Texting While Driving: A Literature Review on Driving Simulator Studies

Abstract

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

Full text

Citation: Voinea, G.-D.; Boboc, R.G.;
Buzdugan, I.-D.; Antonya, C.; Yannis,
G. Texting While Driving: A
Literature Review on Driving
Simulator Studies. Int. J. Environ. Res.
Public Health 2023,20, 4354. https://
doi.org/10.3390/ijerph20054354
Academic Editors: Kun Wang,
Bo Yang and Fangtong Jiao
Received: 8 February 2023
Revised: 23 February 2023
Accepted: 25 February 2023
Published: 28 February 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
International Journal of
Environmental Research
and Public Health
Review
Texting While Driving: A Literature Review on Driving
Simulator Studies
Gheorghe-Daniel Voinea 1, Răzvan Gabriel Boboc 1,* , Ioana-Diana Buzdugan 1, Csaba Antonya 1
and George Yannis 2
1Department of Automotive and Transport Engineering, Transilvania University of Bras
,ov, 29 Eroilor Blvd.,
500036 Brasov, Romania
2Department of Transportation Planning and Engineering, National Technical University of Athens,
5 Heroon Polytechniou str., GR-15773 Athens, Greece
*Correspondence: razvan.boboc@unitbv.ro
Abstract:
Road safety is increasingly threatened by distracted driving. Studies have shown that
there is a significantly increased risk for a driver of being involved in a car crash due to visual
distractions (not watching the road), manual distractions (hands are off the wheel for other non-
driving activities), and cognitive and acoustic distractions (the driver is not focused on the driving
task). Driving simulators (DSs) are powerful tools for identifying drivers’ responses to different
distracting factors in a safe manner. This paper aims to systematically review simulator-based studies
to investigate what types of distractions are introduced when using the phone for texting while
driving (TWD), what hardware and measures are used to analyze distraction, and what the impact
of using mobile devices to read and write messages while driving is on driving performance. The
review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension
for Scoping Reviews (PRISMA-ScR) guidelines. A total of 7151 studies were identified in the database
search, of which 67 were included in the review, and they were analyzed in order to respond to four
research questions. The main findings revealed that TWD distraction has negative effects on driving
performance, affecting drivers’ divided attention and concentration, which can lead to potentially
life-threatening traffic events. We also provide several recommendations for driving simulators that
can ensure high reliability and validity for experiments. This review can serve as a basis for regulators
and interested parties to propose restrictions related to using mobile phones in a vehicle and improve
road safety.
Keywords: texting while driving; distracted driving; simulator study; literature review
1. Introduction
Road safety is increasingly threatened by distracted driving. One of the highest-risk
forms of distracted driving is texting while driving (TWD) [
1
,
2
] alongside talking on the
phone while driving (TPWD) [
3
,
4
]. After decades of research, the statistics show that the
risks associated with TWD are very high [
5
]. According to the United Nations Road Safety
statistical data [
6
], car traffic crashes cause more than 1.35 million deaths and injure as
many as 50 million people annually worldwide, and a significant cause of such crashes is
distracted driving [
7
]. Considering that, distracted driving has become a common topic in
studies that aim to find solutions to reduce traffic injuries and death.
A general approach to road safety is to identify and analyze all distraction activities
that can lead to a crash [
8
,
9
]. For example, in 2019, the road traffic injuries statistics
showed that a total of 36,096 deaths were reported in the US, of which 8.7 percent were
attributed to driver distraction due to phone use, eating, and so on [
10
]. In the EU, the
European Commission reported a decrease in the number of fatal crashes in 2020 compared
to 2019 by up to 17%, a year in which it was estimated that 18,800 people lost their lives
in car crashes [
11
]. Lower traffic due to the pandemic restrictions during the COVID-19
Int. J. Environ. Res. Public Health 2023,20, 4354. https://doi.org/10.3390/ijerph20054354 https://www.mdpi.com/journal/ijerph

Int. J. Environ. Res. Public Health 2023,20, 4354 2 of 30
pandemic had a clear, though unmeasurable, contribution to this. Although the average
number of fatalities has decreased (for example, Romania showed a decrease of 12%),
some countries reported an increase (Switzerland reported an increase of 21%) [
12
], which
indicates that there is still a need for more countermeasures. Romania, on the other hand,
is at the top of the list when it comes to road traffic fatalities, with 85 car crashes per
million inhabitants [
13
]. These crashes are caused by distraction factors, both internal
(e.g., a smartphone) and external (e.g., a roadside advertisement), in addition to situations
in which the driver has consumed alcohol or prohibited substances [10].
Road safety could be improved if it is analyzed from several perspectives. For example,
a bibliometric review covering 10 years of research focused on cyclist safety has proposed
several recommendations that can lead to well-designed and safer bike networks [
14
].
In [
15
], the authors investigated the effect of cardiovascular and respiratory physiological
parameters on driver’s mental workload. The findings are conflicting, with some studies
suggesting that variations in heart rate (HR) and heart-rate variability (HRV) can reflect
changes in mental workload. Due to external influences, respiratory rate (RR) demonstrated
little importance in most studies, and it has not been a popular choice for researching driv-
ing mental workload. The authors conclude that machine learning algorithms combined
with subjective and objective data may yield accurate results in assessing mental effort.
Driver distraction can be defined as “any activity that diverts attention from driving,
including talking or texting on the cell phone, eating and drinking, talking to people
in the vehicle, fiddling with the stereo, entertainment or navigation system” [
16
]. The
most common sources of distractions are mobile phone use, interaction with passengers,
drinking, eating, and controlling in-vehicle devices [
9
]. There are three basic techniques
to determine the distracted state of the driver: studying drivers’ visual scanning patterns,
detecting physiological signals, and evaluating driving performance. Driver distraction
is often studied and analyzed using various equipment, such as driving simulators, eye-
tracking devices, and so on [
17
–
20
]. Most of the studies demonstrated that a driver’s
performance could be influenced when a non-driving secondary task is performed at the
same time while driving (e.g., cell phone use, TWD, etc.). Therefore, many governments,
including those in Europe, the United States, and other countries across the world, have
approved restrictions on cell-phone use while driving [21–23].
According to [
24
], driving performance is defined as “performance of the driving task”,
where the driving task includes “all aspects involved in mastering a vehicle to achieve
a certain goal (e.g., reach a destination), including tracking, regulating, monitoring and
targeting”. The driving task requires a wide range of cognitive and physical abilities, such
as perception, attention, decision-making, and situational awareness [
25
]. Thus, driving
performance is a crucial indicator of a driver’s ability to operate a vehicle safely and ef-
fectively. To comprehensively assess a driver’s capabilities while driving, it is essential to
analyze all relevant driving performance parameters, such as lateral control through the
standard deviation of lateral position [
26
], lateral clearance and time-to-danger [
27
], longi-
tudinal control, reaction time, gap acceptance, eye movement, and workload measures [
28
].
However, drivers might get so distracted by an activity or event that they cannot react
promptly, thus compromising their ability to drive safely. Different types of distractions
can influence driving performance, such as visual (the driver is not looking at the road),
manual (one hand or both hands are off the steering wheel, e.g., text messaging), and
cognitive (the driver is not mentally present while driving, as the attention is focused on
the secondary task, e.g., focus on phone) [
29
]. For example, initiating, writing, and sending
a text message while driving involves visual, manual, and cognitive resources. The main
effects of distracted driving are increased steering-wheel deviations [
30
], higher standard
deviations of lateral lane position [
17
], increased reaction time [
18
,
31
], lower longitudinal
control [32], increased brake time [33], and decreased driving speed [34].
In recent years, several smart devices that are worn or attached to the body have
been developed that have hands-free functions and can stay connected to the network at
any time. Wearables frequently utilize various input modalities (such as touch, speech, or

Int. J. Environ. Res. Public Health 2023,20, 4354 3 of 30
gesture), making their functionalities even more accessible to drivers on the road than a
cell phone. Several studies have concluded that the use of mobile or portable devices while
driving, such as smartwatches, navigation systems, and Google Glass, has been found to
pose a risk to driving safety comparable to conversing on a mobile phone [
35
]. For example,
Glass-delivered messages did not eliminate the distracting cognitive demands, finding
that both Google Glass and writing a message on the phone require the same attention
resources. Moreover, whether it comes from a smartwatch or smartphone, engaging with
notifications carries the risk of taking the attention from the driving task [36].
Many researchers have used driving simulators to collect data that can improve road
safety, identify and analyze driving profiles, and propose recommendations or policies.
Experiments employed in a secure, versatile, and controlled environment have allowed
scholars to study potentially dangerous driving scenarios and infer valuable knowledge.
However, some possible drawbacks should be mentioned, mainly the external validity (the
degree to which a real-world environment can be replicated), the high initial acquisition
cost, and the simulator sickness which may be experienced by novice participants [37,38].
Research driving simulators in the early eighties, such as HYSIM—Highway Driving
Simulator [
39
], consisted mainly of a fixed-based platform and an interactive visual–audio
application. The main improvements that followed were increased graphics quality, ad-
vanced motion representation through Stewart motion platforms (Six Degrees of Freedom,
6DOF), cabin and control equipment, realistic vehicle sounds, and environmental fac-
tors [
40
]. Driving simulators were typically described using a three-level system (low-level,
mid-level, and high-level) but without having a specific classification criterion [
37
]. Other
classifications were proposed by [
41
] (Levels 1, 2, 3, and 4; however, the criteria are not
explicitly defined), [
42
] (their approach included a five-band classification with six main
parameters), and [
37
] (A, B, C, and D levels; the criteria were adapted from Helicopter
Flight Simulation Classification and include four sets of parameters: general, motion sys-
tem, visual system, and sound system). The papers included in this work were classified
according to [37] because of their explicit and well-defined methodology.
High-level driving simulators can offer some advantages, such as increased awareness
of the surrounding environment due to high-resolution and wide field-of-view display
systems [
43
]. Low-level driving simulators also have well-documented benefits, such as
decreased simulator sickness and increased portability and affordability. The work of [
44
]
highlighted the issue of visual fidelity and proposed a methodology to design, calibrate,
and use driving simulators. Moreover, [
45
] showed that visual fidelity significantly impacts
driving performance. Based on the acquired knowledge from the current work, we propose
several recommendations for driving simulators that can ensure high reliability and validity
of the experiments.
This review aims to highlight the impact of using mobile devices to read and write
messages while driving in a simulated environment, with the overarching goal of enhancing
traffic safety through several recommendations and pointing out future research directions.
The paper’s content focuses on four research questions (RQs) that emphasize the general
characteristics that contribute to the need of improving traffic safety:
RQ1: What types of distractions are introduced when using the phone for TWD?
RQ2: What types of hardware devices were used during experiments to analyze the
driver’s performance?
RQ3: What measures were used to predict and analyze distractions?
RQ4: What is the impact of using mobile devices to read and write messages while driving?
The overall structure of the paper is as follows: Section 2describes the research
methodology. Section 3presents the results, with a focus on answering to the RQs men-
tioned above. Section 4presents the main findings, the proposed recommendations for
future research, and the limitations of the work. Finally, Section 5draws the conclusions of
this review of the literature.

Int. J. Environ. Res. Public Health 2023,20, 4354 4 of 30
2. Method
The review was conducted by following the Preferred Reporting Items for Systematic
Reviews and Meta-Analyses extension for Scoping Review (PRISMA-ScR). Scoping reviews
aim to determine the scope or coverage of a body of the literature on a given topic [
46
] and
identify key concepts and types and sources of evidence to inform practice, policymaking,
and research [47]. For this review, we followed the checklist given in [48].
2.1. Protocol
The manuscript was not previously recorded on PROSPERO or published before, even
if the protocol was written before the work began.
2.2. Eligibility Criteria and Study Selection
The studies that met the following criteria were included in the review: full-text,
original research in a peer-reviewed journal, published in the English language, and
included driving simulators. There was no restriction on the publication year.
Studies were excluded from the review according to the following criteria: commentary
manuscripts; reviews of the literature; editorials; short papers; magazines; dissertations;
book chapters; conference papers; non-academic publications; papers that are not available
in full text; and studies irrelevant to the research, i.e., that did not investigate the relationship
between distracted drivers, mobile phone, use and driving simulators.
We preferred to include only journal articles in our review to maintain high scien-
tific relevance, as they are subject to rigorous review, unlike other types of publications,
including conference articles.
2.3. Information Sources
The following databases were searched in three phases (on 08 January 2021,
10 May 2021
,
and 14 November 2022): ISI Web of Knowledge, Scopus, Science Direct, SAGE Journals,
and ProQuest.
2.4. Search
The review of the literature was conducted with a combination of keywords: “dis-
traction”, “phone”, and “driving simulator”. Additional terms were identified during
the first investigation and were used in combination in the search process: “distracted”,
“disruptive”, “smartphone”, “mobile phone”, “cell phone”, and “simulation”. Example of
search strategy for Scopus database:
ALL ((“distracted” OR “disruptive” OR “disturbing” OR “distraction”) AND (“driv-
ing” OR “driver” OR “driver behaviour”) AND (“car” OR “vehicle” OR “automobile” OR
“truck”) AND (“simulator” OR “simulation” OR “virtual environment” OR “simulated
environment”)) AND (LIMIT-TO (DOCTYPE, “ar”)).
As can be seen, no limit was imposed for the year of publication.
2.5. Study Selection
The five abovementioned electronic databases were searched, and the title, abstracts,
and other details were downloaded to EndNote (version X9, Clarivate, Philadelphia, PA,
USA) for screening. In the first phase, they were screened only by the title and abstract,
and after removing the irrelevant articles, the full-text documents of the remaining ones
were uploaded in EndNote for the second screening phase. Screening and selection were
performed independently by two of the authors (RGB and GDV) and were validated by the
third author (CA). Disagreements were resolved through consensus.
The search strategy is shown in Figure 1. Through this selection procedure, 7151 pa-
pers were obtained. After removing the duplicated ones, this number was reduced to
5904 papers. Titles and abstracts were analyzed, and articles were included in the review
if they were related to studies that investigated the use of mobile phones while driving
in a simulator. A total of 542 articles were found, but 475 of them were excluded due

Int. J. Environ. Res. Public Health 2023,20, 4354 5 of 30
to the following reasons: some of them were conference articles, some did not use a car
simulator, others were not available for download or were review articles, some assessed
pedestrian distraction or the car’s navigation system, others did not use the telephone as
a distraction factor, 1 was scholarly paper, 1 used listening audiobooks as a distraction
factor, 1 was about e-hailing, and 2 were duplicated. In addition, this paper is intended to
be a second part of the work [
3
], in which the distraction caused by talking on the phone
was taken into account. In this regard, the papers focused on talking on the phone were
excluded. However, the articles that dealt with the evaluation of both activities—talking
and texting—were not removed. Finally, 67 articles were selected for data extraction in this
systematic review of the literature.
Int. J. Environ. Res. Public Health 2023, 20, x FOR PEER REVIEW
5 of 29
USA) for screening. In the first phase, they were screened only by the title and abstract,
and after removing the irrelevant articles, the full-text documents of the remaining ones
were uploaded in EndNote for the second screening phase. Screening and selection were
performed independently by two of the authors (RGB and GDV) and were validated by
the third author (CA). Disagreements were resolved through consensus.
The search strategy is shown in Figure 1. Through this selection procedure, 7151 pa-
pers were obtained. After removing the duplicated ones, this number was reduced to 5904
papers. Titles and abstracts were analyzed, and articles were included in the review if they
were related to studies that investigated the use of mobile phones while driving in a sim-
ulator. A total of 542 articles were found, but 475 of them were excluded due to the fol-
lowing reasons: some of them were conference articles, some did not use a car simulator,
others were not available for download or were review articles, some assessed pedestrian
distraction or the car’s navigation system, others did not use the telephone as a distraction
factor, 1 was scholarly paper, 1 used listening audiobooks as a distraction factor, 1 was
about e-hailing, and 2 were duplicated. In addition, this paper is intended to be a second
part of the work [3], in which the distraction caused by talking on the phone was taken
into account. In this regard, the papers focused on talking on the phone were excluded.
However, the articles that dealt with the evaluation of both activities—talking and tex-
ting—were not removed. Finally, 67 articles were selected for data extraction in this sys-
tematic review of the literature.
Figure 1. Study identification and selection based on the PRISMA-ScR flow diagram.
Figure 1. Study identification and selection based on the PRISMA-ScR flow diagram.
2.6. Data Extraction
As previously mentioned, the data extraction was performed by two authors (RGB
and GDV) and was then validated by a third author (CA). A Microsoft Excel spreadsheet
was created to centralize the following information: first author, year of publication,
journal name, region (the country where the experiment took place), institution where
the research was conducted, sample size, age, gender, and driving experience, type of
simulator, driving scenario, tracking device, type of distraction factors, distraction task,
type of evaluated measures, effect on a performance measure, independent variables, and
statistical analysis technique.
Each reference was read in its entirety by the designated author, and the extracted data
were added to the table. The location was based on the country from where the participants

Int. J. Environ. Res. Public Health 2023,20, 4354 6 of 30
were recruited. If the user study involved samples from different countries, we considered
the institution’s location that managed the experiment.
The extracted information was classified into 4 categories related to the characteristics
of the studies and the four research questions: “What types of distractions are introduced
when using the phone for TWD?”, “What types of hardware devices were used during
experiments to analyze the driver’s performance?”, ”What measures were used to predict
and analyze distraction?”, and “What is the impact of using mobile devices to read and
write messages while driving?”.
2.7. Synthesis of the Results
The results of the literature review are given in the following section, with each
subsection corresponding to an objective or a research question proposed in this study.
3. Results
3.1. Characteristics of Studies
The main characteristics of the papers, such as publication date and demographic data,
are briefly presented in Appendix ATable 1. The 67 studies selected for the review cover
a range of 21 years (2002–2022). The number of published papers varies, from 1 paper
in 2002 and 2003 to 10 papers in 2021. The highest number of articles were published
in 2021. The studies included in the review were published in the following journals:
Transportation Research Part F: Traffic Psychology and Behaviour (n= 13); Accidents Analysis
and Prevention (
n= 12
); Applied Ergonomics (n= 4); Transportation Research Record (n= 4);
Human Factors (
n= 3
); Traffic Injury Prevention (n= 3); and several other journals, such
as Safety Science,IEEE Access,Journal of Safety Research, and Transportation Research Part
C: Emerging Technologies.
Most of the studies were developed in North America (n= 22), and more particularly
in the USA (n= 18) (Figure 2). The other studies were conducted in Europe (n= 19), Asia
(
n= 17
), and Oceania (n= 9). In Europe, most publications are from Greece (n= 4), Germany
(n= 3), and The Netherlands (n= 3). In Asia, most of the publications are from China (
n= 7
)
and India (n= 5), and from Oceania, most studies were developed in Australia (n= 8).
Int. J. Environ. Res. Public Health 2023, 20, x FOR PEER REVIEW
7 of 29
Figure 2. Distribution of papers by country/region.
American, Indian, and Australian research institutions dominate the total number of
articles focused on assessing the impact of phone use while driving in virtual environ-
ments (Figure 3). Most studies were developed at the Indian Institute of Technology (IIT)
Bombay (n = 5), followed by the University of Alabama at Birmingham (n = 4), Monash
University (n = 3), and Queensland University of Technology (n = 3).
Figure 3. Distribution of papers by research institution.
The analysis of co-occurrence terms was performed using VOS Viewer software ver-
sion 1.6.18 in order to identify the most frequently used terms and the relationship be-
tween them. The minimum number of occurrences of a keyword was selected to be 10,
resulting in 35 terms that meet the threshold of the total of 716 keywords. The result of the
co-occurrence analysis is presented in Figure 4. As can be observed, the most frequently
used keyword was “human”, with 31 occurrences, followed by “automobile drivers”, “car
driving”, “driving simulator”, and “mobile phone”. The co-occurrence network map gen-
erated by VOS Viewer suggested the division be into three clusters differentiated by col-
ors.
Figure 2. Distribution of papers by country/region.
American, Indian, and Australian research institutions dominate the total number of
articles focused on assessing the impact of phone use while driving in virtual environments
(Figure 3). Most studies were developed at the Indian Institute of Technology (IIT) Bombay

Int. J. Environ. Res. Public Health 2023,20, 4354 7 of 30
(n= 5), followed by the University of Alabama at Birmingham (n= 4), Monash University
(n= 3), and Queensland University of Technology (n= 3).
Int. J. Environ. Res. Public Health 2023, 20, x FOR PEER REVIEW
7 of 29
Figure 2. Distribution of papers by country/region.
American, Indian, and Australian research institutions dominate the total number of
articles focused on assessing the impact of phone use while driving in virtual environ-
ments (Figure 3). Most studies were developed at the Indian Institute of Technology (IIT)
Bombay (n = 5), followed by the University of Alabama at Birmingham (n = 4), Monash
University (n = 3), and Queensland University of Technology (n = 3).
Figure 3. Distribution of papers by research institution.
The analysis of co-occurrence terms was performed using VOS Viewer software ver-
sion 1.6.18 in order to identify the most frequently used terms and the relationship be-
tween them. The minimum number of occurrences of a keyword was selected to be 10,
resulting in 35 terms that meet the threshold of the total of 716 keywords. The result of the
co-occurrence analysis is presented in Figure 4. As can be observed, the most frequently
used keyword was “human”, with 31 occurrences, followed by “automobile drivers”, “car
driving”, “driving simulator”, and “mobile phone”. The co-occurrence network map gen-
erated by VOS Viewer suggested the division be into three clusters differentiated by col-
ors.
Figure 3. Distribution of papers by research institution.
The analysis of co-occurrence terms was performed using VOS Viewer software
version 1.6.18 in order to identify the most frequently used terms and the relationship
between them. The minimum number of occurrences of a keyword was selected to be 10,
resulting in 35 terms that meet the threshold of the total of 716 keywords. The result of the
co-occurrence analysis is presented in Figure 4. As can be observed, the most frequently
used keyword was “human”, with 31 occurrences, followed by “automobile drivers”,
“car driving”, “driving simulator”, and “mobile phone”. The co-occurrence network map
generated by VOS Viewer suggested the division be into three clusters differentiated
by colors.
Int. J. Environ. Res. Public Health 2023, 20, x FOR PEER REVIEW
8 of 29
Figure 4. Network diagram of the most frequently used terms.
In order to infer connections between the authors and their research topics, the co-
citation network was also examined using VOS Viewer. This network entails recognizing
pairs of authors who were referenced together in the same publications. Figure 5 shows
the results in which the minimum number of citations of an author was set to 20. A num-
ber of 39 authors meet the threshold, and four clusters are distinguished.
Figure 5. Author co-citations network.
Figure 4. Network diagram of the most frequently used terms.

Int. J. Environ. Res. Public Health 2023,20, 4354 8 of 30
In order to infer connections between the authors and their research topics, the co-
citation network was also examined using VOS Viewer. This network entails recognizing
pairs of authors who were referenced together in the same publications. Figure 5shows the
results in which the minimum number of citations of an author was set to 20. A number of
39 authors meet the threshold, and four clusters are distinguished.
Int. J. Environ. Res. Public Health 2023, 20, x FOR PEER REVIEW
8 of 29
Figure 4. Network diagram of the most frequently used terms.
In order to infer connections between the authors and their research topics, the co-
citation network was also examined using VOS Viewer. This network entails recognizing
pairs of authors who were referenced together in the same publications. Figure 5 shows
the results in which the minimum number of citations of an author was set to 20. A num-
ber of 39 authors meet the threshold, and four clusters are distinguished.
Figure 5. Author co-citations network.
Figure 5. Author co-citations network.
The selected studies included a sample of 3033 participants (n=1984 male; n= 1049 female)
who participated in simulated driving experiments. The minimum number was 14 [
49
],
and the maximum was 134 [
50
] participants per study. The gender distribution was not
mentioned in two of the extracted studies.
The age of the participants is between 16 and 79 years old; however, in 17 studies, the
age interval is not reported. However, the mean age is reported in more studies (
n= 59
),
and the unweighted mean age is 39.6 years across all of these studies. Moreover, the
standard deviation is mentioned in 52 studies and is 4.98 across all studies. Only two
articles do not mention the age range, the mean age, and the standard deviation.
All participants were assumed to be clinically healthy, except for the participants in
one study focusing on teens with and without ADHD [51].
3.2. RQ1: What Types of Distractions Are Introduced When Using the Phone for TWD
To find out what sources of distraction were used in the studies, we extracted the
information on the type of distraction and divided the distractions into the following
categories according to [
52
,
53
]: visual (V), auditory (Au), manual (M) (physical), and
cognitive (C) distraction. The results are presented in Figure 6, as well as in Appendix A
Table 1for each individual study. As can be seen, most articles (34% of the total number
of papers, n= 23) considered both manual and visual components when assessing the
effects of performing secondary tasks while driving. Each secondary task contains one
or more components. Examples of visual distractions include interaction with in-vehicle
devices [
54
], the use of smartphone applications while driving [
55
], looking around, and so

Int. J. Environ. Res. Public Health 2023,20, 4354 9 of 30
on. Auditory distractions emerge when drivers focus on other sounds, such as the ringing
of the phone, voice conversations, the radio, etc. Manual distractions involve eating [
56
],
drinking [
29
] while driving, or doing anything other than manipulating the steering wheel.
Finally, cognitive distractions occur when the driver has his/her mind in another place
and fails to see what is important on the road. Studies showed that TWD could introduce
all of these types of distractions, and even for short durations, they might lead to driving
errors and even crashes [
57
]. Furthermore, most activities unrelated to the driving task
combine these four modes [
58
]. For instance, the most common compound distraction
is a visual–manual distraction, defined as a secondary activity that involves using hand
gestures to manipulate a visual interface [59].
Int. J. Environ. Res. Public Health 2023, 20, x FOR PEER REVIEW
9 of 29
The selected studies included a sample of 3033 participants (n =1984 male; n = 1049
female) who participated in simulated driving experiments. The minimum number was
14 [49], and the maximum was 134 [50] participants per study. The gender distribution
was not mentioned in two of the extracted studies.
The age of the participants is between 16 and 79 years old; however, in 17 studies,
the age interval is not reported. However, the mean age is reported in more studies (n =
59), and the unweighted mean age is 39.6 years across all of these studies. Moreover, the
standard deviation is mentioned in 52 studies and is 4.98 across all studies. Only two ar-
ticles do not mention the age range, the mean age, and the standard deviation.
All participants were assumed to be clinically healthy, except for the participants in
one study focusing on teens with and without ADHD [51].
3.2. RQ1: What Types of Distractions Are Introduced When Using the Phone for TWD
To find out what sources of distraction were used in the studies, we extracted the
information on the type of distraction and divided the distractions into the following cat-
egories according to [52,53]: visual (V), auditory (Au), manual (M) (physical), and cogni-
tive (C) distraction. The results are presented in Figure 6, as well as in Appendix Table A1
for each individual study. As can be seen, most articles (34% of the total number of papers,
n = 23) considered both manual and visual components when assessing the effects of per-
forming secondary tasks while driving. Each secondary task contains one or more com-
ponents. Examples of visual distractions include interaction with in-vehicle devices [54],
the use of smartphone applications while driving [55], looking around, and so on. Audi-
tory distractions emerge when drivers focus on other sounds, such as the ringing of the
phone, voice conversations, the radio, etc. Manual distractions involve eating [56], drink-
ing [29] while driving, or doing anything other than manipulating the steering wheel. Fi-
nally, cognitive distractions occur when the driver has his/her mind in another place and
fails to see what is important on the road. Studies showed that TWD could introduce all
of these types of distractions, and even for short durations, they might lead to driving
errors and even crashes [57]. Furthermore, most activities unrelated to the driving task
combine these four modes [58]. For instance, the most common compound distraction is
a visual–manual distraction, defined as a secondary activity that involves using hand ges-
tures to manipulate a visual interface [59].
Figure 6. Distribution of papers by the source of distraction type (V—visual; Au—auditory; M—
manual (physical); and C—cognitive).
While some articles focused on the visual component [55,60,61], others considered
two, three, or even four types of distractions. For instance, both cognitive and visual com-
ponents were highlighted in [29,62,63]; cognitive and manual components were presented
Figure 6.
Distribution of papers by the source of distraction type (V—visual; Au—auditory;
M—manual (physical); and C—cognitive).
While some articles focused on the visual component [
55
,
60
,
61
], others considered two,
three, or even four types of distractions. For instance, both cognitive and visual components
were highlighted in [
29
,
62
,
63
]; cognitive and manual components were presented in [
64
,
65
];
and visual–manual distraction was evaluated in [
35
,
66
,
67
]. As we have seen, only one
article considered all four components of distraction: [
68
]. In this paper, visual–manual and
auditory–vocal interfaces were evaluated, but also the subjective workload was considered
as a measure of cognitive distraction.
Some studies investigated the effects of cell phone use in comparison with other
secondary tasks, such as talking to a passenger (two studies: [
49
,
69
]), eating (four stud-
ies: [
56
,
57
,
70
,
71
]), radio tuning (five studies: [
67
,
69
,
72
–
74
]), using navigation systems (three
studies: [
33
,
58
,
74
]), taking pictures [
75
] or selfies [
76
], adjusting climate control [
72
], reading
emails (three studies: [
55
,
63
,
77
]), drinking [
29
], watching video and using social media [
63
],
switching display view and searching songs [
55
], and sharing numbers [
76
]. Other studies
compare phone use with other types of devices, such as the smartwatch (three stud-
ies: [
36
,
68
,
78
]) and Google Glass (two studies: [
54
,
79
]). Moreover, instead of using the
phone for texting, some researchers used smartphones to perform tasks on social media,
such as using Facebook (three studies: [
20
,
80
,
81
]), Snapchat, Instagram [
82
], Whatsapp [
83
],
or some self-developed applications [
60
,
84
]. In one study, the use of mobile phones while
driving was evaluated in parallel with drunk driving: [85].
The distraction tasks were divided into two categories: handheld (HH)—holding the
device in hand; or hands-free (HF)—performing the task without using hands to hold the
device. In 86% of the studies (n= 51), the task was performed using HH devices. In 5 studies,
both HH and HF devices were used, and in 11 studies, the HF devices were preferred.

Int. J. Environ. Res. Public Health 2023,20, 4354 10 of 30
3.3. RQ2: What Types of Hardware Devices Were Used during Experiments to Analyze the
Driver’s Performance?
3.3.1. Driving Simulator Equipment
Regarding the simulators used in the analyzed studies, 84% of experiments (
n= 56 studies
)
were conducted in fixed-based simulators. The other experiments were carried out in
driving simulators equipped with motion systems having from 2 to 6 degrees of freedom
(DOFs). Each study was classified according to the work of [
40
], which proposed a classifi-
cation method for driving simulators that was adapted from flight-simulator classification
standards (see Appendix ATable 1). The proposed classes were defined by taking into
consideration four sets of criteria: general information, such as environmental modeling
and the hardware complexity of the replicated vehicle; the presence of a motion system
and the number of degrees of freedom; visual capabilities, especially the field of view; and
the sound system which is essential for driver immersion. Class A simulators are at the
bottom of the list with no requirement for the motion platform, basic cabin equipment,
and basic visual and sound capabilities. Custom-made driving simulators in class A in-
clude a desktop computer, steering wheel, gas pedal, and brake pedal, as in the following
works: [
61
,
67
,
86
,
87
]. On the other end, class D simulators require a motion platform with a
minimum of six DOFs, at least 180 degrees field of view, and a realistic visual and acoustic
environment. Class B simulators were the most popular, as they were used in 36 studies,
followed by class A, with 21 studies; class C, with 4 studies; and last but not least, class
D simulators, with 6 studies.
The following class C and D simulators were identified: CARRS-Q Advanced Driv-
ing Simulator [
76
,
88
,
89
], the moving-base driving simulator from Würzburg Institute for
Traffic Sciences [
63
], DS-600c Advanced Research Simulator developed by DriveSafety
(3 studies: [
20
,
73
,
82
]), Ford’s VIRtual Test Track EXperiment [
72
], and VS500M driving
simulator [
30
]. One experiment was performed in a driving simulator with three DOFs: [
90
],
and three experiments were performed in two-DOF driving simulators: [
20
,
55
,
82
]. We also
extracted some commercially available class A and B driving simulators: Foerst Driving
Simulator (three studies: [
81
,
91
,
92
]), PatrolSim high-fidelity driving simulator [
66
], NADS
MiniSim [
36
], and EF-X from ECA-Faros (two studies: [
31
,
80
]). Most systems are developed
by Systems Technology Inc., Hawthorne, CA, USA, both hardware and software (used in
10 of the included articles).
The type of display varies among the studies between screen-based projection systems
and systems containing monitors. Thirty-nine studies used monitors, ranging from a single
monitor to a system of five monitors, and twenty-seven studies in which the display system
was based on projectors. The number of screens on which the images were projected ranged
from 1 to 7. One paper did not clearly report the information related to the display. The
visual field of view (FOV) varied between 40
◦
and 300
◦
for horizontal view and between
24
◦
and 60
◦
for vertical view. However, this information is not reported in a large number
of articles (over 16). The most advanced display is installed on the DS-600c advanced
simulator, which is composed of seven high-definition projectors that provide 300 FOV to
drivers [
82
]. In terms of vertical FOV, the highest value is found in [
93
] due to the use of
large screens surrounding the simulator.
The simulated scenarios contain various types of roads (urban, rural, highway, single
lane, and multilane), with lengths varying from 1 to 38.6 km. The lengths were reported by
the authors in either kilometers, meters, miles, or feet but were transformed into kilometers
in this paper. The longest route is presented in [
94
], having 24 miles (equivalent to 38.6 km).
As for the duration of the experiments, it varies from 2 min [
33
] to 120 min [
63
,
95
]. In this
case, only 40 of the articles reported the duration of the experiment.
Fourteen studies reported that the simulator uses an automatic transmission, seven
studies stated that a manual transmission was used in the experiments, and the rest of the
papers did not explicitly state this information.
The impact of the secondary task was assessed in various driving scenarios. Of these,
two types were identified as the majority: 19% of studies (n= 13) used a car-following

Int. J. Environ. Res. Public Health 2023,20, 4354 11 of 30
scenario, which requires following a lead vehicle and responding to its behavior [
96
] and
which is the most common routine driving situation [
97
]. In 50 studies (75% of the total
number of articles), the first task was to free drive on a route or to follow a path along
which one or more incidents occurred. Examples of such incidents include the sudden
appearance of an animal on the roadway [
29
,
81
], the sudden appearance of a pedestrian
crossing the street [
18
–
20
,
51
,
60
,
65
,
76
,
90
], a cyclist entering the roadway [
36
,
51
,
65
], a parked
car pulls out onto the road [18,90], and so on.
Apart from car-following and free-driving scenarios, the other articles contain the
following scenarios: a crossing road [
88
], rail level crossing [
31
], steering along the lane’s
center [87], and lane changing [98].
3.3.2. Driver-Tracking Equipment
The information about the driver’s performance was collected through the hardware
and software systems of the simulator, but in 33% of the total number of studies, addi-
tional driver-tracking devices were used. Thus, in twenty articles, a device for tracking
the driver’s gaze was used; in one article, brain–computer interface (BCI) systems were
used; and in one article, the whole body of the user was tracked. For eye-tracking, some re-
searchers used simple video cameras and extracted the information by manual coding of the
recorded video: [
54
,
58
,
60
,
68
,
93
,
99
]. Others used specialized eye-tracking devices: Fovio eye
tracker [
20
]; Ergoneers’ Dikablis Essential head-mounted eye tracker [
36
,
55
]; eye-tracking
system developed by Seeing Machines, Ltd. (Canberra, Australia): faceLAB
™
4.1 [
90
];
faceLAB
™
5.0 [
31
]; Pupil Lab’s Pro head-mounted eye tracker [
100
]; SmartEye6.0 [
69
];
eye-tracking glasses developed by SensoMotoric Instruments, Berlin, Germany [
74
,
78
,
101
];
Tobii Pro Glasses 2 [
80
,
84
], Ergoneers Dikablis Eye Tracker 3 glasses [
102
]; and one paper
did not mention the device. A MindCap XL headband equipped with a NeuroSky sensor
was used to measure brain activity [
59
]. In [
33
], a high-speed infrared camera Motion
Analysis Corp., Santa Rosa, CA, USA, was used to track the full body of the participants.
Four papers considered the physiological data taken from the participants during
the experiment. In these studies, heart rate and skin conductance were measured using
devices such as the MEDAC System/3 instrumentation unit by NeuroDyne Medical Corpo-
ration [
54
,
68
] and Biopac BioNomadix3 MP150WSW system [
60
], and heart rate plus other
cardiovascular reactivity indicators (root mean square of successive differences, systolic
blood pressure, diastolic blood pressure, and mean arterial pressure) were measured in [
65
].
3.4. RQ3: What Measures Were Used to Analyze and Predict Distraction?
The selected studies include several measures to assess driving distractions. Most of
them are driving-simulator-dependent variables used to assess the driver’s performance
under the influence of distractions. Choosing such measures is an appropriate approach in
the context of car simulators, as no additional sensors are needed. We grouped driving-
performance measures into seven categories, starting from the classifications found in [
103
]
and [
104
] and adding a new category regarding variables that are not necessarily related
to vehicle-performance parameters: traffic violations (TrVs), driving maintenance (DM),
attention lapses (ALs), response time (RT), hazard anticipation (HA), accident probability
(AP), other measures (OMs). The distribution of papers according to these categories is
presented in Figure 7. In some studies, variables belonging to only one category are used,
while in others, they are part of two, three, or even all four categories. Most articles used
measures from the DM category (49 studies), followed by RT (22 studies), OMs (21 studies),
TrVs (12 studies), AP (4 studies), ALs (2 studies), and HA (1 study).
In the DM category, the following measures were included: lane-keeping measured
by the standard deviation of lateral position (SDLP) [
35
,
60
]; speed variables, such as mean
speed [
19
,
34
,
105
] and standard deviation (SD) of speed [
34
]; steering control, including
steering angle [
106
,
107
] and SD of steering angle [
17
]; time to collision [
64
]; and headway
measured in space–distance headway [88] or in time–time headway [108].

Int. J. Environ. Res. Public Health 2023,20, 4354 12 of 30
Int. J. Environ. Res. Public Health 2023, 20, x FOR PEER REVIEW
12 of 29
Four papers considered the physiological data taken from the participants during the
experiment. In these studies, heart rate and skin conductance were measured using de-
vices such as the MEDAC System/3 instrumentation unit by NeuroDyne Medical Corpo-
ration [54,68] and Biopac BioNomadix3 MP150WSW system [60], and heart rate plus other
cardiovascular reactivity indicators (root mean square of successive differences, systolic
blood pressure, diastolic blood pressure, and mean arterial pressure) were measured in
[65].
3.4. RQ3: What Measures Were Used to Analyze and Predict Distraction?
The selected studies include several measures to assess driving distractions. Most of
them are driving-simulator-dependent variables used to assess the driver’s performance
under the influence of distractions. Choosing such measures is an appropriate approach
in the context of car simulators, as no additional sensors are needed. We grouped driving-
performance measures into seven categories, starting from the classifications found in
[103] and [104] and adding a new category regarding variables that are not necessarily
related to vehicle-performance parameters: traffic violations (TrVs), driving maintenance
(DM), attention lapses (ALs), response time (RT), hazard anticipation (HA), accident prob-
ability (AP), other measures (OMs). The distribution of papers according to these catego-
ries is presented in Figure 7. In some studies, variables belonging to only one category are
used, while in others, they are part of two, three, or even all four categories. Most articles
used measures from the DM category (49 studies), followed by RT (22 studies), OMs (21
studies), TrVs (12 studies), AP (4 studies), ALs (2 studies), and HA (1 study).
Figure 7. Distribution of papers according to driving performance measure categories (TrVs—traffic
violations; DM—driving maintenance; ALs—attention lapses; RT—response time; HA—hazard an-
ticipation; AP—accident probability; and OMs—other measures).
In the DM category, the following measures were included: lane-keeping measured
by the standard deviation of lateral position (SDLP) [35,60]; speed variables, such as mean
speed [19,34,105] and standard deviation (SD) of speed [34]; steering control, including
steering angle [106,107] and SD of steering angle [17]; time to collision [64]; and headway
measured in space–distance headway [88] or in time–time headway [108].
RT includes brake reaction time [20,109] and other time variables in response to a
pop-up event [18]. In the TrVs category, variables such as speed violation [72] and the
number of collisions [77] were considered. ALs include results related to cognitively de-
manding and texting compared to four different blood-alcohol-concentration (BAC) lev-
els: 0.00, 0.04, 0.07, and 0.10 [85]. OMs consist of other variables that cannot be included
in the categories presented above: task completion time [67,68]; workload [87]; or
Figure 7.
Distribution of papers according to driving performance measure categories (TrVs—traffic
violations; DM—driving maintenance; ALs—attention lapses; RT—response time; HA—hazard
anticipation; AP—accident probability; and OMs—other measures).
RT includes brake reaction time [
20
,
109
] and other time variables in response to a
pop-up event [
18
]. In the TrVs category, variables such as speed violation [
72
] and the
number of collisions [
77
] were considered. ALs include results related to cognitively
demanding and texting compared to four different blood-alcohol-concentration (BAC)
levels: 0.00, 0.04, 0.07, and 0.10 [
85
]. OMs consist of other variables that cannot be included
in the categories presented above: task completion time [
67
,
68
]; workload [
87
]; or variables
related to eye tracking, such as the number of glances [
78
,
84
], off-road glances [
54
,
69
], and
saccade amplitude [
102
]. The most common measures that were examined in the analyzed
studies are presented in Figure 8.
Int. J. Environ. Res. Public Health 2023, 20, x FOR PEER REVIEW
13 of 29
variables related to eye tracking, such as the number of glances [78,84], off-road glances
[54,69], and saccade amplitude [102]. The most common measures that were examined in
the analyzed studies are presented in Figure 8.
Figure 8. Main measures used in the experiments of the examined studies (SD—standard devia-
tion).
In addition to measures related to the driving performance or other types of out-
comes measured using sensors or self-reported, some of the studies also took into account
additional parameters or independent parameters, such as the age of participants (A),
driving experience (E), gender of participants (G), weather (W), road configuration (RC),
and traffic flow (T). There are 18 articles that analyzed these additional parameters. In
most studies, age was considered to be an independent parameter (11 studies), followed
by gender (3 studies), driving experience (3 studies), traffic flow (2 studies), road config-
uration (2 studies), and weather (2 studies). There are studies that consider two or more
parameters: A and E [32,100], A and G [19,86], RC and T [18], and RC and W [91].
Related to the statistical analysis of data, the most used technique was the analysis of
variance (ANOVA), being applied in 33 of the selected studies. Other statistical methods
used in the works were multivariate analysis of variance (MANOVA; 1 study), Wilcoxon
signed rank test (10 studies), Wald test (6 studies), t-test (8 studies), regression analysis (3
studies), logistic regression analysis (1 study), linear mixed models (2 studies), and gen-
eralized linear model (2 studies).
3.5. RQ4: What Is the Impact of Using Mobile Devices to Read and Write Messages
While Driving?
The selected studies were found to vary in several aspects: the proposed objective,
the number of participants in the experiments, the infrastructure used to pursue the pro-
posed objective, the outcomes, and so on. However, there is an agreement between the
main outcomes of these studies. That is that text messaging, which mostly involves visual
and manual distraction, has a significantly larger influence on driving performance [66]
than a phone conversation. The main effects of this secondary task are increased variabil-
ity in lane position and missed lane changes [90], increased brake reaction time [82],
greater speed variability [110], increased steering variation per second [30], and higher
completion times [88], as well as a higher risk of accidents than other in-vehicle tasks, such
Figure 8.
Main measures used in the experiments of the examined studies (SD—standard deviation).
In addition to measures related to the driving performance or other types of out-
comes measured using sensors or self-reported, some of the studies also took into account
additional parameters or independent parameters, such as the age of participants (A),

Int. J. Environ. Res. Public Health 2023,20, 4354 13 of 30
driving experience (E), gender of participants (G), weather (W), road configuration (RC),
and traffic flow (T). There are 18 articles that analyzed these additional parameters. In
most studies, age was considered to be an independent parameter (11 studies), followed by
gender (3 studies), driving experience (3 studies), traffic flow (2 studies), road configuration
(2 studies), and weather (2 studies). There are studies that consider two or more parameters:
A and E [32,100], A and G [19,86], RC and T [18], and RC and W [91].
Related to the statistical analysis of data, the most used technique was the analysis of
variance (ANOVA), being applied in 33 of the selected studies. Other statistical methods
used in the works were multivariate analysis of variance (MANOVA; 1 study), Wilcoxon
signed rank test (10 studies), Wald test (6 studies), t-test (8 studies), regression analysis
(3 studies), logistic regression analysis (1 study), linear mixed models (2 studies), and
generalized linear model (2 studies).
3.5. RQ4: What Is the Impact of Using Mobile Devices to Read and Write Messages While Driving?
The selected studies were found to vary in several aspects: the proposed objective, the
number of participants in the experiments, the infrastructure used to pursue the proposed
objective, the outcomes, and so on. However, there is an agreement between the main
outcomes of these studies. That is that text messaging, which mostly involves visual and
manual distraction, has a significantly larger influence on driving performance [
66
] than
a phone conversation. The main effects of this secondary task are increased variability
in lane position and missed lane changes [
90
], increased brake reaction time [
82
], greater
speed variability [
110
], increased steering variation per second [
30
], and higher completion
times [
88
], as well as a higher risk of accidents than other in-vehicle tasks, such as tuning
the car radio [
67
]. Even though drivers are aware that it is dangerous [
98
] and illegal in
many countries to use a mobile phone while driving, they cannot resist the temptation
to read and reply to messages, especially in the case of younger drivers [
64
]. Sending or
reading a text from a smartphone takes the driver’s eyes off the road for 5 s, and, at a
speed of 55 mph, that is similar to driving the length of an entire football field with the
eyes closed [111].
Another secondary activity that has a negative impact on the driver’s performance is
using social media [
63
]. However, this was not found to be as detrimental as texting [
20
]
since image-based interfaces may provide a safer way to stay connected while driving than
text-based interfaces [
82
]. Moreover, the side effects of using social media can be prevented
with the help of advanced driver-assistance systems (ADASs) [80].
Visual–manual distractions negatively influence lateral lane position variability [
112
]
and the average speed [
57
] by taking the driver’s eyes off the road [
58
] and increasing the
mental workload [
78
]. Auditory distraction has been studied less, but it also seems to affect
drivers’ performance by negatively affecting situation awareness and mean speed [
113
].
However, driving performance is less affected when travel information is presented in
auditory mode [
93
]. A proper user interface (UI) design of smartphone applications could
reduce the visual and cognitive demands of the driver when engaged in secondary activities.
However, there is plenty of room for improvement of UIs in the automotive context. One
design feature that could alleviate the drivers’ visual–manual demands is the integration of
speech-to-text technology in either mobile phones or in-vehicle systems [55].
Using a mobile phone while driving can lead to compensatory measures to mitigate
the effect of the distraction. Drivers could increase their vigilance [
106
], adopt a reduced
speed [
19
,
67
], increase their distance from the leading vehicle [
114
], and self-regulate the
secondary task [
112
]. It is worth noting that the driving task also negatively influences the
texting task by inducing accuracy errors [115] and an increased response [116].
Regarding the independent variables, some findings can be extracted from the ana-
lyzed studies. The driver ’s age can be used to predict driving performance significantly
when it is correlated with the driving experience. To illustrate this aspect, [
72
] found that
teens are not responsible enough while driving, as they have inadequate vehicle-control
abilities and are more likely to be distracted from HH phone tasks compared to older drivers.

Int. J. Environ. Res. Public Health 2023,20, 4354 14 of 30
However, young people have lower longitudinal control during distracted driving [
32
] and
are more likely to accept a gap in intersections [
88
]. The age may be counterbalanced by
driving experience, but in the case of TWD, it does not have any influence. In terms of
gender, it was found that male drivers drove at higher speeds [
19
], while female drivers
performed a higher number of lane excursions and had a higher reaction time compared to
male drivers [
17
,
18
,
75
]. Moreover, male drivers tend to be more positive toward on-board
traffic messages and in-vehicle systems [86].
Regarding the road configuration variable, it was observed that road geometry (es-
pecially curved road and vertical alignments) has a more significant influence on speed
and lateral position than mobile-phone distraction [
89
]. Furthermore, it was found that text
messaging could lead to behaviors that can obstruct traffic flow [94].
Another relevant outcome is that weather does not seem to influence the mean speed,
but it can negatively affect the mean reaction time [91].
Some secondary tasks, such as eating and drinking while driving, have fewer distract-
ing effects on the driver’s performance than phone texting [
29
,
56
]. In addition, operating
a music player was found to be less risky than texting, which was reported to be an ex-
tremely risky task [
71
]. Studies that analyzed drivers’ physiological data showed that TWD
increases cardiovascular reactivity [
65
] and skin conductance [
68
] compared to driving
with no secondary tasks.
Several studies that explore the impact of texting on driving behavior have shown
that engagement in secondary tasks directly influences safe driving performance [
33
]. For
instance, regardless of the device, whether it is a mobile phone or a smartwatch, if the
driver’s gaze is not on the road scene and all attention is on the device and its contents,
then the driving performance is affected [
68
,
78
], and this, in turn, increases the risk of a
crash [
36
]. The probability of a crash increases up to four times when drivers are engaged
in distractions related to using a mobile phone [
19
]. The use of augmented-reality glasses
did not eliminate the distracting cognitive demands while driving and still influenced
driving performance [
54
]. The age of the participants is the main limitation of the analyzed
studies, which included the use of Google Glass, as they include mainly a younger segment
of the population. A summary of the results of the selected papers can be found in
Appendix ATable 1.
4. Discussion
The primary focus of this comprehensive review is to summarize the existing knowl-
edge regarding the impact of texting and reading on a mobile phone while driving in a
simulator. The review addressed four research questions that can help to better understand
the distractions that influence the drivers’ performance, what simulators were used by
researchers, and what measures were considered to assess the impact of distracted driving.
The review found a relatively large number of studies (n= 67) that addressed texting as a
secondary task while driving in a simulator. The results of the review are in line with those
of previous research, which found that TWD has a negative effect on a number of parame-
ters related to driving performance that can be investigated in experiments conducted in
car simulators.
The included studies can be divided into two broad categories depending on the device
type: handheld or hands-free devices. The sources of distractions were also classified into
the following four types: cognitive, visual, manual, and auditory. Most secondary tasks
include at least two distractions that can influence the driver’s ability to reach his/her
destination in a safe manner. The driver’s brain has to manage all of the abovementioned
distractions when operating a vehicle. Any additional distractions can increase the mental
workload, thus compromising the driver’s performance.
Drivers are subject to various distractions that can hamper their driving ability. Manual
and visual sources of distraction are the most common and correspond to activities such
as interaction with in-vehicle devices or the use of a mobile phone. Driver-assistance
systems that offer warnings could reduce the time the driver is not focused on the driving

Int. J. Environ. Res. Public Health 2023,20, 4354 15 of 30
task. Some high-end vehicles already have integrated devices that track the driver’s gaze.
However, technology needs to become more accessible, reliable, and mainstream. We
expect to see rapid progress in deep learning algorithms that can accurately identify and
track the driver’s gaze by using a simple video camera.
The driver’s behavior has been exhaustively researched in naturalistic and simulator-
based studies [
117
,
118
]. Even so, there is still work to be performed to fully understand
the combination of measures most effective in predicting road safety. The most popular
variables used by researchers to analyze driving patterns are mean speed, reaction time,
and the standard deviation of the lane position.
Driving scenarios investigating hazard anticipation and traffic violation measures in a
simulator are gaining more and more interest. The negative effects of using a mobile phone
for TWD have been confirmed by numerous studies. The main effects include an increased
brake reaction time, a decrement in lane control, and higher speed variability.
4.1. Recommendations and Directions for Future Research
What is evident from the findings is that typing and reading text messages while
driving, regardless of the device used, should be prohibited in order to reduce the number
of traffic-related deaths and injuries. Although it is advisable not to use a phone while
driving, this is not very likely to happen, as it is used for various purposes, and the tendency
to check the smartphone’s screen cannot be easily inhibited [
119
]. To support this idea,
it was shown that even the experience of a minor accident is not enough to discourage
drivers from sending messages while driving [
120
]. A possible solution would be to reduce
as much as possible the unnecessary use of the phone and provide easy access to its screen
by placing it in the field of view of the driver in a way that he/she is still attentive to the
traffic scene or by sharing the screen on built-in display systems, which should be safer
to use while driving. Moreover, built-in driver-assistance systems that prevent distracted
driving should become mainstream as soon as possible, especially considering the rising
number of traffic participants involved in car crashes due to phone use. A solution that has
been shown to be effective would be the intervention by interactive text message [121].
A topic that still requires attention is how to increase the use of advanced driving-
assistance systems (ADAS) to prevent drivers from engaging in distracting secondary tasks.
For instance, ADAS systems may reduce or prevent the excessive use of a mobile phone by
giving visual–audio notifications when the driver takes his/her eyes off the road. Future
studies should focus on reducing the number of false alerts and propose adaptive ADAS
models that can modify their behavior according to the characteristics of a driver (some
initial work is presented in [
122
]). The use of safety functions should not impose other
costs, as most drivers would not pay extra for such systems [
123
]. Another key aspect
that could increase the acceptance of ADAS is related to the education of the driver, which
should fully understand the safety benefits and limitations of such systems.
After analyzing the included studies, we noticed a lack of consensus regarding the
methods and materials used for running experiments in driving simulators. In the context
of automation, we suggest some minimum features for DS to ensure high reliability, validity,
and replicability of the obtained results. The need for a systematic comparison of DSs
concerning their validity and fidelity was also expressed in a scientometric analysis in [
124
].
Other issues identified are related to simulation sickness, how drivers perceive risks in
a virtual environment, and the lack of detailed descriptions in research studies. A DS
that offers high validity has the ability to reproduce as accurately as possible real-world
driving [
125
], but the validity should be investigated in-depth to better approach the real
conditions of driving [126].
Several aspects need to be considered when testing whether a driving simulator
provides valid results: the simulator itself, the user samples, the task studied, the design
of the experiment, and even the terminology used [
34
]. In view of these, and given that
car manufacturers, taking advantage of the latest technologies, are setting new standards

Int. J. Environ. Res. Public Health 2023,20, 4354 16 of 30
for car simulators [
127
], we propose several recommendations for future research in the
context of driving simulators (the summary is shown in Table 1):
•
Hardware characteristics: The simulator should have a dashboard resembling that
of a real car, providing at least three DOFs in terms of motion and having a display
system that offers a minimum horizontal field of view of 135
◦
[
128
]. It should have
the basic vehicle controls, a sound system, and at least a system capable of monitoring
the driver’s behavior, which includes functions that can detect distracted driving.
Distraction-detection systems are important in the case of autonomous driving because
automated-vehicle drivers will still need to be in the loop in order to take over the
controls when necessary [129].
•
Scenario—Driving scenarios should provide a similar experience to naturalistic driv-
ing [
130
] and highlight the different types of driving behavior [
131
]. Therefore, we
consider that it is not enough to consider a single basic scenario and suggest that
experiments should include at least two driving situations, having multiple driving
conditions (for example, driving in urban, rural areas, less or more traffic, simpler or
more complex road geometry, etc.).
Table 1. Minimum feature recommendations for experiments using a driving simulator.
Immersion:
Motion Platform Display Other Features
Hardware features 3 DOFs At least 135◦horizontal
FOV and 40◦vertical FOV
- Dashboard similar to
that of a real car
-
Basic vehicle controls
- Sound system
Driver tracking:
Movement Distraction detection Physiological metrics
Head tracking Eye and/or hand tracking Electrocardiogram (ECG)
103A/m
Number Type (difficulty) Driving conditions
Scenarios Minimum 2 scenarios,
including a baseline
- urban/rural
environment
- Low/ heavy traffic
- Curves, hills,
intersections, and
roundabouts
- Day/night
- Rain/snow/sun/fog
The driving task should not be too long in order to avoid fatigue and boredom, but not
too short in order to be able to extract relevant results. Participants need to be monitored
in case they experience simulator sickness during the practice session and in the study
itself. A subjective evaluation of the experiment, for example, using questionnaires to better
understand how the experiment influenced the driver’s psychological state (e.g., discomfort,
fatigue, workload, frustration, mind wandering, and so on), can be beneficial and generate
other valuable insights.
Therefore, punctual research studies that focus on a particular subject or concern are
frequently carried out over a shorter period and might utilize a smaller sample size and a
limited number of techniques to gather data. These studies might also look at the efficacy
of measures taken to reduce the harmful effects caused by particular driving distractions.
On the other hand, in order to gain a thorough understanding of a specific topic, it is crucial
to gather a large amount of data over time and under different driving conditions, which,
in turn, can reveal significant trends and patterns.
4.2. Limitations
Certain limitations need to be mentioned for this review. First, since the use of the
mobile phone while driving is a widely studied field of research, it is possible that some
relevant articles may have been missed even after a rigorous search of the literature. The

Int. J. Environ. Res. Public Health 2023,20, 4354 17 of 30
review was limited to excluding studies published in conference proceedings or book
chapters, as well as those published in languages other than English. Some shortcomings
are related to the data, which were not fully reported in several papers. There are also
methodological limitations, including the lack of valid and reliable measures to assess the
effects of TWD, the use of small samples, the duration of experiments, and so on.
The proposed recommendations aim to offer guidelines for experiments using a
driving simulator. However, they cannot consider all the possible scenarios that could
be investigated. The suggested minimum requirements are based on the knowledge
gained from the literature review analysis and on our partially subjective vision of driving
simulators. It can be argued that a consensus regarding this topic will, perhaps, never be
reached, as researchers will just use the infrastructure available.
5. Conclusions
This study presents the results of a review of the literature using a structured search to
examine drivers’ use of mobile phones and wearable devices concerning simulated driving.
Through a rigorous selection process, fifty-nine studies published in the past 20 years
were extracted, analyzed, and classified into four categories. Advanced driving simulators
with a motion system were used in less than 20% of the studies due to the high costs and
complexity of operation and maintenance. According to [
132
], studies that include low-cost
simulators to identify and analyze the driver’s performance can offer meaningful and even
similar findings as those obtained from experiments with advanced driving simulators.
Nonetheless, the lack of a motion platform significantly affects the realism of the simulated
scenario, as the participant cannot experience the vehicle’s inertia when accelerating or
when negotiating a curve.
Mobile phone use in the vehicle is a major component of distracted driving that
requires drivers to take their eyes off the road and one or both hands off the steering wheel,
thus impairing their driving performance and increasing the likelihood of crashes [
133
].
Most studies reached the conclusion that activities such as texting a message on the phone,
manipulating the phone, or the use of different types of phone-connected devices can
introduce cognitive, manual, visual, or even auditory distractions [
134
] that can have
serious negative effects on drivers’ attention and concentration, and this can lead to serious
traffic incidents [135].
Many studies based on driving simulators show that performing secondary tasks (such as
manual input) while driving leads to a compromised driving performance
[17–19,32,70,101,136]
.
Distraction can be achieved by removing the driver’s gaze from the road. However,
cognitive distractions can be just as dangerous by taking his/her mind away from the
driving process [137].
The ubiquity of mobile phones; the increasing number of traffic participants; and their
need/desire to engage in secondary tasks, such as games, texting, or social media, could
have a negative effect on road safety, despite the integrated or mobile driver assistance
systems. This review can serve as a basis for regulators and interested parties to propose
restrictions related to using mobile phones in a vehicle and improve road safety. It also
points out the significance of informing drivers about the dangers of using mobile phones
while driving and the importance of enforcing strict rules and sanctions for those who have
a habit of doing this. Moreover, the study provides researchers with an overview of the
types of distractions that can affect the driver at a cognitive, visual, manual, or auditory
level, as well as the measures that can be used to predict and analyze those distractions. The
review recommends that future research should concentrate on creating more sophisticated
driver assistance systems and technologies that can better detect and prevent distractions
caused by TWD.
Future research should focus on finding a consensus regarding driving-simulator
studies that will enable scholars to directly compare their work with similar studies, thus
ensuring high validity of results, especially in the context of automated driving.

Int. J. Environ. Res. Public Health 2023,20, 4354 18 of 30
Author Contributions:
Conceptualization, R.G.B. and I.-D.B.; methodology, R.G.B.; software, G.-D.V.;
validation, I.-D.B., C.A. and G.Y.; formal analysis, R.G.B.; investigation, G.-D.V.; resources, G.-D.V.;
data curation, I.-D.B.; writing—original draft preparation, R.G.B. and G.-D.V.; writing—review
and editing, C.A. and G.Y.; visualization, I.-D.B.; supervision, C.A. and G.Y.; project administra-
tion, R.G.B.; funding acquisition, C.A. All authors have read and agreed to the published version
of the manuscript.
Funding:
This work was supported by a grant from the Romanian Ministry of Education and Re-
search, CCCDI–UEFISCDI, project number PN-III-P2-2.1-PED-2019-4366 (431PED), within
PNCDI III
.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.

Int. J. Environ. Res. Public Health 2023,20, 4354 19 of 30
Appendix A
Table 1. An overview of driving simulators characteristics and classification (n= 67).
ID Ref. NP Sample
Characteristics aDriving Simulator
Class bLSR
(km) TD MT Type of Device—Distraction Task Findings
1 [70] 35 NR; 22.5; NR; 21–14 B 2,65 V, C, M TrVs,
DM HH—texting Based on vehicle dynamics, it is possible to identify
specific distraction tasks with a level of accuracy that
is adequate.
2 [54] 25 22–33; 25; 2.6; NR A NR V, M, C OMs HF—destination entry In comparison to the primary visual-manual interaction
with the Samsung Touch interface, voice entry (from
Google Glass and Samsung) resulted in lower subjective
workload ratings, lower standard deviation of lateral lane
position, shorter task durations, faster remote Detection
Response Task (DRT) reaction times, lower DRT miss rates,
and less time looking off-road.
3 [50] 134 20–30, 65–75; 23.2,
70.0; 2.8, 3.0; 23–40,
39–22
A 25.7 V, Au + A DM HF—typing a number into a keypad, conversation
with a car passenger, memorizing Braking responses are affected by distractions, and this
effect can last for up to 11.5 s.
4 [78] 31 18–47; 25.61; 6.24;
16–15 A NR V, C, M TrVs HH—received and answered text messages Any mobile gadget, like a smartwatch, smartphone, or
voice assistant, could affect how well you drive, especially
if you have to pay attention to it when your eyes are off
the road.
5 [93] 24 NR; 33, 26.3; NR;
8–4, 8–4 B NR V, C, Au DM HF—receives traffic information The two other systems required the participants to glance
away from the road (too) long, endangering their safety,
and reading an SMS took longer than scanning a PDA. The
auditory information provision system, however, provided
for the best driving performance.
6 [83] 39 19–32; 21.5; 2.6;
27–12 A NR V, C, M TrVs HF—respond to a call, replay several WhatsApp
messages, use Instagram Young drivers who use mobile phones while operating a
vehicle experience impairments that limit their ability to
control the vehicle.
7 [108] 53 22–34; 25.25; 3.08;
37–16 B 3 V, C, M RT HH—speech-based texting and handheld texting
(two difficulty levels in each task) Drivers undertake risk-compensation behavior by
extending time headway in order to offset the higher
accident risk associated with using a mobile phone while
driving. Drivers perceive a rise in accident risk during
distracted driving.
8 [102] 41 <25, 26–40, >41;
NR; NR; 30–11 B 20 V, M + A DM,
OMs HF—enter the application interface of 3, 4, or 6 icons In the HMI design of in-vehicle information, there is a
statistically significant difference in driver perception
reaction time for varying numbers of icons (IVI).
9 [17] 100 <30, 30–50, >50;
24.14, 36.05; 54.67;
2.79, 5.43, 5.04;
87–13
B 3.5 V, C DM HH—simple conversation, complex conversation,
and simple-texting and complex-texting tasks Both talking on the phone and texting while driving
impair a driver’s ability to pay enough attention to the
road ahead, to react appropriately to unexpected traffic
situations, and to control the car within a lane and in
relation to other vehicles.

Int. J. Environ. Res. Public Health 2023,20, 4354 20 of 30
Table 1. Cont.
ID Ref. NP Sample
Characteristics aDriving Simulator
Class bLSR
(km) TD MT Type of Device—Distraction Task Findings
10 [18] 100 <30, 30–50, >51;
24.14, 36.05, 54.68;
2.79, 5.43, 5.05;
87–13
B 3.5 V, C + RC, T RT HH—simple conversation, complex conversation,
and simple-texting and complex-texting tasks Simple conversations, complicated conversations, basic texts,
and complex texts all increased reaction times for pedestrian
crossing events by 40%, 95%, 137%, and 204%, respectively. For
parked car crossing events, the tasks increased reaction times
by 48%, 65%, 121%, and 171%, respectively.
11 [19] 100 <30, 30–50, >52;
24.14, 36.05, 54.69;
2.79, 5.43, 5.06;
87–13
B 3.5 V, C + A, G DM, AP HH—simple conversation, complex conversation,
simple texting and complex texting tasks When engaged in conversation or texting duties, the
drivers significantly decreased their mean speed by
2.62 m/s and 5.29 m/s, respectively, to offset the
increased strain.
12 [32] 49 22.12, 37.62; 22.12,
37.62; 2.45, 7.22;
22–3, 25–0
B 3.5 V, C + A, E DM HH—simple conversation, complex conversation,
simple texting and complex texting tasks Younger drivers are less able to compensate for
distractions while driving and have poorer
longitudinal control.
13 [71] 90 <30, 30–55; 25.31,
37.00; 2.74, 6.29;
83–7
B NR V, M + A DM, RT HH—conversation, texting, eating, music player Most of the drivers (72.06%) reported texting as an
extremely risky task
14 [49] 14 18–22; NR; NR; B NR C, M DM HH—cell phone conversation, back seat
conversation, text message, Ipod manipulation The iPod task and all wireless communication tasks caused
a noticeable increase in speed variability throughout the
driving scenario.
15 [86] 49 19–65; 35.63; 14.26;
32–17 B 50 V, C + A, G OMs HH—reading and comprehension task (three types
of display) Warnings took longer to read and comprehend (4 s on
average), compared to recommendations.
16 [66] 40 19–23; 21; NR;
20–20 B 51.5 V, M DM, RT HH—text messaging Simulated driving performance suffers when texting while
operating a vehicle. This detrimental effect seems to be
more severe than the consequences of using a cell phone
for conversations while driving.
17 [80] 17 NR; 25.88; 5.82; 14,3 B NR V, M TrVs,
DM HH—accessing social network on the smartphone Even when the driver is distracted, using an in-vehicle
smartphone ADAS application has enhanced driving
performance in a simulator..
18 [56] 101 18–57; 27.8; 8.3;
68,33 A NR C, V, M DM HH—using a handheld cell phone; texting; eating Regardless of their prior experience, multitasking while
driving and distracting activities have a negative influence
on driving performance for both genders and all age
groups. The main factor that negatively affected driving
performance was texting.
19 [109] 56 21–30; 25.13; 2.57;
41–15 B 3 V, C, M RT HF, HH—speech-based and handheld texting Compared to the baseline, handheld texting tasks caused a
delayed reaction to the unexpected braking occurrences.
20 [36] 26 22–31, 22–29; 25.5,
23.9; 3.33, 2.27; 3–3,
20–0
B NR V, M + A RT, DM HH—receive notification The use of smartwatches could affect traffic safety. There
may be a discrepancy between drivers’ actual performance
and their views regarding using a wristwatch while
driving, given that participants generally believed that
smartwatch use resulted in similar or fewer traffic fines
than smartphone use.

Int. J. Environ. Res. Public Health 2023,20, 4354 21 of 30
Table 1. Cont.
ID Ref. NP Sample
Characteristics aDriving Simulator
Class bLSR
(km) TD MT Type of Device—Distraction Task Findings
21 [55] 48 20–79, 19–66; 34.8,
35.3; 16.0, 13.9;
17–7, 16–8
C NR V OMs HH—email reading, view-switching, song
searching, email replying Compared to using standard smartphone apps, an
automotive-specific application reduced the visual
demand and visual distraction potential of in-car duties.
22 [72] 63 25–66, 8–18; NR;
NR; 32–31 D NR V, M + A DM HH, HF—answer incoming calls, dialing, retrieve a
voicemail message from a specific person using
either the handheld or hands-free phone
Teenagers were shown to adopt risky following distances,
to drive poorly, and to be more easily distracted by
handheld phone tasks than adults.
23 [20] 36 NR; 20.95; 2.36;
16,10 C 6.8 V, C, M RT, DM HH—social media browsing Performance is impacted by both texting and using social
media, but texting while driving is more harmful.
24 [90] 20 18–21; NR; NR; 12,8 C 8 V, M DM, HA HH—retrieve and send text messages Text messaging has negative consequences on driving
ability, which could explain the higher crash risks.
25 [99] 24 18–64; 32.1; 12.5;
10,14 A 3.55 V, M DM HH—manual dialing, voice-dialing When participants utilized voice-activated dialing as
opposed to manual dialing, there were 22% fewer
lane-keeping mistakes and 56% fewer looks away from the
road scene.
26 [69] 40 20–52; 32.5; NR;
11,29 B NR V, C OMs HH—touching the touch-screen telephone menu to
a certain song, talking with laboratory assistant,
answering a telephone via Bluetooth headset, and
finding the navigation system from Ipad4 compute
The attention of the driver is substantially diverted from
the road when engaging in secondary tasks while driving,
and the evaluation model used in this study could
accurately predict driving safety under various driving
circumstances.
27 [61] 24 20–45; 33.43; 6.32;
22–2 A NR V DM, RT HF—ordering, route check, destination search Usability and driving safety were higher when the phone
was placed on the left side of the steering wheel as
opposed to the right.
28 [33] 29 NR; 56.6, 55.9; 4.1,
3.0; 16, 13 A NR V, M, N RT, OMs HH—sending a text message, searching navigation When driving while sending a text message or using
navigation, the jerk-cost function, medial-lateral coefficient
of variation, and braking time were all higher than when
driving alone.
29 [58] 20 27–59; 37.65; 9.75;
14,6 B 10 + 9 V, M, C DM,
OMs HH—conversation, texting, destination entry,
following route guidance Only when individuals engaged in visual-manual tasks,
such as texting and entering a location, when they
frequently glanced away from the forward road, did
lateral performance decline.
30 [64] 30 18–30; 22.7; 3.51;
15,15 A 13 C, M DM,
TrVs HH—“temptation to text” The “Temptation to Text” condition revealed noticeably
more workload. Similarly, it was discovered that texting
while driving drastically reduced vehicle performance.
31 [85] 20 23–30; 26.20; 2.58;
10,10 A NR C, M TrVs,
DM,
ALs, RT
HF—conversation, HF cognitive demanding
conversation, texting Comparatively to legal BAC limits, very basic mobile
phone conversations may not pose a substantial risk to
driving, but cognitively taxing hands-free talks and, most
notably, texting, do pose significant dangers.

Int. J. Environ. Res. Public Health 2023,20, 4354 22 of 30
Table 1. Cont.
ID Ref. NP Sample
Characteristics aDriving Simulator
Class bLSR
(km) TD MT Type of Device—Distraction Task Findings
32 [88] 41 18-61; 31; 9.7; 23,18 B 5 C + G ALs HF, HH—conversation Drivers’ decisions regarding accepting gaps were unaffected
by the distraction task, although the crossing’s completion
time increased by over 10% in comparison to the baseline.
Also, when using a phone at an intersection, drivers exhibited
conservative behavior, slowing down more quickly, waiting
longer, and keeping a greater distance from the vehicle in
front of them.
33 [101] 29 22–49; 30; 6; 15,14 A 1 V, M DM HH—help, browse, filter task The filtering task’s slider widget was overly demanding
and hindered performance, whereas kinetic scrolling
produced an equal amount of visual distraction although
requiring less precise finger pointing.
34 [59] 15 NR; 28; 4.08; 12,3 A NR C, V, M OMs HH—button, slider, Insert data, dropdown,
radio buttons When evaluating the mental workload related to wide
differences in task complexity in terms of the amount of
information to be processed, a commercial BCI device may
be helpful.
35 [75] 60 16–17; 16.8; 0.4; 20,
40 B NR V, M + G OMs HH—looking at the phone, picking up the phone,
taking a picture, sending the picture, hand mani-
pulation of phone (mimicking writing a text), ans-
wering a call, and looking at a picture on the phone
Self-reported distracted driving habits grew with time,
with a significant effect of visit on self-report outcomes.
36 [67] 28 18–28; 21.0, 2.4; _;
16,12 B 1.1–1.5 V, M DM HH—type and send a text message vs,. tunning
car radio Even in the simplest of driving situations, multitasking while
operating a motor vehicle can have a negative impact on
performance and increase risk. Comparing text messaging to
other in-car activities like changing the radio, text messaging
may present a “perfect storm” of risks.
37 [82] 18 18–22; 20.4; NR; NR C NR V, M RT HH—text messaging, reading Facebook posts
(text/self-paced), exchanging photos via Snapchat,
and viewing updates on Instagram
When compared to the image-based scenario
(mean = 0.92 s) and the baseline, the brake reaction times
(BRTs) in the text-based scenarios were substantially
longer (mean = 1.16 s) (0.88 s). Both the task-pacing impact
and the difference between BRTs in the image-based and
baseline conditions were not statistically significant.
38 [63] 64 22–60; 33; 10; 34, 30 D NR V, C RT HH—reading, texting, video, social media, gaming,
phoning, music Reaction times did decrease when performing non-driving
related tasks (NDRTs), suggesting that the NDRT assisted
the drivers in keeping their focus during the partially
automated drive. Drowsiness and the NDRT’s
motivational appeal thus raised situation criticality,
whereas the NDRT’s cognitive load decreased it.
39 [89] 35 18–29; 22.9; 4.0; 22,
13 D 10 V, M, C + RC DM HF, HH—calling, texting vs. road environment Compared to distraction from a cell phone or other road
elements like pedestrians and approaching vehicles, road
geometry has a greater impact on driver behavior.
40 [76] 35 18–29; 22.9; 4.0; 22,
13 D NR V, M, C OMs HH—ring a doctor and cancel an appointment, text
a friend and tell him/her that the participant will be
arriving 10 min late, share the doctor’s phone
number with a friend, and take a ‘selfie
The three types of self-regulation that distracted drivers
use most frequently are tactical, operational, and strategic.

Int. J. Environ. Res. Public Health 2023,20, 4354 23 of 30
Table 1. Cont.
ID Ref. NP Sample
Characteristics aDriving Simulator
Class bLSR
(km) TD MT Type of Device—Distraction Task Findings
41 [30] 50 27–55; 36.8; 5.8;
50,0 D NR V, M, C DM HH—driving while having a conversation on the
mobile phone, driving while reading out loud text
messages and driving while texting
The “reading of text messages” and “texting” had a big
impact on the “change of the steering position per second.
For all three cell phone assignments, a substantial main
effect was seen in terms of “following distance per second”
and “change of the lateral lane position per second”.
42 [29] 90 NR; NR; NR; 73,17 A 3.6 C, V DM, RT,
TrVs HH—using the mobile phone, drinking and
text messaging The disruptive variables have a negative impact on road
safety due to cognitive distraction and mobility limitation
(e.g., longer response times and more errors), on the one
hand, and have a bad impact on the environment and the
economy (e.g., increased fuel consumption), on the other.
43 [105] 36 21–54; 33.3; 8.6;
21–15 B 4.8 V, Au DM, RT HF—features presented via a mobile phone
mounted near the line of sight The findings indicated that new features with the greatest
levels of urgency and criticality, such as Emergency Vehicle
Warning (EVW) and Emergency Electronic Brake Lights
(EEBL), would improve safety and make it easier for
emergency vehicles to reach their intervention site.
44 [68] 36 NR; NR; NR; 18,18 A NR V, C, M, Au RT, DM,
OMs HH—smartwatch vs. smartphone calling By using a phone instead of just driving, participants
shown increased off-road visual attention.
45 [73] 32 17–21; 19.0, 19.3;
NR; 7,9 B NR V, M DM,
TrVs, RT HH—manipulating controls of a radio/tape deck
and dialing a handheld cellular phone The time spent on tasks was marginally longer for
participants who anticipated dangers compared to those
who did not, but the difference was stable across tasks.
46 [87] 45 NR; 62.8, 24.3; 7.2,
4.8; 30–0, 11–4 B NR V, P DM,
OMs HH—texting on a smartphone and while sitting on
a stable or unstable surface When drivers were texting, the perceived workload
increased, but balancing training decreased it. While
seated on the unsteady surface, perceived workload was
higher; however, it decreased after balance training.
47 [35] 40 NR; 20.47; 4.76; 24,
16 B 8.04 V, M DM, RT HH—use Google Glass or a smartphone-based
messaging interface Glass-delivered messages served to reduce distracting
cognitive demands, but they did not completely remove
them. Comparatively speaking to driving when not
multitasking, messaging while using either gadget
impairs driving.
48 [81] 37 18–33; 24.7; 3.6;
20–17 B NR V DM, RT,
AP HF—navigating on the Facebook newsfeed, reading
and sending text messages in Facebook Messenger,
searching for a location in Google Maps
Web browsing and texting-related distraction raise the
likelihood of an accident, the headway, and the lateral
distance deviation by 32%, 27%, and 6%, respectively.
49 [84] 123 18–64; 34.46; 13.04;
62,61 B 26.4 V, Au DM,
OMs HH—audio warning, flashing display There was no difference in the number of vehicles
overtaken between the groups, and the existence of the
speed warnings had no effect on overtaking.
50 [51] 34 16–18; 17.25, 17.09;
0.99, 0.89; 12–4,
14–4
B 8.04 C, M DM, RT,
TrVs HH—conversing on a cell phone, text messaging Compared to the no task and the cell-phone task, the lane
position varied significantly more while texting. Teens
with ADHD spent noticeably less time to finish the
scenario while texting in particular. There were no
discernible group-wide major effects detected.

Int. J. Environ. Res. Public Health 2023,20, 4354 24 of 30
Table 1. Cont.
ID Ref. NP Sample
Characteristics aDriving Simulator
Class bLSR
(km) TD MT Type of Device—Distraction Task Findings
51 [77] 50 24–54; 39.8; 8.4;
49, 1 B 36.2 C, M, V TrVs,
DM,
OMs
HH—cell phone conversation, text message
interaction, emailing interaction Poorer driving performance was associated with more
visually demanding jobs. Yet, using a cell phone caused
fewer off-road eye looks. Drivers who described
themselves as “extremely skilled” drove less well than
those who described themselves as “talented.”
52 [94] 75 16–18, 19–25; 17.67,
23.39; 1.18, 1.81;
11–19, 23–22
B 38,6 C, M + T TrVs,
DM HH—cell phone, texting Texting generally resulted in more lane deviations and
collisions. Text messaging was the most common form of
distraction, which had a major negative influence on traffic
flow. As a result, participants’ speeds fluctuated more,
changed lanes less frequently, and took longer to finish
the scenario.
53 [60] 32 18–25; 20.6; 2.1;
32–0 D 13 V DM,
TrVs HH—gamified boredom intervention The gamified boredom intervention promoted anticipatory
driving while reducing risky coping strategies
like speeding.
54 [132] 36 NR; 28.44; 9.26; 30,6 A NR C, V, M DM HH—conversation, texting Driver performance in the longitudinal and lateral control
of the vehicle for the texting event significantly declined
during the texting task.
55 [113] 37 NR; 21; 3.63; 11,26 B NR C, Au DM,
OMs HH—text-message distractions For at least 10 s but no more than 30 s following the text
message alert, situation awareness is negatively impacted.
Participants’ mean speed increased during periods of
distraction in the 10 s after receiving a mobile phone
notification, which also resulted in a decrease in context
awareness.
56 [100] 27 24–59; 42.4; 9.1; 11,
16 B 4.4 V, M + A, E DM,
OMs HH vs. dashboard—texting with the smartphone in
one hand (handheld drive) and texting while the
phone is placed in a dashboard mount
Texting while driving when using a dashboard-mounted
device impairs driving safety at least as much as texting
while using a handheld device.
57 [98] 40 NR; 28; 12.6; 10,30 A NR V, M + E DM HH—texting Mobile phone texting dramatically reduced the ability to
drive. Driving experience had no bearing on the results,
however highly skilled phone users’ texting use had a
noticeably reduced negative impact.
58 [95] 40 NR; 18.6; 1.8; 11–29 B NR V, M, C DM,
OMs HF, HH—conversation, texting, selecting a song Although the amount of interference varied depending on
the task, hands-free smartphone call created substantially
less interference than texting and listening to music on an
MP3 player.
59 [65] 60 NR; 19.74; 2.4; 30,3 A 8.04 C, M OMs HF—conversation, texting Driving while texting was similar to driving while not doing
anything. The results of this study highlight the need for
further investigation into the long-term effects of secondary
task use while driving on cardiovascular reactivity as well as
the dangers of secondary task use while driving on the risk of
cardiovascular disease or stroke.

Int. J. Environ. Res. Public Health 2023,20, 4354 25 of 30
Table 1. Cont.
ID Ref. NP Sample
Characteristics aDriving Simulator
Class bLSR
(km) TD MT Type of Device—Distraction Task Findings
60 [110] 36 18–56; 26.95; 5.076;
23,13 A 2.5 M DM, RT HH—cell-phone texting Driver groups with phone-texting distractions exhibited
larger speed variability, longer average following HWDs,
considerably slower reaction times, and longer distances
needed for quick recovery in response to front-car braking
events than driver groups without such distractions.
61 [91] 34 18–28; NR; NR;
19,15 A NR V, M + RC, W DM, RT,
AP HH—texting In both urban and rural road contexts, texting results in a
statistically significant decrease in mean speed and an
increase in mean reaction time. Due to driver distraction
and delayed response at the time of the incident, it also
increases the likelihood of an accident.
62 [92] 34 18–24; NR; NR;
19,15 B 3 V, M + W DM, AP HH—navigation, tuning the radio, replying to a text
message, replying to a voice message, and making a
phone call
On highways, texting appears to cause drivers to exhibit
compensatory behavior, which statistically significantly
reduces the mean speed and increases headway in both
normal and particular traffic and weather conditions.
63 [74] 34 NR; 47.6, 23.05; NR;
23, 11 A NR V, M + A OMs HF—normal conversation (non-emotional cellular
conversation), and seven-level mathematical
calculations
Making a call, returning a voicemail, and responding to
texts are high-visual-load secondary chores that drivers
shouldn’t engage in while operating a vehicle.
64 [62] 43 NR; 24.09; 3.27;
25–18 B 4.1 V, C DM,
OMs HF—texting, talking For basic road portions, texting considerably raised the
SDLP, although conversational tasks showed less lateral
variance than when there was no distraction.
65 [31] 28 18–55; 29.4; 11.3; 16,
12 B 9 V, M, Au RT, DM,
OMs HH—text messaging Although Glass enables drivers to better maintain their
visual attention on the front scene, they are still unable to
efficiently divide their cognitive attention between the
Glass display and the road environment, which impairs
their ability to drive.
66 [79] 20 22–47; 32.2; 6.3; 16,
4A 3 V, C DM,
OMs HH—reading text on Glass and on a smartphone When approaching active urban rail level crossings (RLXs),
texting had a negative effect on how well the
driver performed.
67 [57] 101 18–57; 27.8; 8.3; 68,
33 A 6 V, C, M DM HH—texting, talking on the phone, or eating According to the simulation results, texting and, to a lesser
extent, talking on the phone cause traffic to move more
slowly on average and with higher coefficients of variation.
Note: TD—type of distraction: C—cognitive, V—visual, M—manual, Au—auditory; MT—measure type: AL—attention lapses, AP—accident probability, DM—driving maintenance,
HA- hazard anticipation, RT—response time, TrV—traffic violations, OM—other measures; HH—hand-held, HF—hands-free, NP—number of participants; LSR—length of simulated
route; NR—not reported.
a
Values include age, mean, standard deviation, and gender (M, F).
b
Driving Simulator Classification: A—fixed-based, basic visual capability, FOV minimum
H:40 and V:30; B—fixed-based, FOV minimum H:40, and V:30; C—motion platform, FOV minimum H:120 and V:30; D—minimum 6 DOF motion platform, FOV minimum H:180 and
V:40 [40].

Int. J. Environ. Res. Public Health 2023,20, 4354 26 of 30
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