cell phone use while driving research paper

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

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

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Received : 15 December 2015

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

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Research In Action

Cell phone use while driving has been linked to increased crash and near-crash risk. Despite the implementation of bans on handheld cell phone use while driving in many states, crash reduction results are inconsistent .

While distracted driving is dangerous enough among adult experienced drivers, it’s even more dangerous for young drivers, particularly those with little experience behind the wheel. This is because young novice drivers may have limited abilities to focus their attention and control their impulses .

While young novice drivers are at the greatest risk of crashing overall, not all engage in risky driving behaviors or crash.

With colleagues Flaura Winston, MD, PhD and Dan Romer, PhD , I recently published study findings in the  International Journal of Environmental Research and Public Health that revealed young adult drivers (ages 18-24) who self-report cell phone use while driving also engage in other risky driving behaviors, such as  speeding , running red lights, and impatiently passing a car in front on the right.

A Pattern of Risky-Driving Behaviors?

While young novice drivers are at the greatest risk of crashing overall, not all engage in risky driving behaviors or crash. Our finding describing cell phone use while driving as part of a pattern of risk-taking may explain why some young adult drivers are more prone to crash involvement than other drivers their age.

This finding, however, is not new: We previously published a paper with the same finding. This newer study builds and expands on this prior work in two key ways:

• It goes further by replicating the same finding in a larger sample of 384 young drivers from across the United States, not just in one geographical area.
• Those who more frequently engaged in this pattern of risk-taking were more impulsive (act-without-thinking) than those who didn’t take as many risks on the road.
• Sensation seeking was also associated with crashes but independently of risky driving practices and impulsivity.

Taken together, these two studies suggest that it may be more beneficial to promote safe driving behavior more broadly than concentrating on combating one risky driving behavior, such as texting while driving. This makes sense since teen drivers who engage in one risky behavior are also likely to engage in other dangerous behaviors that can lead to crashes.

Our newer study also suggests that assessment of personality traits, such as impulsivity, may be helpful to identify drivers most at risk in order to provide more targeted interventions promoting safe driving, particularly among those with weaker impulse control.

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Cell phone use and distracted driving begins in the mind

Researchers pinpoint 'attention disengagement' lag as cause for impaired driving when talking on cell phone

University of Iowa

We all know that talking on a cell phone impedes your driving ability. But new research from the University of Iowa is helping us understand how even a simple conversation can affect your brain's ability to focus on the roadway.

UI researchers used computerized experiments that tracked eye movements while asking subjects to answer true or false questions. Respondents who answered the questions took about twice as long to direct their eyes to a new object on the screen than those not required to respond or who were asked no questions at all.

The experiments mimic a scenario in which a driver is using a cell phone or having a conversation with a passenger, says Shaun Vecera, professor in the UI Department of Psychological and Brain Sciences and corresponding author on the paper, published online June 5 in the journal Psychonomic Bulletin and Review .

It's the first study known to examine attentional disengagement as the possible cause of poor driving while using a cell phone.

"What this study suggests is the reason you should be cautious (when talking on the phone while driving) is it slows your attention down, and we're just not aware of it because it happens so fast," Vecera says.

The delay is about 40 milliseconds, or four-hundredths of a second, which may not seem like a long time. But that delay compounds: Every time the brain is distracted, the time to disengage from one action and initiate another action gets longer.

"It's a snowball effect," Vecera says, "and that's what contributes to the problem, because eventually you're oblivious to a lot that's around you."

There's little dispute cell phone use--whether texting or talking--is hazardous for drivers. The U.S. National Highway Safety Administration reports that in 2015, 3,477 people were killed and 391,000 were injured in motor vehicle crashes involving drivers engaged in cell phone conversations, texting, and other distractions.

That's why a growing number of states--Iowa included--have either limited or banned some uses of a cell phone while driving.

Research has demonstrated cell phone use reduces a driver's field of vision, creating a cone-like field of view akin to tunnel vision. Other studies have suggested using a cell phone while driving places a mental burden, or "cognitive load," on drivers, making them less likely to detect and react to the appearance of a new object.

Vecera and his team wanted to explore why the brain was burdened with something as simple as having a conversation. After all, why would talking on the phone affect your ability to pay attention to the road?

Engaging in conversation, whether on the phone or with someone in the vehicle, "seems effortless," Vecera says. But it's far more complex than one would think. The brain is absorbing information, overlaying what you know (and what you don't), and then preparing to construct a thoughtful reply.

"That's all very effortful," Vecera says. "We do it extremely rapidly--so rapidly we don't grasp how difficult it really is."

In a study published in 2011 in the Journals of Gerontology, Series B, Vecera and colleagues documented that older adults with poorer mental and visual abilities took longer to switch their attention from one object to another than older adults with diminished vision only. In his current study, he hypothesized that younger, healthy individuals asked to answer questions while training their eyes on objects would mimic the older adults with cognitive decline.

The experiment was simple enough. The participants answered a series of true or false questions, termed "active listening," while researchers used high-speed cameras to track how rapidly their eyes located and fixed on a new object that appeared on a computer screen. Other groups either were asked a question but were not required to answer ("passive listening") or were not asked a question.

Among the simple questions was: "C-3P0 is the name of a tall golden robot, and he was in the popular film Star Wars."

Among the more difficult questions was: "The Magna Carta was written as a legal proclamation, subjecting the king to the law."

It took nearly 100 milliseconds, on average, for participants answering questions to disengage their vision from one object and locate and fixate their vision on a new object that appeared on the screen.

"Active listening delays the disengagement of attention, which must occur before attention can be moved to a new object or event," Vecera says.

In addition, the eye movements of participants asked to answer both simple and difficult questions also lagged. Researchers believe that's because the brain needs to be engaged when actively listening, no matter how elementary the topic of conversation.

The solution? Don't talk on the phone while driving, Vecera says.

"There's no evidence that I know of that says you can eliminate the mental distraction of cell phone use with practice or conditioning," he says. "But that is an open question that should be studied."

Benjamin Lester, a UI graduate who majored in psychology and former post-doctoral research scholar at the UI, is the paper's first author. The U.S. National Science Foundation and the Toyota Collaborative Safety Research Center funded the research.

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Texting While Driving: A Literature Review on Driving Simulator Studies

Gheorghe-daniel voinea.

1 Department of Automotive and Transport Engineering, Transilvania University of Brașov, 29 Eroilor Blvd., 500036 Brasov, Romania

Răzvan Gabriel Boboc

Ioana-diana buzdugan, csaba antonya, george yannis.

2 Department of Transportation Planning and Engineering, National Technical University of Athens, 5 Heroon Polytechniou str., GR-15773 Athens, Greece

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Not applicable.

Road safety is increasingly threatened by distracted driving. Studies have shown that there is a significantly increased risk for a driver of being involved in a car crash due to visual distractions (not watching the road), manual distractions (hands are off the wheel for other non-driving activities), and cognitive and acoustic distractions (the driver is not focused on the driving task). Driving simulators (DSs) are powerful tools for identifying drivers’ responses to different distracting factors in a safe manner. This paper aims to systematically review simulator-based studies to investigate what types of distractions are introduced when using the phone for texting while driving (TWD), what hardware and measures are used to analyze distraction, and what the impact of using mobile devices to read and write messages while driving is on driving performance. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR) guidelines. A total of 7151 studies were identified in the database search, of which 67 were included in the review, and they were analyzed in order to respond to four research questions. The main findings revealed that TWD distraction has negative effects on driving performance, affecting drivers’ divided attention and concentration, which can lead to potentially life-threatening traffic events. We also provide several recommendations for driving simulators that can ensure high reliability and validity for experiments. This review can serve as a basis for regulators and interested parties to propose restrictions related to using mobile phones in a vehicle and improve road safety.

1. Introduction

Road safety is increasingly threatened by distracted driving. One of the highest-risk forms of distracted driving is texting while driving (TWD) [ 1 , 2 ] alongside talking on the phone while driving (TPWD) [ 3 , 4 ]. After decades of research, the statistics show that the risks associated with TWD are very high [ 5 ]. According to the United Nations Road Safety statistical data [ 6 ], car traffic crashes cause more than 1.35 million deaths and injure as many as 50 million people annually worldwide, and a significant cause of such crashes is distracted driving [ 7 ]. Considering that, distracted driving has become a common topic in studies that aim to find solutions to reduce traffic injuries and death.

A general approach to road safety is to identify and analyze all distraction activities that can lead to a crash [ 8 , 9 ]. For example, in 2019, the road traffic injuries statistics showed that a total of 36,096 deaths were reported in the US, of which 8.7 percent were attributed to driver distraction due to phone use, eating, and so on [ 10 ]. In the EU, the European Commission reported a decrease in the number of fatal crashes in 2020 compared to 2019 by up to 17%, a year in which it was estimated that 18,800 people lost their lives in car crashes [ 11 ]. Lower traffic due to the pandemic restrictions during the COVID-19 pandemic had a clear, though unmeasurable, contribution to this. Although the average number of fatalities has decreased (for example, Romania showed a decrease of 12%), some countries reported an increase (Switzerland reported an increase of 21%) [ 12 ], which indicates that there is still a need for more countermeasures. Romania, on the other hand, is at the top of the list when it comes to road traffic fatalities, with 85 car crashes per million inhabitants [ 13 ]. These crashes are caused by distraction factors, both internal (e.g., a smartphone) and external (e.g., a roadside advertisement), in addition to situations in which the driver has consumed alcohol or prohibited substances [ 10 ].

Road safety could be improved if it is analyzed from several perspectives. For example, a bibliometric review covering 10 years of research focused on cyclist safety has proposed several recommendations that can lead to well-designed and safer bike networks [ 14 ]. In [ 15 ], the authors investigated the effect of cardiovascular and respiratory physiological parameters on driver’s mental workload. The findings are conflicting, with some studies suggesting that variations in heart rate (HR) and heart-rate variability (HRV) can reflect changes in mental workload. Due to external influences, respiratory rate (RR) demonstrated little importance in most studies, and it has not been a popular choice for researching driving mental workload. The authors conclude that machine learning algorithms combined with subjective and objective data may yield accurate results in assessing mental effort.

Driver distraction can be defined as “any activity that diverts attention from driving, including talking or texting on the cell phone, eating and drinking, talking to people in the vehicle, fiddling with the stereo, entertainment or navigation system” [ 16 ]. The most common sources of distractions are mobile phone use, interaction with passengers, drinking, eating, and controlling in-vehicle devices [ 9 ]. There are three basic techniques to determine the distracted state of the driver: studying drivers’ visual scanning patterns, detecting physiological signals, and evaluating driving performance. Driver distraction is often studied and analyzed using various equipment, such as driving simulators, eye-tracking devices, and so on [ 17 , 18 , 19 , 20 ]. Most of the studies demonstrated that a driver’s performance could be influenced when a non-driving secondary task is performed at the same time while driving (e.g., cell phone use, TWD, etc.). Therefore, many governments, including those in Europe, the United States, and other countries across the world, have approved restrictions on cell-phone use while driving [ 21 , 22 , 23 ].

According to [ 24 ], driving performance is defined as “performance of the driving task”, where the driving task includes “all aspects involved in mastering a vehicle to achieve a certain goal (e.g., reach a destination), including tracking, regulating, monitoring and targeting”. The driving task requires a wide range of cognitive and physical abilities, such as perception, attention, decision-making, and situational awareness [ 25 ]. Thus, driving performance is a crucial indicator of a driver’s ability to operate a vehicle safely and effectively. To comprehensively assess a driver’s capabilities while driving, it is essential to analyze all relevant driving performance parameters, such as lateral control through the standard deviation of lateral position [ 26 ], lateral clearance and time-to-danger [ 27 ], longitudinal control, reaction time, gap acceptance, eye movement, and workload measures [ 28 ]. However, drivers might get so distracted by an activity or event that they cannot react promptly, thus compromising their ability to drive safely. Different types of distractions can influence driving performance, such as visual (the driver is not looking at the road), manual (one hand or both hands are off the steering wheel, e.g., text messaging), and cognitive (the driver is not mentally present while driving, as the attention is focused on the secondary task, e.g., focus on phone) [ 29 ]. For example, initiating, writing, and sending a text message while driving involves visual, manual, and cognitive resources. The main effects of distracted driving are increased steering-wheel deviations [ 30 ], higher standard deviations of lateral lane position [ 17 ], increased reaction time [ 18 , 31 ], lower longitudinal control [ 32 ], increased brake time [ 33 ], and decreased driving speed [ 34 ].

In recent years, several smart devices that are worn or attached to the body have been developed that have hands-free functions and can stay connected to the network at any time. Wearables frequently utilize various input modalities (such as touch, speech, or gesture), making their functionalities even more accessible to drivers on the road than a cell phone. Several studies have concluded that the use of mobile or portable devices while driving, such as smartwatches, navigation systems, and Google Glass, has been found to pose a risk to driving safety comparable to conversing on a mobile phone [ 35 ]. For example, Glass-delivered messages did not eliminate the distracting cognitive demands, finding that both Google Glass and writing a message on the phone require the same attention resources. Moreover, whether it comes from a smartwatch or smartphone, engaging with notifications carries the risk of taking the attention from the driving task [ 36 ].

Many researchers have used driving simulators to collect data that can improve road safety, identify and analyze driving profiles, and propose recommendations or policies. Experiments employed in a secure, versatile, and controlled environment have allowed scholars to study potentially dangerous driving scenarios and infer valuable knowledge. However, some possible drawbacks should be mentioned, mainly the external validity (the degree to which a real-world environment can be replicated), the high initial acquisition cost, and the simulator sickness which may be experienced by novice participants [ 37 , 38 ].

Research driving simulators in the early eighties, such as HYSIM—Highway Driving Simulator [ 39 ], consisted mainly of a fixed-based platform and an interactive visual–audio application. The main improvements that followed were increased graphics quality, advanced motion representation through Stewart motion platforms (Six Degrees of Freedom, 6DOF), cabin and control equipment, realistic vehicle sounds, and environmental factors [ 40 ]. Driving simulators were typically described using a three-level system (low-level, mid-level, and high-level) but without having a specific classification criterion [ 37 ]. Other classifications were proposed by [ 41 ] (Levels 1, 2, 3, and 4; however, the criteria are not explicitly defined), [ 42 ] (their approach included a five-band classification with six main parameters), and [ 37 ] (A, B, C, and D levels; the criteria were adapted from Helicopter Flight Simulation Classification and include four sets of parameters: general, motion system, visual system, and sound system). The papers included in this work were classified according to [ 37 ] because of their explicit and well-defined methodology.

High-level driving simulators can offer some advantages, such as increased awareness of the surrounding environment due to high-resolution and wide field-of-view display systems [ 43 ]. Low-level driving simulators also have well-documented benefits, such as decreased simulator sickness and increased portability and affordability. The work of [ 44 ] highlighted the issue of visual fidelity and proposed a methodology to design, calibrate, and use driving simulators. Moreover, [ 45 ] showed that visual fidelity significantly impacts driving performance. Based on the acquired knowledge from the current work, we propose several recommendations for driving simulators that can ensure high reliability and validity of the experiments.

This review aims to highlight the impact of using mobile devices to read and write messages while driving in a simulated environment, with the overarching goal of enhancing traffic safety through several recommendations and pointing out future research directions. The paper’s content focuses on four research questions (RQs) that emphasize the general characteristics that contribute to the need of improving traffic safety:

RQ1: What types of distractions are introduced when using the phone for TWD?

RQ2: What types of hardware devices were used during experiments to analyze the driver’s performance?

RQ3: What measures were used to predict and analyze distractions?

RQ4: What is the impact of using mobile devices to read and write messages while driving?

The overall structure of the paper is as follows: Section 2 describes the research methodology. Section 3 presents the results, with a focus on answering to the RQs mentioned above. Section 4 presents the main findings, the proposed recommendations for future research, and the limitations of the work. Finally, Section 5 draws the conclusions of this review of the literature.

The review was conducted by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Review (PRISMA-ScR). Scoping reviews aim to determine the scope or coverage of a body of the literature on a given topic [ 46 ] and identify key concepts and types and sources of evidence to inform practice, policymaking, and research [ 47 ]. For this review, we followed the checklist given in [ 48 ].

2.1. Protocol

The manuscript was not previously recorded on PROSPERO or published before, even if the protocol was written before the work began.

2.2. Eligibility Criteria and Study Selection

The studies that met the following criteria were included in the review: full-text, original research in a peer-reviewed journal, published in the English language, and included driving simulators. There was no restriction on the publication year.

Studies were excluded from the review according to the following criteria: commentary manuscripts; reviews of the literature; editorials; short papers; magazines; dissertations; book chapters; conference papers; non-academic publications; papers that are not available in full text; and studies irrelevant to the research, i.e., that did not investigate the relationship between distracted drivers, mobile phone, use and driving simulators.

We preferred to include only journal articles in our review to maintain high scientific relevance, as they are subject to rigorous review, unlike other types of publications, including conference articles.

2.3. Information Sources

The following databases were searched in three phases (on 08 January 2021, 10 May 2021, and 14 November 2022): ISI Web of Knowledge, Scopus, Science Direct, SAGE Journals, and ProQuest.

2.4. Search

The review of the literature was conducted with a combination of keywords: “distraction”, “phone”, and “driving simulator”. Additional terms were identified during the first investigation and were used in combination in the search process: “distracted”, “disruptive”, “smartphone”, “mobile phone”, “cell phone”, and “simulation”. Example of search strategy for Scopus database:

ALL ((“distracted” OR “disruptive” OR “disturbing” OR “distraction”) AND (“driving” OR “driver” OR “driver behaviour”) AND (“car” OR “vehicle” OR “automobile” OR “truck”) AND (“simulator” OR “simulation” OR “virtual environment” OR “simulated environment”)) AND (LIMIT-TO (DOCTYPE, “ar”)).

As can be seen, no limit was imposed for the year of publication.

2.5. Study Selection

The five abovementioned electronic databases were searched, and the title, abstracts, and other details were downloaded to EndNote (version X9, Clarivate, Philadelphia, PA, USA) for screening. In the first phase, they were screened only by the title and abstract, and after removing the irrelevant articles, the full-text documents of the remaining ones were uploaded in EndNote for the second screening phase. Screening and selection were performed independently by two of the authors (RGB and GDV) and were validated by the third author (CA). Disagreements were resolved through consensus.

The search strategy is shown in Figure 1 . Through this selection procedure, 7151 papers were obtained. After removing the duplicated ones, this number was reduced to 5904 papers. Titles and abstracts were analyzed, and articles were included in the review if they were related to studies that investigated the use of mobile phones while driving in a simulator. A total of 542 articles were found, but 475 of them were excluded due to the following reasons: some of them were conference articles, some did not use a car simulator, others were not available for download or were review articles, some assessed pedestrian distraction or the car’s navigation system, others did not use the telephone as a distraction factor, 1 was scholarly paper, 1 used listening audiobooks as a distraction factor, 1 was about e-hailing, and 2 were duplicated. In addition, this paper is intended to be a second part of the work [ 3 ], in which the distraction caused by talking on the phone was taken into account. In this regard, the papers focused on talking on the phone were excluded. However, the articles that dealt with the evaluation of both activities—talking and texting—were not removed. Finally, 67 articles were selected for data extraction in this systematic review of the literature.

Study identification and selection based on the PRISMA-ScR flow diagram.

2.6. Data Extraction

As previously mentioned, the data extraction was performed by two authors (RGB and GDV) and was then validated by a third author (CA). A Microsoft Excel spreadsheet was created to centralize the following information: first author, year of publication, journal name, region (the country where the experiment took place), institution where the research was conducted, sample size, age, gender, and driving experience, type of simulator, driving scenario, tracking device, type of distraction factors, distraction task, type of evaluated measures, effect on a performance measure, independent variables, and statistical analysis technique.

Each reference was read in its entirety by the designated author, and the extracted data were added to the table. The location was based on the country from where the participants were recruited. If the user study involved samples from different countries, we considered the institution’s location that managed the experiment.

The extracted information was classified into 4 categories related to the characteristics of the studies and the four research questions: “What types of distractions are introduced when using the phone for TWD?”, “What types of hardware devices were used during experiments to analyze the driver’s performance?”, ”What measures were used to predict and analyze distraction?”, and “What is the impact of using mobile devices to read and write messages while driving?”.

2.7. Synthesis of the Results

The results of the literature review are given in the following section, with each subsection corresponding to an objective or a research question proposed in this study.

3.1. Characteristics of Studies

The main characteristics of the papers, such as publication date and demographic data, are briefly presented in Appendix A Table A1 . The 67 studies selected for the review cover a range of 21 years (2002–2022). The number of published papers varies, from 1 paper in 2002 and 2003 to 10 papers in 2021. The highest number of articles were published in 2021. The studies included in the review were published in the following journals: Transportation Research Part F: Traffic Psychology and Behaviour ( n = 13); Accidents Analysis and Prevention ( n = 12); Applied Ergonomics ( n = 4); Transportation Research Record ( n = 4); Human Factors ( n = 3); Traffic Injury Prevention ( n = 3); and several other journals, such as Safety Science , IEEE Access , Journal of Safety Research , and Transportation Research Part C: Emerging Technologies .

Most of the studies were developed in North America ( n = 22), and more particularly in the USA ( n = 18) ( Figure 2 ). The other studies were conducted in Europe ( n = 19), Asia ( n = 17), and Oceania ( n = 9). In Europe, most publications are from Greece ( n = 4), Germany ( n = 3), and The Netherlands ( n = 3). In Asia, most of the publications are from China ( n = 7) and India ( n = 5), and from Oceania, most studies were developed in Australia ( n = 8).

Distribution of papers by country/region.

American, Indian, and Australian research institutions dominate the total number of articles focused on assessing the impact of phone use while driving in virtual environments ( Figure 3 ). Most studies were developed at the Indian Institute of Technology (IIT) Bombay ( n = 5), followed by the University of Alabama at Birmingham ( n = 4), Monash University ( n = 3), and Queensland University of Technology ( n = 3).

Distribution of papers by research institution.

The analysis of co-occurrence terms was performed using VOS Viewer software version 1.6.18 in order to identify the most frequently used terms and the relationship between them. The minimum number of occurrences of a keyword was selected to be 10, resulting in 35 terms that meet the threshold of the total of 716 keywords. The result of the co-occurrence analysis is presented in Figure 4 . As can be observed, the most frequently used keyword was “human”, with 31 occurrences, followed by “automobile drivers”, “car driving”, “driving simulator”, and “mobile phone”. The co-occurrence network map generated by VOS Viewer suggested the division be into three clusters differentiated by colors.

Network diagram of the most frequently used terms.

In order to infer connections between the authors and their research topics, the co-citation network was also examined using VOS Viewer. This network entails recognizing pairs of authors who were referenced together in the same publications. Figure 5 shows the results in which the minimum number of citations of an author was set to 20. A number of 39 authors meet the threshold, and four clusters are distinguished.

Author co-citations network.

The selected studies included a sample of 3033 participants ( n =1984 male; n = 1049 female) who participated in simulated driving experiments. The minimum number was 14 [ 49 ], and the maximum was 134 [ 50 ] participants per study. The gender distribution was not mentioned in two of the extracted studies.

The age of the participants is between 16 and 79 years old; however, in 17 studies, the age interval is not reported. However, the mean age is reported in more studies ( n = 59), and the unweighted mean age is 39.6 years across all of these studies. Moreover, the standard deviation is mentioned in 52 studies and is 4.98 across all studies. Only two articles do not mention the age range, the mean age, and the standard deviation.

All participants were assumed to be clinically healthy, except for the participants in one study focusing on teens with and without ADHD [ 51 ].

3.2. RQ1: What Types of Distractions Are Introduced When Using the Phone for TWD

To find out what sources of distraction were used in the studies, we extracted the information on the type of distraction and divided the distractions into the following categories according to [ 52 , 53 ]: visual (V), auditory (Au), manual (M) (physical), and cognitive (C) distraction. The results are presented in Figure 6 , as well as in Appendix A Table A1 for each individual study. As can be seen, most articles (34% of the total number of papers, n = 23) considered both manual and visual components when assessing the effects of performing secondary tasks while driving. Each secondary task contains one or more components. Examples of visual distractions include interaction with in-vehicle devices [ 54 ], the use of smartphone applications while driving [ 55 ], looking around, and so on. Auditory distractions emerge when drivers focus on other sounds, such as the ringing of the phone, voice conversations, the radio, etc. Manual distractions involve eating [ 56 ], drinking [ 29 ] while driving, or doing anything other than manipulating the steering wheel. Finally, cognitive distractions occur when the driver has his/her mind in another place and fails to see what is important on the road. Studies showed that TWD could introduce all of these types of distractions, and even for short durations, they might lead to driving errors and even crashes [ 57 ]. Furthermore, most activities unrelated to the driving task combine these four modes [ 58 ]. For instance, the most common compound distraction is a visual–manual distraction, defined as a secondary activity that involves using hand gestures to manipulate a visual interface [ 59 ].

Distribution of papers by the source of distraction type (V—visual; Au—auditory; M—manual (physical); and C—cognitive).

While some articles focused on the visual component [ 55 , 60 , 61 ], others considered two, three, or even four types of distractions. For instance, both cognitive and visual components were highlighted in [ 29 , 62 , 63 ]; cognitive and manual components were presented in [ 64 , 65 ]; and visual–manual distraction was evaluated in [ 35 , 66 , 67 ]. As we have seen, only one article considered all four components of distraction: [ 68 ]. In this paper, visual–manual and auditory–vocal interfaces were evaluated, but also the subjective workload was considered as a measure of cognitive distraction.

Some studies investigated the effects of cell phone use in comparison with other secondary tasks, such as talking to a passenger (two studies: [ 49 , 69 ]), eating (four studies: [ 56 , 57 , 70 , 71 ]), radio tuning (five studies: [ 67 , 69 , 72 , 73 , 74 ]), using navigation systems (three studies: [ 33 , 58 , 74 ]), taking pictures [ 75 ] or selfies [ 76 ], adjusting climate control [ 72 ], reading emails (three studies: [ 55 , 63 , 77 ]), drinking [ 29 ], watching video and using social media [ 63 ], switching display view and searching songs [ 55 ], and sharing numbers [ 76 ]. Other studies compare phone use with other types of devices, such as the smartwatch (three studies: [ 36 , 68 , 78 ]) and Google Glass (two studies: [ 54 , 79 ]). Moreover, instead of using the phone for texting, some researchers used smartphones to perform tasks on social media, such as using Facebook (three studies: [ 20 , 80 , 81 ]), Snapchat, Instagram [ 82 ], Whatsapp [ 83 ], or some self-developed applications [ 60 , 84 ]. In one study, the use of mobile phones while driving was evaluated in parallel with drunk driving: [ 85 ].

The distraction tasks were divided into two categories: handheld (HH)—holding the device in hand; or hands-free (HF)—performing the task without using hands to hold the device. In 86% of the studies ( n = 51), the task was performed using HH devices. In 5 studies, both HH and HF devices were used, and in 11 studies, the HF devices were preferred.

3.3. RQ2: What Types of Hardware Devices Were Used during Experiments to Analyze the Driver’s Performance?

3.3.1. driving simulator equipment.

Regarding the simulators used in the analyzed studies, 84% of experiments ( n = 56 studies) were conducted in fixed-based simulators. The other experiments were carried out in driving simulators equipped with motion systems having from 2 to 6 degrees of freedom (DOFs). Each study was classified according to the work of [ 40 ], which proposed a classification method for driving simulators that was adapted from flight-simulator classification standards (see Appendix A Table A1 ). The proposed classes were defined by taking into consideration four sets of criteria: general information, such as environmental modeling and the hardware complexity of the replicated vehicle; the presence of a motion system and the number of degrees of freedom; visual capabilities, especially the field of view; and the sound system which is essential for driver immersion. Class A simulators are at the bottom of the list with no requirement for the motion platform, basic cabin equipment, and basic visual and sound capabilities. Custom-made driving simulators in class A include a desktop computer, steering wheel, gas pedal, and brake pedal, as in the following works: [ 61 , 67 , 86 , 87 ]. On the other end, class D simulators require a motion platform with a minimum of six DOFs, at least 180 degrees field of view, and a realistic visual and acoustic environment. Class B simulators were the most popular, as they were used in 36 studies, followed by class A, with 21 studies; class C, with 4 studies; and last but not least, class D simulators, with 6 studies.

The following class C and D simulators were identified: CARRS-Q Advanced Driving Simulator [ 76 , 88 , 89 ], the moving-base driving simulator from Würzburg Institute for Traffic Sciences [ 63 ], DS-600c Advanced Research Simulator developed by DriveSafety (3 studies: [ 20 , 73 , 82 ]), Ford’s VIRtual Test Track EXperiment [ 72 ], and VS500M driving simulator [ 30 ]. One experiment was performed in a driving simulator with three DOFs: [ 90 ], and three experiments were performed in two-DOF driving simulators: [ 20 , 55 , 82 ]. We also extracted some commercially available class A and B driving simulators: Foerst Driving Simulator (three studies: [ 81 , 91 , 92 ]), PatrolSim high-fidelity driving simulator [ 66 ], NADS MiniSim [ 36 ], and EF-X from ECA-Faros (two studies: [ 31 , 80 ]). Most systems are developed by Systems Technology Inc., Hawthorne, CA, USA, both hardware and software (used in 10 of the included articles).

The type of display varies among the studies between screen-based projection systems and systems containing monitors. Thirty-nine studies used monitors, ranging from a single monitor to a system of five monitors, and twenty-seven studies in which the display system was based on projectors. The number of screens on which the images were projected ranged from 1 to 7. One paper did not clearly report the information related to the display. The visual field of view (FOV) varied between 40° and 300° for horizontal view and between 24° and 60° for vertical view. However, this information is not reported in a large number of articles (over 16). The most advanced display is installed on the DS-600c advanced simulator, which is composed of seven high-definition projectors that provide 300 FOV to drivers [ 82 ]. In terms of vertical FOV, the highest value is found in [ 93 ] due to the use of large screens surrounding the simulator.

The simulated scenarios contain various types of roads (urban, rural, highway, single lane, and multilane), with lengths varying from 1 to 38.6 km. The lengths were reported by the authors in either kilometers, meters, miles, or feet but were transformed into kilometers in this paper. The longest route is presented in [ 94 ], having 24 miles (equivalent to 38.6 km). As for the duration of the experiments, it varies from 2 min [ 33 ] to 120 min [ 63 , 95 ]. In this case, only 40 of the articles reported the duration of the experiment.

Fourteen studies reported that the simulator uses an automatic transmission, seven studies stated that a manual transmission was used in the experiments, and the rest of the papers did not explicitly state this information.

The impact of the secondary task was assessed in various driving scenarios. Of these, two types were identified as the majority: 19% of studies ( n = 13) used a car-following scenario, which requires following a lead vehicle and responding to its behavior [ 96 ] and which is the most common routine driving situation [ 97 ]. In 50 studies (75% of the total number of articles), the first task was to free drive on a route or to follow a path along which one or more incidents occurred. Examples of such incidents include the sudden appearance of an animal on the roadway [ 29 , 81 ], the sudden appearance of a pedestrian crossing the street [ 18 , 19 , 20 , 51 , 60 , 65 , 76 , 90 ], a cyclist entering the roadway [ 36 , 51 , 65 ], a parked car pulls out onto the road [ 18 , 90 ], and so on.

Apart from car-following and free-driving scenarios, the other articles contain the following scenarios: a crossing road [ 88 ], rail level crossing [ 31 ], steering along the lane’s center [ 87 ], and lane changing [ 98 ].

3.3.2. Driver-Tracking Equipment

The information about the driver’s performance was collected through the hardware and software systems of the simulator, but in 33% of the total number of studies, additional driver-tracking devices were used. Thus, in twenty articles, a device for tracking the driver’s gaze was used; in one article, brain–computer interface (BCI) systems were used; and in one article, the whole body of the user was tracked. For eye-tracking, some researchers used simple video cameras and extracted the information by manual coding of the recorded video: [ 54 , 58 , 60 , 68 , 93 , 99 ]. Others used specialized eye-tracking devices: Fovio eye tracker [ 20 ]; Ergoneers’ Dikablis Essential head-mounted eye tracker [ 36 , 55 ]; eye-tracking system developed by Seeing Machines, Ltd. (Canberra, Australia): faceLAB™ 4.1 [ 90 ]; faceLAB™ 5.0 [ 31 ]; Pupil Lab’s Pro head-mounted eye tracker [ 100 ]; SmartEye6.0 [ 69 ]; eye-tracking glasses developed by SensoMotoric Instruments, Berlin, Germany [ 74 , 78 , 101 ]; Tobii Pro Glasses 2 [ 80 , 84 ], Ergoneers Dikablis Eye Tracker 3 glasses [ 102 ]; and one paper did not mention the device. A MindCap XL headband equipped with a NeuroSky sensor was used to measure brain activity [ 59 ]. In [ 33 ], a high-speed infrared camera Motion Analysis Corp., Santa Rosa, CA, USA, was used to track the full body of the participants.

Four papers considered the physiological data taken from the participants during the experiment. In these studies, heart rate and skin conductance were measured using devices such as the MEDAC System/3 instrumentation unit by NeuroDyne Medical Corporation [ 54 , 68 ] and Biopac BioNomadix3 MP150WSW system [ 60 ], and heart rate plus other cardiovascular reactivity indicators (root mean square of successive differences, systolic blood pressure, diastolic blood pressure, and mean arterial pressure) were measured in [ 65 ].

3.4. RQ3: What Measures Were Used to Analyze and Predict Distraction?

The selected studies include several measures to assess driving distractions. Most of them are driving-simulator-dependent variables used to assess the driver’s performance under the influence of distractions. Choosing such measures is an appropriate approach in the context of car simulators, as no additional sensors are needed. We grouped driving-performance measures into seven categories, starting from the classifications found in [ 103 ] and [ 104 ] and adding a new category regarding variables that are not necessarily related to vehicle-performance parameters: traffic violations (TrVs), driving maintenance (DM), attention lapses (ALs), response time (RT), hazard anticipation (HA), accident probability (AP), other measures (OMs). The distribution of papers according to these categories is presented in Figure 7 . In some studies, variables belonging to only one category are used, while in others, they are part of two, three, or even all four categories. Most articles used measures from the DM category (49 studies), followed by RT (22 studies), OMs (21 studies), TrVs (12 studies), AP (4 studies), ALs (2 studies), and HA (1 study).

Distribution of papers according to driving performance measure categories (TrVs—traffic violations; DM—driving maintenance; ALs—attention lapses; RT—response time; HA—hazard anticipation; AP—accident probability; and OMs—other measures).

In the DM category, the following measures were included: lane-keeping measured by the standard deviation of lateral position (SDLP) [ 35 , 60 ]; speed variables, such as mean speed [ 19 , 34 , 105 ] and standard deviation (SD) of speed [ 34 ]; steering control, including steering angle [ 106 , 107 ] and SD of steering angle [ 17 ]; time to collision [ 64 ]; and headway measured in space–distance headway [ 88 ] or in time–time headway [ 108 ].

RT includes brake reaction time [ 20 , 109 ] and other time variables in response to a pop-up event [ 18 ]. In the TrVs category, variables such as speed violation [ 72 ] and the number of collisions [ 77 ] were considered. ALs include results related to cognitively demanding and texting compared to four different blood-alcohol-concentration (BAC) levels: 0.00, 0.04, 0.07, and 0.10 [ 85 ]. OMs consist of other variables that cannot be included in the categories presented above: task completion time [ 67 , 68 ]; workload [ 87 ]; or variables related to eye tracking, such as the number of glances [ 78 , 84 ], off-road glances [ 54 , 69 ], and saccade amplitude [ 102 ]. The most common measures that were examined in the analyzed studies are presented in Figure 8 .

Main measures used in the experiments of the examined studies (SD—standard deviation).

In addition to measures related to the driving performance or other types of outcomes measured using sensors or self-reported, some of the studies also took into account additional parameters or independent parameters, such as the age of participants (A), driving experience (E), gender of participants (G), weather (W), road configuration (RC), and traffic flow (T). There are 18 articles that analyzed these additional parameters. In most studies, age was considered to be an independent parameter (11 studies), followed by gender (3 studies), driving experience (3 studies), traffic flow (2 studies), road configuration (2 studies), and weather (2 studies). There are studies that consider two or more parameters: A and E [ 32 , 100 ], A and G [ 19 , 86 ], RC and T [ 18 ], and RC and W [ 91 ].

Related to the statistical analysis of data, the most used technique was the analysis of variance (ANOVA), being applied in 33 of the selected studies. Other statistical methods used in the works were multivariate analysis of variance (MANOVA; 1 study), Wilcoxon signed rank test (10 studies), Wald test (6 studies), t-test (8 studies), regression analysis (3 studies), logistic regression analysis (1 study), linear mixed models (2 studies), and generalized linear model (2 studies).

3.5. RQ4: What Is the Impact of Using Mobile Devices to Read and Write Messages While Driving?

The selected studies were found to vary in several aspects: the proposed objective, the number of participants in the experiments, the infrastructure used to pursue the proposed objective, the outcomes, and so on. However, there is an agreement between the main outcomes of these studies. That is that text messaging, which mostly involves visual and manual distraction, has a significantly larger influence on driving performance [ 66 ] than a phone conversation. The main effects of this secondary task are increased variability in lane position and missed lane changes [ 90 ], increased brake reaction time [ 82 ], greater speed variability [ 110 ], increased steering variation per second [ 30 ], and higher completion times [ 88 ], as well as a higher risk of accidents than other in-vehicle tasks, such as tuning the car radio [ 67 ]. Even though drivers are aware that it is dangerous [ 98 ] and illegal in many countries to use a mobile phone while driving, they cannot resist the temptation to read and reply to messages, especially in the case of younger drivers [ 64 ]. Sending or reading a text from a smartphone takes the driver’s eyes off the road for 5 s, and, at a speed of 55 mph, that is similar to driving the length of an entire football field with the eyes closed [ 111 ].

Another secondary activity that has a negative impact on the driver’s performance is using social media [ 63 ]. However, this was not found to be as detrimental as texting [ 20 ] since image-based interfaces may provide a safer way to stay connected while driving than text-based interfaces [ 82 ]. Moreover, the side effects of using social media can be prevented with the help of advanced driver-assistance systems (ADASs) [ 80 ].

Visual–manual distractions negatively influence lateral lane position variability [ 112 ] and the average speed [ 57 ] by taking the driver’s eyes off the road [ 58 ] and increasing the mental workload [ 78 ]. Auditory distraction has been studied less, but it also seems to affect drivers’ performance by negatively affecting situation awareness and mean speed [ 113 ]. However, driving performance is less affected when travel information is presented in auditory mode [ 93 ]. A proper user interface (UI) design of smartphone applications could reduce the visual and cognitive demands of the driver when engaged in secondary activities. However, there is plenty of room for improvement of UIs in the automotive context. One design feature that could alleviate the drivers’ visual–manual demands is the integration of speech-to-text technology in either mobile phones or in-vehicle systems [ 55 ].

Using a mobile phone while driving can lead to compensatory measures to mitigate the effect of the distraction. Drivers could increase their vigilance [ 106 ], adopt a reduced speed [ 19 , 67 ], increase their distance from the leading vehicle [ 114 ], and self-regulate the secondary task [ 112 ]. It is worth noting that the driving task also negatively influences the texting task by inducing accuracy errors [ 115 ] and an increased response [ 116 ].

Regarding the independent variables, some findings can be extracted from the analyzed studies. The driver’s age can be used to predict driving performance significantly when it is correlated with the driving experience. To illustrate this aspect, [ 72 ] found that teens are not responsible enough while driving, as they have inadequate vehicle-control abilities and are more likely to be distracted from HH phone tasks compared to older drivers. However, young people have lower longitudinal control during distracted driving [ 32 ] and are more likely to accept a gap in intersections [ 88 ]. The age may be counterbalanced by driving experience, but in the case of TWD, it does not have any influence. In terms of gender, it was found that male drivers drove at higher speeds [ 19 ], while female drivers performed a higher number of lane excursions and had a higher reaction time compared to male drivers [ 17 , 18 , 75 ]. Moreover, male drivers tend to be more positive toward on-board traffic messages and in-vehicle systems [ 86 ].

Regarding the road configuration variable, it was observed that road geometry (especially curved road and vertical alignments) has a more significant influence on speed and lateral position than mobile-phone distraction [ 89 ]. Furthermore, it was found that text messaging could lead to behaviors that can obstruct traffic flow [ 94 ].

Another relevant outcome is that weather does not seem to influence the mean speed, but it can negatively affect the mean reaction time [ 91 ].

Some secondary tasks, such as eating and drinking while driving, have fewer distracting effects on the driver’s performance than phone texting [ 29 , 56 ]. In addition, operating a music player was found to be less risky than texting, which was reported to be an extremely risky task [ 71 ]. Studies that analyzed drivers’ physiological data showed that TWD increases cardiovascular reactivity [ 65 ] and skin conductance [ 68 ] compared to driving with no secondary tasks.

Several studies that explore the impact of texting on driving behavior have shown that engagement in secondary tasks directly influences safe driving performance [ 33 ]. For instance, regardless of the device, whether it is a mobile phone or a smartwatch, if the driver’s gaze is not on the road scene and all attention is on the device and its contents, then the driving performance is affected [ 68 , 78 ], and this, in turn, increases the risk of a crash [ 36 ]. The probability of a crash increases up to four times when drivers are engaged in distractions related to using a mobile phone [ 19 ]. The use of augmented-reality glasses did not eliminate the distracting cognitive demands while driving and still influenced driving performance [ 54 ]. The age of the participants is the main limitation of the analyzed studies, which included the use of Google Glass, as they include mainly a younger segment of the population. A summary of the results of the selected papers can be found in Appendix A Table A1 .

4. Discussion

The primary focus of this comprehensive review is to summarize the existing knowledge regarding the impact of texting and reading on a mobile phone while driving in a simulator. The review addressed four research questions that can help to better understand the distractions that influence the drivers’ performance, what simulators were used by researchers, and what measures were considered to assess the impact of distracted driving. The review found a relatively large number of studies ( n = 67) that addressed texting as a secondary task while driving in a simulator. The results of the review are in line with those of previous research, which found that TWD has a negative effect on a number of parameters related to driving performance that can be investigated in experiments conducted in car simulators.

The included studies can be divided into two broad categories depending on the device type: handheld or hands-free devices. The sources of distractions were also classified into the following four types: cognitive, visual, manual, and auditory. Most secondary tasks include at least two distractions that can influence the driver’s ability to reach his/her destination in a safe manner. The driver’s brain has to manage all of the abovementioned distractions when operating a vehicle. Any additional distractions can increase the mental workload, thus compromising the driver’s performance.

Drivers are subject to various distractions that can hamper their driving ability. Manual and visual sources of distraction are the most common and correspond to activities such as interaction with in-vehicle devices or the use of a mobile phone. Driver-assistance systems that offer warnings could reduce the time the driver is not focused on the driving task. Some high-end vehicles already have integrated devices that track the driver’s gaze. However, technology needs to become more accessible, reliable, and mainstream. We expect to see rapid progress in deep learning algorithms that can accurately identify and track the driver’s gaze by using a simple video camera.

The driver’s behavior has been exhaustively researched in naturalistic and simulator-based studies [ 117 , 118 ]. Even so, there is still work to be performed to fully understand the combination of measures most effective in predicting road safety. The most popular variables used by researchers to analyze driving patterns are mean speed, reaction time, and the standard deviation of the lane position.

Driving scenarios investigating hazard anticipation and traffic violation measures in a simulator are gaining more and more interest. The negative effects of using a mobile phone for TWD have been confirmed by numerous studies. The main effects include an increased brake reaction time, a decrement in lane control, and higher speed variability.

4.1. Recommendations and Directions for Future Research

What is evident from the findings is that typing and reading text messages while driving, regardless of the device used, should be prohibited in order to reduce the number of traffic-related deaths and injuries. Although it is advisable not to use a phone while driving, this is not very likely to happen, as it is used for various purposes, and the tendency to check the smartphone’s screen cannot be easily inhibited [ 119 ]. To support this idea, it was shown that even the experience of a minor accident is not enough to discourage drivers from sending messages while driving [ 120 ]. A possible solution would be to reduce as much as possible the unnecessary use of the phone and provide easy access to its screen by placing it in the field of view of the driver in a way that he/she is still attentive to the traffic scene or by sharing the screen on built-in display systems, which should be safer to use while driving. Moreover, built-in driver-assistance systems that prevent distracted driving should become mainstream as soon as possible, especially considering the rising number of traffic participants involved in car crashes due to phone use. A solution that has been shown to be effective would be the intervention by interactive text message [ 121 ].

A topic that still requires attention is how to increase the use of advanced driving-assistance systems (ADAS) to prevent drivers from engaging in distracting secondary tasks. For instance, ADAS systems may reduce or prevent the excessive use of a mobile phone by giving visual–audio notifications when the driver takes his/her eyes off the road. Future studies should focus on reducing the number of false alerts and propose adaptive ADAS models that can modify their behavior according to the characteristics of a driver (some initial work is presented in [ 122 ]). The use of safety functions should not impose other costs, as most drivers would not pay extra for such systems [ 123 ]. Another key aspect that could increase the acceptance of ADAS is related to the education of the driver, which should fully understand the safety benefits and limitations of such systems.

After analyzing the included studies, we noticed a lack of consensus regarding the methods and materials used for running experiments in driving simulators. In the context of automation, we suggest some minimum features for DS to ensure high reliability, validity, and replicability of the obtained results. The need for a systematic comparison of DSs concerning their validity and fidelity was also expressed in a scientometric analysis in [ 124 ]. Other issues identified are related to simulation sickness, how drivers perceive risks in a virtual environment, and the lack of detailed descriptions in research studies. A DS that offers high validity has the ability to reproduce as accurately as possible real-world driving [ 125 ], but the validity should be investigated in-depth to better approach the real conditions of driving [ 126 ].

Several aspects need to be considered when testing whether a driving simulator provides valid results: the simulator itself, the user samples, the task studied, the design of the experiment, and even the terminology used [ 34 ]. In view of these, and given that car manufacturers, taking advantage of the latest technologies, are setting new standards for car simulators [ 127 ], we propose several recommendations for future research in the context of driving simulators (the summary is shown in Table 1 ):

• 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.).

Minimum feature recommendations for experiments using a driving simulator.

The driving task should not be too long in order to avoid fatigue and boredom, but not too short in order to be able to extract relevant results. Participants need to be monitored in case they experience simulator sickness during the practice session and in the study itself. A subjective evaluation of the experiment, for example, using questionnaires to better understand how the experiment influenced the driver’s psychological state (e.g., discomfort, fatigue, workload, frustration, mind wandering, and so on), can be beneficial and generate other valuable insights.

Therefore, punctual research studies that focus on a particular subject or concern are frequently carried out over a shorter period and might utilize a smaller sample size and a limited number of techniques to gather data. These studies might also look at the efficacy of measures taken to reduce the harmful effects caused by particular driving distractions. On the other hand, in order to gain a thorough understanding of a specific topic, it is crucial to gather a large amount of data over time and under different driving conditions, which, in turn, can reveal significant trends and patterns.

4.2. Limitations

Certain limitations need to be mentioned for this review. First, since the use of the mobile phone while driving is a widely studied field of research, it is possible that some relevant articles may have been missed even after a rigorous search of the literature. The review was limited to excluding studies published in conference proceedings or book chapters, as well as those published in languages other than English. Some shortcomings are related to the data, which were not fully reported in several papers. There are also methodological limitations, including the lack of valid and reliable measures to assess the effects of TWD, the use of small samples, the duration of experiments, and so on.

The proposed recommendations aim to offer guidelines for experiments using a driving simulator. However, they cannot consider all the possible scenarios that could be investigated. The suggested minimum requirements are based on the knowledge gained from the literature review analysis and on our partially subjective vision of driving simulators. It can be argued that a consensus regarding this topic will, perhaps, never be reached, as researchers will just use the infrastructure available.

5. Conclusions

This study presents the results of a review of the literature using a structured search to examine drivers’ use of mobile phones and wearable devices concerning simulated driving. Through a rigorous selection process, fifty-nine studies published in the past 20 years were extracted, analyzed, and classified into four categories. Advanced driving simulators with a motion system were used in less than 20% of the studies due to the high costs and complexity of operation and maintenance. According to [ 132 ], studies that include low-cost simulators to identify and analyze the driver’s performance can offer meaningful and even similar findings as those obtained from experiments with advanced driving simulators. Nonetheless, the lack of a motion platform significantly affects the realism of the simulated scenario, as the participant cannot experience the vehicle’s inertia when accelerating or when negotiating a curve.

Mobile phone use in the vehicle is a major component of distracted driving that requires drivers to take their eyes off the road and one or both hands off the steering wheel, thus impairing their driving performance and increasing the likelihood of crashes [ 133 ]. Most studies reached the conclusion that activities such as texting a message on the phone, manipulating the phone, or the use of different types of phone-connected devices can introduce cognitive, manual, visual, or even auditory distractions [ 134 ] that can have serious negative effects on drivers’ attention and concentration, and this can lead to serious traffic incidents [ 135 ].

Many studies based on driving simulators show that performing secondary tasks (such as manual input) while driving leads to a compromised driving performance [ 17 , 18 , 19 , 32 , 70 , 101 , 136 ]. Distraction can be achieved by removing the driver’s gaze from the road. However, cognitive distractions can be just as dangerous by taking his/her mind away from the driving process [ 137 ].

The ubiquity of mobile phones; the increasing number of traffic participants; and their need/desire to engage in secondary tasks, such as games, texting, or social media, could have a negative effect on road safety, despite the integrated or mobile driver assistance systems. This review can serve as a basis for regulators and interested parties to propose restrictions related to using mobile phones in a vehicle and improve road safety. It also points out the significance of informing drivers about the dangers of using mobile phones while driving and the importance of enforcing strict rules and sanctions for those who have a habit of doing this. Moreover, the study provides researchers with an overview of the types of distractions that can affect the driver at a cognitive, visual, manual, or auditory level, as well as the measures that can be used to predict and analyze those distractions. The review recommends that future research should concentrate on creating more sophisticated driver assistance systems and technologies that can better detect and prevent distractions caused by TWD.

Future research should focus on finding a consensus regarding driving-simulator studies that will enable scholars to directly compare their work with similar studies, thus ensuring high validity of results, especially in the context of automated driving.

An overview of driving simulators characteristics and classification ( n = 67).

Note: TD—type of distraction: C—cognitive, V—visual, M—manual, Au—auditory; MT—measure type: AL—attention lapses, AP—accident probability, DM—driving maintenance, HA- hazard anticipation, RT—response time, TrV—traffic violations, OM—other measures; HH—hand-held, HF—hands-free, NP—number of participants; LSR—length of simulated route; NR—not reported. a Values include age, mean, standard deviation, and gender (M, F). b Driving Simulator Classification: A—fixed-based, basic visual capability, FOV minimum H:40 and V:30; B—fixed-based, FOV minimum H:40, and V:30; C—motion platform, FOV minimum H:120 and V:30; D—minimum 6 DOF motion platform, FOV minimum H:180 and V:40 [ 40 ].

Funding Statement

This work was supported by a grant from the Romanian Ministry of Education and Research, CCCDI–UEFISCDI, project number PN-III-P2-2.1-PED-2019-4366 (431PED), within PNCDI III.

Author Contributions

Conceptualization, R.G.B. and I.-D.B.; methodology, R.G.B.; software, G.-D.V.; validation, I.-D.B., C.A. and G.Y.; formal analysis, R.G.B.; investigation, G.-D.V.; resources, G.-D.V.; data curation, I.-D.B.; writing—original draft preparation, R.G.B. and G.-D.V.; writing—review and editing, C.A. and G.Y.; visualization, I.-D.B.; supervision, C.A. and G.Y.; project administration, R.G.B.; funding acquisition, C.A. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The authors declare no conflict of interest.

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