ArticlePDF Available

Adolescent Cellphone Use While Driving: An Overview of the Literature and Promising Future Directions for Prevention



Motor vehicle crashes are the leading cause of death in adolescents, and drivers aged 16–19 are the most likely to die in distracted driving crashes. This paper provides an overview of the literature on adolescent cellphone use while driving, focusing on the crash risk, incidence, risk factors for engagement, and the effectiveness of current mitigation strategies. We conclude by discussing promising future approaches to prevent crashes related to cellphone use in adolescents. Handheld manipulation of the phone while driving has been shown to have a 3 to 4-fold increased risk of a near crash or crash, and eye glance duration greater than 2 seconds increases crash risk exponentially. Nearly half of U.S. high school students admit to texting while driving in the last month, but the frequency of use according to vehicle speed and high-risk situations remains unknown. Several risk factors are associated with cell phone use while driving including: parental cellphone use while driving, social norms for quick responses to text messages, and higher levels of temporal discounting. Given the limited effectiveness of current mitigation strategies such as educational campaigns and legal bans, a multi-pronged behavioral and technological approach addressing the above risk factors will be necessary to reduce this dangerous behavior in adolescents.
Media and Communication, 2016, Volume 4, Issue 3, Pages 79-89 79
Media and Communication (ISSN: 2183-2439)
2016, Volume 4, Issue 3, Pages 79-89
Doi: 10.17645/mac.v4i3.536
Adolescent Cellphone Use While Driving: An Overview of the Literature
and Promising Future Directions for Prevention
M. Kit Delgado 1,*, Kathryn J. Wanner 1 and Catherine McDonald 2
1 Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA;
E-Mails: (M.D.), (K.J.W.)
2 School of Nursing, University of Pennsylvania, Philadelphia, PA 19104, USA; E-Mail:
* Corresponding author
Submitted: 17 December 2015 | Accepted: 11 March 2016 | Published: 16 June 2016
Motor vehicle crashes are the leading cause of death in adolescents, and drivers aged 1619 are the most likely to die
in distracted driving crashes. This paper provides an overview of the literature on adolescent cellphone use while driv-
ing, focusing on the crash risk, incidence, risk factors for engagement, and the effectiveness of current mitigation strat-
egies. We conclude by discussing promising future approaches to prevent crashes related to cellphone use in adoles-
cents. Handheld manipulation of the phone while driving has been shown to have a 3 to 4-fold increased risk of a near
crash or crash, and eye glance duration greater than 2 seconds increases crash risk exponentially. Nearly half of U.S.
high school students admit to texting while driving in the last month, but the frequency of use according to vehicle
speed and high-risk situations remains unknown. Several risk factors are associated with cell phone use while driving
including: parental cellphone use while driving, social norms for quick responses to text messages, and higher levels of
temporal discounting. Given the limited effectiveness of current mitigation strategies such as educational campaigns
and legal bans, a multi-pronged behavioral and technological approach addressing the above risk factors will be neces-
sary to reduce this dangerous behavior in adolescents.
accidents prevention; adolescent; cell phones; distracted driving; text messaging
This review is part of the issue Adolescents in the Digital Age: Effects on Health and Development, edited by Dan
Romer (University of Pennsylvania, USA).
© 2016 by the author(s); licensee Cogitatio (Lisbon, Portugal). This article is licensed under a Creative Commons Attrib-
ution 4.0 International License (CC BY).
1. Introduction
Cellphones, and the connectivity they provide, have
become a part of everyday life. In recent years, cell-
phone use, in particular communication by text mes-
saging, has dramatically increased in prevalence and
popularity across the world. In 2014, an estimated
169.3 billion text messages were sent worldwide, com-
pared to 110 billion in 2009 (CTIA, 2013). Adolescents
report that texting is the most common way that they
stay in contact with friends (Lepp, Barkley, & Karpinski,
2014), sending an average of 100 texts per day
(Nielson, 2010). Problematic cellphone use and texting
has been likened to other addictive behaviors, and may
have negative effects on both academic performance
and mental health (Lee, Chang, Lin, & Cheng, 2014;
Lepp, et al., 2014; Walsh, White, & Young, 2008).
However, texting has also become a way that adoles-
cents forge social bonds, and texting between adoles-
cents often serves to promote social cohesion in peer
groups (Ling, 2012). More than half of adolescents text
their friends every day, and many of them are texting
their friends multiple times a day (Lenhart, Smith, An-
derson, Duggan, & Perrin, 2015).
The phenomenon of distracted driving from cell-
phone use has caught the attention of the national
Media and Communication, 2016, Volume 4, Issue 3, Pages 79-89 80
media in the United States (U.S.). There have been
numerous reports on its dangers (CNN, 2014; DePalma,
2014; Muskal, 2015), prevalence (Richtel, 2015), and
possible solutions (Richtel, 2014). The U.S. federal gov-
ernment’s Healthy People 2020 objectives pinpoints
distracted driving related to cellphone use as the top
emerging cause of injury and highlights the need for fu-
ture research (Office of Disease Prevention and Health
Promotion, 2015). Several prominent public awareness
campaigns have been aimed at promoting safety while
driving, and in 2010 there was a national summit that
brought together safety experts, senators, and industry
leaders, to focus on this issue (AAA, 2013). Given the
gravity of the problem of distracted driving, and in con-
cert with this special issue on “Adolescents in the Digi-
tal Age: Effects on Health and Development,” the ob-
jectives of this paper are to provide an overview on the
incidence, crash risk, risk factors for engagement, and
the effectiveness of current mitigation strategies. We
conclude by proposing promising future approaches to
prevent crashes due to cellphone use in adolescents.
2. Public Health Magnitude of Distracted Driving in
Motor vehicle crashes (MVCs) are the leading cause of
death and disability in adolescents in the U.S. and
globally (World Health Organization, 2013). Based on
police crash report data collected by the U.S. National
Highway Traffic Safety Administration (NHTSA), in
2013, 2,650 adolescents, aged 1619, died as a result
of a motor vehicle collision (MVC), making this the
number one cause of death in the U.S. for this age
group; another 292,000 were treated for injuries (CDC,
2013). A disproportionate amount of MVCs related to
distracted driving involve teenagers: although they
comprise 6% of all drivers killed in MVCs, teenagers ac-
count for 10% of all drivers determined to be distract-
ed at the time of a crash and 11% of all drivers killed in
crashes related to documented cellphone use (NHTSA,
2015b). NHTSA reports that there were a total 45 teen-
age drivers and 161 drivers (aged 2029) killed in cell-
phone distraction crashes in 2013. These numbers un-
derestimate the true magnitude of the problem since
the statistics are based on documented cellphone use
while driving as measured through police reports.
3. Incidence of Cellphone Use While Driving in
The majority of evidence on the proportion of the ado-
lescent population that uses their cellphone while driv-
ing has been obtained through population-level self-
report surveys. In 2014, 94% of U.S. drivers aged 1829
reported owning a smartphone (State Farm, 2014). A
Centers for Disease Control and Prevention survey of
8,505 students 16 years of age and younger, found that
42% of U.S. high school students admit to engaging in
texting while driving, which included both text messag-
ing and emailing while driving, at least once per month
(Olsen, Hanowski, Hickman, & Bocanegra, 2009). A
more recent nationally representative survey of 1,243
high school students, funded by the National Institutes
of Health (NIH), found that 83% reported engaging in
electronic device use while driving at least once in the
last 30 days (Ehsani, Li, & Simons-Morton, 2015). Spe-
cifically, 71% made or answered a phone call, 64% read
or sent a text message, 20% read or sent an email, 29%
checked a website, 71% changed music, 12% used a
tablet, and 53% looked at directions or a map. Young
drivers reported using electronic devices while driving
on 19% of the days they drove. Males were more likely
to use a tablet or a computer while driving, teens from
moderate and high affluence households were more
likely to check websites, and rural participants were
less likely to look at directions or a map than urban
participants (Ehsani et al., 2015).
It appears that social media use while driving is in-
creasing among adolescents and young adults based on
a survey, conducted annually since 2009 by the State
Farm insurance company, of 1,000 drivers, aged 18 and
older. According to this survey, the proportion of
young drivers, aged 1829, who read social media
websites while driving doubled from 21% in 2009 to
41% in 2014 (State Farm, 2014). Likewise, the propor-
tion of this population who actually post to social media
while driving increased from 20% in 2009 to 30% in
2014. This form of communication may eventually sup-
plant text messaging, as the same survey found the pro-
portion of young adults age 1829 who texted while
driving was 58% in 2014, down from 71% in 2009.
NHTSA’s National Occupant Protection Use Survey
(NOPUS) provides the only nationwide probability-
based observed data on driver electronic device use
in the U.S. Data are collected by trained observers
standing at the roadside of probabilistically sampled
intersections, who are observing drivers while
stopped at the intersection. The overall percentage of
drivers who are text-messaging, or visibly manipulat-
ing handheld devices while driving, increased from 1.7
% in 2013 to 2.2% in 2014. However, among the 16
24 year old age group, this proportion was much
higher, and increased from 2.9% in 2013 to 4.8% in
2014 (NHTSA, 2015a). These statistics likely underes-
timate the true incidence of handheld cellphone use
since the below eye-level view beneath the windows
and windshield is not captured.
Local roadside observation based studies suggest a
higher prevalence of cellphone use while driving. A
study conducted at 11 intersections in the Birmingham
Alabama metro area found that among drivers pre-
sumed to be less than 30 years olds (N=853), 8.4%
were observed to be texting and another 11.7% were
observed to be talking on the phone (Huisingh, Griffin,
Media and Communication, 2016, Volume 4, Issue 3, Pages 79-89 81
& McGwin, 2015). Among drivers of all ages who were
witnessed to be texting, 49% of these episodes were at
estimated speed of more than 25 miles per hour. A
similar study conducted in one intersection in Pennsyl-
vania in 2014 of 2,000 observed drivers, found 3% of
drivers in motion were texting or visibly manipulating
handheld devices and 5% were engaged in handheld
phone calls. Among the stopped drivers, 14.5% were
texting and 6.3% were talking (Bernstein & Bernstein,
2015). Further work is necessary to describe the pro-
portion of time individual drivers use their phone while
the car is in motion.
Naturalistic studies using non-obtrusive video
event recorders installed in drivers’ cars can provide a
much more nuanced incidence of cellphone use and
other distracted driving behaviors. Typically, the re-
corder runs continuously, it only saves information
when a vehicle movement (decelerating, accelerating,
or turning) produces a g-force that exceeds a prede-
termined threshold. Lower thresholds can be set such
that clips can be recorded intermittently during nor-
mal periods of driving. A naturalistic study using
event-triggered recording in 52 high-school aged
drivers found that cellphone use was present in 6.7%
video clips, followed by adjusting vehicle controls
(6.2%) and grooming (3.8%) (Foss & Goodwin, 2014).
Of episodes of cellphone use, one third involved hold-
ing the phone to the ear, with the rest involving
handheld manipulation. Only 1% of these recorded
episodes involved hands-free talking. Interestingly,
cellphone use while driving was much less likely to
occur if there was a passenger in the vehicle.
A naturalistic study using continuous video record-
ing of young drivers 2030 years old (n=36) for 4 weeks
in 20062007 found that these drivers had a mean 2.1
phone conversations per hour for drive time for a
mean average conversation duration of 2.6 minutes
(Funkhouser & Sayer, 2012). They also had a mean av-
erage of 4.0 visual-manual cellphone use task per hour
of drive time with a mean average duration of 0.51
minutes. When data from all drivers aged 2070 years
old was analyzed (n=108), it was found that 23% of all
visual manual tasks were initiated when stopped and
another 5% were initiated at 5 miles per hour or less.
Of concern, this indicated that nearly three quarters of
handheld phone use episodes occurred while moving.
In fact, more than 45% of these episodes were initiated
at speeds of more than 25 miles per hour, consistent
with the estimated 49% of texting episodes witnessed
in the roadside observation study from Alabama
(Huisingh et al., 2015). Given that speed is the biggest
predictor of injury severity in motor vehicle collisions
(Kockelman & Kweon, 2002), these findings suggest
that the riskiness of cellphone use episodes in terms of
causing serious crashes is likely to be heterogeneous,
and needs further clarification in future research (see
Knowledge gaps).
4. Safety Risk of Engaging in Cellphone Use While
The first large scale study to evaluate the safety risk of
cellphone use while driving was published in the New
England Journal of Medicine in 1997 (Redelmeier &
Tibshirani, 1997). This epidemiologic study compared
detailed time-stamped phone bill usage records of in-
dividuals, moments before a motor vehicle crash as
well as records one week before the crash. The risk of
collision was found to be 4 times higher during a phone
call. However, subsequent research has suggested a bi-
as to this design; study subjects were less likely to have
been driving during the control period, reducing their
potential exposure to a crash (Young, 2012). Since
then, dozens of studies with more robust designs have
been published evaluating the risk of cellphone use
while driving, and in particular texting while driving, in
adult drivers. A meta-analysis of 28 epidemiologic, driv-
ing simulator, and naturalistic studies, which use vehi-
cle instrumentation to measure actual driving, found
that texting while driving increases the risk of crashing
by at least 3 to 4-fold (Caird, Johnston, Wilness, As-
bridge, & Steel, 2014).
Fewer studies have examined the crash risk of cell
phone use among adolescent drivers. Klauer et al.
(2014) recently conducted a systematic review of
quantitative epidemiologic, driving simulator, and nat-
uralistic studies examining secondary task engagement
while driving with adolescents; they identified 15 stud-
ies that met inclusion criteria (Klauer et al., 2014). Alt-
hough this systematic review investigated more than
just cell phone use (secondary task was defined as eat-
ing, using a cellphone, inserting a compact disc), their
findings about the common mechanism that increases
crash risk is notable. Overall, this systematic review
found that secondary tasks while driving, where eyes
were not on the forward roadway, increased crash risk
(e.g. looking down at a phone while texting) (Fitch,
Hanowski, & Guo, 2014; Klauer, Dingus, & Neal, 2006;
Olsen et al., 2009); however, secondary tasks where
eyes were not required to be off the forward roadway
(e.g. talking on a cell phone) did not significantly in-
crease crash risk (Harbluk, Noy, Trbovich, & Eisenman,
2007; Klauer, Ehsani, McGehee, & Manser, 2015).
One of the most rigorous studies included in Klauer
et al.’s review followed 42 newly licensed adolescent
drivers for 18 months immediately after licensure with
in-vehicle event-triggered cameras (Klauer et al., 2014).
This study found that dialing the phone was associated
with the highest risk of a crash or near crash event
(Odds Ratio [OR] 8.32), followed by reaching for the
phone (OR 7.05), and texting or using the Internet (OR
3.87); talking was not associated with the crash risk
(OR 0.61). Secondary analysis of these data revealed
that the duration of glancing away from the forward
roadway steadily increases the risk of a crash beginning
Media and Communication, 2016, Volume 4, Issue 3, Pages 79-89 82
with glances longer than 1 second. Glances of 2 sec-
onds or more while engaging in handheld cellphone
use were associated with a 5.5-fold increase in the risk
of crash or near crash event (Simons-Morton, Guo,
Klauer, Ehsani, & Pradhan, 2014). These important
findings imply that interventions and policies to reduce
the crash risk of distracted driving, and in particular
distraction from cellphone use, need to focus on main-
taining the driver’s eyes on the forward roadway.
5. Knowledge of the Risks of Cellphone Use
Generally, adolescents report that texting or talking on a
handheld phone while driving is dangerous. A survey
found that the 97% of U.S. adolescents know texting and
driving is dangerous based on a survey of 1,200 teenag-
ers aged 1519 years old (AT&T, 2012). However,
knowledge of safety risks does not necessarily indicate
adolescents will not engage in the behavior. In a focus
group study of 1618 year olds with less than 1 year of
licensure, participants indicated that they understand
the dangers of cellphone use while driving, however,
they still reported driving while engaging in talking, tex-
ting and social media app use (McDonald & Sommers,
2015). This suggests that simply continuing to raise
awareness of the risks of cellphone use while driving
may not be very effective for reducing this behavior, giv-
en that most adolescents are aware of the risks. A recent
survey of college students found they were much more
likely to text behind the wheel than drink and drive, de-
spite perceiving that the risks of texting were similar to
drinking (Terry & Terry, 2015). Furthermore, participants
perceived their peers as being more accepting toward
cellphone use while driving than themselves, suggesting
that one factor underlying the discrepancy between per-
ceived risk and risk exposure may be the weakness of
social norms opposed to texting while driving.
6. Risk Factors for Engagement: Development, Peers
and Families
Adolescents are a particularly vulnerable group at risk
for crashes. Adolescent drivers are at greatest risk for a
crash in the first 612 months of licensure (Mayhew,
Simpson, & Pak, 2003; McCartt, Shabanova, & Leaf,
2003; Williams & Tefft, 2014). As adolescents drive,
they acquire more experience and skill; this skill acqui-
sition for newly licensed drivers strongly influences
crash risk reduction in the first year of driving (McKnight
& McKnight, 2003). However, experience is not the only
contributor to crashes, as the developmental changes
during adolescence can influence crash risk.
Major changes in the brain occur throughout ado-
lescence that can lead to increased risk taking and sen-
sation seeking, and a movement towards a greater af-
filiation with their peers (Giedd, 2012). This is not to
indicate that adolescents are taking risks with their cell
phones while driving simply to challenge safety limits.
Rather, adolescents may drive with incomplete matu-
ration of cognitive and motor skills, and decision-
making may be modulated by emotional and social fac-
tors.(Romer, Lee, McDonald, & Winston, 2014) The ad-
olescent pre-frontal cortex has not fully matured; ade-
quate experience in risk assessment may not have
occurred, nor may adolescents fully exert control over
those risksand all the while, there is a rise in sensa-
tion seeking (Giedd, 2012). Impulsivity and present bi-
ased preferences (Atchley & Warden, 2012), the ten-
dency to place more weight on benefits realized now
and less weight on costs realized in the future, is asso-
ciated with a higher likelihood of engaging in texting
while driving (Hayashi, Russo, & Wirth, 2015). Adoles-
cents are more present-biased than adults indicating a
greater cognitive difficulty with delaying gratification
(Romer, Duckworth, Sznitman, & Park, 2010), and in this
context, delaying checking their phone and/or respond-
ing to a text message until they have stopped driving.
A 2014 systematic review of 29 papers identified
several other psychological factors associated with
cellphone use while driving in young drivers. These in-
cluded the importance of an incoming or outgoing call,
social acceptance, possession attachment, and a positive
attitude toward cellphone use while driving (Cazzulino,
Burke, Muller, Arbogast, & Upperman, 2014). The im-
portance of answering or making the call while driving
was found to have greater weight than the perceived
risk associated with cellphone use while driving.
The proximity of relationship of the individual who
is communicating with the adolescent influences cell-
phone use (Atchley & Warden, 2012; LaVoie, Lee, &
Parker, 2015). In a focus group study, adolescent par-
ticipants indicated that context mattered; the individu-
al involved in the communication, and the reason be-
hind it, would influence whether they would use the
cell phone while driving (McDonald & Sommers, 2015).
A survey study of 395 adolescent drivers found that
adolescents most often spoke to parents while driving
(50%), rather than a significant other (16%) or friend
(21%) (LaVoie et al., 2015). This indicates that reducing
check in calls from parents may reduce cellphone use
while driving. However, adolescent drivers were more
likely to text a significant other (30%) or friend (27%)
rather than their parents (16%) (LaVoie et al., 2015).
Social norms strongly influence texting behavior, as
89% of adolescents expect a response to a text mes-
sage within 5 minutes (Bowen et al., 2009). Together
these findings indicate that interventions to reduce
texting should alleviate the urge to respond immedi-
ately to close social contacts, such as setting up auto-
mated responses to incoming text messages.
Carter, Bingham, Zakrajsek, Shope and Sayer (2014)
also conducted a survey with adolescentparent dyads
and found that actual and perceived distracted driving
behaviors of parents, and perceived distracted driving
Media and Communication, 2016, Volume 4, Issue 3, Pages 79-89 83
behaviors of peers, were predictive of adolescent dis-
tracted driving behavior. Finally, there is increasing ev-
idence for compulsive cellphone use as a diagnosable
behavioral addiction, given the behavioral and neuro-
biological characteristics of this behavior (Billieux,
Maurage, Lopez-Fernandez, Kuss, & Griffiths, 2015).
Use in dangerous situations, such as while driving, is
measured as a factor in scales of problematic cellphone
use (Merlo, Stone, & Bibbey, 2013). More research is
needed to better determine the correlation between
measures of general problematic or compulsive phone
use and risky cellphone use while driving.
7. Social and Logistical Barriers to Reducing Cellphone
Use While Driving
Understanding why adolescents may not want to ab-
stain from in-vehicle cellphone use provides insights in-
to behavioral strategies that may be more effective for
reducing use while driving. Dominant disadvantages of
abstaining from in-vehicle cellphone use among ado-
lescents include: the inability to communicate location
or letting others know their time of arrival, the inability
to get help if the driver got lost or forgot something,
and increased difficulty for parents to get in touch with
the driver (Hafetz, Jacobsohn, García-España, Curry, &
Winston, 2010). Other disadvantages of abstaining
from in-vehicle cellphone use are giving up the ability
to call for emergency help. This may include calling 911
if being followed by a potential stalker, calling to report
a drunk driver on the road, or calling for emergency
medical care in the case of a MVC. In fact, in the land-
mark 1997 New England Journal of Medicine study on
drivers who owned cellphones and were involved in
MVCs, 39% of drivers called 911 from the scene of the
crash on their cellphone (Redelmeier & Tibshirani,
1997). Therefore, interventions to reduce risky cell-
phone use while driving should make allowances for
calls in emergency situations and should safely balance
needs related to navigation and trip communication.
8. Effectiveness of Current Mitigation Strategies
8.1. Legal Bans
In the U.S., states have enacted policies to help de-
crease cellphone use while driving. For example, ac-
cording to the Insurance Institute of Highway Safety
(2015), as of the end of December 2015, talking on a
hand-held cellphone while driving has been banned for
all drivers in 14 states and the District of Columbia; ad-
ditionally, the use of all cellphones by novice drivers is
restricted in 37 states and the District of Columbia.
Text messaging has been banned for all drivers in 46
states and the District of Columbia. In addition, novice
drivers are banned from texting in Missouri and Texas
(Insurance Institute for Highway Safety, 2015).
There are mixed results on the effectiveness of
cellphone restrictions. One of the earliest studies ex-
amining the effect on the general population investi-
gated the relationship between collision claim frequen-
cies and texting bans in 4 states (Highway Loss Data
Institute, 2010). This study found that texting bans
were actually associated with increased frequencies of
collision claims. The authors posited that this increase
may have stemmed from the unintended consequence
of drivers lowering their phones from view to avoid ci-
tations and fines and, in doing so, taking their eyes off
the road more than they did before the implementa-
tion of the bans. Two other studies using observation
and self-report outcomes in the adolescent driver pop-
ulation showed that laws restricting cellphone use have
not had long-term effects on adolescent drivers’ cell-
phone use while driving (Ehsani, Bingham, Ionides, &
Childers, 2014; Goodwin, O’Brien, & Foss, 2012).
Studies examining the effect of cell phone bans on
MVC fatalities and hospitalizations have demonstrated
modestly positive outcomes. Primary enforced laws
banning all drivers from texting was associated with a
3% reduction in fatalities in all age groups; banning on-
ly young drivers from texting had the greatest impact
on reducing deaths among those aged 15 to 21 years
(Ferdinand et al., 2014). A similar study found an 8%
decrease in fatalities in states that universally banned
texting while driving and made it a primary offense.
However, this effect was only apparent for the law’s
first three months (Abouk & Adams, 2013). The study
also found that this loss of effect was lessened in states
that had universal bans against handheld use of cell
phones. The authors suggest that the lack of effective-
ness of texting bans was due to poor enforcement;
drivers refrained from texting immediately after the
law’s announcement and implementation but returned
to texting if they believed the law was not being en-
forced (Abouk & Adams, 2013). Finally, texting bans
were also significantly associated with reductions in
hospitalizations among those aged 22 to 64 years and
those aged 65 years or older, but did not significantly
reduce hospitalizations for adolescents (Ferdinand et
al., 2015). While in these analyses it cannot be deter-
mined whether the crashes and hospitalizations ana-
lyzed were caused by distracted driving or not, these
studies suggest that bans with primary enforcement
can reduce the burden of injury from cellphone use.
This is further supported by the results of a high visibil-
ity law enforcement campaign “Phone in One Hand,
Ticket in the Other” implemented in Connecticut and
New York, which was shown to have a modest reduc-
tion in observed handheld cellphone usage rates over
the course of a year (Chaudhary, Cassanova-Powell,
Cosgrove, Reagan, & Williams, 2012). Given the logisti-
cal difficulty needed to enforce bans, such as catching a
driver using a phone out of view, additional mitigation
strategies may be necessary.
Media and Communication, 2016, Volume 4, Issue 3, Pages 79-89 84
8.2. Education to Increase Awareness
Several national public health campaigns have
emerged aimed at the prevention of distracted driving.
For example, NHTSA’s is a national
campaign to increase awareness of distracted driving
through informational videos, facts, and personal nar-
ratives (, 2015). There have been sever-
al industry sponsored campaigns aimed at the preven-
tion of distracted driving, such as AT&T’s “It Can Wait”
wait campaign, which encourages individuals to reach
out to friends and family and to pledge to abstain from
texting and driving (AAA, 2013; AT&T, 2012). These
campaigns consist of online pledges, where individuals
can pledge to abstain from texting and driving, and ed-
ucational videos with the goal of increasing awareness
of the dangers of distracted driving. Despite these ma-
jor investments, there are no data to suggest that
these campaigns have had any effect on cellphone use
while driving. Given 97% of adolescent drivers already
know that cellphone use while driving is dangerous
(AT&T, 2012), solely increasing awareness of risks is un-
likely to lead to wide scale behavior change.
There are few published studies of more targeted
educational interventions in the adolescent driver
population. One effective intervention led by staff from
a pediatric trauma center hospital invited 61 student
leaders from a local high school for a half-day educa-
tional session. The student leaders then went back to
their two high schools to implement a yearlong peer-
to-peer campaign focused on a clear no texting while
driving campaign (Unni, Morrow, Shultz, & Tian, 2013).
There was a decrease in unannounced observation of
actual texting and driving (from 17% to 8%, p<0.001)
among high school students driving on roads near the
school a year after the intervention compared to just
prior to the intervention (Unni, et al., 2013).
8.3. Technological Interventions
Over the last decade, in-vehicle technologies have
been developed and tested with the aim of improving
adolescent driving behavior through monitoring and
feedback. Feedback on g-force events, recorded using
an in-vehicle event triggered video recording device in
which parents were involved in the feedback loop, has
been shown to be effective in reducing the occurrence
of these near-crash events (Simons-Morton et al.,
2013). Some auto insurance companies have moved to
offer use of event-triggered video monitors and paren-
tal feedback on recorded driving errors and distracted
driving behavior (American Family Insurance, 2016).
More recently, in the last five years, smartphone
applications have been developed to directly measure
cellphone use while driving. “Software only” applica-
tions rely on the phone’s sensors (e.g. accelerometer
and GPS) to determine whether the phone is traveling
at a speed consistent with driving (e.g. >25 mph). If
traveling over a certain speed threshold, the applica-
tion can be set to disable the phone unlock screen and
block incoming and outgoing text messaging and calls.
Most of these applications have been developed for
the Android platform and there are currently dozens of
such applications available in the Google Play store.
Because of the more stringent developer restrictions of
the iOS, there are fewer such applications available in
the iTunes store. The major barriers to adoption of
“software only” applications are the current inability to
detect whether the phone is being used on a car vs.
another vehicle such as a bus or train, and battery
drain from continuous use of GPS in the background.
“Software–Hardware” applications have also been de-
veloped to overcome some of these limitations. This
involves the instillation of a device in the car that pairs
with a smartphone application via Bluetooth technolo-
gy. These devices were developed to be installed in the
car’s OBD-II (On-board diagnostics-II) data port and
more recently have included solar powered designs
that can be installed on the windshield.
To our knowledge there are three completed stud-
ies of smartphone applications to block cellphone use
while driving, with all three demonstrating a reduction
in cellphone use while driving at non-zero speeds
(Creaser, Edwards, Morris, & Donath, 2015; Ebel et al.,
2015; Funkhouser & Sayer, 2013). For example, in the
largest study to date, involving 274 novice teen drivers
followed for 1 year, the rate of text messages sent per
mile driven for each given month post licensure was at
least 5 to 10 times higher in the control group (0.05 to
0.20 texts per mile driven) than in the blocking group
(0.0 to 0.02 texts per mile driven) (Creaser et al., 2015).
The number of text messages sent tripled by one year
since licensure in the control group compared with the
first 8 months of driving. On the other hand, the rate
remained stable in the blocking group (Creaser et al.,
2015). However, behavioral engagement strategies will
likely be necessary to enable the success and sustaina-
bility of cellphone blocking indicating a low likelihood
of use beyond the study. In the above mentioned
study, 15% of the teen drivers in the treatment group
were caught trying to game the system either by find-
ing ways to bypass the blocking system or by borrow-
ing a phone (Creaser et al., 2015). In one study of adult
drivers, after an intervention period of blocking was
disabled, there was no lasting behavior change as cell-
phone use while driving returned to baseline levels
(Funkhouser & Sayer, 2013). Furthermore, when sur-
veyed, the adult participants had overall not very posi-
tive views of the blocking technology.
9. Knowledge Gaps in the Science
Despite the widespread emergence of cellphone use
while driving among adolescent drivers over the last
Media and Communication, 2016, Volume 4, Issue 3, Pages 79-89 85
decade, and the associated research activity on this
behavior, several critical knowledge gaps persist. One
of the major challenges in understanding the preva-
lence of this behavior and measuring effectiveness of
intervention strategies is a lack of readily collectable,
reliable, and valid measures of cellphone use while
driving. Despite the ease of collection of survey data on
magnitude of self-reported cellphone use while driving,
there is scant evidence to support its validity. Based on
the comparison between self-reported general
smartphone use episodes and actual recorded epi-
sodes, survey self-report methods likely underestimate
the number of cellphone use episodes (Andrews, Ellis,
Shaw, & Piwek, 2015). The biggest problem with widely
accepted survey self-report measures of cellphone use
while driving is that there is no distinction between use
while stopped vs. use while the car is in motion (Olsen,
Shults, & Eaton, 2013). Only one study to our
knowledge measured self-reported cellphone use and
actual cellphone use while driving using a cellphone
app and in vehicle monitoring device, but these
measures were not directly compared (Creaser et al.,
2015). There is a need for future naturalistic studies to
clarify the correlation between self-reported cellphone
use and actual cellphone use as well as the frequency
and duration of use given the transient risks of the ex-
posure on crash risk.
The association between driving context in which
cellphone use is initiated and crash risk has also not
been well elucidated. For example, it is not known if
handheld use while stopped or in very low speed traffic
actually poses risk of injury. Additionally, it is not
known how type of handheld cellphone use (e.g. tex-
ting vs. checking email vs. looking at GPS directions) af-
fects level of risk in terms eye glance duration, real
driving performance, and near crash and crash events.
These knowledge gaps are difficult to fill because they
would require naturalistic studies with large sample
sizes. Leveraging smartphone apps that can track type
of phone use while driving may be a cost-effective way
to study these questions in larger and broader popula-
tions than have been studied to date in instrumented
vehicle naturalistic studies.
Furthermore, there is a great need to better under-
stand the effectiveness of current countermeasures
and assess why many countermeasures have failed to
reduce this behavior. Specifically, it should be deter-
mined whether cellphone bans have led to the unin-
tended consequence of drivers holding their phone be-
low window-level view to avoid detection when texting
thereby taking their eyes off the road longer. If con-
firmed, this could undermine the effectiveness of en-
forcing cellphone bans. Additionally, further qualitative
research with adolescent drivers and their parents
would shed light on addressable barriers to the adop-
tion of several available smartphone based apps and
settings that limit the temptation to text while driving.
Finally, as smartphone and in-vehicle technology rapid-
ly evolves, there is an urgent need to determine
whether hands-free features actually reduce cognitive
distraction and keep the drivers eyes on the road. Stud-
ies to date suggest that most of these features, such as
voice to text functions, do not reduce distracted driving
and may even increase the risk of distraction (Strayer,
Turrill, Coleman, Ortiz, & Cooper, 2014).
10. Promising Future Directions
Given the limited effectiveness of current isolated miti-
gation strategies, a multi-pronged regulatory, behav-
ioral, and technological approach addressing the above
risk factors will be necessary to reduce this dangerous
behavior in adolescents. For legal bans to be effective,
they must be aggressively enforced. As evidenced by
prior research, bans lose effectiveness shortly after im-
plementation, likely due to lack of enforcement
(Highway Loss Data Institute, 2010). As demonstrated
in a pilot program in two northeastern U.S. states, high
visibility law enforcement campaigns that increase
awareness of the legal and financial repercussions of
texting in addition to actually enforcing the bans, im-
prove the effectiveness of the legal ban (Chaudhary, et
al., 2012). It is also likely that increasing the financial
and legal repercussions of getting caught using a cell-
phone while driving would further increase the effec-
tiveness of legal bans. The combination of these ap-
proaches are theoretically sound given that individuals
value and are more sensitive to losses than equivalent
gains based on the behavioral economic phenomenon
of loss aversion (Tversky & Kahneman, 1991). Never-
theless, the logistical challenges of enforcing bans, par-
ticularly the accurate detection of the behavior (hold-
ing and manipulating phone in hand vs. holding
another object or looking down in car) will continue to
limit the overall effectiveness of bans, necessitating
other strategies to reduce use.
Educational interventions aimed at reducing texting
while driving and distracted driving in general should
focus on targeting the mechanism by which distraction
causes crashesby getting drivers to keep their eyes
on the forward roadway. Furthermore, efforts to re-
duce cellphone use while driving in adolescents may be
more successful if the intervention also addresses the
parents’ behavior. There is promising evidence that ac-
tive parental involvement enhances the effectiveness
of adolescent driving interventions (Curry, Peek-Asa,
Hamann, & Mirman, 2015). Furthermore, given the
strong correlation between parental engagement with
texting and driving and their child’s behavior, strategies
that enable parents to be better role models for their
children are highly promising (Carter et al., 2014). Edu-
cational interventions that can be delivered online
would increase the scalability of these efforts and po-
tential adoption in driver’s education classes.
Media and Communication, 2016, Volume 4, Issue 3, Pages 79-89 86
While smartphone applications that disable
handheld use while driving are effective in research
settings, it is questionable whether individuals will
continue to use such applications without a behavior-
al strategy to sustain use. This is evidenced by the fact
that a significant proportion of adolescent drivers
tried to bypass cellphone blocking in one study
(Creaser et al., 2015). In the short term, the apps can
be designed to be more user friendly by allowing au-
tomated responses to incoming messages and hands
free navigation, enabling emergency calls, and balanc-
ing functionality with maintaining battery life. Incor-
porating adolescents and young adults into the design
process would like also increased adoption. In the
long term, as with all mobile devices and apps, sus-
taining use will require behavioral engagement strat-
egies (Patel, Asch, & Volpp, 2015). Feedback loops
could be better designed to sustain engagement with
cellphone blocking apps using concepts from behav-
ioral economics. Given that some of those who en-
gage in texting while driving overweigh immediate
benefits (Hayashi et al., 2015), promising intervention
would be to provide frequently delivered (e.g. daily)
rewards to make the cognitive appraisal of abstaining
from handheld phone use more attractive than the
urge to engage in texting while driving (Loewenstein,
Asch, & Volpp, 2013). Making a portion of parental
weekly allowances contingent on good behavior may
be one way to operationalize this through the use of
smartphone based apps that monitor cellphone use
behavior while driving (e.g. $1/day of allowance given
for each day with no measured texting while driving).
In the future, financial rewards could be scaled up and
implemented on a large scale through repurposing ex-
isting auto-insurer teen driver discounts into discounts
or rewards based on actual driving performance, as
measured by in vehicle devices and smartphone appli-
cations (Cambridge Mobile Telematics, 2014). Auto in-
surance and car rental companies are already providing
in-vehicle devices and associated smartphone applica-
tions to reduce cellphone distraction (Insurance and
Technology, 2013; Jackson, 2016).
11. Conclusions
Cellphones are a mainstay of connectivity in most ado-
lescents’ daily lives, as a form of entertainment, infor-
mation and communication. The pervasiveness of ado-
lescent cellphone use can have negative effects on
driving behavior and increase crash risk. Current strat-
egies to decrease adolescent cellphone use while driv-
ing fall short of what is needed to curb teen driver
crashes and improve adolescent health. Interdiscipli-
nary approaches show promise, and those that inte-
grate cellphone policies, technology, and individual and
family behaviors will be necessary to reduce this dan-
gerous behavior in adolescents.
This work was supported by Research Career Develop-
ment Program in Emergency Medicine of the National
Institutes of Health under award number K12HL109009
(Delgado), as well as the Penn Roybal Center for Behav-
ioral Economics under award number P30AG034546
(Delgado). Catherine C. McDonald was supported by
the National Institute of Nursing Research under award
number R00NR013548. The content is solely the re-
sponsibility of the authors and does not necessarily
represent the official views of the National Institutes of
Conflict of Interests
The authors declare no conflict of interests.
AAA. (2013). AAA Campaign aims to pass texting while
driving bans in all 50 states by 2013. AAA Newsroom.
Retrieved from
Abouk, R., & Adams, S. (2013). Texting bans and fatal ac-
cidents on roadways: do they work? Or do drivers
just react to announcements of bans? American Eco-
nomic Journal: Applied Economics, 5(2), 179-199.
American Family Insurance. (2016). Teen safe driver pro-
gram. Retrieved from http://www.teensafedriver.
Andrews, S., Ellis, D., Shaw, H., & Piwek, L. (2015). Be-
yond self-report: Tools to compare estimated and
real-world smartphone use. PLoS One, 10(10),
AT&T. (2012). AT&T teen driver survey: Executive sum-
mary. Retrieved from
Atchley, P., & Warden, A. C. (2012). The need of young
adults to text now: Using delay discounting to assess
informational choice. Journal of Applied Research in
Memory and Cognition, 1(4), 229-234.
Bernstein, J. J., & Bernstein, J. (2015). Texting at the light
and other forms of device distraction behind the
wheel. BMC Public Health, 15(1), 968.
Billieux, J., Maurage, P., Lopez-Fernandez, O., Kuss, D., &
Griffiths, M. (2015). Can disordered mobile phone
use be considered a behavioral addiction? An update
on current evidence and a comprehensive model for
future research. Current Addiction Reports, 2(2), 156-
Bowen, D., Kreuter, M., Spring, B., Cofta-Woerpel, L.,
Linnan, L., Weiner, D., . . . Fernandez, M. (2009). How
we design feasibility studies. American Journal of
Preventive Medicine, 36(5), 452-457.
Caird, J., Johnston, K., Wilness, C., Asbridge, M., & Steel,
P. (2014). A meta-analysis of the effects of texting on
Media and Communication, 2016, Volume 4, Issue 3, Pages 79-89 87
driving. Accident Analysis and Prevention, 71, 311-
Cambridge Mobile Telematics. (2014). Leading global in-
surer expands partnership with innovative telematics
technology provider. Cambridge Mobile Telematics.
Retrieved from
Carter, P., Bingham, C., Zakrajsek, J., Shope, J., & Sayer,
T. (2014). Social norms and risk perception: Predic-
tors of distracted driving behavior among novice
adolescent drivers. Journal of Adolescent Health,
54(5), S32-S41.
Cazzulino, F., Burke, R., Muller, V., Arbogast, H., & Up-
perman, J. (2014). Cell phones and young drivers: A
systematic review regarding the association between
psychological factors and prevention. Traffic Injury
Prevention, 15(3), 234-242.
CDC. (2013). Centers for Disease Control and Prevention.
Web-based injury statistics query and reporting sys-
tem (WISQARS). 2010. Retrieved from: www.cdc.
Chaudhary, N., Cassanova-Powell, T., Cosgrove, L.,
Reagan, I., & Williams, A. (2012). Evaluation of
NHTSA distracted driving demonstration projects in
Connecticut and New York. Washington DC: National
Highway Traffic Safety Administration.
CNN. (2014). Distracted driving a real danger for teens.
CNN. Retrieved from
Creaser, J., Edwards, C., Morris, N., & Donath, M. (2015).
Are cellular phone blocking applications effective for
novice teen drivers? Journal of Safety Research,
54(June), 75.e29-78.
CTIA. (2013). CTIA's wireless industry summary report,
year end 2014 results. Computers in Human Behav-
ior, 29(6), 2632-2639.
Curry, A. E., Peek-Asa, C., Hamann, C.,& Mirman, J.
(2015). Effectiveness of parent-focused interventions
to increase teen driver safety: A critical review. Jour-
nal of Adolescent Health, 57(1), S6-S14.
DePalma, A. (2014). Did a text kill my brother? The New
York Times. Retrieved from http://www.nytimes.
brother.html (2015). Official US government website
for distracted driving. Retrieved from http://www.
Ebel, B., Boyle, L., O'Connor, S., Bresnahan, B., Maeser,
J., Kernic, M., & Rowhani-Rahbar, A. (2015). Random-
ized trial of cell phone blocking and in-vehicle cam-
era to reduce high-risk driving events among novice
drivers. Pediatric Academic Societies.
Ehsani, J., Bingham, C., Ionides, E., & Childers, D. (2014).
The impact of Michigan's text messaging restriction
on motor vehicle crashes. Journal of Adolescent
Health, 54(5), s68-s74.
Ehsani, J., Li, K., & Simons-Morton, B. (2015). Teenage
drivers portable electronic device use while driving.
The National Academies of Sciences, Engineering
Medicine, 219-225.
Ferdinand, A. O., Menachemi, N., Blackburn, J. L., Sen,
B., Nelson, L., & Morrisey, M. (2015). The impact of
texting bans on motor vehicle crash-related hospital-
izations. American Journal of Public Health, 105(5),
859-865. doi: 10.2105/AJPH.2014.302537
Ferdinand, A. O., Menachemi, N., Sen, B., Blackburn, J.
L., Morrisey, M., & Nelson, L. (2014). Impact of tex-
ting laws on motor vehicular fatalities in the United
States. American Journal of Public Health, 104(8),
1370-1377. doi: 10.2105/ajph.2014.301894
Fitch, G., Hanowski, R., & Guo, F. (2014). The risk of a
safety-critical event associated with mobile device
use in specific driving contexts. Traffic Injury Preven-
tion, 16(2), 124-132. doi:10.1080/15389588.2014.
Foss, R., & Goodwin, A. (2014). Distracted driver behav-
iors and distracting conditions among adolescent
drivers. Journal of Adolescent Health, 54(5).
Funkhouser, D., & Sayer, J. (2012). Naturalistic census of
cell phone use. Transportation Research Record:
Journal of The Transportation Research Board, 2321,
1-6. doi: 10.3141/2321-01
Funkhouser, D., & Sayer, J. (2013). Cellphone fil-
ter/blocker techonology field test. Washington, DC:
National Highway and Traffic Safety Administration.
Giedd, J. N. (2012). The digital revolution and adolescent
brain evolution. The Journal of Adolescent Health,
51(2), 101-105. doi:10.1016/j.jadohealth.2012.06.002
Goodwin, A., O’Brien, N., & Foss, R. (2012). Effect of
North Carolina's restriction on teenage driver cell
phone use two years after implementation. Accident
Analysis & Prevention, 48, 363-367.
Hafetz, J. S., Jacobsohn, L. S., García-España, J. F., Curry,
A. E., & Winston, F. K. (2010). Adolescent drivers
perceptions of the advantages and disadvantages of
abstention from in-vehicle cell phone use. Accident
Analysis & Prevention, 42(6), 1570-1576.
Harbluk, J., Noy, Y., Trbovich, P., & Eisenman, M. (2007).
An on-road assessment of cognitive distraction: Im-
pacts on drivers’ visual behavior and braking perfor-
mance. Accident Analysis & Prevention, 39, 372-379.
Hayashi, Y., Russo, C. T., & Wirth, O. (2015). Texting
while driving as impulsive choice: A behavioral eco-
nomic analysis. Accident Analysis & Prevention, 83,
182-189. doi:10.1016/j.aap.2015.07.025
Highway Loss Data Institute. (2010). Texting laws and
collision claim frequencies. Ruckersville, VA: Highway
Loss Data Institute.
Huisingh, C., Griffin, R., & McGwin, G. (2015). The preva-
lence of distraction among passenger vehicle drivers:
A roadside observational approach. Traffic Injury
Prevention, 16(2), 140-146. doi:10.1080/15389588.
Media and Communication, 2016, Volume 4, Issue 3, Pages 79-89 88
Insurance and Technology. (2013). Esurance takes on
distracted driving. Information Week. Retrieved from
Insurance Institute for Highway Safety. (2015, December
2015). Cellphones and texting: Map of texting bans.
Retrieved from
ed driving \# map
Jackson, C. (2016). TextNinja makes a game of banning
texting while driving. Chicago Tribune. Retrieved
Klauer, S., Dingus, T., & Neal, V. (2006). The impact on
driver inattention on near crash/crash risk: An analy-
sis using the 100 car naturalistic driving study data.
Washington, DC: National Highway Traffic Safety
Klauer, S. G., Ehsani, J. P., McGehee, D. V., & Manser, M.
(2015). The effect of secondary task engagement on
adolescents’ driving performance and crash risk.
Journal of Adolescent Health, 57(1), S36-S43.
Klauer, S. G., Guo, F., Simons-Morton, B. G., Ouimet, M.
C., Lee, S. E., & Dingus, T. A. (2014). Distracted driv-
ing and risk of road crashes among novice and expe-
rienced drivers. New England Journal of Medicine,
370(1), 54-59.
Kockelman, K. M., & Kweon, Y.-J. (2002). Driver injury
severity: An application of ordered probit models.
Accident Analysis & Prevention, 34(3), 313-321.
LaVoie, N., Lee, Y.-C., & Parker, J. (2015). Preliminary re-
search developing a theory of cell phone distraction
and social relationships. Accident Analysis & Preven-
tion, 86, 155-160.
Lee, Y.-K., Chang, C.-T., Lin, Y., & Cheng, Z.-H. (2014). The
dark side of smartphone usage: Psychological traits,
compulsive behavior and technostress. Computers in
Human Behavior, 31, 373-383.
Lenhart, B. Y. A., Smith, A., Anderson, M., Duggan, M., &
Perrin, A. (2015). Teens, technology, and freindship:
Pew Research Center. Retrieved from http://www.
Lepp, A., Barkley, J., & Karpinski, A. (2014). The relation-
ship between cell phone use, academic performance,
anxiety, and satisfaction with life in college students.
Computers in Human Behavior, 31(1), 343-350.
Ling, R., Bertel, T., & Sundsoy, P. (2012). The socio-
demographics of texting: An analysis of traffic data.
New Media & Society, 14(2), 281-298.
Loewenstein, G., Asch, D., & Volpp, K. (2013). Behavioral
economics holds potential to deliver better results
for patients, insurers, and employers. Health Affairs,
32(7), 1244-1250.
Mayhew, D. R., Simpson, H. M., & Pak, A. (2003). Chang-
es in collision rates among novice drivers during the
first months of driving. Accident Analysis & Preven-
tion, 35(5), 683-691.
McCartt, A. T., Shabanova, V. I., & Leaf, W. a. (2003).
Driving experience, crashes and traffic citations of
teenage beginning drivers. Accident Analysis and
Prevention, 35(3), 311-320.
McDonald, C. C., & Sommers, M. S. (2015). Teen drivers’
perceptions of inattention and cell phone use while
driving. Traffic Injury Prevention, 16, S52-58.
McKnight, J., & McKnight, S. (2003). Young novice driv-
ers: Careless or clueless? Accident Analysis and Pre-
vention, 35(6), 921-925.
Merlo, L. J., Stone, A. M., & Bibbey, A. (2013). Measuring
problematic mobile phone use: Development and
preliminary psychometric properties of the PUMP
scale. Journal of Addiction, 2013.
Muskal, M. (2015). Teen drivers distracted by cell-
phones, talking in most crashes. Los Angeles Times.
Retrieved from
NHTSA. (2015a). Driver electronic device use in 2014.
U.S. Department of Transportation. Retrieved from
NHTSA. (2015b). Traffic safety facts: Distracted driving
2013. NHTSA's National Center for Statistics and
Analysis. Washington, DC: U.S. Department of Trans-
portation. Retrieved from http://www.distraction.
Nielson. (2010). U.S. teen mobile report calling yester-
day, texting today, using apps tomorrow. The Nielson
Company. Retrieved from
Office of Disease Prevention and Health Promotion.
(2015). Healthy People 2020. Retrieved from http://
Olsen, E. O. M., Shults, R. A., & Eaton, D. K. (2013). Tex-
ting while driving and other risky motor vehicle be-
haviors among US high school students. Pediatrics,
131(6), e1708-e1715.
Olsen, R., Hanowski, R., Hickman, J., & Bocanegra, J.
(2009). Driver distraction in commercial vehicle oper-
ations. Washington DC: US Department of Transpor-
Patel, M. S., Asch, D. A., & Volpp, K. G. (2015). Wearable
devices as facilitators, not drivers, of health behavior
change. Journal of the American Medical Association,
313(5), 459-460.
Redelmeier, D. A., & Tibshirani, R. J. (1997). Association
between cellular-telephone calls and motor vehicle
collisions. New England Journal of Medicine, 336(7),
Richtel, M. (2014). Trying to hit the brake on texting
while driving. The New York Times. Retrieved from
Media and Communication, 2016, Volume 4, Issue 3, Pages 79-89 89
Richtel, M. (2015). Some people do more than text while
driving. Bits. Retrieved from http://bits.blogs.ny
Romer, D., Duckworth, A. L., Sznitman, S., & Park, S.
(2010). Can adolescents learn self-control? Delay of
gratification in the development of control over risk
taking. Prevention Science : The Official Journal of the
Society for Prevention Research, 11(3), 319-330.
Romer, D., Lee, Y., McDonald, C., & Winston, F. (2014).
Adolescence, attention allocation, and driving safety.
Journal of Adolescent Health, 54(5), S6--S15.
Simons-Morton, B. G., Bingham, C. R., Ouimet, M. C.,
Pradhan, A. K., Chen, R., Barretto, A., & Shope, J. T.
(2013). The effect on teenage risky driving of feed-
back from a safety monitoring system: A randomized
controlled trial. Journal of Adolescent Health, 53(1),
Simons-Morton, B. G., Guo, F., Klauer, S. G., Ehsani, J. P.,
& Pradhan, A. K. (2014). Keep your eyes on the road:
Young driver crash risk increases according to dura-
tion of distraction. The Journal of Adolescent Health,
54(5), S61-67.
State Farm. (2014). Distracted driving state. Farm Auto-
mobile Insurance Company. Retrieved from http://
Strayer, D., Turrill, J., Coleman, J., Ortiz, E., & Cooper, J.
(2014). Measuring cognitive distraction in the auto-
mobile II: Assessing in-vehicle voice-based interactive
technologies. Washington DC: AAA Foundation for
Traffic Safety.
Terry, C. P., & Terry, D. L. (2015). Distracted driving
among college students: Perceived risk versus reality.
Current Psychology, 35(1), 115-120.
Tversky, A., & Kahneman, D. R. (1991). Loss aversion in
riskless choice: A reference-dependent model. Quar-
terly Journal of Economics, 106(4), 1039-1061.
Unni, P., Morrow, S. E., Shultz, B. L., & Tian, T. T. (2013).
A pilot hospitalschool educational program to ad-
dress teen motor vehicle safety. Journal Trauma
Acute Care Surgery, 75(4), S285-289.
Walsh, S. P., White, K. M., & Young, R. M. (2008). Over-
connected? A qualitative exploration of the relation-
ship between Australian youth and their mobile
phones. Journal of Adolescence, 31(1), 77-92.
Williams, A. F., & Tefft, B. C. (2014). Characteristics of
teens-with-teens fatal crashes in the United States,
20052010. Journal of Safety Research, 48, 37-42.
World Health Organization. (2013). Global status report
on road safety: Time for action.Retrieved from:
Young, R. (2012). Cell phone use and crash risk: Evidence
for positive bias. Epidemiology, 23(1), 116-118.
About the Authors
Dr. M. Kit Delgado
M. Kit Delgado, MD, MS, is an Assistant Professor in the Department of Emergency Medicine at the
University of Pennsylvania. Dr. Delgado has secondary appointments as an Assistant Professor in the
Department of Biostatistics and Epidemiology and as a Senior Fellow in the Leonard Davis Institute of
Health Economics. Dr. Delgado’s research focuses on reducing the public health burden caused by in-
Kathryn J. Wanner
Kathryn J. Wanner, MA, is a Research Project Manager for the Center for Emergency Medicine Policy
Research at the University of Pennsylvania. She received her Master of Art’s in the Sociology of Edu-
cation from New York University.
Dr. Catherine McDonald
Catherine McDonald, PhD, RN is an Assistant Professor at the University of Pennsylvania School of
Nursing and has a secondary appointment at Children’s Hospital of Pennsylvania as an Assistant Pro-
fessor of Nursing in Pediatrics in the Department of Pediatrics. Her work focuses on the factors that
contribute to adolescent morbidity and mortality associated with injury and violence.
... Driver education programs are a popular and structured approach targeting novice drivers to improve risk perception, alter aggressive driving maneuvers, and develop critical driving skills. However, conventional training has little effect in lessening cellphone use while driving (Delgado, Wanner, & McDonald, 2016). Several studies have recommended adding the contextual understanding of risk factors into the training contents that could depict drivers' actual crash scenarios (Arnold et al., 2019;Delgado et al., 2016). ...
... However, conventional training has little effect in lessening cellphone use while driving (Delgado, Wanner, & McDonald, 2016). Several studies have recommended adding the contextual understanding of risk factors into the training contents that could depict drivers' actual crash scenarios (Arnold et al., 2019;Delgado et al., 2016). In this regard, the associations revealed in this study can be helpful to strengthen the existing educational interventions. ...
... Electronic stability control (ESC) is substantially effective in reducing single-vehicle crashes as the sensor-based braking system can quickly respond to sudden instabilities (Sivinski, 2011). Few studies have stated the necessity to implement integrations of multiple countermeasures (e.g., improved education programs, strict enforcement of regulations, widespread safety awareness campaigns, etc.) against cellphone-distracted driving (Arnold et al., 2019;Delgado et al., 2016). However, the biggest challenge is to find an approach that will not only decrease the risk of cellphone use while driving, but also permit adolescents to enjoy the benefits of advanced smartphone features. ...
Full-text available
More than 30% of cellphone-distracted fatal crashes occurred to drivers younger than 25-years-old in 2018, even though they constitute less than 12% of total licensed drivers in the U.S. Using joint correspondence analysis (JCA), this study analyzed six years (2014-2019) of cellphone-related fatal crashes involving young drivers based on the data from the Fatality Analysis Reporting System (FARS). This unsupervised learning algorithm can graphically display the co-occurrence of variable categories in a lower-dimensional space by effectively summarizing the knowledge of a complex crash dataset. The Boruta algorithm was applied to select the relevant features from the preliminary crash dataset. The empirical results of JCA manifest a few interesting fatal crash patterns. For example, young male drivers in light trucks were involved in deadly collisions while performing specific cellphone activities (other than talking and listening), cellphone-related fatal crashes occurred to young females with prior crash records, and so on. Apart from alcohol and drug involvement, this study identified young drivers’ additional risk-taking maneuvers while engaged in cellphone usage, including- disregarding traffic signs and signals, speeding, and unrestrained driving. The associations could guide the safety officials and policymakers in developing appropriate engineering, education, and enforcement strategies when dealing with cellphone-distracted young drivers.
... In studies of younger teenage drivers, parents are effective at reducing mobile-device use in teenage drivers with directed feedback from access to applications that track their children's phone-use while driving [15]. Parents are also important role models for their children's future driving habits and can help set norms for safe driving practices by modeling attentive driving even while their children are young [8]. Recent legislative interventions have focused on restrictions on the use of cellphones with the goal of decreasing driver distraction, including specific text messaging bans, handheld cellphone bans, and novice driver all cellphone-use bans. ...
... However, there has been great concern about the detrimental effects of distracted driving that occurs with cell phone use [62,63]. Although much of this research has focused on cell phone use among younger adults [64], there is some evidence that, like teenagers, the driving of adults aged 65+ may be especially affected by concomitant cell phone use [65]. Simulator studies suggest that older adults with MCI have slower reaction times and a greater probability of being in an accident when distracted by cell phone use compared to those with normal cognition [66,67]. ...
Full-text available
Research on how preclinical and early symptomatic Alzheimer’s disease (AD) impacts driving behavior is in its infancy, with several important research areas yet to be explored. This paper identifies research gaps and suggests priorities for driving studies over the next few years among those at the earliest stages of AD. These priorities include how individual differences in demographic and biomarker measures of AD pathology, as well as differences in the in-vehicle and external driving environment, affect driving behavior. Understanding these differences is important to developing future interventions to increase driving safety among those at the earliest stages of AD.
... The risky riding behavior in this study was the driver doing other activities such as using a handphone. That devices which are currently the main human needs, make it impossible for a person to easily let their hands off, as well as participants who often operate handphones anywhere and anytime (6,39). We found that doing other activities such as operating handphones, smoking and excessive interaction with drivers is at risk of experiencing 1.514 traffic accidents. ...
Background: According to police reported crash, in 2020 there have been 510 road traffic accidents among adolescents aged 16-25 years. The problem is that although restrictions on social activities have been implemented, 9.80% of accidents have caused deaths in Semarang City. There were many factors that influence the occurrence of road traffic accidents; one of those is the poor knowledge about safe riding behavior. The aim of this study is to determine the factors that contribute to the occurrence of road traffic accidents in adolescents during the pandemic. Methods: This was a cross-sectional study, collected data using an online questionnaire distributed to adolescents aged 15-20 years in Semarang City, Indonesia. It was distributed during February-April 2021. The data included participant's demographic information, riding behavior, and knowledge about safe riding. We analyzed using chi-square and logistic regression to determine the most influential factors. Results: The sample included 725 participants with a mean age of 17.4 years (SD=0.97); 260 (35.9%) males. We have found that gender was associated with the incidence of road traffic accidents (AOR=1.455, 95% CI [1.048-2.020], P=0.025) after adjusting for experience road safety education, vehicle type, and knowledge of safe riding. Conclusion: It is necessary to carry out Road Safety Education efforts to male students during the pandemic to reduce the incidence of traffic accidents.
... Teens in general have a stronger tendency to use their mobile phones [8], and studies have shown that nearly half of adolescent drivers engage in MPUWD [9,10]. Legal bans on phone use while driving have shown only modest results, likely due to the difficulties of enforcement [11]. Modern mobile phones have built-in functionality to detect when a phone is in a moving vehicle; however, those libraries are unable to differentiate between drivers and passengers, and commercially available MPUWD software typically includes a simple option for users to manually disable it during drives where they are a passenger. ...
Full-text available
Traffic-related injuries and fatalities are major health risks in the United States. Mobile phone use while driving quadruples the risk for a motor vehicle crash. This work demonstrates the feasibility of using the mobile phone camera to passively detect the location of the phone's user within a vehicle. In a large, varied dataset we were able correctly identify if the user was in the driver's seat or one of the passenger seats with 94.9% accuracy. This model could be used by application developers to selectively change or lock functionality while a user is driving, but not if the user is a passenger in a moving vehicle.
In the United States, motor vehicle crashes are a leading cause of injury and death in adolescents. Driving is a complex task that requires integrating several cognitive processes. However, the prefrontal cortex, which is the brain area responsible for these higher-level functions, is not fully developed in adolescents. Slow development of the prefrontal cortex further contributes to teens’ impulsivity and risk-taking behavior. This chapter presents research on the risky behaviors undertaken by teen drivers, including cell phone use, peer passengers, speeding, inconsistent seat belt use, alcohol/substance use, and sleep deprivation. The chapter then outlines existing intervention efforts to reduce car crash injury and death among teens, such as graduated driver's licensing laws and regulations regarding distracted and impaired driving. Finally, the authors provide recommendations for parents and health care providers to address teen driving safety.
Purpose: This randomized clinical trial tested the efficacy of a 6-week text message program to reduce texting while driving (TWD) for young adults. Methods: Eligible individuals recruited from four emergency departments from December 2019 to June 2021 were aged 18-25 years who reported TWD in the past 2 weeks. Participants were randomly assigned 1:1 to intervention:assessment control. The intervention arm (n = 57) received an automated interactive text message program, including weekly queries about TWD for 6 weeks with feedback and goal support to promote cessation of TWD. The assessment control arm (n = 55) received identical weekly TWD queries but no additional feedback. Outcomes were collected via web-based self-assessments at 6- and 12 weeks and analyzed under intent-to-treat models, presented as adjusted odds ratios (ORs) with 95% confidence intervals (CIs). Results: The mean (SD) age was 21.7 (2.1) years, 73 (65%) were female, and 40 (36%) were White. The 6-week follow-up rate was 77.7% (n = 87) and 12-week follow-up rate was 64.3% (n = 72). At 6 weeks, 52.6% (95% CI, 39.0%-66.0%) of intervention participants reported TWD versus 63.6% (95% CI, 49.6%-76.2%) of control participants (adjusted OR, 0.71; 95% CI, 0.32-1.59). At 12 weeks, 38.2% (95% CI, 22.8%-53.5%) of intervention participants reported TWD versus 69.3% (95% CI, 53.8%-84.7%) of control participants (adjusted OR, 0.29; 95% CI, 0.11-0.80). Discussion: An interactive text message intervention was more effective at reducing self-reported TWD among young adults than assessment control at 12 weeks.
Objective: Motorcycles comprised over 60% of motor vehicles in Taiwan. There were still many motorcycle crashes in Taiwan, especially among young riders. This study investigated the characteristics of novice motorcyclist crashes in Taiwan over the period January 2011 to December 2016. Various risk factors affecting the severity of novice motorcyclist crashes, such as the rider characteristics, licensing conditions, and the environment, were examined. Methods: To model the count data with multiple crash severities, several regression models were considered. The multinomial logit (MNL) model, ordered logit (OL) model, and partial proportional odds (PPO) model were chosen and investigated for the relationships between the severity of novice motorcyclist crashes and potential risk factors. Results: The results showed that the novice rider who was underage or unlicensed had a higher probability of a fatal crash. Male sex, helmet use, drinking, college student, frontal impact, urban or dry road, and daytime all played significant roles in novice motorcyclist crashes. Conclusions: Taiwan traffic safety needs further policy adjustments and public education toward novice motorcycle crashes. Adequate driving training and providing a user-friendly environment for novice riders could help. Taiwan should consider graduated driver licensing systems for skill-building and riding supervision for new motorcyclists.
Young drivers, aged 17–25 years, are more likely than other age groups to access social interactive technologies (e.g., Snapchat, Facebook) on their smartphones while driving. Many of these young drivers do so in a concealed manner, thereby diverting their eyes from the road for extended periods and increasing their crash risk. In accordance with previous research, an extended Theory of Planned Behaviour (TPB) was applied in this survey study to investigate psychosocial predictors of young drivers’ intention, and behaviour, of responding to social interactive technology on a smartphone in a concealed manner. Participants (N = 154) resided in Australia, were aged 17–25 years, owned a provisional or an open licence, and owned a smartphone. Participants completed two online surveys administered 1-week apart. The first survey measured intention and assessed the TPB standard constructs of attitude, subjective norm, and perceived behavioural control, as well as the additional constructs of anticipated action regret, anticipated inaction regret, and problematic mobile phone usage. The first survey also assessed whether there were any differences in the salient beliefs (elicited in a previous study) about smartphone use between high and low intenders to engage in this behaviour. The second survey measured engagement in the behaviour of responding in a concealed manner in the previous week. Hierarchical multiple regression analyses showed the standard TPB accounted for 69% of variance in intention, and a further 4% was accounted for by the extended constructs. In the final model, all variables, except anticipated inaction regret, were significant predictors of intention. Intention was the only significant predictor of behaviour. A series of MANOVAs found significant differences in the salient belief items between high and low intenders (e.g., high intenders were more likely to believe that friends/peers and other drivers would approve of them engaging in this behaviour). These key findings can be used as focal points for public education messages to persuade young drivers to reduce the frequency of their smartphone use, which is vital to improve road safety for all users.
Full-text available
Phenomenon map is a method used in producing reliable evidence syntheses on complex topics corresponding to user needs. It has been developed by Sofi, the Science Advice Initiative of Finland. This summary report includes all parts of the first phenomenon map, which explored the impacts of digital media. It provides useful reading material for anyone interested in the effects of digital media, the media use of children, young people and older people as well as evidence syntheses and their production.
Full-text available
Psychologists typically rely on self-report data when quantifying mobile phone usage, despite little evidence of its validity. In this paper we explore the accuracy of using self-reported estimates when compared with actual smartphone use. We also include source code to process and visualise these data. We compared 23 participants' actual smartphone use over a two-week period with self-reported estimates and the Mobile Phone Problem Use Scale. Our results indicate that estimated time spent using a smartphone may be an adequate measure of use, unless a greater resolution of data are required. Estimates concerning the number of times an individual used their phone across a typical day did not correlate with actual smartphone use. Neither estimated duration nor number of uses correlated with the Mobile Phone Problem Use Scale. We conclude that estimated smartphone use should be interpreted with caution in psychological research.
Full-text available
Background: Cell phones are a well-known source of distraction for drivers, and owing to the proliferation of text messaging services, web browsers and interactive apps, modern devices provide ever-increasing temptation for drivers to take their eyes off the road. Although it is probably obvious that drivers' manual engagement of a device while their vehicles are in motion is potentially dangerous, it may not be clear that such engagement when the vehicle is at rest (an activity broadly labeled "texting at the light") can also impose risks. For one thing, a distracted driver at rest may fail to respond quickly to sudden changes in road conditions, such as an ambulance passing through. In addition, texting at the light may decrease so-called "situational awareness" and lead to driving errors even after the device is put down. To our knowledge, the direct comparison of the rate of device usage by drivers at rest with the rate of device usage by drivers in motion has not been reported. Methods: We collected information on 2000 passenger vehicles by roadside observation. For the first group of 1000 passenger vehicles stopped at a traffic light, device usage ("texting", "talking", "none"), gender of the driver, vehicle type, seatbelt usage and presence of front seat passengers were recorded. For a second set of 1000 vehicles in motion, device usage alone was noted. Statistical significance for differences in rates was assessed with the chi-square test. Results: We found that 3 % of drivers in motion were texting and 5 % were talking. Among the stopped drivers, 14.5 % were texting and 6.3 % were talking. In the stopped-vehicle set, gender and vehicle type were not associated with significant differences in device usage, but having a front seat passenger and using seatbelts were. Conclusions: Device usage is markedly higher among drivers temporarily at rest compared with those in motion, and the presence of a front seat passenger, who may help alleviate boredom or reprimand bad behavior, is associated with lower device usage rates among vehicles stopped at a light. These observations may help identify suitable steps to decrease distracted driving and thereby minimize traffic trauma.
Full-text available
We used a panel design and the Nationwide Inpatient Sample from 19 states between 2003 and 2010 to examine the impact of texting bans on crash-related hospitalizations. We conducted conditional negative binomial regressions with state, year, and month fixed effects to examine changes in crash-related hospitalizations in states after the enactment of a texting ban relative to those in states without such bans. Results indicate that texting bans were associated with a 7% reduction in crash-related hospitalizations among all age groups. Texting bans were significantly associated with reductions in hospitalizations among those aged 22 to 64 years and those aged 65 years or older. Marginal reductions were seen among adolescents. States that have not passed strict texting bans should consider doing so.
Although the rate of alcohol-impaired driving among adolescents has declined in the past two decades, distracted driving has become a major public safety concern. The present study compared perceptions of accident risk and social norms related to cell phone use while driving (CPWD), as well as alcohol-impaired driving, with self-reported behavior among a sample of 726 college students. Results indicated that although participants perceived sending text messages while driving as posing a similar accident risk as driving while legally intoxicated, they were much more likely to text behind the wheel. Furthermore, participants perceived their peers as being more accepting of and having more liberal views toward CPWD than their own, suggesting that one factor underlying the discrepancy between perceived risk and risk exposure may be the level of social acceptability attributed to texting while driving. Future interventions may benefit from focusing not only on risk perception, but on social norms, legal consequences, and adaptive alternatives.
Objective: Inattention to the roadway, including cell phone use while driving (cell phone calls, sending and reading texts, mobile app use, and Internet use), is a critical problem for teen drivers and increases risk for crashes. Effective behavioral interventions for teens are needed in order to decrease teen driver inattention related to cell phone use while driving. However, teens' perceptions of mobile device use while driving is a necessary component for theoretically driven behavior change interventions. The purpose of this study was to describe teen drivers' perceptions of cell phone use while driving in order to inform future interventions to reduce risky driving. Methods: We conducted 7 focus groups with a total of 30 teen drivers, ages 16-18, licensed for ≤1 year in Pennsylvania. The focus group interview guide and analysis were based on the Theory of Planned Behavior, identifying the attitudes, perceived behavioral control, and norms about inattention to the roadway. Directed descriptive content analysis was used to analyze the focus group interviews. All focus groups were coded by 2 research team members and discrepancies were reconciled. Themes were developed based on the data. Results: Teens had a mean age of 17.39 (SD = 0.52), mean length of licensure of 173.7 days (SD = 109.2; range 4-364), were 50% male and predominately white (90%) and non-Hispanic (97%). From the focus group data, 3 major themes emerged: (1) Recognizing the danger but still engaging; (2) Considering context; and (3) Formulating safer behaviors that might reduce risk. Despite recognizing that handheld cell phone use, texting, and social media app use are dangerous and distracting while driving, teens and their peers often engaged in these behaviors. Teens described how the context of the situation contributed to whether a teen would place or answer a call, write or respond to a text, or use a social media app. Teens identified ways in which they controlled their behaviors, although some still drew attention away from the roadway. Conclusions: Cell phone use while driving is a contributor to motor vehicle crashes in teens, and effective interventions to decrease risks are needed. Teens viewed some types of cell phone use as unsafe and describe methods in which they control their behaviors. However, some of their methods still take attention off the primary task of driving. Teens could benefit from behavior change interventions that propose strategies to promote focused attention on the roadway at all times during the driving trip.
Problem Distracted driving is a significant concern for novice teen drivers. Although cellular phone bans are applied in many jurisdictions to restrict cellular phone use, teen drivers often report making calls and texts while driving. Method The Minnesota Teen Driver Study incorporated cellular phone blocking functions via a software application for 182 novice teen drivers in two treatment conditions. The first condition included 92 teens who ran a driver support application on a smartphone that also blocked phone usage. The second condition included 90 teens who ran the same application with phone blocking but which also reported back to parents about monitored risky behaviors (e.g., speeding). A third control group consisting of 92 novice teen drivers had the application and phone-based software installed on the phones to record cellular phone (but not block it) use while driving. Results The two treatment groups made significantly fewer calls and texts per mile driven compared to the control group. The control group data also demonstrated a higher propensity to text while driving rather than making calls. Discussion Software that blocks cellular phone use (except 911) while driving can be effective at mitigating calling and texting for novice teen drivers. However, subjective data indicates that some teens were motivated to find ways around the software, as well as to use another teen's phone while driving when they were unable to use theirs. Practical applications Cellular phone bans for calling and texting are the first step to changing behaviors associated with texting and driving, particularly among novice teen drivers. Blocking software has the additional potential to reduce impulsive calling and texting while driving among novice teen drivers who might logically know the risks, but for whom it is difficult to ignore calling or texting while driving. © 2015 National Safety Council and Elsevier Ltd. All rights reserved.
The goal of the present study was to examine the utility of a behavioral economic analysis to investigate the role of delay discounting in texting while driving. A sample of 147 college students completed a survey to assess how frequently they send and read text messages while driving. Based on this information, students were assigned to one of two groups: 19 students who frequently text while driving and 19 matched-control students who infrequently text while driving but were similar in gender, age, years of education, and years driving. The groups were compared on the extent to which they discounted, or devalued, delayed hypothetical monetary rewards using a delay-discounting task. In this task, students made repeated choices between $1000 available after a delay (ranging from 1 week to 10 years) and an equal or lesser amount of money available immediately. The results show that the students who frequently text while driving discounted delayed rewards at a greater rate than the matched control students. The study supports the conclusions that texting while driving is fundamentally an impulsive choice made by drivers, and that a behavioral economic approach may be a useful research tool for investigating the decision-making processes underlying risky behaviors. Copyright © 2015 Elsevier Ltd. All rights reserved.
We critically reviewed recent parent-directed teen driving interventions to summarize their success in meeting stated goals; identify promising intervention components and knowledge gaps; aid in the selection, adaptation, and dissemination of effective interventions; and guide future research efforts. We focused on interventions that included a direct parent component, explicitly stated outcomes related to the teen and/or their parents, were evaluated for parent or teen outcomes, targeted drivers younger than the age of 21 years, and had at least one evaluation study published since 1990 and in English. We conducted a comprehensive systematic search of 26 online databases between November 2013 and January 2014 and identified 34 articles representing 18 interventions. Several interventions-in particular, those that had an active engagement component, incorporated an in-vehicle data recorder system, and had a strong conceptual approach-show promise in improving parental supervisory behaviors during the learner and early independent phases, increasing teen driver skill acquisition, and reducing teens' risky driving behaviors. We identify essential characteristics of effective parent-involved teen driving interventions and their evaluation studies, propose a comprehensive and multitiered approach to intervention, and discuss several research areas and overarching issues for consideration. Copyright © 2015 Society for Adolescent Health and Medicine. All rights reserved.
The purpose of this review was to synthesize the evidence of the effects of secondary task engagement on novice adolescent's driving performance and crash risk. Searches of multiple databases were conducted using search terms related to secondary task engagement and teenage drivers. Articles were selected for inclusion if they were: written in English, an empirical study assessing the impact of secondary task engagement on driving, and included study participants who were licensed drivers between the ages of 14 and 17 years (if research was conducted in the United States) or within 18 months licensure in other countries. Thirty-eight abstracts were reviewed. Fifteen studies met the inclusion criteria. Most studies examined the effects of electronic device use as the secondary task. Effects were assessed using crash databases, simulator, instrumented vehicle, and naturalistic driving studies. Texting resulted in increased lane deviations and eyes off road time in simulated driving, whereas talking on a cell phone had little effect. Naturalistic studies, which use vehicle instrumentation to measure actual driving, found secondary tasks that required drivers to look away from the forward roadway also increased the risk of crashes and near-crashes for young novice drivers, whereas tasks that did not require eyes to be off the forward roadway (e.g., talking on cell phone) had no effect on crash risk. Methodological differences in the definition and measurement of driving performance make it difficult to directly compare findings, even among the limited number of studies conducted. Despite this, results suggest that secondary tasks degrade driving performance and increase risk only when they require drivers to look away from the forward roadway. Future research needs to focus more explicitly on the ways in which secondary task engagement influences drivers' behavior (e.g., interfering with information acquisition or manual control of the vehicle). This, along with the use of standard measures across studies, would build a more useful body of literature on this topic. Copyright © 2015 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.