Accident Analysis and Prevention 74 (2015) 145–149
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The effects of texting on driving performance in a driving simulator: The influence of driver age Gordon Rumschlag a , Theresa Palumbo a , Amber Martin a , Doreen Head b , Rajiv George b , Randall L. Commissaris a, * a b
Department of Pharmaceutical Sciences, Eugene Applebaum College of Pharmacy and Health Sciences, Wayne State University, Detroit, MI 48202, USA Department of Health Care Sciences, Eugene Applebaum College of Pharmacy and Health Sciences, Wayne State University, Detroit, MI 48202, USA
A R T I C L E I N F O
A B S T R A C T
Article history: Received 17 December 2013 Received in revised form 5 October 2014 Accepted 7 October 2014 Available online 31 October 2014
Distracted driving is a significant contributor to motor vehicle accidents and fatalities, and texting is a particularly significant form of driver distraction that continues to be on the rise. The present study examined the influence of driver age (18–59 years old) and other factors on the disruptive effects of texting on simulated driving behavior. While ‘driving’ the simulator, subjects were engaged in a series of brief text conversations with a member of the research team. The primary dependent variable was the occurrence of Lane Excursions (defined as any time the center of the vehicle moved outside the directed driving lane, e.g., into the lane for oncoming traffic or onto the shoulder of the road), measured as (1) the percent of subjects that exhibited Lane Excursions, (2) the number of Lane Excursions occurring and (3) the percent of the texting time in Lane Excursions. Multiple Regression analyses were used to assess the influence of several factors on driving performance while texting, including text task duration, texting skill level (subject-reported), texting history (#texts/week), driver gender and driver age. Lane Excursions were not observed in the absence of texting, but 66% of subjects overall exhibited Lane Excursions while texting. Multiple Regression analysis for all subjects (N = 50) revealed that text task duration was significantly correlated with the number of Lane Excursions, and texting skill level and driver age were significantly correlated with the percent of subjects exhibiting Lane Excursions. Driver gender was not significantly correlated with Lane Excursions during texting. Multiple Regression analysis of only highly skilled texters (N = 27) revealed that driver age was significantly correlated with the number of Lane Excursions, the percent of subjects exhibiting Lane Excursions and the percent of texting time in Lane Excursions. In contrast, Multiple Regression analysis of those drivers who self-identified as not highly skilled texters (N = 23) revealed that text task duration was significantly correlated with the number of Lane Excursions. The present studies confirm past reports that texting impairs driving simulator performance. Moreover, the present study demonstrates that for highly skilled texters, the effects of texting on driving are actually worse for older drivers. Given the increasing frequency of texting while driving within virtually all age groups, these data suggest that ‘no texting while driving’ education and public service messages need to be continued, and they should be expanded to target older drivers as well. ã 2014 Elsevier Ltd. All rights reserved.
Keywords: Driving Simulator Performance Texting and Driving Distracted Driving Driver Age Effects on Driving
1. Introduction Distracted driving is a significant and increasing contributor to motor vehicle accidents and distraction-related accident fatalities (Olson et al., 2009; Lee et al., 2013). Lam (2002) estimated that distractions were responsible for approximately 4% of traffic crashes; more recently, Bakiri et al. (2013) suggested that distraction-related factors accounted for 8% of injurious road
* Corresponding author. Tel.: +1 313 577 0813; fax: +1 313 577 2033. E-mail address: [email protected] (R.L. Commissaris). http://dx.doi.org/10.1016/j.aap.2014.10.009 0001-4575/ ã 2014 Elsevier Ltd. All rights reserved.
crashes. The National Safety Council (NSC, 2010) reported that cell phone use of any sort causes 28% of all crashes each year. Hoff et al. (2013) reported that nearly 10% of drivers reported being involved in a motor vehicle accident related to distracted driving. Consistent with these reports, various distractions also impair driving performance in driving simulator studies (Neyens and Boyle, 2008; Lam, 2002; Strayer and Drews, 2004; Beede and Kass, 2006). Indeed, cell phone use while driving has been reported to be more disruptive than ethanol intoxication (Strayer et al., 2006). Driver age and experience have been demonstrated to affect the extent of distraction-induced driving impairment, with mature drivers typically being found to be less affected by distraction than
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either much younger or much older drivers. Several investigators (Horberry et al., 2006; McPhee et al., 2004; Cooper et al., 2003; Lee et al., 2003) have reported that older drivers (e.g., 60–73 years old) are more affected by distraction when compared to mature drivers (e.g., 31–44 years old). Moreover, Strayer and Drews (2004) have reported that the effects of distraction in older drivers (e.g., 65–74 years old) are comparable to the effects of distraction in younger, less experienced, drivers (18–25 years old). Recently, Klauer et al. (2014) have reported that the odds ratio for a distraction-related motor vehicle crash or near-crash was greater for novice drivers (newly licensed; Mean = 16.4 years old) when compared to experienced drivers (Mean = 36.2 years; data from 100 car study; Klauer et al., 2006). Taken together, these studies suggest that middle-age mature drivers are less affected by distractions than either older or younger drivers. Texting while driving is a particularly potent yet increasingly likely form of distraction, and incidents of texting while driving and accidents relating to texting while driving continue to be on the rise (O’Malley et al., 2013; Wilson and Stimpson, 2010). In driving simulator studies, texting has been reported to be very disruptive as well (Neyens and Boyle, 2008; Hosking and Young, 2009). Past studies on the impact of texting on driving have focused primarily on young adults (Neyens and Boyle, 2008; Hosking and Young, 2009), in part because this group represents the ‘texting generation’ and because these drivers are generally less experienced and therefore are presumably more likely to be affected by distractions while driving (Fofanova and Vollrath, 2011). While texting continues to be more prevalent in young adults, many older individuals now use texting as a frequent form of communication (CTIA, 2012). Furthermore, it is likely that many of these ‘older texters’ are also texting while driving (Harris Interactive, 2011). At present, there are no studies examining the effects of texting on driving performance across a broad range of driver ages. The purpose of the present study, therefore, was to examine the effects of texting on driving simulator performance across a broad range of driver ages. One might hypothesize that mature drivers would be less affected by texting because of their maturity and driving experience. Alternatively, one might hypothesize that mature drivers will be more affected by texting while driving because they are not as practiced and efficient as technological multi-taskers, particularly as texters, when compared to younger drivers. In addition to driver age the influence of driver gender, driver texting skill level and text task duration also were evaluated. 2. Materials and methods
Room or (2) simulator sickness after the pre-test drive. This study was approved by the WSU Behavioral IRB (#063413B3X). 2.1.1. Subject demographics There were 27 female and 23 male subjects, distributed across a range of ages (18–24, N = 12; 25–34, N = 16; 35–44, N = 9; 45–59, N = 13; overall, 34.5 11.7 [Mean SD]). The subjects reported 18 12 (Mean SD) years of driving experience, with approximately 246 246 (Mean SD) miles driven per week (range: 10–1000). Texting Skill Level was self-reported by subjects and sorted into one of three categories: (1) limited (hunt and peck; N = 7); (2) good (use two hands; N = 16); (3) skilled (one-handed texting; N = 27). Texting history was self-reported by subjects and sorted into one of four categories: (1) 6–10 texts/week (N = 6); (2) 11–50 texts/week (N = 15); (3) 51–500 texts/week (N = 22); (4) >500 texts/week (N = 7). 2.2. The EACPHS driving simulator Study participants were seated in a fixed base driving simulator (DriveSafety, Inc) that consists of a four-door vehicle (2001 Chevrolet Impala) fully equipped with steering wheel, pedals, ignition switch, gear shift, rear and side view mirrors, headlights, turn signals and a radio. A fully immersed virtual driving experience was created by six networked computers generating the simulated roadway via three forward projection screens (left, center, right) to provide a 150 forward field of view, and one rear projection screen. Driving scenarios were created using HyperDrive software, a tile-based scripting tool. The ‘road’ for the present study was created to assess primarily the ability of the drivers/subjects to remain in their intended lane during periods of texting and not texting; it was a two lane ‘country’ road with several minor turns, no intersections, no stop signs and no stop lights. There was no oncoming traffic. 2.3. Experimental design The Study consisted of four phases, all conducted within a single 30-min test session. In phase 1, subjects ‘drove’ the simulator in a brief (3–5 min) drive (50–60 mph) to acclimate to the driving experience. In phase 2, the subjects completed a brief survey asking about demographic information, their texting behavior and some of their driving habits. In phase 3, the subjects again drove the simulator; on this drive the subjects were engaged in series of brief text conversations (three questions requiring brief answers) with a member of the research team. In phase 4, the subjects reflected on their experience in a brief exit survey.
2.1. Subjects 2.4. Data collection and analysis All research subjects were unpaid volunteers, over 18 years of age; most subjects were recruited from the population of students, faculty and staff at the Eugene Applebaum College of Pharmacy and Health Sciences (EACPHS) at Wayne State University. Volunteers who did not use texting as a regular form of communication (operationally defined as 5 text messages/week) were excluded, as were volunteers who did not have a valid drivers license. Volunteers who did not have reliable and fast cell phone service in the driving simulator room (there was a pre-test of cell phone/ texting service prior to the experiment; the driving simulator room turned out to be a ‘dead zone’ for one cell phone service provider) were excluded. Finally, volunteers who experienced ‘simulator sickness’ (dizziness, queasiness, nausea) during the pre-test drive (see details below) were not tested or included as experimental subjects. Overall, approximately 10% of initial volunteers were excluded from participating as subjects; the reasons for exclusion were either (1) lack of cell phone service in the Driving Simulator
Data from the simulator computers were collected and sorted into the following categories/bins: (1) 30-s periods before texting, (2) the periods when the subject was texting and (3) the 30-s periods after texting. The Text Task Duration was defined as starting at the time the subject received a text message, and ending at the time the subject sent a text reply. Texting status (i.e., preduring-post) was judged by a trained ‘texting judge’ who was watching a projected video image from an in-car camera; the texting judge was ‘blind’ regarding any other aspects of the drive (car location on the road, etc).
2.5. Statistical analyses The primary dependent variable monitored during driving was Lane Excursions, which were defined as any time the center of the
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vehicle moved outside the intended driving lane, e.g., either into the lane for oncoming traffic or onto the shoulder of the road. These data were measured as (1) the percent of subjects that exhibited Lane Excursions, (2) the number of Lane Excursions occurring and (2) the percent of the texting time in Lane Excursions. Data from the three text conversations were combined prior to statistical analysis. Multiple linear regression analyses were used to assess the influence of several factors on driving performance while texting, including text task duration, texting skill level, texting history, driver gender and driver age. In the multiple regression analysis, the various factors were treated as continuous variables. Because multiple regression analysis of the full data set (N = 50) revealed a significant effect of texting skill and because texting while driving is more likely to occur among highly skilled texters, additional separate multiple regression analyses were conducted on the data from skilled texters only (N = 27) and not skilled texters only (N = 23). In all statistical comparisons, p < 0.05 was used as the criterion for statistical significance. 3. Results Table 1 summarizes the results of the multiple regression analyses for the data (1) pooled across all subjects (left portion), (2) from the skilled texters only (middle portion) and (3) from the not skilled texters only (right portion). When the data from all 50 subjects were evaluated (left portion), the text task duration was a significant predictor for the number of Lane Excursions, whereas driver age and driver texting skill level were marginally significant predictors for the percent of subjects exhibiting Lane Excursions. There was no significant single predictor for the percent of texting
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time in Lane Excursions. In the skilled texters (middle portion), driver age was a significant predictor for the number of Lane Excursions, the percent of subjects exhibiting Lane Excursions and the percent of texting time in Lane Excursions (marginally significant). In contrast, in the not skilled texters (right portion), the text task duration was a significant predictor for the number of Lane Excursions; there was no significant single predictor for either the percent of subjects exhibiting Lane Excursions or the percent of texting time in Lane Excursions in this group. Driver gender was not a significant predictor for the effects of texting on Lane Excursions in any of the analyses. Fig. 1 depicts the percent of subjects exhibiting Lane Excursions while texting as a function of driver age group in the group of skilled texters only. As can be seen, the percent of subjects exhibiting Lane Excursions increases linearly and dramatically with driver age group. This is true for both the observed data (striped columns) and for values predicted based upon the multiple regression analysis (open columns). Finally, there was one rather notable effect of this driving simulator experience on attitudes toward texting while driving. Although in the initial survey 100 percent of the subjects (50 of 50) reported that texting while driving was dangerous, after participation in the study, 60% of subjects (30 of 50) reported that texting while driving was MORE dangerous than they had previously thought, compared to 0% of subjects (0 of 50) who reported that texting was less dangerous than they had previously thought; 40% of the subjects reported no change in perceived danger. This difference (60% reported MORE dangerous; 0% reported LESS dangerous; 40% reported no change) was statistically significant (Chi square = 27.94, df = 2; p < 0.05). Thus, simply participating in a
Table 1 Results of Multiple Regression Analyses. Dependent variable: number of Lane Excursions Data from skilled texters only (27) F[4,22] = 2.70, p = 0.056 Overall r = 0.57
Data from all subjects (50) F[5,44] = 3.44, p < 0.05 Overall r = 0.53 Predictor Text Task Duration Texts per Week Driver Gender Driver Age Driver Texting Skill
Beta 0.42 0.07 0.05 0.19 -0.03
p
Predictor
0.004 0.64 0.73 0.22 0.19
Text Task Duration Texts per Week Driver Gender Driver Age
Beta 0.15 0.19 0.04 0.41
Data from not skilled texters only (23) F[4,18] = 3.20, p = 0.038 Overall r = 0.63 p
Predictor
0.43 0.39 0.85 0.05
Text Task Duration Texts per Week Driver Gender Driver Age
Beta 0.66 0.08 0.02 0.04
p 0.004 0.68 0.90 0.86
Dependent variable: percent of subjects exhibiting Lane Excursions Data from skilled texters only (27) F[4,22] = 3.25, p = 0.03 Overall r = 0.61
Data from all subjects (50) F[5,44] = 2.54, p < 0.05 Overall r = 0.47 Predictor Text Task Duration Texts per Week Driver Gender Driver Age Driver Texting Skill
Beta 0.11 0.03 0.02 0.27 0.28
p
Predictor
0.46 0.82 0.89 0.09 0.053
Text Task Duration Texts per Week Driver Gender Driver Age
Beta 0.05 0.17 0.17 0.48
Data from not skilled texters only (23) F[4,18] = 0.27, p = 0.89 Overall r = 0.11 p
Predictor
0.80 0.42 0.37 0.019
Text Task Duration Texts per Week Driver Gender Driver Age
Beta 0.11 0.19 0.08 0.06
p 0.67 0.41 0.73 0.82
Dependent variable: percent of texting time in Lane Excursions Data from skilled texters only (27) F[4,22] = 2.38, p = 0.008 Overall r = 0.55
Data from all subjects (50) F[5,44] = 1.99, p = 0.098 Overall r = 0.43 Predictor Text Task Duration Texts per Week Driver Gender Driver Age Driver Texting Skill
Beta 0.18 0.18 0.00 0.23 0.01
p
Predictor
0.23 0.24 0.98 0.16 0.97
Text Task Duration Texts per Week Driver Gender Driver Age
Beta 0.15 0.19 0.06 0.38
Data from not skilled texters only (23) F[4,18] = 0.52, p = 0.72 Overall r = 0.14 p
Predictor
0.44 0.38 0.78 0.074
Text Task Duration Texts per Week Driver Gender Driver Age
Beta 0.21 0.10 0.09 0.08
p 0.40 0.68 0.69 0.76
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Fig. 1. Percent of subjects exhibiting Lane Excursions during texting as a function of driver age group – data from skilled texters only. Data in the striped columns reflect the observed measures from the various driver age groups. Data in the open columns reflect values predicted for the various ages (for 20, 30, 40 and 50 years old) based upon the multiple regression using driver gender, text task duration, driver texting frequency and driver age as predictors (see text for details).
texting while driving simulator study increased the recognition and appreciation of the hazards of texting while driving in these subjects. 4. Discussion In the present study, texting dramatically increased Lane Excursions in the driving simulator. The present studies are consistent with past reports that texting impairs driving simulator performance (Hosking et al., 2009; Drews et al., 2009; Neyens and Boyle, 2008); these findings also are consistent with studies showing an increased frequency of accidents associated with texting while driving in the real world (Fernando and Stimpson, 2008; O’Malley et al., 2013). When the data from all drivers (N = 50) were evaluated using multiple regression, several factors, including text task duration, driver age and driver texting skill were significant predictors for Lane Excursions. In contrast, when the data from only not skilled texters were analyzed, only text task duration was a significant predictor for the number of Lane Excursions. This is perhaps not very surprising; given that texting is disruptive to driving, one might predict that longer text task durations in not skilled texters (greater than 60 s in some drivers) would increase the probability that a Lane Excursion would happen (i.e., it’s just a matter of time). Probably the most important discovery in the present study comes from the drivers who self-reported as skilled texters (N = 27), since this category of drivers are more likely to be texting while driving. In this multiple regression analysis, driver age was a statistically significant predictor for (1) the likelihood of having a Lane Excursion, (2) the number of Lane Excursions, and (3) the percent of texting time in Lane Excursions (marginally significant). Multiple regression-based predicted values for the effects of texting on driving for different driver ages were in close agreement with observed values for different driver age groups. In these skilled texters, text task duration was not a significant predictor for Lane Excursions; this probably relates to the shorter mean text task duration and the reduced variability of this measure among the skilled texters. The finding that the disruptive effects of texting on driving performance are directly related to driver age across a wide range of driver age groups is not entirely consistent with the results of
other studies on the relationship between driver age and distracted driving, where the general picture painted by the results of past studies is that mature drivers are less affected by distraction. Klauer et al. (2014) have recently reported that newly-minted young drivers (16 years old) are more susceptible to a variety of distractions when compared to more mature drivers (texting was not evaluated in both groups). In addition, young drivers have been reported as comparable to older drivers (Strayer and Drews, 2004; Kass et al., 2007), whereas older drivers are more adversely affected by distraction when compared to mature drivers (Horberry et al., 2006; McPhee et al., 2004; Cooper et al., 2003; Fofanova and Vollrath, 2011; Lee et al., 2003), suggesting that younger drivers might be more affected by distractions like texting when compared to more mature, seasoned drivers. Nonetheless, in the present study young drivers were significantly less affected by the distraction of texting when compared to more mature drivers. The reason for these discrepancies is at the present time unresolved, although they may relate to the nature and magnitude of the ‘distraction’. In the previous studies noted above, the driving distraction being compared across age groups was not texting, and it may be that the ‘distracting’ effects of texting are less dramatic in younger drivers, in part because texting is almost second nature to many younger individuals (Lee, 2007). Nonetheless, even among the younger drivers in the present study, texting significantly impaired simulated driving performance. The reason for the dramatic effect of driver age on the disruptive effects of texting observed in the present study is not known at this time. Although text task duration was not different as a function of driver age it is possible that, relative to younger drivers, older drivers spend a greater percentage of their texting time looking at their cell phones (reading and composing/sending a reply), and not looking at the road, when texting. An increase in actual percent of texting time spent reading/composing/sending text messages would very likely increase driving errors. Alternatively, it is possible that older drivers do not differ from younger drivers with respect to the percent of time spent looking at their cell phones (reading/composing/sending), but are more distracted because they spend more time thinking about texting even though their eyes are on the road (i.e., older drivers are less proficient mental mult-taskers). This too, would increase driving errors. Of course, it is possible that both of these factors (loss of visual contact with the road; diminished multi-tasking skills) may be contributing to the greater degree of texting-induced impairment of driving observed in older drivers. Future studies monitoring eye glances during texting in older and younger drivers will be useful to test these various hypotheses. One unexpected finding in the present study was the impact of the experience of texting while driving in the simulator on the subjects' attitude regarding the dangers of texting while driving. In the pre-drive survey, 100% of the subjects indicated that texting while driving was not safe; nonetheless, after the study, 60% reported that their attitude had changed and they believed that texting was actually more dangerous than they had initially thought, whereas no drivers reported that texting while driving was less dangerous than they had initially thought. Thus, it appears that the experience of texting while driving in this simulator has the potential to increase the appreciation of the dangers of texting while driving, and thereby reduce the frequency of texting while driving in the ‘real world’. In contrast to the present study, Lesch and Hancock (2004) and Tison et al. (2011) reported that subjects often were not aware of, and therefore were unaffected by, their driving performance decrements associated with cell phone use while driving in a driving range or simulator. Whether the discrepancy between these studies and the present findings relates to the nature of the two driving situations or some other factor remains undetermined. Nonetheless, our results suggest that mere
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exposure to the driving simulator, and the opportunity to experience ‘first hand’ the effects of texting while driving in this safe situation, might be an effective deterrent to texting while driving in the ‘real world’. In summary, across a range of driver ages, texting while driving significantly impaired driving performance in a human driving simulator. Among the subjects who self-identified as skilled texters, older drivers were more adversely affected by the texting task than younger drivers. Given the increasing frequency of texting among older drivers, and by deduction, the increasing likelihood of texting while driving within this group, these data suggest that ‘no texting while driving’ education and public service messages need to be continued, and they should be expanded to target older drivers as well. Acknowledgements The authors are grateful to the EACPHS students, faculty and staff of who participated as subjects. This research was supported in part by the Department of Pharmaceutical Sciences and the Department of Health Sciences, EACPHS, and by a research grant award from the EACPHS Faculty Research Award Program (FRAP) to RLC and DH. GR was supported by a Summer Undergraduate Research Fellowship (SURF) Award, Department of Pharmaceutical Sciences. References Bakiri, S., Galera, C., Lagarde, E., Laborey, M., Contrand, B., Ribereau-Gayon, R., Salmi, L.-R., Gabaude, C., Fort, A., Maury, B., Lemercier, C., Cours, M., Bouvard, M.-P., Orriols, L., 2013. Distraction and driving: results from a case-control responsibility study of traffic crash injured drivers interviewed at the emergency room. Accid. Anal. Prev. 59, 588–592. Beede, K.E., Kass, S.J., 2006. Engrossed in conversation: the impact of cell phones on simulated driving performance. Accid. Anal. Prev. 38, 414–421. CTIA. The Wireless Association (2012). CTIA–The Wireless Association semiannual survey shows significant demand by Americans for wireless broadband. CTIA–The Wireless Association. Retrieved from http://ctia.org/media/press/ body.cfm/prid/2171. Drews, Frank A., Hina Yazdani, Celeste N. Godfrey, Joel M. Cooper, and David L. Strayer. Text Messaging During Simulated Driving. Human Factors: The Journal of the Human Factors and Ergonomics Society, 16 Dec. 2009. Harris. Most drivers with cell phones use them while driving even though they know it is unsafe; more than one in five text while driving. Harris Interactive: Harris Polls. N.p., 2011. Web. 20 Nov. 2011.
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Accident Analysis and Prevention journal homepage: www.elsevier.com/locate/aap
The effects of texting on driving performance in a driving simulator: The influence of driver age Gordon Rumschlag a , Theresa Palumbo a , Amber Martin a , Doreen Head b , Rajiv George b , Randall L. Commissaris a, * a b
Department of Pharmaceutical Sciences, Eugene Applebaum College of Pharmacy and Health Sciences, Wayne State University, Detroit, MI 48202, USA Department of Health Care Sciences, Eugene Applebaum College of Pharmacy and Health Sciences, Wayne State University, Detroit, MI 48202, USA
A R T I C L E I N F O
A B S T R A C T
Article history: Received 17 December 2013 Received in revised form 5 October 2014 Accepted 7 October 2014 Available online 31 October 2014
Distracted driving is a significant contributor to motor vehicle accidents and fatalities, and texting is a particularly significant form of driver distraction that continues to be on the rise. The present study examined the influence of driver age (18–59 years old) and other factors on the disruptive effects of texting on simulated driving behavior. While ‘driving’ the simulator, subjects were engaged in a series of brief text conversations with a member of the research team. The primary dependent variable was the occurrence of Lane Excursions (defined as any time the center of the vehicle moved outside the directed driving lane, e.g., into the lane for oncoming traffic or onto the shoulder of the road), measured as (1) the percent of subjects that exhibited Lane Excursions, (2) the number of Lane Excursions occurring and (3) the percent of the texting time in Lane Excursions. Multiple Regression analyses were used to assess the influence of several factors on driving performance while texting, including text task duration, texting skill level (subject-reported), texting history (#texts/week), driver gender and driver age. Lane Excursions were not observed in the absence of texting, but 66% of subjects overall exhibited Lane Excursions while texting. Multiple Regression analysis for all subjects (N = 50) revealed that text task duration was significantly correlated with the number of Lane Excursions, and texting skill level and driver age were significantly correlated with the percent of subjects exhibiting Lane Excursions. Driver gender was not significantly correlated with Lane Excursions during texting. Multiple Regression analysis of only highly skilled texters (N = 27) revealed that driver age was significantly correlated with the number of Lane Excursions, the percent of subjects exhibiting Lane Excursions and the percent of texting time in Lane Excursions. In contrast, Multiple Regression analysis of those drivers who self-identified as not highly skilled texters (N = 23) revealed that text task duration was significantly correlated with the number of Lane Excursions. The present studies confirm past reports that texting impairs driving simulator performance. Moreover, the present study demonstrates that for highly skilled texters, the effects of texting on driving are actually worse for older drivers. Given the increasing frequency of texting while driving within virtually all age groups, these data suggest that ‘no texting while driving’ education and public service messages need to be continued, and they should be expanded to target older drivers as well. ã 2014 Elsevier Ltd. All rights reserved.
Keywords: Driving Simulator Performance Texting and Driving Distracted Driving Driver Age Effects on Driving
1. Introduction Distracted driving is a significant and increasing contributor to motor vehicle accidents and distraction-related accident fatalities (Olson et al., 2009; Lee et al., 2013). Lam (2002) estimated that distractions were responsible for approximately 4% of traffic crashes; more recently, Bakiri et al. (2013) suggested that distraction-related factors accounted for 8% of injurious road
* Corresponding author. Tel.: +1 313 577 0813; fax: +1 313 577 2033. E-mail address: [email protected] (R.L. Commissaris). http://dx.doi.org/10.1016/j.aap.2014.10.009 0001-4575/ ã 2014 Elsevier Ltd. All rights reserved.
crashes. The National Safety Council (NSC, 2010) reported that cell phone use of any sort causes 28% of all crashes each year. Hoff et al. (2013) reported that nearly 10% of drivers reported being involved in a motor vehicle accident related to distracted driving. Consistent with these reports, various distractions also impair driving performance in driving simulator studies (Neyens and Boyle, 2008; Lam, 2002; Strayer and Drews, 2004; Beede and Kass, 2006). Indeed, cell phone use while driving has been reported to be more disruptive than ethanol intoxication (Strayer et al., 2006). Driver age and experience have been demonstrated to affect the extent of distraction-induced driving impairment, with mature drivers typically being found to be less affected by distraction than
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either much younger or much older drivers. Several investigators (Horberry et al., 2006; McPhee et al., 2004; Cooper et al., 2003; Lee et al., 2003) have reported that older drivers (e.g., 60–73 years old) are more affected by distraction when compared to mature drivers (e.g., 31–44 years old). Moreover, Strayer and Drews (2004) have reported that the effects of distraction in older drivers (e.g., 65–74 years old) are comparable to the effects of distraction in younger, less experienced, drivers (18–25 years old). Recently, Klauer et al. (2014) have reported that the odds ratio for a distraction-related motor vehicle crash or near-crash was greater for novice drivers (newly licensed; Mean = 16.4 years old) when compared to experienced drivers (Mean = 36.2 years; data from 100 car study; Klauer et al., 2006). Taken together, these studies suggest that middle-age mature drivers are less affected by distractions than either older or younger drivers. Texting while driving is a particularly potent yet increasingly likely form of distraction, and incidents of texting while driving and accidents relating to texting while driving continue to be on the rise (O’Malley et al., 2013; Wilson and Stimpson, 2010). In driving simulator studies, texting has been reported to be very disruptive as well (Neyens and Boyle, 2008; Hosking and Young, 2009). Past studies on the impact of texting on driving have focused primarily on young adults (Neyens and Boyle, 2008; Hosking and Young, 2009), in part because this group represents the ‘texting generation’ and because these drivers are generally less experienced and therefore are presumably more likely to be affected by distractions while driving (Fofanova and Vollrath, 2011). While texting continues to be more prevalent in young adults, many older individuals now use texting as a frequent form of communication (CTIA, 2012). Furthermore, it is likely that many of these ‘older texters’ are also texting while driving (Harris Interactive, 2011). At present, there are no studies examining the effects of texting on driving performance across a broad range of driver ages. The purpose of the present study, therefore, was to examine the effects of texting on driving simulator performance across a broad range of driver ages. One might hypothesize that mature drivers would be less affected by texting because of their maturity and driving experience. Alternatively, one might hypothesize that mature drivers will be more affected by texting while driving because they are not as practiced and efficient as technological multi-taskers, particularly as texters, when compared to younger drivers. In addition to driver age the influence of driver gender, driver texting skill level and text task duration also were evaluated. 2. Materials and methods
Room or (2) simulator sickness after the pre-test drive. This study was approved by the WSU Behavioral IRB (#063413B3X). 2.1.1. Subject demographics There were 27 female and 23 male subjects, distributed across a range of ages (18–24, N = 12; 25–34, N = 16; 35–44, N = 9; 45–59, N = 13; overall, 34.5 11.7 [Mean SD]). The subjects reported 18 12 (Mean SD) years of driving experience, with approximately 246 246 (Mean SD) miles driven per week (range: 10–1000). Texting Skill Level was self-reported by subjects and sorted into one of three categories: (1) limited (hunt and peck; N = 7); (2) good (use two hands; N = 16); (3) skilled (one-handed texting; N = 27). Texting history was self-reported by subjects and sorted into one of four categories: (1) 6–10 texts/week (N = 6); (2) 11–50 texts/week (N = 15); (3) 51–500 texts/week (N = 22); (4) >500 texts/week (N = 7). 2.2. The EACPHS driving simulator Study participants were seated in a fixed base driving simulator (DriveSafety, Inc) that consists of a four-door vehicle (2001 Chevrolet Impala) fully equipped with steering wheel, pedals, ignition switch, gear shift, rear and side view mirrors, headlights, turn signals and a radio. A fully immersed virtual driving experience was created by six networked computers generating the simulated roadway via three forward projection screens (left, center, right) to provide a 150 forward field of view, and one rear projection screen. Driving scenarios were created using HyperDrive software, a tile-based scripting tool. The ‘road’ for the present study was created to assess primarily the ability of the drivers/subjects to remain in their intended lane during periods of texting and not texting; it was a two lane ‘country’ road with several minor turns, no intersections, no stop signs and no stop lights. There was no oncoming traffic. 2.3. Experimental design The Study consisted of four phases, all conducted within a single 30-min test session. In phase 1, subjects ‘drove’ the simulator in a brief (3–5 min) drive (50–60 mph) to acclimate to the driving experience. In phase 2, the subjects completed a brief survey asking about demographic information, their texting behavior and some of their driving habits. In phase 3, the subjects again drove the simulator; on this drive the subjects were engaged in series of brief text conversations (three questions requiring brief answers) with a member of the research team. In phase 4, the subjects reflected on their experience in a brief exit survey.
2.1. Subjects 2.4. Data collection and analysis All research subjects were unpaid volunteers, over 18 years of age; most subjects were recruited from the population of students, faculty and staff at the Eugene Applebaum College of Pharmacy and Health Sciences (EACPHS) at Wayne State University. Volunteers who did not use texting as a regular form of communication (operationally defined as 5 text messages/week) were excluded, as were volunteers who did not have a valid drivers license. Volunteers who did not have reliable and fast cell phone service in the driving simulator room (there was a pre-test of cell phone/ texting service prior to the experiment; the driving simulator room turned out to be a ‘dead zone’ for one cell phone service provider) were excluded. Finally, volunteers who experienced ‘simulator sickness’ (dizziness, queasiness, nausea) during the pre-test drive (see details below) were not tested or included as experimental subjects. Overall, approximately 10% of initial volunteers were excluded from participating as subjects; the reasons for exclusion were either (1) lack of cell phone service in the Driving Simulator
Data from the simulator computers were collected and sorted into the following categories/bins: (1) 30-s periods before texting, (2) the periods when the subject was texting and (3) the 30-s periods after texting. The Text Task Duration was defined as starting at the time the subject received a text message, and ending at the time the subject sent a text reply. Texting status (i.e., preduring-post) was judged by a trained ‘texting judge’ who was watching a projected video image from an in-car camera; the texting judge was ‘blind’ regarding any other aspects of the drive (car location on the road, etc).
2.5. Statistical analyses The primary dependent variable monitored during driving was Lane Excursions, which were defined as any time the center of the
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vehicle moved outside the intended driving lane, e.g., either into the lane for oncoming traffic or onto the shoulder of the road. These data were measured as (1) the percent of subjects that exhibited Lane Excursions, (2) the number of Lane Excursions occurring and (2) the percent of the texting time in Lane Excursions. Data from the three text conversations were combined prior to statistical analysis. Multiple linear regression analyses were used to assess the influence of several factors on driving performance while texting, including text task duration, texting skill level, texting history, driver gender and driver age. In the multiple regression analysis, the various factors were treated as continuous variables. Because multiple regression analysis of the full data set (N = 50) revealed a significant effect of texting skill and because texting while driving is more likely to occur among highly skilled texters, additional separate multiple regression analyses were conducted on the data from skilled texters only (N = 27) and not skilled texters only (N = 23). In all statistical comparisons, p < 0.05 was used as the criterion for statistical significance. 3. Results Table 1 summarizes the results of the multiple regression analyses for the data (1) pooled across all subjects (left portion), (2) from the skilled texters only (middle portion) and (3) from the not skilled texters only (right portion). When the data from all 50 subjects were evaluated (left portion), the text task duration was a significant predictor for the number of Lane Excursions, whereas driver age and driver texting skill level were marginally significant predictors for the percent of subjects exhibiting Lane Excursions. There was no significant single predictor for the percent of texting
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time in Lane Excursions. In the skilled texters (middle portion), driver age was a significant predictor for the number of Lane Excursions, the percent of subjects exhibiting Lane Excursions and the percent of texting time in Lane Excursions (marginally significant). In contrast, in the not skilled texters (right portion), the text task duration was a significant predictor for the number of Lane Excursions; there was no significant single predictor for either the percent of subjects exhibiting Lane Excursions or the percent of texting time in Lane Excursions in this group. Driver gender was not a significant predictor for the effects of texting on Lane Excursions in any of the analyses. Fig. 1 depicts the percent of subjects exhibiting Lane Excursions while texting as a function of driver age group in the group of skilled texters only. As can be seen, the percent of subjects exhibiting Lane Excursions increases linearly and dramatically with driver age group. This is true for both the observed data (striped columns) and for values predicted based upon the multiple regression analysis (open columns). Finally, there was one rather notable effect of this driving simulator experience on attitudes toward texting while driving. Although in the initial survey 100 percent of the subjects (50 of 50) reported that texting while driving was dangerous, after participation in the study, 60% of subjects (30 of 50) reported that texting while driving was MORE dangerous than they had previously thought, compared to 0% of subjects (0 of 50) who reported that texting was less dangerous than they had previously thought; 40% of the subjects reported no change in perceived danger. This difference (60% reported MORE dangerous; 0% reported LESS dangerous; 40% reported no change) was statistically significant (Chi square = 27.94, df = 2; p < 0.05). Thus, simply participating in a
Table 1 Results of Multiple Regression Analyses. Dependent variable: number of Lane Excursions Data from skilled texters only (27) F[4,22] = 2.70, p = 0.056 Overall r = 0.57
Data from all subjects (50) F[5,44] = 3.44, p < 0.05 Overall r = 0.53 Predictor Text Task Duration Texts per Week Driver Gender Driver Age Driver Texting Skill
Beta 0.42 0.07 0.05 0.19 -0.03
p
Predictor
0.004 0.64 0.73 0.22 0.19
Text Task Duration Texts per Week Driver Gender Driver Age
Beta 0.15 0.19 0.04 0.41
Data from not skilled texters only (23) F[4,18] = 3.20, p = 0.038 Overall r = 0.63 p
Predictor
0.43 0.39 0.85 0.05
Text Task Duration Texts per Week Driver Gender Driver Age
Beta 0.66 0.08 0.02 0.04
p 0.004 0.68 0.90 0.86
Dependent variable: percent of subjects exhibiting Lane Excursions Data from skilled texters only (27) F[4,22] = 3.25, p = 0.03 Overall r = 0.61
Data from all subjects (50) F[5,44] = 2.54, p < 0.05 Overall r = 0.47 Predictor Text Task Duration Texts per Week Driver Gender Driver Age Driver Texting Skill
Beta 0.11 0.03 0.02 0.27 0.28
p
Predictor
0.46 0.82 0.89 0.09 0.053
Text Task Duration Texts per Week Driver Gender Driver Age
Beta 0.05 0.17 0.17 0.48
Data from not skilled texters only (23) F[4,18] = 0.27, p = 0.89 Overall r = 0.11 p
Predictor
0.80 0.42 0.37 0.019
Text Task Duration Texts per Week Driver Gender Driver Age
Beta 0.11 0.19 0.08 0.06
p 0.67 0.41 0.73 0.82
Dependent variable: percent of texting time in Lane Excursions Data from skilled texters only (27) F[4,22] = 2.38, p = 0.008 Overall r = 0.55
Data from all subjects (50) F[5,44] = 1.99, p = 0.098 Overall r = 0.43 Predictor Text Task Duration Texts per Week Driver Gender Driver Age Driver Texting Skill
Beta 0.18 0.18 0.00 0.23 0.01
p
Predictor
0.23 0.24 0.98 0.16 0.97
Text Task Duration Texts per Week Driver Gender Driver Age
Beta 0.15 0.19 0.06 0.38
Data from not skilled texters only (23) F[4,18] = 0.52, p = 0.72 Overall r = 0.14 p
Predictor
0.44 0.38 0.78 0.074
Text Task Duration Texts per Week Driver Gender Driver Age
Beta 0.21 0.10 0.09 0.08
p 0.40 0.68 0.69 0.76
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Fig. 1. Percent of subjects exhibiting Lane Excursions during texting as a function of driver age group – data from skilled texters only. Data in the striped columns reflect the observed measures from the various driver age groups. Data in the open columns reflect values predicted for the various ages (for 20, 30, 40 and 50 years old) based upon the multiple regression using driver gender, text task duration, driver texting frequency and driver age as predictors (see text for details).
texting while driving simulator study increased the recognition and appreciation of the hazards of texting while driving in these subjects. 4. Discussion In the present study, texting dramatically increased Lane Excursions in the driving simulator. The present studies are consistent with past reports that texting impairs driving simulator performance (Hosking et al., 2009; Drews et al., 2009; Neyens and Boyle, 2008); these findings also are consistent with studies showing an increased frequency of accidents associated with texting while driving in the real world (Fernando and Stimpson, 2008; O’Malley et al., 2013). When the data from all drivers (N = 50) were evaluated using multiple regression, several factors, including text task duration, driver age and driver texting skill were significant predictors for Lane Excursions. In contrast, when the data from only not skilled texters were analyzed, only text task duration was a significant predictor for the number of Lane Excursions. This is perhaps not very surprising; given that texting is disruptive to driving, one might predict that longer text task durations in not skilled texters (greater than 60 s in some drivers) would increase the probability that a Lane Excursion would happen (i.e., it’s just a matter of time). Probably the most important discovery in the present study comes from the drivers who self-reported as skilled texters (N = 27), since this category of drivers are more likely to be texting while driving. In this multiple regression analysis, driver age was a statistically significant predictor for (1) the likelihood of having a Lane Excursion, (2) the number of Lane Excursions, and (3) the percent of texting time in Lane Excursions (marginally significant). Multiple regression-based predicted values for the effects of texting on driving for different driver ages were in close agreement with observed values for different driver age groups. In these skilled texters, text task duration was not a significant predictor for Lane Excursions; this probably relates to the shorter mean text task duration and the reduced variability of this measure among the skilled texters. The finding that the disruptive effects of texting on driving performance are directly related to driver age across a wide range of driver age groups is not entirely consistent with the results of
other studies on the relationship between driver age and distracted driving, where the general picture painted by the results of past studies is that mature drivers are less affected by distraction. Klauer et al. (2014) have recently reported that newly-minted young drivers (16 years old) are more susceptible to a variety of distractions when compared to more mature drivers (texting was not evaluated in both groups). In addition, young drivers have been reported as comparable to older drivers (Strayer and Drews, 2004; Kass et al., 2007), whereas older drivers are more adversely affected by distraction when compared to mature drivers (Horberry et al., 2006; McPhee et al., 2004; Cooper et al., 2003; Fofanova and Vollrath, 2011; Lee et al., 2003), suggesting that younger drivers might be more affected by distractions like texting when compared to more mature, seasoned drivers. Nonetheless, in the present study young drivers were significantly less affected by the distraction of texting when compared to more mature drivers. The reason for these discrepancies is at the present time unresolved, although they may relate to the nature and magnitude of the ‘distraction’. In the previous studies noted above, the driving distraction being compared across age groups was not texting, and it may be that the ‘distracting’ effects of texting are less dramatic in younger drivers, in part because texting is almost second nature to many younger individuals (Lee, 2007). Nonetheless, even among the younger drivers in the present study, texting significantly impaired simulated driving performance. The reason for the dramatic effect of driver age on the disruptive effects of texting observed in the present study is not known at this time. Although text task duration was not different as a function of driver age it is possible that, relative to younger drivers, older drivers spend a greater percentage of their texting time looking at their cell phones (reading and composing/sending a reply), and not looking at the road, when texting. An increase in actual percent of texting time spent reading/composing/sending text messages would very likely increase driving errors. Alternatively, it is possible that older drivers do not differ from younger drivers with respect to the percent of time spent looking at their cell phones (reading/composing/sending), but are more distracted because they spend more time thinking about texting even though their eyes are on the road (i.e., older drivers are less proficient mental mult-taskers). This too, would increase driving errors. Of course, it is possible that both of these factors (loss of visual contact with the road; diminished multi-tasking skills) may be contributing to the greater degree of texting-induced impairment of driving observed in older drivers. Future studies monitoring eye glances during texting in older and younger drivers will be useful to test these various hypotheses. One unexpected finding in the present study was the impact of the experience of texting while driving in the simulator on the subjects' attitude regarding the dangers of texting while driving. In the pre-drive survey, 100% of the subjects indicated that texting while driving was not safe; nonetheless, after the study, 60% reported that their attitude had changed and they believed that texting was actually more dangerous than they had initially thought, whereas no drivers reported that texting while driving was less dangerous than they had initially thought. Thus, it appears that the experience of texting while driving in this simulator has the potential to increase the appreciation of the dangers of texting while driving, and thereby reduce the frequency of texting while driving in the ‘real world’. In contrast to the present study, Lesch and Hancock (2004) and Tison et al. (2011) reported that subjects often were not aware of, and therefore were unaffected by, their driving performance decrements associated with cell phone use while driving in a driving range or simulator. Whether the discrepancy between these studies and the present findings relates to the nature of the two driving situations or some other factor remains undetermined. Nonetheless, our results suggest that mere
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exposure to the driving simulator, and the opportunity to experience ‘first hand’ the effects of texting while driving in this safe situation, might be an effective deterrent to texting while driving in the ‘real world’. In summary, across a range of driver ages, texting while driving significantly impaired driving performance in a human driving simulator. Among the subjects who self-identified as skilled texters, older drivers were more adversely affected by the texting task than younger drivers. Given the increasing frequency of texting among older drivers, and by deduction, the increasing likelihood of texting while driving within this group, these data suggest that ‘no texting while driving’ education and public service messages need to be continued, and they should be expanded to target older drivers as well. Acknowledgements The authors are grateful to the EACPHS students, faculty and staff of who participated as subjects. This research was supported in part by the Department of Pharmaceutical Sciences and the Department of Health Sciences, EACPHS, and by a research grant award from the EACPHS Faculty Research Award Program (FRAP) to RLC and DH. GR was supported by a Summer Undergraduate Research Fellowship (SURF) Award, Department of Pharmaceutical Sciences. References Bakiri, S., Galera, C., Lagarde, E., Laborey, M., Contrand, B., Ribereau-Gayon, R., Salmi, L.-R., Gabaude, C., Fort, A., Maury, B., Lemercier, C., Cours, M., Bouvard, M.-P., Orriols, L., 2013. Distraction and driving: results from a case-control responsibility study of traffic crash injured drivers interviewed at the emergency room. Accid. Anal. Prev. 59, 588–592. Beede, K.E., Kass, S.J., 2006. Engrossed in conversation: the impact of cell phones on simulated driving performance. Accid. Anal. Prev. 38, 414–421. CTIA. The Wireless Association (2012). CTIA–The Wireless Association semiannual survey shows significant demand by Americans for wireless broadband. CTIA–The Wireless Association. Retrieved from http://ctia.org/media/press/ body.cfm/prid/2171. Drews, Frank A., Hina Yazdani, Celeste N. Godfrey, Joel M. Cooper, and David L. Strayer. Text Messaging During Simulated Driving. Human Factors: The Journal of the Human Factors and Ergonomics Society, 16 Dec. 2009. Harris. Most drivers with cell phones use them while driving even though they know it is unsafe; more than one in five text while driving. Harris Interactive: Harris Polls. N.p., 2011. Web. 20 Nov. 2011.
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