the translation of the mixed-logit utility function coefficients into how those attribute levels affect the choice to read a text message. The levels for Importance had the largest effect on choice and the Road Type had the smallest effect on choice. If the text in the scenario was Very Important, the scenario was 27 % more likely to be chosen. If the text in the scenario was Moderately or Not Important the scenario was 17 % more likely and 27 % less likely to be chosen, respectively. For the Relationship to the Sender attribute, if Family Member, Significant Other, or Boss was in the scenario, then that scenario was more likely to be chosen, but if Casual Friend was in the scenario, then it was 14 % less likely to be selected.

The percent change in the probability of the choice to read a text while driving in comparison to the reference case (on a Rural Road when you receive a Not Important text from a Casual Friend).

4. Discussion

4.1. factors affecting the decision to text while driving.

In the present study, a DCE was used to investigate the decision making of drivers when faced with hypothetical TWD scenarios. When drivers were asked under which of two circumstances they are most likely to read a text message, the factor that had the greatest effect on choice was the importance of the message. These results are consistent with previous studies that found that the perceived importance of a phone call was a strong predictor of talking on a cellphone while driving ( Nelson et al., 2009 ).

The sender of the text message had a smaller but still significant effect on drivers’ decisions to read a text message. The present findings are consistent with other studies on social distance. For example, when the sender of a message or a caller was more socially distant, teen drivers were more likely to ignore texts ( McDonald and Sommers, 2015 ) and less likely to talk on the phone ( LaVoie et al., 2016 ) while driving. In a study that examined texting while driving and social distance, participants were more likely to text while driving as the sender became closer to them in social distance (e.g., dearest friend or relative) ( Foreman et al., 2019 ). The present findings are also consistent with texting behavior outside of a driving context, as a study by Atchley and Warden (2012) that participants were more willing to wait to reply to a text message from someone who was more socially distant compared to senders who were closer to them in social distance.

In the present study, drivers were less likely to read a text message in city traffic (i.e., stop-and-go) compared to highway or rural road conditions. These results contrast the finding that young adult drivers reported that they were more likely to read a text message when stopped at a stop sign as well as in “calm” road conditions, and less likely to read a text message on the highway or in “intense” road conditions ( Atchley et al., 2011 ). The divergence in our results from the prior study may have occurred because the road conditions in the present study included both road type (city, highway, or rural) and amount of traffic (heavy, moderate, or light). It is possible that participants may not have read or completely comprehended the definitions for the different levels of road conditions that were provided. If we had fully separated the type of road conditions (e.g., congested, not congested) from the actions of the cars on that road (e.g., stopped traffic, slow traffic, fast traffic, etc.) then the results may have been consistent with previous research.

The analyses comparing participants who drove for work and those who do not drive for work were exploratory, and thus our conclusions are also limited. The utility of the attributes in the DCE scenarios were significantly different for those who drove for work compared to those who did not drive for work based on the significant interactions between Drive for Work and the levels of the attributes. Compared to those who did not drive for work, participants who drove for work derived less utility from the relationship of the sender (Family Member and Significant Other) and the importance of the message (Very Important and Moderately Important) and derived greater utility from the road conditions (City/Heavy Traffic and Highway/Moderate Traffic). Although the difference in the coefficients between the two groups were statistically significant, the reasons for these differences are unknown. Future studies could ask about the rules concerning distracted driving at their jobs because the policies and practices within organizations related to TWD may affect workers’ driving behavior at work and outside of work. Perhaps stricter policies around cellphone use while driving within workplaces would encourage those who drive for work to behave more safely during non-work driving. Additionally, including the amount of a potential fine for being caught TWD as an attribute could have helped to differentiate the groups. It is possible that those who drive for work are less risk averse while driving than those who do not, and there is evidence that those who drive for work in a company vehicle are involved in more car accidents than those who use a personal vehicle ( Clarke et al., 2005 ; Downs et al., 1999 ). The DCE in the present study only asked about decision making associated with TWD during non-work activities (e.g., personal errands), whereas decision making may differ depending on whose car is being driven or whether the driver is currently working.

Although the present study was primarily a demonstration of the DCE methodology with drivers’ decision making, there may be some implications for public policy and guidance. The results indicate that the factor with the greatest effect on the decision to read a text message while driving was the importance of the message, and message importance has been a focus of some insurance campaigns. For example, in 2018, many Allstate Insurance slogans included the phrase, “No text is important enough to risk a life,” in their social media advertisements for that company’s Drivewise program ( Kevin Olp: Allstate Insurance, 2018 ). In relation to texting while driving for work, a study of a cohort of individuals who drive for work found that one of the predictors of safety performance was management commitment to fleet and driver safety ( Wills et al., 2006 ). Therefore, adherence to company texting while driving policies by managers and supervisors (e.g., not sending drivers text messages while they are known to be driving) may help ensure a safer climate for their driving workers.

4.2. Study limitations and future directions

There are several limitations to the present study. First, the amount of information that could be extracted from the DCE design was somewhat limited given that only three attributes were included in the study design. In any well-designed DCE, there is a tradeoff between the number of attributes and levels in the study design and the amount of cognitive burden imposed on the participants. If there are too many attributes, then the quality of the data may suffer because the participants are not attending to all of the attributes ( Alemu et al., 2013 ). Future research could investigate a greater number of attributes, perhaps by using a partial profile design in which only a select number of attributes are presented to each participant ( Kessels et al., 2011 ). Although these designs do require a relatively large number of participants, the design decreases the potential cognitive burden on the participants while still allowing the researchers to examine a larger number of attributes.

A second limitation was that all of the attributes studied were categorical variables. Including a continuous variable, like cost of a driving citation, permits the computation of equivalence calculations, such as maximum acceptable risk ( Bridges et al., 2011 ). In the present study, inclusion of a variable like a monetary fine or penalty for TWD could have expanded the present analysis beyond only ranking the importance of the attributes and levels. Future research could incorporate continuous variables, such as risk of a crash or the amount of a fine, into a discrete choice experiment to assess how much risk would be tolerated under different texting scenarios.

Third, there was no “opt out” option in which the participant could select neither scenario. In the present study participants were forced to make a choice between texting in two different scenarios. It is quite possible that some participants would not have responded to a text message under any condition in a more naturalistic or real-life situation. This may have limited the realism of the DCE choice sets because, in real life, drivers can always choose to not engage in cellphone use while driving. Future studies could expand the DCE methodology to include writing text messages and other types of cellphone use while driving and include continuous variables and opt-out options to increase both the potential implications of the findings and the realism of the DCE, respectively.

Additionally, our sample was relatively young (the mean age was 36.2) and was primarily composed of non-Hispanic whites who had at least some college education. Although the demographic characteristics of drivers were not a focus of the present study, future research could examine differences in decision making across diverse groups.

DCEs can further expand avenues of research on distracted driving. In addition to assessing driver decision making and behavior, DCEs can also be used to assess preferences among different driver monitoring technologies, such as smartphone applications that block calls and screen notifications of email and text messages while driving. For example, a DCE could evaluate the acceptability of attributes of potential new technologies, such as the ease of use and cost of a new smartphone application, related to decreasing or preventing cellphone use while driving, especially considering that some current technologies (e. g., software that blocks phone use while driving) have not been adopted by drivers outside of research study protocols ( Creaser et al., 2015 ; Funkhouser and Sayer, 2013 ). Similarly, DCEs can be used with relevant driver populations to evaluate the potential effectiveness of new public service campaigns in changing driver behavior (cf. Hayashi et al. (2019) ). The aspects of a potential campaign, such as the tagline, included statistics (e.g., X number of drivers crash due to texting), and message framing (e.g., negative or positive), could be manipulated and shown to samples of drivers to assess under which combination of attributes they would be more compelled to alter their texting-while-driving behavior.

4.3. Conclusions

The present study demonstrated the use of a DCE to examine decision making of drivers related to reading text messages while driving. When choosing between two hypothetical scenarios in which the relevant factors were evaluated simultaneously, participants were more likely to read a text message while driving if the sender of the message was a significant other, the message was perceived to be very important, and the participant was driving on rural roads. DCEs offer a promising approach to studying decision making in drivers and other populations because they allow for an analysis of multiple factors simultaneously and the trade-offs among different choices. DCE methods provide safety researchers with additional survey designs and analytical tools to more effectively assess factors that directly influence safety-related decisions and behavior, which would contribute to the development of effective prevention and intervention strategies for the problem.

This work was supported by a Research Development Grant, Office of Academic Affairs, Pennsylvania State University, Hazleton.

Publisher's Disclaimer: Disclaimer

The findings and conclusions in this report have not been formally disseminated by the National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention and should not be construed to represent any agency determination or policy.

Declaration of Competing Interest

The authors have no conflicts of interest to declare. All co-authors have seen and agree with the contents of the manuscript and there is no financial interest to report. The submission is original work and is not under review at any other publication.

Appendix B. Supplementary data

Supplementary material related to this article can be found, in the online version, at doi: https://doi.org/10.1016/j.aap.2020.105823 .

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• Original Contribution
• Open access
• 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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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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Tackling Texting While Driving: ‘The Decision to Reach for That Phone Can Be Impulsive’

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You already know that you’re not supposed to text and drive. Your parents have lectured you endlessly about it, you’ve been taught the horror stories about it in driver’s ed class, and you probably live in one of the 49 states where it’s illegal for teens to text behind the wheel.

But the numbers suggest you’re not always getting the message.

Teens were responsible for 9% of all the fatal crashes involving distracted drivers in 2017, according to government figures. While the percentage seems small, that’s nearly 300 deaths that could have been prevented. Not to mention countless injuries.

Kit Delgado, an emergency room physician who’s also an assistant professor at the Perelman School of Medicine at the University of Pennsylvania, gets that it’s hard to keep your hands off your phone. He sees it all the time in patients who come into his ER, like the college student who was heading down the highway to pick up his girlfriend when he heard his phone ding. He picked it up, dropped it on the floorboard, reached down to get it and crashed into the guardrail.

“You talk to any teenager in the country, and they’ve been beaten over the head that texting while driving is dangerous,” Delgado says. “But the decision to reach for that phone can be impulsive, it can be emotional, it can be subconscious and automatic. Even though if you were to step out of the situation, you would say you shouldn’t be doing this.”

The Imperfection of Human Decision-making

Years of treating people who have been hurt in distracted driving crashes is a big reason why Delgado is researching this topic. He’s heading up a multimillion-dollar grant, one of the largest ever funded by the federal government, to figure out the best ways to use technology to help drivers put down their phones. The research team includes experts from the fields of medicine, behavioral economics , psychology, insurance and technology. They hope their work leads to the development of more smartphone programs that can nudge drivers into the correct behavior, like apps that automatically switch on to prevent incoming notifications while in the car.

“What my research group is trying to focus on is how can we design around the imperfection of human decision-making,” Delgado says. “I think we can make a big difference if we can solve for it the right way.”

For starters, Delgado says, “texting while driving” is an antiquated term for talking about the problem. Distracted driving means anything that takes your attention away from the road, whether it’s that Starbucks frappuccino you’re trying to sip, or arguing with your best friend about your Spotify play list. Conversations, eating, drinking, texting, checking emails and social media notifications, using navigation and music apps, even putting on lipstick all contribute to distracted driving.

“For me, it’s not necessarily about cell phones, it’s about all the facets that can be distracting,” notes Catherine McDonald, an assistant professor at the University of Pennsylvania School of Nursing who has been studying teen driving for a decade. She’s working on the grant with Delgado and, like him, is motivated by her own experiences as a nurse treating young people injured in car crashes.

“What’s important to remember about driving is that you’re making decisions not just about yourself, but about other cars that you’re not controlling.” — Catherine McDonald

The research is still in the data -collection phase. Some of that data is coming from an app developed by TruMotion and being used by Progressive Insurance to capture all kinds of driving information – like hard breaking, speed, acceleration and distance traveled. The information will help the researchers figure out how to best use smartphones to help drivers of all ages.

“Tech is pervasive in the lives of teens. It’s a part of their very fabric, and the technology that we think of often is their smartphones that are with them all the time,” says McDonald, who also works at the Children’s Hospital of Pennsylvania Center for Injury Research and Prevention. “This generation of drivers has grown up with the cellphone. They’ve had phones, they’ve seen parents with cellphones, so that piece of tech is a big part of their lives. When we move to the role of driving with teens, it’s figuring out how to keep them safe with that technology.”

To be fair, teens aren’t the worst offenders. Delgado says millennials – typically people between 25 and 34 – are the most distracted drivers of any age group. But the professors say that the lack of driving experience makes distractions most dangerous for teens. “We know it’s one of the leading contributors to fatal crashes in that group,” Delgado notes.

Teens may be doing things they think are safer, like waiting until they’re stopped at a red light to check notifications. But that’s time they could be using to assess what’s coming next – changes in cross-traffic patterns, a ball rolling into the street, a stalled car, and so on. “They need to be using all opportunities to take in information about the road,” McDonald says. “What’s important to remember about driving is that you’re making decisions not just about yourself, but about other cars that you’re not controlling.”

McDonald believes the distracted driving will decrease when society accepts the danger as a norm. For example, smoking, drunken driving and not wearing seat belts are all risky behaviors that have been reduced as people have internalized the message that they are dangerous. She also believes individualized approaches are needed, including assistive technologies.

The professors, guided by their research, were asked to give their best advice for teen drivers, and here’s what they suggest:

• Use a Do Not Disturb app, which is automatically activated on many smartphones. The app prevents you from receiving notifications while driving and sends auto-responses to calls or texts. Some have settings that allow certain notifications to get through, so you can be reached in an emergency.
• Use Apply Auto or Android Auto, available in newer cars, so you can give voice commands for most functions.
• Get a phone mount for your dashboard. This will help you avoid looking down to find or use your phone.
• Pick your playlist ahead of time. Music is one of the biggest distractions for teens, so set up your tunes before you start the vehicle.
• Designate a passenger to handle your phone so that you don’t have to.
• Talk to your parents so they understand you will not answer their calls when driving. Call them back as soon as you’ve reached your destination. “That’s a really simple conversation for a parent and a teen to have,” McDonald says. “Teens can initiate that, and it makes them really responsible.”
• Know the laws in your state. Each jurisdiction is different, but 20 states and Washington, D.C., ban all handheld phone use.
• Turn off your phone.

The professors practice what they preach. McDonald uses Apple Auto, and Delgado has a phone mount and a Do Not Disturb app. “It helps keep me honest,” Delgado says. “I’m busy like everyone else, and taking a few minor steps to counteract those urges to use the phone helps. It’s not easy, but there are a few things you can do that help more than willpower, which almost never works.”

That college student who crashed into the guardrail survived, but he had a head injury. Delgado wants to see more of his patients walk away from car crashes, and that starts with drivers understanding that nothing is more important than what they are doing behind the steering wheel.

“Because, at the end of the day, what really matters is not taking your eyes off the road,” Delgado says. “Anything that takes your eyes off the road for more than a second exponentially increases your crash risk.”

Hear the story of safe-driving advocate Liz Marks , who was 17 when she crashed her car while trying to read a text from her mom. She suffered a traumatic brain injury and facial injuries, and lost her sight in one eye and sense of smell.

Related Links

• State Laws about Texting
• Government Crash Statistics
• Penn Medicine Grant
• Insurance Institute for Highway Safety: Distracted Driving
• Take the Pledge to End Distracted Driving
• For More Tips on Using a DND Function

Conversation Starters

Dr. Delgado says that his study is trying to figure out “how can we design around the imperfection of human decision-making.” What does that mean and how does it apply to the issue of texting while driving? What other issues might it involve?

How many of the professors’ driving tips do you follow?

As a passenger, are you confident enough to speak up if you think the driver is distracted by their phone or just not paying attention? Why or why not?

7 comments on “ Tackling Texting While Driving: ‘The Decision to Reach for That Phone Can Be Impulsive’ ”

As a teenager myself, I experience firsthand the sudden urges to respond to snapchats at a red light or skip to the next song on my phone. So I understand the misconceived notion—that taking your eyes off the road for a second or less is a relatively innocuous action. But it’s these several milliseconds that could change someone’s life forever, or worse, your own life…or worse, death. Think about the impact that checking a text has on others now with a, hopefully, new perspective.

While distracted driving is a serious issue, and while I could fill pages with my thoughts on it, I couldn’t help but think about another issue many teens (and people in general) have faced that is more or less out of their control. Given that the outline of this contest is to “practice critical and reflective thinking,” and “connect ideas, insights and opinions with what [has been] read,” I think that it is appropriate to share all that I have been able to think about recently, despite what I have been reading.

Reflecting on the article about texting and driving, I found it hard to concentrate on the issue that was presented. Rather, my mind kept drifting off to think about how many lives have been affected in the past two weeks. We can thank…

Santino Legan, who decided that an annual garlic festival with four decades of history would be a suitable setting to open fire on young children and their families,

Patrick W. Crusius, who decided to take the issue of illegal immigration into his own hands and target Mexicans in his mission,

and Connor Betts, who decided to kill his biological sister, as well as eight other bystanders with a pistol with a rapid fire rate, for shaking up the country and instilling a new level of fear in US citizens.

We shouldn’t be scared to go shopping, nor should we be scared to enjoy a garlic festival, let alone grabbing a drink with friends. Yet, averaging more than a shooting a day since the start to 2019 is enough for the masses to be “scared.”

Still aligning with the outline of Round 3, which asks for “a personal story,” I have two.

The first one is that tomorrow, I am going to a music festival with my friends and a small part of me is afraid, which shouldn’t have to be the case. The second one is that I am living in a time where unnecessary fear has accrued as a result of lacking administration. In fact, we are all living that story, every day.

I hope that my usage of this platform can help spark discussion and ultimately lead to change.

#endgunviolence

“McDonald believes the distracted driving will decrease when society accepts the danger as a norm. For example, smoking, drunken driving and not wearing seat belts are all risky behaviors that have been reduced as people have internalized the message that they are dangerous.”

McDonald’s claim that risky behaviors like distracted driving will decrease when we internalize the danger behind those behaviors seems to make sense. After all, most people do not put their hand back on the stove after being burned once. However, as the article acknowledges, we already know that we shouldn’t take a call, eat, or daydream while driving, yet we still do it. There’s a gap between knowing something is dangerous, or filling in the correct bubble on a permit test, and internalizing its danger and choosing not to drive distracted.

Maybe a clue to this gap lies in how drivers education teaches danger. After an hour and a half of writing down boring rules in our notebooks (if you are parking uphill with a curb, point the wheels away from the curb…), my driving instructor would play the next episode of a safety film produced by the California Highway Patrol, and it was magic. When the lights came off, our heads would perk up, and we’d all spend the next half hour with our eyes glued on the screen. We couldn’t get enough of the vivid, greater-than-life depiction of high school. After a wild night partying, virtuous teens would make the mistake of driving drunk instead of calling a taxi. While still having wild fun in the car, what was about to happen next would ruin their lives forever. A bump in the road or a patch of ice on a bridge would send the vehicle flipping through the air or spinning out of control. Teens would be rushed to the hospital, and police would later interrogate and arrest some of them. The driver of the car that fateful night would see their friends disappear and forever receive only hateful glances from every direction. Teachers and parents would come on the screen and talk about the bright future the unfortunate victims once had. A scientist would recreate the exact scene of the accident, including a slow-motion of the car flipping through the air, talking about how if they had missed that one pothole, bump, or patch of ice, they might have ended up okay. The movie would end with an officer reminding us sternly that accidents from distracted driving could happen to anyone at any time.

We didn’t think that would happen to us. Of course, some people choose to drive drunk, and maybe cars can flip that many times in the air. But that was entertainment, and it wasn’t us. We were good students who knew the rules of the road, and we had been driving for months without an accident. Perhaps one issue is that the movies seemed too exaggerated, too un-lifelike.

California Highway Patrol must have thought this as well because their older driver’s ed films tried to be more realistic. Red Asphalt, for instance, had been put together from footage of real accidents. While some experts argue that those horror films have lost their effectiveness due to the widespread violence in video games and movies, many drivers education instructors believe that the gorier films are more effective. Most people, including Tom Marshall, a spokesman for the California Highway Patrol, acknowledge that the film won’t permanently change driving habits, but “if it can get kids to focus on it for the first month or two [that they’re driving], it has done its job.” Whether gore is more effective than drama is up to debate, but educational films’ shift to emotion shows that shock was not effective enough in changing long-term behavior. Indeed, there’s a value in safety films to increase attention in the first few months of supervised driving. However, it seems that after that supervision, we think that those films can’t be us, and return to bad habits.

Unfortunately, this trend holds for other behavior as well. We think that the past will repeat itself in the future, which can lure us into a false sense of security. We are aware of economic bubbles, most famously the Dutch tulip-mania, yet a lot of us continued buying houses up to the Great Recession because the price had risen for the past few years. We cheat on exams because we haven’t been caught before and “only the bad cheaters get caught.” One of my favorite statistics is that 73% of drivers think they’re better than average. After a shock like a bubble collapse or getting caught on a test, we may swear we’ve learned our lesson and change our behavior only to return to bad habits days or weeks later. We’re creatures of habit, and it’s easier not to start a bad habit than to get out of one.

Maybe no driving film can pull us away from already-developed technology addiction. However, there is still another issue on the table: driver’s ed movies may promote the behavior they intend to prevent by glamorizing danger. As journalist Martin Smith notes, Red Asphalt may be one of the most-viewed movies ever, and that may be due to reasons of entertainment, not education.

In his riveting memoir This Boy’s Life, Tobias Wolff speaks to the risk of glamorizing harmful behavior. The World War II dramas he watched are hauntingly similar to the scare films of today, “always with a somber narrator to remind us that this wasn’t make-believe but actual history, that what we were seeing had really happened and could happen again.” While Wolff acknowledges that the depiction of the Nazis’ downfall produced “glimpses of humiliation and loss,” they only lasted a few minutes. Wolff believes that the point of the show was not to discourage Nazism: “the real point was to celebrate snappy uniforms and racy Mercedes staff cars and great marching, thousands of boots slamming down together on cobbled streets while banners streamed overhead and strong voices sang songs that stirred our blood though we couldn’t understand a word. These shows instructed us further in the faith we were already beginning to hold: that victims are contemptible, no matter how much people pretend otherwise, that it is more fun to be inside than outside, to be arrogant than to be kind, to be with a crowd than to be alone.”

Certainly, not everyone is driven to dangerous behavior in the way that Wolff was. However, the risk of glamorizing danger is real. In one famous example, the DARE program may have encouraged drug use through its aggressive scare tactics.

The dilemma of human nature is that we learn more from putting our hand on the stove than being lectured about the dangers of burning ourselves. Even when we get burned, our learning may be temporary. However, we can’t afford to burn ourselves when it comes to driving. Therefore, the paradox of safety education is to make the danger seem real and instill fear but not to glamorize risky behavior. The gap between learning and internalizing is how much we believe in the world inside the television screen. Through the difference between greater-than-life reality TV and my experiences in the world outside my window, the world on the screen seems slightly foreign. At times, it can even be enticing.

Sources on the effectiveness of Red Asphalt: https://medium.com/@martinjsmith/the-cinematic-genius-of-the-red-asphalt-road-splatter-series-5289d382ffa3 https://www.latimes.com/archives/la-xpm-2003-jan-21-me-wheel21-story.html

Due to my research, I have found many surprising things about being on your phone while driving. At any given time in the day, 660,000 are attempting to use their phones while behind the wheel of an automobile. To me, this stat shows how many potential accidents there could be on any given day. Another stat that worries me is that 1 in 4 car accidents every day are caused by texting while driving. If we just tried to put down our phones while driving we could reduce the amount of deaths and injuries every day caused by distracted driving.

As a teen I see lots of people risking there lives and mine in cars where texting and driving is normal. I have never had any type or urge myself but that would be mainly due to my minimal use of my phone other than for calling or texting (I dont pick up often). Mainly I keep my phone on silent due to attending classes 5 days a week on top of working. To avoid interrupting situations like my phone going off in class or getting a call at work I’m not supposed to answer. I agree with the statement it goes farther than just the cell phone as well and that our actions that involve anything other than focusing on the road can be detrimental to our lives and it’s not acknowledged enough. Driving is treated lightly until an accident happens and if it is escaped it is regretted when it should not had even taken place. We live our lives through trial and error and it is a dangerous way to live. It’s a way that can end our lives at any moment. It’s like you have to come out lucky to have had the ability to reflect and change your ways but everyone doesn’t get those chances. Your life can be taken away from you at any second without you knowing and texting while driving in a vehicle that doesn’t have 100% protection rate is a risk it may only be 9% now but that can add up the more it’s not changed. -DeMarcus Kilgo kwhs wshs NC

Hey DeMarcus! You are so right that this is a big problem. During my commute, I literally see so many drivers looking down at their phones. Thank you for sharing your perspective. I especially like your line “We live our lives through trial and error and it is a dangerous way to live.” If we just acknowledged the statistics and used some common sense, we could avoid this experimental lifestyle and not put our lives or those of others at risk. Stay safe!

Texting while driving is a horrific yet common act that many still do every day on their commute. While this is not the only distraction a driver faces, it is one of the major causes of distracted driving. While the solution of setting one’s phone on the side seems viable, it is a natural instinct for people nowadays to pick up and check their phones. Even preventions such as turning on “Do Not Disturb” might not work in some cases. Indeed, it does block out notifications, but it still doesn’t prevent the actual act of a driver from reaching their phone to check on it. Despite that these simple approaches are great in preventing some of the causes of distracted driving, they do not cover all aspects of it.

Therefore, I propose a new solution to reduce the amount of distracted driving: tracking sensors on frame glasses or prescription glasses of the driver, which the driver would either wear when they get into the car or in their daily lives. Because being distracted refers to any aspect of not focusing on the road, a sensor on the glasses can detect a movement that is unnatural to driving, such as looking down below the dashboard and not onto the road or mirrors. These driver glasses can connect to a wireless relay box when they enter the car, and once on the road, whenever the driver looks down into an unnatural position, the relay box will beep back in consideration of how alert the driver is on the road, beeping louder the more unaware the driver is through its position of the sensors. We can expand this idea by disabling the phone when it senses movement of the driver trying to beat the system by raising the phone on top of the dashboard or just the standard looking down below the dashboard and reaching for the smartphone.

One might say this system is complicated, as one can just turn off their phone in general, but constantly shutting ones’ phone down may become annoying, which might result in the driver giving up the habit as a whole. On the other hand, studies from the NCBI have found that noise induction will most likely make a person respond accordingly to their surroundings, as the human race has evolved in humanity from nature, so humans will generally react to a sound to perceive danger. Therefore, we can use this ideology to direct our eyes to only focus on the road. Although this system might not beat out all the distractions a driver might face — such as daydreaming or getting distracted by the outer world — it still blocks out the core causes of distracted driving, which include checking the phone, eating, and arguing with someone else about a Spotify Playlist. I believe this innovation can help our society immensely, especially younger generations who are attached to smartphones, as this technology will help cut down their loss of attention on the road.

As Catherine McDonald explained when she stated, “What’s important to remember about driving is that you’re making decisions not just about yourself, but about other cars that you’re not controlling,” driving is a privilege given to us that requires a large amount of responsibility and control. When driving, it is your responsibility to keep yourself safe and to not do anything rash that would risk the safety of the fellow drivers.

There’s no doubt that we’ve all heard the phrase, “Don’t text while driving!” numerous times in the past. We’ve all seen the consequences of behaviors such as these, and yet, why do people still do it? Despite knowing just how dangerous and risky these actions may be, why do thousands of drivers do this on a daily basis? Perhaps the thought of “that’ll never happen to me because I’ll be careful” deceives us, but the severity of the situation cannot be taken lightly.

Many different ideas have been suggested to the public over recent years, with recent ones including a Do Not Disturb While Driving addition to the iPhones, and Auto Apply/Android Auto for newer models of cars. Although we’ve definitely all tried these methods at some point, our temptations may get the better of us at the end of the day. So is there really any method that can prevent texting while driving for sure? As of right now, there really isn’t. But that doesn’t mean that there can’t be one in the near future.

The only way to solve a problem is to get rid of the source of the problem itself. The source of the problem would be the phones, right? The easiest and best thing to do in this scenario would be to remove the phones from plain sight, so the driver could focus on driving. As John Heywood once said, “Out of sight, out of mind.” By implementing current technology, there could be a surefire way to prevent texting while driving. In the car, there could be a compartment installed, and that compartment would be there for one purpose: to hold your phone. However, this compartment would be directly connected to the car’s ability to move, and without the device being inside the compartment, the car wouldn’t be able to be put into drive. To prevent any possible loophole, the compartment would also have a sensor installed, and this sensor would be used to detect that the device has been put into the compartment. After the device has been placed into the compartment, it wouldn’t be able to be removed until the proper destination has been reached. The phone would then be released from the constraints of the compartment, and you could go on to do whatever you needed to do safely. In case of an emergency, the phone would be automatically linked to the car via bluetooth, and with a single sentence, you would be able to call 911. The compartment would then send a GPA location directly to the police, and within minutes, they would arrive to help you in your time of need. With the addition of a new gadget like this, driving while texting would no longer be a hindrance. Everyone could drive safely, and they could rest assured knowing that something as rash as texting while driving wouldn’t be the cause of injury or fatality.

If there’s anything that commenting on KWHS has taught me, it’s that nothing is impossible. Young scholars from all over the world are coming up with new innovative ways to make the world a much better place everyday, and with the current technology that we possess, creating new things is no longer a burden. With the combined innovative thoughts from scholars all over the world, problems such as these will no longer cause us so much harm. It’s all up to whether we’re willing to work together to achieve this goal.

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Texting while driving: a literature review on driving simulator studies.

1. Introduction

2.1. protocol, 2.2. eligibility criteria and study selection, 2.3. information sources, 2.4. search, 2.5. study selection, 2.6. data extraction, 2.7. synthesis of the results, 3.1. characteristics of studies, 3.2. rq1: what types of distractions are introduced when using the phone for twd, 3.3. rq2: what types of hardware devices were used during experiments to analyze the driver’s performance, 3.3.1. driving simulator equipment, 3.3.2. driver-tracking equipment, 3.4. rq3: what measures were used to analyze and predict distraction, 3.5. rq4: what is the impact of using mobile devices to read and write messages while driving, 4. discussion, 4.1. recommendations and directions for future research.

• Hardware characteristics: The simulator should have a dashboard resembling that of a real car, providing at least three DOFs in terms of motion and having a display system that offers a minimum horizontal field of view of 135° [ 128 ]. It should have the basic vehicle controls, a sound system, and at least a system capable of monitoring the driver’s behavior, which includes functions that can detect distracted driving. Distraction-detection systems are important in the case of autonomous driving because automated-vehicle drivers will still need to be in the loop in order to take over the controls when necessary [ 129 ].
• Scenario—Driving scenarios should provide a similar experience to naturalistic driving [ 130 ] and highlight the different types of driving behavior [ 131 ]. Therefore, we consider that it is not enough to consider a single basic scenario and suggest that experiments should include at least two driving situations, having multiple driving conditions (for example, driving in urban, rural areas, less or more traffic, simpler or more complex road geometry, etc.).

4.2. Limitations

5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Voinea, G.-D.; Boboc, R.G.; Buzdugan, I.-D.; Antonya, C.; Yannis, G. Texting While Driving: A Literature Review on Driving Simulator Studies. Int. J. Environ. Res. Public Health 2023 , 20 , 4354. https://doi.org/10.3390/ijerph20054354

Voinea G-D, Boboc RG, Buzdugan I-D, Antonya C, Yannis G. Texting While Driving: A Literature Review on Driving Simulator Studies. International Journal of Environmental Research and Public Health . 2023; 20(5):4354. https://doi.org/10.3390/ijerph20054354

Voinea, Gheorghe-Daniel, Răzvan Gabriel Boboc, Ioana-Diana Buzdugan, Csaba Antonya, and George Yannis. 2023. "Texting While Driving: A Literature Review on Driving Simulator Studies" International Journal of Environmental Research and Public Health 20, no. 5: 4354. https://doi.org/10.3390/ijerph20054354

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Expert Commentary

Multitasking, texting and distracted driving: Researchers discuss cognitive effects and risks

Researchers from Kentucky University, West Virginia University, Harvard and Stanford discuss the risks of using mobile devices while driving and the impact of multitasking on how we process information.

Republish this article

This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License .

by Jeremy Venook, The Journalist's Resource August 12, 2014

This <a target="_blank" href="https://journalistsresource.org/economics/multitasking-texting-and-distracted-driving-academics-discuss-the-cognitive-effects-and-risks/">article</a> first appeared on <a target="_blank" href="https://journalistsresource.org">The Journalist's Resource</a> and is republished here under a Creative Commons license.<img src="https://journalistsresource.org/wp-content/uploads/2020/11/cropped-jr-favicon-150x150.png" style="width:1em;height:1em;margin-left:10px;">

An often-observed human trait is for us to overestimate our own abilities — for example, a 1980 study of 161 U.S. and Swedish residents found that 88% of the Americans considered themselves to be safer-than-average drivers, while 77% of the Swedes held the same belief. A similar contradiction appears to apply the use of mobile devices while in a car: A 2012 survey of more than 3,900 U.S. adults found that 82.9% felt that texting while driving was “completely unacceptable,” yet 34.7% admitted to reading an email or text in the past 30 days — and the actual rate is probably much higher.

Laws against such behavior are on the rise , but their effectiveness depends on the legal specifics and the level of enforcement: A 2014 study in the American Journal of Public Health found that laws allowing police officers to pull over all drivers who are texting, regardless of age, resulted in a 5% reduction in fatal accidents among individuals ages 15-21. “Secondary” bans, which only allow texting citations when drivers are pulled over for other reasons, had no effect on fatality rates.

A growing body of work is looking at the effects of multitasking in the digital age. Research suggests that multitasking can actually reduce productivity because the brain is forced to jump back and forth between tasks rather than simultaneously focusing on two things. Neurobiologists, psychologists and social scientists have also begun to delve into the longer-term effects of living in a state of near-constant multitasking.

—————-

“The Dangers of Texting and Driving,” Kentucky University’s Paul Atchley discusses the substantial risks that drivers take by sending text messages from behind the wheel. Atchley has authored a number of papers on the subject, including “The Effects of Perception of Risk and Importance of Answering and Initiating a Cellular Phone Call While Driving,” 2009; and “The Choice to Text and Drive in Younger Drivers: Behavior May Shape Attitude,” 2011.

“Preventing Deadly Distracted Driving,” Harvard School of Public Health. U.S. Secretary of Transportation Anthony Foxx and Jay A. Winsten , HSPH Associate Dean, talk about distracted driving as a public health issue. Winsten is the author of “Promoting Designated Drivers: The Harvard Alcohol Project,” among other studies.

“What Were You Thinking? The Myth of Multitasking,” Clifford Nass of Stanford University talks about how our desire to constantly multitask plays out in the car, even when a driver is not using his or her phone. Deborah Trombley of the National Safety Council then specifically discusses driving while using the phone. Among other relevant papers, Nass co-authored “Cognitive Control in Media Multitaskers” in 2009.

“The (Cognitive) Trouble with Multitasking” and “Media Multitasking: Scope and Consequences.” Professor Elizabeth Cohen of West Virginia University discusses the psychological underpinnings of how we multitask and its negative effects on everyday brain function. She also discusses “media multitasking,” the increasing tendency to utilize multiple media at once, such as using a computer or cellphone while watching television. Among other papers, she is the co-author of “How Low-Income Residents Decide Between Emergency and Primary Health Care for Non-Urgent Treatment.”

Related research: A 2013 study in Public Health Reports , “Distracted Driving: Voice-Activated Systems and Drivers’ Reaction Times,” found that the number of pedestrians and cyclists killed by distracted drivers has risen significantly — for pedestrians, the fatality rate due to distracted driving increased 45% from 2005 to 2010, and for cyclists it jumped 32%.

Keywords: cognition, multitasking, road safety, driving, technology, video roundup

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Jeremy Venook

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An evidence-based review: distracted driver

Affiliation.

• 1 From the University of South Florida (L.E.L.), Tampa, Florida; Westchester Medical Center (K.A.), Valhalla, New York; East Carolina University (M.B.), Greenville, North Carolina; Advocate Medical Group (S.S.), Chicago, Illinois; Howard University Hospital (W.G.), Washington, District of Columbia; and Johns Hopkins University (A.D., A.S.), Baltimore Maryland; and Aga Khan University Hospital (J.B.), Karachi, Pakistan Karen Hospital Consulting Clinics (J.M.), Nairobi, Kenya.
• PMID: 25539216
• DOI: 10.1097/TA.0000000000000487

Background: Cell phone use and texting are prevalent within society and have thus pervaded the driving population. This technology is a growing concern within the confines of distracted driving, as all diversions from attention to the road have been shown to increase the risk of crashes. Adolescent, inexperienced drivers, who have the greatest prevalence of texting while driving, are at a particularly higher risk of crashes because of distraction.

Methods: Members of the Injury Control Violence Prevention Committee of the Eastern Association for the Surgery of Trauma performed a PubMed search of articles related to distracted driving and cell phone use as a distractor of driving between 2000 and 2013.

Results: A total of 19 articles were found to merit inclusion as evidence in the evidence-based review. These articles provided evidence regarding the relationship between distracted driving and crashes, cell phone use contributing to automobile accidents, and/or the relationship between driver experience and automobile accidents. (Adjust methods/results sections to the number of articles that correctly corresponds to the number of references, as well as the methodology for reference inclusion.)

Conclusion: Based on the evidence reviewed, we can recommend the following. All drivers should minimize all in-vehicle distractions while on the road. All drivers should not text or use any touch messaging system (including the use of social media sites such as Facebook and Twitter) while driving. Younger, inexperienced drivers should especially not use cell phones, texting, or any touch messaging system while driving because they pose an increased risk for death and injury caused by distractions while driving.

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Teens and Distracted Driving

Texting, talking and other uses of the cell phone behind the wheel.

by Mary Madden, Senior Research Specialist and Amanda Lenhart, Senior Research Specialist, Pew Internet & American Life Project

Over the summer of 2009, the Pew Research Center’s Internet & American Life Project conducted a survey of 800 teens ages 12-17 asking about their experiences with cell phone use in cars. All of the teens in our survey were asked about their experiences as passengers, and if they were age 16 or older and have a cell phone, they were also asked about their own actions behind the wheel including both talking and text messaging. Additionally, the Pew Internet Project and the University of Michigan conducted nine focus groups with teens ages 12-18 between June and October 2009 where the topic of driving and mobile phones was addressed. The following are the major findings from the survey and focus groups: 

• 75% of all American teens ages 12-17 own a cell phone, and 66% use their phones to send or receive text messages.
• Older teens are more likely than younger teens to have cell phones and use text messaging; 82% of teens ages 16-17 have a cell phone and 76% of that cohort are cell phone texters.
• One in three (34%) texting teens ages 16-17 say they have texted while driving. That translates into 26% of all American teens ages 16-17.
• Half (52%) of cell-owning teens ages 16-17 say they have talked on a cell phone while driving. That translates into 43% of all American teens ages 16-17.
• 48% of all teens ages 12-17 say they have been in a car when the driver was texting.
• 40% say they have been in a car when the driver used a cell phone in a way that put themselves or others in danger.

Introduction

As early as 2006, and well before texting had become mainstream in the United States, the Pew Research Center’s Internet & American Life Project reported that more than a quarter of adult cell phone owners felt their cell phone had at some point compromised their driving ability. In the survey, 28% admitted they sometimes did not drive as safely as they should while using their mobile devices. 1

Over time, cell phones have become increasingly important fixtures in Americans’ lives and public concern over their use while driving has grown. 2 At the time of the 2006 survey, just 35% of adult cell phone owners said they used the text messaging feature on their phones. By April 2009, the use of text messaging by cell phone owners had nearly doubled to 65%. 3

Several states including California, Connecticut and Oregon have already passed laws to ban all texting or talking with a handheld phone while driving, and the Senate is now considering a bill that would provide federal funding to states that enact similar laws. 4 In September 2009 U.S. Transportation Secretary Ray LaHood convened policy makers, safety advocates, law enforcement representatives and academics to address the risk of text-messaging and other “distracted driving” behavior. At the conclusion of the summit, Secretary LaHood announced an executive order from President Obama that forbids federal workers from texting while driving government vehicles or their own vehicles while on the job. 5

According to the latest research from the National Highway Traffic Safety Administration, in 2008 alone, there were 5,870 fatalities and an estimated 515,000 people were injured in police-reported crashes in which at least one form of driver distraction was reported. Distractions among young drivers are of particular concern, as the highest incidence of distracted driving occurs in the under-20 age group. 6

New research released in July 2009 by the Virginia Tech Transportation Institute (VTTI) examines a variety of tasks that draw drivers’ eyes away from the roadway and suggests that text messaging on a cell phone is associated with the highest risk among all cell phone-related tasks observed among drivers. 7 The VTTI has also noted that teen drivers are generally at a much higher crash risk when compared with other drivers, but there is a gap in understanding to what extent specific behaviors and relative lack of driving experience may contribute to this elevated risk. An 18-month study of newly-licensed teen drivers is currently underway to further examine these factors. 8

Research conducted at the University of Utah’s Applied Cognition Laboratory over the past decade raises further problems with cell phone use in the car and suggests that talking on a cell phone while driving impairs driving ability in ways that conversing with a person in the car does not. 9

Read the the full report at pewresearch.org/pewresearch-org/internet .

1. Lee Rainie and Scott Keeter, “ Americans and their cell phones ,” Pew Internet & American Life Project, April 3, 2006. 2. Marjorie Connelly, “ Many in U.S. Want Texting at the Wheel to Be Illegal ,” The New York Times , Nov. 1, 2009. 3. John Horrigan, “ Wireless Internet Use,” Pew Internet & American Life Project , July 22, 2009. Both the 2006 and 2009 surveys were dual frame, interviewing respondents via landlines and cell phones. 4. Kim Geiger, “ Support in Senate for cellphone driving ban ,” Los Angeles Times , October 14, 2009. Available at:  5. Michael Dresser, “ Don’t text while driving, Obama orders U.S. workers,” The Baltimore Sun , October 2, 2009. 6. Debra Ascone, Tonja Lindsey, and Cherian Varghese, “ An Examination of Driver Distraction as Recorded in NHTSA Databases ,” Data Reporting and Information Division, National Center for Statistics and Analysis, NHTSA, September 2009. 7. Sherri Box, “ New data from Virginia Tech Transportation Institute provides insight into cell phone use and driving distraction ,” VTTI, July 29, 2009. 8. VTTI In the News . 9. See Strayer, D.L. and Johnston, W.A., (2001), Strayer, D.L. Drews, F.A., and Crouch, D.J. (2003) and Drews, F.A., Pasupathi, M. and Strayer, D.L. (2008) The findings from these studies assert that talking on a cell phone while driving results in “inattention blindness,” slower reaction times and other impairments of driving skills that are similar to driving while intoxicated. Find these papers and others at Applied Cognition Lab

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