Paper on Hazardous Behavior

RESEARCH ARTICLE

Mobile phone applications use while driving in

Ukraine: Self-reported frequencies and

psychosocial factors underpinning this risky

behaviour

Tetiana HillID 1*, Amanda N. Stephens2, Mark J. M. Sullman3

1 Hertfordshire Business School, University of Hertfordshire, Hatfield, United Kingdom, 2 Monash University

Accident Research Centre, Monash University, Melbourne, Australia, 3 Department of Social Sciences,

School of Humanities and Social Sciences, University of Nicosia, Nicosia, Cyprus

* [email protected]

Abstract

Despite the fact that mobile phones have been transformed over the last decade into infor-

mation and communication hubs that are fundamental to modern life, there is little informa-

tion on how this has impacted on mobile phone use while driving. The present study was

conducted in Ukraine, where this risky behaviour remains a common driving practice,

despite legislative bans. A total of 220 (male = 82%; mean age = 35.53; SD = 10.54) drivers

completed an online survey assessing frequency of engaging in a range of mobile phone

applications while driving. Four variables of the theory of planned behaviour (general atti-

tude and intention towards phone use while driving, social norms towards mobile phone

use, perceived behavioural control, the specific beliefs about being able to engage in dis-

tracting activities and drive safely), and type A behaviour pattern were also collected. The

results showed that, during the last year, 65% of drivers had read a text message and 49%

had written a text using mobile phone applications. Likewise, a substantial proportion of the

sample reported using social media while driving, by checking (34%), sending or typing a

post (25%) on social network applications. Hierarchical stepwise regressions showed that a

positive attitude towards mobile phone use while driving and beliefs about being able to

drive safely and write or read a text message were significantly associated with the mobile

phone applications use while driving. No associations were found between the type A

behaviour pattern and mobile phone applications use.

Introduction

Distracted driving is one of the major risk factors for road traffic injuries and fatalities for driv-

ers, passengers and other road user groups in the world. It often occurs when a driver divides

their attention, voluntarily or involuntarily, between driving and an unrelated secondary task.

According to the estimates provided by the National Highway Traffic Safety Administration

[1], it accounts for approximately 8% of all fatal crashes and 15% of all injury crashes, as well as

PLOS ONE

PLOS ONE | https://doi.org/10.1371/journal.pone.0247006 February 17, 2021 1 / 15

a1111111111

a1111111111

a1111111111

a1111111111

a1111111111

OPEN ACCESS

Citation: Hill T, Stephens AN, Sullman MJM (2021)

Mobile phone applications use while driving in

Ukraine: Self-reported frequencies and

psychosocial factors underpinning this risky

behaviour. PLoS ONE 16(2): e0247006. https://doi.

org/10.1371/journal.pone.0247006

Editor: Feng Chen, Tongii University, CHINA

Received: October 31, 2020

Accepted: January 30, 2021

Published: February 17, 2021

Copyright: © 2021 Hill et al. This is an open access article distributed under the terms of the Creative

Commons Attribution License, which permits

unrestricted use, distribution, and reproduction in

any medium, provided the original author and

source are credited.

Data Availability Statement: The data that support

the findings of this study are openly available in

Zenodo (DOI: 10.5281/zenodo.4115826).

Funding: The authors received no specific funding

for this work.

Competing interests: The authors have declared

that no competing interests exist.

14% of all police-reported motor vehicle traffic crashes. Driving distraction may take different

forms and vary in terms of the level of interference it can cause, meaning that not all distrac-

tion activities have the same impairment on driving performance. Young et al. [2] categorised

driver distraction into four distinct types: visual (focusing one’s visual attention on a secondary

task), auditory (focusing one’s attention on auditory signals and misfocusing on the road envi-

ronment), physical (removing one’s hand(s) from the steering wheel to manipulate other

objects), and cognitive (thinking about something unrelated to the driving task). Each second-

ary task may cause one or more types of driver distractions depending on its complexity and

demands on driver mental workload [3,4].

There is ongoing debate regarding which secondary tasks have the most detrimental effect

on driving performance, however, mobile phone use while driving is considered as one of the

most important global road safety issues. This is primarily due to the fact that interactions with

mobile phones can encompass all four types of distractions (i.e. visual, auditory, manual, and

cognitive). By way of example, the results of the three-year Second Strategic Highway Research

Program Naturalistic Driving Study (SHRP 2 NDS), employing a sample of more than 3500

drivers, reported that browsing, handheld dialling and handheld text interactions with a

mobile phone are associated with an increased crash risk [5,6].

These findings are also supported by the results of the recent systematic review and meta-

analysis of the on-road and naturalistic studies [7,8], as well as epidemiological studies (e.g.,

[9,10]).

The concern over mobile phone crash risk is increasing, as mobile devices become informa-

tion and communication hubs often fundamental to modern life. For instance, mobile phone

functions are no longer limited to making/receiving calls and writing/sending text messages;

smart technology allows engagement in a wide range of functions (e.g., emails, social media,

personal data for fitness, banking etc.). Some functions may be beneficial for drivers, such as

Global Positioning System (GPS), navigation and real-time traffic updates and these are per-

mitted if the mobile phone is secured in an appropriate holder. However, it is well established

that some mobile phone applications may significantly draw attention from driving. This

includes both observational and naturalistic studies (e.g., [11–13]). For example, naturalistic

driving research conducted on 221 Israeli drivers found that drivers touched their smart-

phones’ screens at least 1.71 times per minute while driving for different purposes including to

use applications [12]. Furthermore, another naturalistic study in Finland tracked the frequen-

cies of use of different mobile phone applications while driving in a sample of 30 drivers. The

results showed that drivers most commonly used WhatsApp messaging application, with one

instance of use having a median duration of 35 seconds and a median of 8 screen touches [14].

An interesting contrast was the fact that navigation application use had a median duration of 3

screen touches and lasted for 11 seconds. The authors concluded that messaging applications

(as opposed to image- or audio-based applications) pose the greatest threat to a driver’s ability

to control a vehicle. A similar finding was also reported by McNabb and Gray [15] who found

that image-based applications, such as Instagram and Snapchat, had no significant effect on

brake reaction time (BRT) and time headway (TH) variability in a driving simulator study.

More specifically, the findings showed that scrolling through and reading the updates from a

Facebook account considerably increased BRT and TH variability in a sample of 18 drivers in

the USA. Similarly, another driving simulator research in Spain found that texting on What-

sApp while driving significantly impaired driving performance for all age groups, and espe-

cially among older drivers [12]. Collectively, most of the abovementioned studies conclude

that the introduction of mobile phone applications has not only dramatically increased mobile

phone use among the general population [16] but has also affected the frequency of mobile

phone use among drivers (e.g., [11,12,17]). Consequently, and despite legislation strictly

PLOS ONE Mobile phone applications use while driving in Ukraine

PLOS ONE | https://doi.org/10.1371/journal.pone.0247006 February 17, 2021 2 / 15

prohibiting handheld mobile phone use in most of the countries, many drivers continue to

interact with their phones while driving [3,6].

A number of studies have shown that enforcement and penalties are not effective in reduc-

ing the use of mobile phones while driving (e.g., [18,19]). Research in to other dangerous and

illegal driving behaviours, such as speeding [20] and drink-driving [21] has found the same.

That is, drivers will engage in these behaviours more frequently when they do not feel they will

get caught, do not believe there is a risk of crash and when they have friends and family who

also engage in the behaviour. This suggests that it is important to understand the attitudes

toward the behaviour in order to address the problem.

Previous studies have also examined driver attitudes toward mobile phone use. These have

focussed on predicting use based on psychosocial variables, such as attitudes (e.g., [22]), beliefs

(e.g., [23]), levels of mobile phone involvement (e.g., [24]), and self-reported frequency of text-

ing and calling behaviours (e.g., [25]). Most of these studies report that the prevalence of

mobile phone use while driving is directly related to positive attitudes drivers have towards

this behaviour and the perceived benefits their use may have [26].

There is also research that has investigated the attitude-behaviour relationship for driver

mobile phone use using the Theory of Planned Behaviour (TPB; [27]). Based on this model,

mobile phone use can be predicted by understanding one’s intentions to engage in it, which

are predetermined by attitudes (positive / negative evaluation of the target behaviour), subjec-

tive norms (perceived approval / disapproval of the target behaviour by significant others), and

perceived behavioural control (PBC; perceived ease / difficulty of performing the target behav-

iour). The TPB has been successfully applied in previous studies to explain texting and calling

behaviour while driving (e.g., [22–24,28,29]). In addition, using an extended TPB, which

included the additional components of anticipated regret, moral norm, mobile phone involve-

ment, and cognitive capture, Gauld et al. [30] explored university student drivers’ intentions to

engage in initiating, monitoring/reading, and responding to social interactive technology on a

smartphone. However, none of these have attempted to explore the associations between the

TPB variables and the proliferation of mobile phone applications use.

Personality is another factor that can underlie the decision to use a mobile phone while

driving. For example, Bianchi and Phillips [31] found that problematic mobile phone use was

more prevalent among extraverted drivers as they tend to make more calls while driving.

Sween et al. [32] identified that greater emotionality, less conscientiousness, openness to expe-

rience and honesty/humility were strongly associated with frequent mobile phone use while

driving. No previous research, however, has been carried out to explore the associations

between the frequency of mobile phone use while driving and personality traits/types that may

explain the tendency to multitask, such as the type A behaviour pattern (TABP). TABP has

been recognised as one of the individual variables increasing the risk of road traffic accidents

(RTAs; e.g., [33–35]).

TABP was originally conceptualised by two cardiologists, Friedman and Rosenman [36,37],

who noted that individuals with heart disease exhibit different behavioural patterns to those

with no heart disease. As such, those individuals who have a greater competitive need for

achievement, time urgency, aggressiveness, and hostility have overall higher odds of having

coronary heart disease. When applied to the driving context, it can be predicted that drivers

who exhibit TABP may approach various driving situations with a heightened sense of urgency

and impatience, which can be a crucial factor leading to RTAs [38]. For example, West et al.

[35] found that TABP was strongly associated with speeding behaviour. The results of a study

employing a sample of bus drivers in the USA and India showed that drivers exhibiting TABP

reported overall higher traffic accident rates per month, in comparison to those who exhibit

the opposite type B behaviour pattern (i.e., these individuals tend to enjoy working steadily

PLOS ONE Mobile phone applications use while driving in Ukraine

PLOS ONE | https://doi.org/10.1371/journal.pone.0247006 February 17, 2021 3 / 15

and do not experience stress due to a lack of achievements; [39]). Lastly, using a large sample

of 11.965 French employees aged 39–52 years, Nabi et al. [33] identified that type A drivers

had an increased risk of RTAs. It can be concluded that the implications of TABP for traffic

safety may be quite severe, meaning that it is important to investigate whether and how TABP

is associated with other safety-critical driving behaviours, such as mobile phone use while

driving.

The present study

The present study was conducted with a sample of Ukrainian drivers, for whom mobile phone

use while driving is a prevalent behaviour. The main aim of this study was to explore the fre-

quencies of use of different mobile phone applications while driving and their underpinning

psychosocial factors. In particular, we investigated the associations between mobile phone

applications use and the prevalence of TABP in the Ukrainian drivers, their beliefs about being

able to engage in secondary tasks and drive safely, as well as the four TPB components, such as

the general attitude and intention towards phone use while driving, social norms towards

mobile phone use, and perceived behavioural control.

Method

Participants

The sample consisted of 220 fully licenced drivers in Ukraine. To be eligible to take part in the

study, participants were required to hold a current driver’s licence, own a mobile phone, and

report driving at least once in the past six months. Most participants were males (82%), aged

19–70 years old (SD = 10.54, mean age = 35.53). Participants reported holding a driving licence for an average of 4.01 years (SD = 1.17, range: 1–5) and driving approximately 17.02 kilometres per week (SD = 18.01, range: 0–150).

Procedure

Firstly, the survey was translated into Ukrainian by a professional translator and checked for

consistency by one of the authors (TH) who is fluent in both languages. Secondly, the survey

was hosted online using the Qualtrics web surveying platform. Data were collected via a link

sent to the personal e-mails of both staff and students of the National Aviation University in

Kyiv, Ukraine, using the existing contacts of one of the authors (TH), who is also an alumna of

this University. Approval for this study was granted by the University’s ethics committee. The

survey was also advertised on a national forum for Ukrainian motorists. All the study partici-

pants were encouraged to pass on the link to eligible friends and family (i.e. those who hold a

current driver’s licence, own a mobile phone, and had driven at least once in the past six

months). Before completing the online survey, the participants were asked to familiarise them-

selves with the information sheet outlining that the study was voluntary with no compensation

for the participation and that their data would be collected and held confidentially and

anonymously.

Measures

Demographic variables. Participants were asked to report their age, sex, marital and

work status, educational attainment as well as the number of hours spent driving on average

each week. They were also asked to indicate the driving purpose based on the ratio of driving

for work versus leisure using a 7- point Likert scale (1 = primarily for work; 7 = primarily for

leisure).

PLOS ONE Mobile phone applications use while driving in Ukraine

PLOS ONE | https://doi.org/10.1371/journal.pone.0247006 February 17, 2021 4 / 15

Frequency of mobile phone applications use while driving. To assess the frequency of

mobile phone application use while driving, participants were also asked “How often do you

do the following on your mobile phone while driving?”:

• Read a text message using mobile phone applications (e.g., Viber, WhatsApp).

• Send a text message using mobile phone applications (e.g., Viber, WhatsApp).

• Read/look at posts on social networks (e.g., Facebook, VK, Odnoklassniki).

• Send or make posts on social networks (e.g., Facebook, VK, Odnoklassniki).

• Make a video call (e.g., FaceTime, Skype).

• Make a call using mobile phone applications (e.g., Viber, WhatsApp).

• Write an email.

• Read an email.

• Type an address into maps (e.g., Google maps, Yandex).

• Use an intellectual assistant (e.g., Siri, Majel).

• Take photos “selfies”.

Participants were asked to rate each of the options using a 7-point Likert scale (1 = more

than once a day; 4 = one or two times a month; 7 = never). The mobile phone applications

included in the survey options were selected based on their popularity among the Ukrainian

population in 2015 when the study was conducted.

Theory of planned behaviour variables. To investigate the associations between the TPB

variables and various mobile phone applications use while driving, participants were asked to

rate four items extracted from the TPB questionnaire developed by Walsh et al. [29]. Using a

7-point Likert scale (1 = strongly disagree; 7 = strongly agree), participants were asked to indi-

cate their level of agreement with the following statements: In the next week, to what extent do

you agree or disagree that:

• It is likely you will use your mobile phone while driving (measuring general intention to use

a mobile phone while driving).

• Using your mobile phone while driving will be good (measuring general attitude towards

mobile phone use while driving).

• Those people who are important to you would want you to use your mobile phone while

driving (measuring social norms towards mobile phone use while driving).

• You have complete control over whether you use your mobile phone while driving (measur-

ing perceived behavioural control).

The Bortner Type A scale. This self-report scale was used to assess TABP [40]. It contains

14 items, each consisting of opposing statements placed on a continuum ranging from the

extreme TABP to the absence of TABP. Participants were asked to indicate their position

between the two extremes using an 11- point Likert scale (e.g., 1 = casual about appointments;

11 = never late). The Bortner Type A scale is a unidimensional scale, the reliability of which in

the current sample was acceptable, with Cronbach’s alpha of 0.74.

Beliefs about being able to engage in secondary tasks and drive safely. To assess partici-

pants’ beliefs about their ability to safely use a mobile phone while driving, four items were

PLOS ONE Mobile phone applications use while driving in Ukraine

PLOS ONE | https://doi.org/10.1371/journal.pone.0247006 February 17, 2021 5 / 15

added to the survey that were retrieved from a questionnaire developed by White et al. [25].

The questionnaire was developed based on a beliefs-based TPB approach aimed at investigat-

ing the direct determinants of intentions to engage in various risky behaviours, such as mobile

phone use while driving. Similar to the original questionnaire, in the current study, partici-

pants were asked to rate these items (e.g., “I am able to drive safely and send a text message at

the same time”, “I am able to drive safely and talk on a handheld phone at the same time”)

using a 7-point Likert scale (1 = strongly disagree; 7 = strongly agree).

Data handling and analysis

All analyses were undertaken using SPSS v.22. There were no missing data. The variable for

the ratio for driving was recoded into a binary variable representing driving “primarily for

work” (scores of 1 to 3) and “primarily for leisure” (scores of 4 to 7). Mobile phone frequency

was recoded as a binary variable for hand-held calls (included making and received calls) and

text messaging (included writing or reading text messages) while driving. To explore the asso-

ciations between the variables Spearman correlations were conducted, which are robust to

ordinal data. Cohen’s effect sizes of � .29 as small, between .30 to .49 as medium and � .50 as

large, were used to describe the relationships [41]. Hierarchical stepwise regressions were used

to explore the psychosocial factors related to different types of mobile phone applications use

while driving.

Results

Frequency of mobile phone use while driving

Table 1 presents the frequencies of self-reported interactions with the mobile phone applica-

tions while driving. The majority of the sample (65%) reported they would read a text message

and 49% reported they would type one while driving. Making a call and/or a video call using

mobile phone applications were less common amongst the Ukrainian drivers, with nearly 25%

of the sample reporting they would make a call using the applications and 21% reporting they

would video call someone while driving. No less concerning, a significant percentage of the

sample reported engaging in social media while driving: 34% of drivers checked and 25% sent

or typed a post on social network applications. Notably, 32% of drivers reported dealing with

Table 1. Self-reported mobile phone applications usage while driving (N = 220).

While driving, how often do you. . . More than once a

day (%)

Daily

(%)

1–2 times per

week (%)

1–2 times per

month (%)

1–2 times in six

months (%)

Once a year

(%)

Never

(%)

Read a text message using mobile phone

applications

15.9 12.3 25.0 5.5 2.7 3.6 35.0

Send a text message using mobile phone

applications

7.7 14.5 13.6 7.3 4.1 1.8 50.9

Read or look at posts on social networks 13.2 2.7 4.1 7.3 2.3 4.1 66.4

Send or make posts on social network 12.7 2.3 2.3 3.6 0.9 3.2 75.0

Make a video call 5.0 5.5 4.1 3.6 0.9 1.8 79.1

Make a call using mobile phone

applications

10.0 3.2 5.9 2.3 2.3 0.9 75.5

Write an email 10.0 5.0 8.2 3.2 2.7 2.7 68.2

Read an email 9.1 5.9 5.0 3.2 5.0 3.2 68.6

Type an address into maps 8.6 5.5 4.5 8.6 4.1 6.8 61.8

Use an intellectual assistant 8.2 0.9 2.7 1.4 2.3 0.0 84.5

Take photos “selfies” 8.6 7.7 5.5 7.7 3.2 8.6 58.6

https://doi.org/10.1371/journal.pone.0247006.t001

PLOS ONE Mobile phone applications use while driving in Ukraine

PLOS ONE | https://doi.org/10.1371/journal.pone.0247006 February 17, 2021 6 / 15

emails while driving and 38% typed an address into maps applications while driving. Just

under half of the participants (41.4%) reported taking selfies while driving at least once a year.

The least common behaviour amongst the drivers was using an intellectual assistant (15.5%).

Descriptive variables and intercorrelations between variables

Table 2 displays the correlations between engagement with a mobile phone while driving and

age, sex, hours driving per week, TABP, beliefs about being able to drive safely and interact

Table 2. Associations between engagement with different mobile phone applications while driving and age, sex, hours driven per week, TABP, specific beliefs about

being able to engage in secondary tasks and drive safely, and TPB variables (N = 220).

While driving,

how often do

you. . .

Age Sex Hours

driving

per week

TABP Able to

drive safely

and read a

text

message

Able to

drive safely

and write a

text

message

Able to

drive

safely

and talk

on

phone

Able to drive

safely and

talk with

handsfree

unit

General

intention

towards

mobile phone

use while

driving

General

attitudes

towards

mobile

phone use

while driving

Social

norms

towards

mobile

phone use

while

driving

Perceived

behavioural

control

Read a text

message using

mobile phone

applications

.03 .07 -.18�� .10 -.21�� -.24�� -.13 -.05 -.05 -.09 -.07 .07

Send a text

message using

mobile phone

applications

.02 -.02 -.13 .07 -.22�� -.27�� -.14� -.05 -.11 -.12 -.14� .52

Read/look at

posts on social

networks

.02 -.07 -.04 .01 -.11 -.18�� -.06 .04 .12 -.16� -.11 -.01

Send or make

posts on social

network

-.01 -.05 .02 .01 -.11 -.14� -.04 .04 -.02 -.11 -.07 .01

Make a video

call

-.06 -.02 .01 .02 -.06 -.10 -.05 .01 -.05 -.12 -.08 .03

Make a call

using mobile

phone

applications

-.03 -.11 -.02 .02 -.11 -.16�� -.09 -.01 -.08 -.14� -.09 .06

Write an email -.05 -.13 -.01 -.01 -.13 -.16� .01 .07 -.08 -.18�� -.14� .06

Read an email -.06 -.12 .01 -.03 -.14� -.18�� -.01 .07 -.08 -.17� -.14� .04

Type an

address into

maps

.01 -.04 -.06 .01 -.19�� -.22��� -.06 .02 -.10 -.15� -.13� .03

Use an

intellectual

assistant

-.01 -.02 -.03 .01 -.07 -.06 -.01 .01 .03 -.05 -.01 .01

Take photos

“selfies”

-.03 -.10 .02 .06 -.20�� -.23��� -.06 -.04 -.03 -.03 -.11 .05

Variable Mean

(SD)

35.53

(10.54)

– 17.20

(18.03)

88.78

(17.57)

2.09 (1.56) 1.91 (1.50) 3.23

(2.07)

4.41 (2.25) 4.87 (2.20) 3.71 (2.12) 3.44 (2.14) 5.11 (1.96)

Variable range 19–70 – 0–150 27–134 1–7 1–7 1–7 1–7 1–7 1–7 1–7 1–7

Notes. ��� Significant at p < .001 �� p < .01 � p < .05. For sex, 1 = male, 2 = female.

https://doi.org/10.1371/journal.pone.0247006.t002

PLOS ONE Mobile phone applications use while driving in Ukraine

PLOS ONE | https://doi.org/10.1371/journal.pone.0247006 February 17, 2021 7 / 15

with a mobile phone, as well as the TPB variables. As can be observed from the table, very few

relationships emerged between these variables. There were no significant relationships

between any type of mobile phone interaction with age, TABP or hours driving per week.

Small but significant associations were found between beliefs about being able to drive safely

and read or write a text message with reading an email, typing an address into maps and taking

“selfie” photos while driving (rs ranged from .14 to .23, ps < .05). General attitudes towards using a mobile phone while driving and the belief about being able to safely drive and write a

text message were also weakly related to reading social media posts and making a call using

applications (rs ranged from .14 to .18, ps < .05). In all instances, drivers with stronger beliefs about their ability to engage in mobile phones use while driving and the benefits of doing so

reported more frequent interactions with their mobile phones while driving.

Table 3 displays the intercorrelations between demographic variables, specific beliefs about

being able to engage in secondary tasks and drive safely, attitudes, intentions, social norms and

perceived behavioural control. Age and hours driven were not significantly related to any of

these variables. TABP showed weak, positive relationships with the general attitude towards

mobile phone use while driving, social norms towards mobile phone use while driving, and

sex.

Relationships between interactions with mobile phones while driving and

perceptions about being able to do this safely

Hierarchical stepwise regressions were conducted on each of the selected interactions with a

mobile phone. These were taking “selfie” pictures while driving, typing an address into a map

application, reading or writing an email, making a video call and voice call using an applica-

tion, reading and writing a text message, as well as reading and writing social network posts.

In the first block, demographic information such as age, sex, driving purpose, and hours spend

driving per week were entered. In the second block, the Bortner (TABP) scale scores were

entered. In the third block, specific beliefs about being able to engage in secondary tasks and

Table 3. Associations between age, sex, hours driven per week, TABP, specific beliefs about being able to engage in secondary tasks and drive safely, and TPB vari-

ables (N = 220).

While driving, how often do you. . . 1 2 3 4 5 6 7 8 9 10 11

1. Age –

2. Sex .10 –

3. Hours driving per week .01 -.13 –

4. TABP .03 -.17� -.02 –

5. Belief about being able to drive safely and read a text message -.08 .10 -.01 -.01 –

6. Belief about being able to drive safely and write a text message -.10 .10 .05 -.03 .83�� –

7. Belief about being able to drive safely and talk on a mobile phone -.05 .02 .01 .02 .44��� .40��� –

8. Belief about being able to drive safely and talk with a handsfree unit .05 -.09 -.01 .05 .21�� .15� .60��� –

9. General intention towards mobile phone use while driving -.01 .02 .09 .10 0.3 .06 .17�� .09 –

10. General attitude towards mobile phone use while driving -.02 -.10 .08 .15� .05 .10 .08 -.02 .68��� –

11. Social norms towards mobile phone use while driving -.02 -.02 -.12 .16� .03 .05 .08 -.03 .60��� .80��� –

12. Perceived behavioural control -.03 -.05 -.06 .03 -.08 -.11 .05 .05 .18�� .20��� .23���

Note. ��� Significant at p < .001 �� p < .01 � p < .05.

https://doi.org/10.1371/journal.pone.0247006.t003

PLOS ONE Mobile phone applications use while driving in Ukraine

PLOS ONE | https://doi.org/10.1371/journal.pone.0247006 February 17, 2021 8 / 15

driving safely were entered. In the final block, the TPB variables such as the general attitude

and intention towards phone use while driving, social norms towards mobile phone use and

perceived behavioural control were entered. All were entered using the stepwise procedure,

the results of which are presented in Table 4.

As can be seen in Table 4, the only factors that were related to mobile phone usage were the

beliefs about being able to engage in secondary tasks and drive safely and positive attitudes

regarding the use of a mobile phone while driving. The regression analyses for using an intel-

lectual assistant, making a video call, writing posts on social networks, and reading / writing

text messages were not significant. With regard to taking “selfies”, typing an address onto a

map application and making a call using applications, drivers who believed they were more

able to drive safely while writing a text were more likely to engage in these behaviours. In

terms of writing an email and reading social network posts, a positive attitude towards the

behaviour was the key factor related to increased engagement in these activities. Age, sex and

type A behaviour were not significantly associated with any of the mobile phone interactions.

Discussion

The aim of the current study was to explore the frequencies of mobile phone applications use

while driving, as well as to examine the psychosocial factors that influence driver willingness to

use these applications despite the legislative bans in Ukraine. As such, we explored the associa-

tions between the TPB variables, TABP, as well as specific beliefs about being able to engage in

secondary tasks and drive safely and the frequencies of self-reported mobile phone applica-

tions usage while driving. Interestingly, no previous studies have attempted to investigate this

issue despite the fact that the functionality of mobile phones has rapidly increased over the last

10 years.

In general, our results showed that the Ukrainian drivers are most likely to use mobile

phone applications for reading text messages, followed by sending text messages, taking selfies,

typing an address into maps and writing / reading an email. The self-reported frequencies

overall suggest that participants regularly use mobile phone applications while driving, which

consequently may pose a safety risk not just to the drivers themselves, but also to other road

users. When exploring the frequencies of standard mobile phone functions usage, scholars

have previously reported that drivers most frequently use their mobile phones for answering /

making calls, with much smaller proportions of drivers reporting reading / sending text

Table 4. Factors related to different types of mobile phone applications use while driving (N = 220).

B (95% CI) β p 1. Take photos “selfies” R

2 0.03; F (1,218) = 7.03, p = 0.009

Belief about being able to drive safely and write a text message -.25 (-.44–.07) -.18 .009

2. Type an address into maps R 2

0.04; F (1,218) = 11.18, p = 0.001 Belief about being able to drive safely and write a text message -.31 (-.49–.13) -.22 .001

3.Write an email R 2

0.01; F (1,218) = 4.15, p = 0.04 General attitude towards mobile phone use while driving -.14 (-.28–.01) -.14 .043

4.Read an email R 2

0.02; F (1,218) = 4.15, p = 0.04 Belief about being able to drive safely and read a text message -.19 (-.37–.01) -.14 .043

5. Make a call using mobile phone applications R 2

0.03; F (1,218) = 7.02, p = 0.009 Belief about being able to drive safely and write a text message -.18 (-.43–.06) -.18 .009

6. Read/look at posts on social networks R 2

0.03; F (2,217) = 4.24, p = 0.041 Belief about being able to drive safely and write a text message -.19 (-.39–.01) -.13 .06

General attitude towards mobile phone use while driving -.15 (-.28–.01) -.14 .04

https://doi.org/10.1371/journal.pone.0247006.t004

PLOS ONE Mobile phone applications use while driving in Ukraine

PLOS ONE | https://doi.org/10.1371/journal.pone.0247006 February 17, 2021 9 / 15

messages while driving (e.g., [22,42]). Our overall findings make an important contribution to

the existing knowledge, suggesting that the frequency of mobile phone applications usage

should be considered alongside the standard mobile phone functions to identify the different

dimensions of this risky behaviour.

Our findings indicated that a large proportion of the participants would read and write a

text message using mobile phone applications while driving. Because the process of writing /

reading text messages using the existing applications, such as Viber and WhatsApp, is exactly

the same as writing / reading using a traditional text messaging function, this finding is partic-

ularly concerning (e.g., [13,15]). This is because when drivers are actively engaged in texting

behaviour, they look 400 times more away from the road [25,43]. In addition, nearly one third

of the drivers read and/or wrote an e-mail at least once while driving in the previous year. This

is concerning, given that this secondary task is as demanding on cognitive resources as writing

a text message. What remains unknown and requires further research, however, is an under-

standing of what kind of emails/text messages (e.g., work-related or personal) drivers tend to

deal with while driving a vehicle. For example, a study conducted by Porter and Kakabadse

[44] on the association between the use of technology and work addiction showed that those

individuals who are workaholics routinely use technology outside work hours / environment.

When applied to the driving context, this could possibly mean that those drivers who tend to

access their work emails / read text messages from outside work hours / environment may also

have an urge to do so while driving. Understanding the motivations across different mediums

will help design strategies to reduce distraction behaviour.

Nearly one quarter of the participants in our study had made a call using mobile phone

applications at least once in the past year. In contrast to this, a previous study by Sullman et al.

[22] employing a sample of the Ukrainian drivers showed that approximately 90% of partici-

pants reported making or answering a call at least once in the past year using a standard mobile

phone function. Given that making a call via a mobile phone application while driving requires

a sufficient Wi-Fi network connection, drivers may choose to use a standard phone call func-

tion over the applications simply because of the practicality. In addition, making a standard

call while driving could be considered a preferable option for drivers in terms of ease of use.

Reading and / or writing posts on social networks were found to be relatively common

amongst drivers, although approximately 70% of the sample reported they had never engaged

in this behaviour. This finding aligns with research from the UK [45] that found 24% of young

adults and 8% of adults admitted to using their mobile phones for social networking while

driving. This behaviour is of particular concern, as the results of a driving simulator study con-

ducted jointly by the IAM RoadSmart charity and the Transport Research Laboratory (TRL)

indicated that using a mobile phone for social networking increases reaction times by 37.6%,

while driving under the influence of cannabis, for instance, increases reaction times by 21%

[17]. One possible explanation for this finding could be that those individuals, who overuse

social networking applications on mobile phones in a day-to-day life, may not be able to con-

trol this behaviour in the driving context. Previous research commonly explains this type of

problematic phone use behaviour within the terms of mobile phone dependency (e.g., [31,46])

social media addiction (e.g., [47,48]), and technology addiction (e.g., [49]). In addition, mak-

ing and / or reading posts on social networks could be used by drivers as a way to deal with

their driving experience or to regulate emotion. For instance, Stephens et al. [50] found that

across a 13-month period, over 80,000 twitter posts # road rage were made and seemingly

while driving. A large amount of these included a 7- second video clip, filmed by the drivers in

situ.

Rather alarmingly, almost half of the drivers in our study reported taking a selfie while driv-

ing at least once in the last year. However, this finding is somewhat unsurprising as it is

PLOS ONE Mobile phone applications use while driving in Ukraine

PLOS ONE | https://doi.org/10.1371/journal.pone.0247006 February 17, 2021 10 / 15

estimated that at least one million selfies are taken per day worldwide [51], which highlights

how prevalent this phenomenon is these days. In order to explain the reasons why people take

selfies in general, previous research has mostly concentrated on finding the associations

between this behaviour and various personality traits, such as narcissism (e.g., [52]), attention

seeking and low self-esteem (e.g., [53]). In the driving context, drivers may possibly take selfies

to reinforce their self-concept given that having a vehicle in Ukraine may still be considered to

be prestigious. In addition, as previously suggested by Stephens et al. [50] taking selfies may

also play a role in the processing of the driving experience or emotion regulation. Future

research should further explore the goals/motivations behind using social networking applica-

tions by drivers and how individual differences may impact their use.

The results of this study indicate that both general positive attitudes towards using mobile

phone while driving and approval of this behaviour by significant others were significantly

related to frequent mobile phone application usage amongst the Ukrainian drivers. This

includes sending a text message and making a call using mobile phone applications, reading /

looking at posts on social networks, writing and reading an email, as well as typing an address

into a map application. The results also showed that a positive attitude towards mobile phone

use while driving was related to frequent writing of emails and reading posts on social net-

works. This is somewhat consistent with previous research, in that drivers who had positive

attitudes towards mobile phone use and who perceive that their significant others would

approve of this behaviour tended to use standard mobile phone functions more frequently

while driving (e.g., [27,54,55]). This finding may indicate that drivers form positive attitudes

towards this dangerous behaviour despite being aware of the risk associated with engaging in

it while driving. In fact, previous research has shown that most drivers are aware of the risk of

using a mobile phone while driving (e.g., [56,57]); however, they still engage in this behaviour

as the perceived benefits of it may outweigh the associated risks [58]. In addition, being aware

of the risks does not necessarily serve as a disincentive factor when it comes to engaging in this

behaviour (e.g., [59]). As for the association between social approval and frequencies of mobile

phone usage while driving, this highlights the necessity of incorporating themes of social influ-

ence when developing interventions to tackle this dangerous behaviour more efficiently (e.g.,

[25]).

Our results also showed that those drivers, who had stronger beliefs about being able to

drive safely and engage in secondary tasks, used their mobile phones while driving more fre-

quently. This finding was supported by the results of the hierarchical stepwise regressions,

indicating that beliefs about being able to drive safely and write or read a text message affect

the frequency of using a mobile phone to take a selfie while driving, as well as typing an

address onto maps and making a call. This finding can be explained in two ways. Firstly, it

suggests that drivers might not always be able to detect and accurately judge changes in

their driving performance caused by simultaneously using a mobile phone and driving. In

addition, De Craen et al. [60] report that those drivers who inaccurately assess their driving

performance and skills tend to engage in driving and secondary tasks that are too cogni-

tively demanding and potentially unsafe. Secondly, most drivers tend to be overconfident in

their driving skills, which may subsequently lead to overestimating their multitasking

abilities.

Finally, in the current sample of the Ukrainian drivers, type A behaviour pattern did not

significantly predict any type of mobile phone applications usage. Although previous research

has reported there to be a strong link between the TABP and RTAs (e.g., [33,34]), our study

revealed that the reasons drivers use mobile phone applications while driving may only be

slightly related to their impatience and time urgency.

PLOS ONE Mobile phone applications use while driving in Ukraine

PLOS ONE | https://doi.org/10.1371/journal.pone.0247006 February 17, 2021 11 / 15

Limitations

This study has several limitations. Firstly, the data were collected using self-report question-

naires, which may have been affected by social desirability bias. However, in order to mitigate

its influence, participants were assured of anonymity and confidentially, which is considered

an efficient measure against this phenomenon (e.g., [61]). Secondly, the vast majority of the

participants were males, which may reduce the ability to generalise these findings to the driv-

ing population of Ukraine. However, given that only 22% of Ukrainian drivers are females,

according to the Marketing Index TNS Global report [62], the study sample was comparatively

representative of the general population, in terms of the sex ratio. In addition, the study partic-

ipants were relatively young, which means that they might have been more familiar with smart

phones applications and therefore would be more likely to use them while driving. Thirdly,

although some of the participants were invited to take part in the study using a snowballing

technique, a large proportion of the sample were students / staff from the National Aviation

University and members of the Ukrainian motorists’ forum. It can be presumed that they may

significantly differ from the general population in terms of their socio-economic status, access

to technology and types of the mobile phones they use (smart phones versus older mobile

phones). Considering these points, future research should aim to examine the psychosocial

factors relating to the mobile phone applications use among a more representative sample.

Summary and practical implications

Despite the growing functionality of mobile phones and continuous introduction of new appli-

cations that significantly increase an individuals’ level of mobile phone involvement in day-to-

day life, there is little information regarding how these innovations affect the prevalence of

mobile phone use while driving. To our knowledge, this study is one of the first to report the

frequencies of mobile phone applications use, which was conducted using a sample of Ukrai-

nian drivers. Our results revealed that the majority of drivers would frequently read / write a

text message, type an address into maps and write / read an email while driving. Given the

known risk associated with writing / reading a text message, the frequency of these behaviours

is concerning. In addition, a large proportion of drivers reported haven taken a selfie while

driving in the past year; suggesting social media may have infiltrated the driving experience.

The engagement with various types of applications may pose additional risks to individuals’

driving performance, thereby potentially increasing the chances of crash involvement. The

reported frequencies highlight the need to include mobile phone applications when exploring

the “real” levels of mobile phone use when driving. In addition, and in line with previous road

safety research, positive attitudes, perceived social approval and lack of risk acceptance were all

positively related to mobile phone use. This suggests that future research should further

explore the goals/motivations behind using mobile phone applications while driving and how

individual differences may impact their use. Lastly, future studies should also explore the per-

ceived risks related to mobile phone applications usage while driving.

Author Contributions

Conceptualization: Amanda N. Stephens, Mark J. M. Sullman.

Data curation: Mark J. M. Sullman.

Formal analysis: Tetiana Hill, Amanda N. Stephens.

Investigation: Tetiana Hill.

Methodology: Amanda N. Stephens, Mark J. M. Sullman.

PLOS ONE Mobile phone applications use while driving in Ukraine

PLOS ONE | https://doi.org/10.1371/journal.pone.0247006 February 17, 2021 12 / 15

Project administration: Tetiana Hill.

Supervision: Mark J. M. Sullman.

Writing – original draft: Tetiana Hill.

Writing – review & editing: Amanda N. Stephens, Mark J. M. Sullman.

References 1. National Highway Traffic Safety Administration. State traffic data: 2018 data. Traffic Safety Facts.

National Center for Statistics and Analysis. 2020 May [cited 2021 Jan 20]. Report No.: DOT HS 812 9.

Available from: https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812948.

2. Young K, Regan M, Hammer M. Driver distraction: A review of the literature. Distracted driving. 2007;

27:79–405.

3. Oviedo-Trespalacios O, Haque MM, King M, Washington S. Should I text or call here? A situation-

based analysis of drivers’ perceived likelihood of engaging in mobile phone multitasking. Risk Anal.

2018; 38(6):2144–2160.

4. Kinnear N, Helman S, Walter L. A segmentation of novice drivers in Great Britain: Factors associated

with intention to take advanced driver training. IHS; 2011.

5. Dingus TA, Guo F, Lee S, Antin JF, Perez M, Buchanan-King M, et al. Driver crash risk factors and prev-

alence evaluation using naturalistic driving data. Proc Natl Acad Sci. 2016; 113:2636–2641. https://doi.

org/10.1073/pnas.1513271113 PMID: 26903657

6. Oviedo-Trespalacios O, Nandavar S, Newton JDA, Demant D, Phillips JG. Problematic use of mobile

phones in Australia. . . is it getting worse?. Front Psychiatry. 2019; 10:105. https://doi.org/10.3389/fpsyt.

2019.00105 PMID: 30914975

7. Oviedo-Trespalacios O, Haque MM, King M, Washington S. Understanding the impacts of mobile

phone distraction on driving performance: A systematic review. Transp Res Part C Emerg Technol.

2016; 72:360–380.

8. Simmons SM, Hicks A, Caird JK. Safety-critical event risk associated with cell phone tasks as measured

in naturalistic driving studies: A systematic review and meta-analysis. Accid Anal Prev. 2016; 87:161–

169. https://doi.org/10.1016/j.aap.2015.11.015 PMID: 26724505

9. Asbridge M, Brubacher JR, Chan H. Cell phone use and traffic crash risk: a culpability analysis. Int J

Epidemiol. 2013; 42(1):259–267. https://doi.org/10.1093/ije/dys180 PMID: 23159829

10. Tivesten E, Dozza M. Driving context and visual-manual phone tasks influence glance behavior in natu-

ralistic driving. Transp Res Part F Traffic Psychol Behav. 2014; 26: 258–272.

11. Huemer AK, Schumacher M, Mennecke M, Vollrath M. Systematic review of observational studies on

secondary task engagement while driving. Accid Anal Prev. 2018; 119:225–236. https://doi.org/10.

1016/j.aap.2018.07.017 PMID: 30055511

12. Luria G. The mediating role of smartphone addiction on the relationship between personality and young

drivers’ smartphone use while driving. Transp Res Part F Traffic Psychol Behav. 2018; 59:203–211.

13. Ortiz C, Ortiz-Peregrina S, Castro JJ, Casares-López M, Salas C. Driver distraction by smartphone use

(WhatsApp) in different age groups. Accid Anal Prev. 2018; 117:239–249. https://doi.org/10.1016/j.aap.

2018.04.018 PMID: 29723735

14. Kujala T, Mäkelä J. Naturalistic study on the usage of smartphone applications among Finnish drivers.

Accid Anal Prev. 2018; 115:53–61. https://doi.org/10.1016/j.aap.2018.03.011 PMID: 29549771

15. McNabb J, Gray R. Staying connected on the road: a comparison of different types of smart phone use

in a driving simulator. PLoS One. 2016; 11(2):e0148555. https://doi.org/10.1371/journal.pone.0148555

PMID: 26886099

16. Ayar D, Bektas M, Bektas I, Kudubes AA, Ok YS, Altan SS et al. The effect of adolescents’ internet

addiction on smartphone addiction. J Addict Nurs. 2016; 28(4):210–214.

17. Hanlon M. Using Facebook While Driving More Dangerous Than Drinking, Texting or Marijuana. Giz-

mag, Mobile Technology [Internet]. 2012 Dec [cited 2021 Jan 20] Available from http://www.gizmag.

com/mobile-phones-and-driving-research-from-iam-institute-of-advanced-motorists/21678/2015.

18. Foss RD, Goodwin AH, McCartt AT, Hellinga LA. Short-term effects of a teenage driver cell phone

restriction. Accid Anal Prev. 2009; 41(3):419–424. https://doi.org/10.1016/j.aap.2009.01.004 PMID:

19393787

PLOS ONE Mobile phone applications use while driving in Ukraine

PLOS ONE | https://doi.org/10.1371/journal.pone.0247006 February 17, 2021 13 / 15

19. Rajalin S, Summala H, Pöysti L, Anteroinen P, Porter BE. In-car cell phone use and hazards following

hands free legislation. Traffic Inj Prev. 2005; 6(3):225–229. https://doi.org/10.1080/

15389580590969166 PMID: 16087462

20. Stephens AN, Nieuwesteeg M, Page-Smith J, Fitzharris M. Self-reported speed compliance and atti-

tudes towards speeding in a representative sample of drivers in Australia. Accid Anal Prev. 2017;

103:56–64. https://doi.org/10.1016/j.aap.2017.03.020 PMID: 28384489

21. Stephens AN, Bishop CA, Liu S, Fitzharris M. Alcohol consumption patterns and attitudes toward drink-

drive behaviours and road safety enforcement strategies. Accid Anal Prev. 2017; 98:241–251. https://

doi.org/10.1016/j.aap.2016.10.011 PMID: 27770690

22. Sullman MJM, Hill T, Stephens AN. Predicting intentions to text and call while driving using the theory of

planned behaviour. Transp Res Part F Traffic Psychol Behav. 2018; 58:405–413.

23. Przepiorka AM, Blachnio AP, Prat F, Sullman MJM. Do beliefs differ between frequent and infrequent

hand-held and hands-free phone users while driving? A Polish study. J of Public Health. 2019; 21:1–9.

24. Hill T, Sullman MJM, Stephens AN. Mobile phone involvement, beliefs, and texting while driving in

Ukraine. Accid Anal Prev. 2019; 125:124–131. https://doi.org/10.1016/j.aap.2019.01.035 PMID:

30743175

25. White KM, Hyde MK, Walsh SP, Watson B. Mobile phone use while driving: An investigation of the

beliefs influencing drivers’ hands-free and hand-held mobile phone use. Transp Res Part F Traffic Psy-

chol Behav. 2010; 13(1):9–20.

26. Rozario M, Lewis I, White KM. An examination of the factors that influence drivers’ willingness to use

hand-held mobile phones. Transp Res Part F Traffic Psychol Behav. 2010; 13(6): 365–376.

27. Ajzen I. The theory of planned behavior. Organ Behav Hum Decis Process. 1991; 50(2):179–211.

28. Waddell LP, Wiener KK. What’s driving illegal mobile phone use? Psychosocial influences on drivers’

intentions to use hand-held mobile phones. Transp Res Part F Traffic Psychol Behav. 2014; 22:1–11.

29. Walsh SP, White KM, Hyde MK, Watson B. Dialling and driving: Factors influencing intentions to use a

mobile phone while driving. Accid Anal Prev. 2008; 40(6):1893–1900. https://doi.org/10.1016/j.aap.

2008.07.005 PMID: 19068291

30. Gauld CS, Lewis I, White KM, Fleiter JJ, Watson B. Smartphone use while driving: What factors predict

young drivers’ intentions to initiate, read, and respond to social interactive technology?. Comput Human

Behav. 2017; 76:174–183.

31. Bianchi A, Phillips JG. Psychological predictors of problem mobile phone use. Cyberpsychol Behav.

2005; 8(1):39–51. https://doi.org/10.1089/cpb.2005.8.39 PMID: 15738692

32. Sween M, Ceschi A, Tommasi F, Sartori R, Weller J. Who is a Distracted Driver? Associations between

Mobile Phone Use while Driving, Domain-Specific Risk Taking, and Personality. Risk Anal. 2017; 37

(11):2119–2131. https://doi.org/10.1111/risa.12773 PMID: 28230266

33. Nabi H, Consoli SM, Chastang JF, Chiron M, Lafont S, Lagarde E. Type A behavior pattern, risky driving

behaviors, and serious road traffic accidents: a prospective study of the GAZEL cohort. Am J Epidemiol.

2005; 161(9):864–870. https://doi.org/10.1093/aje/kwi110 PMID: 15840619

34. Perry AR, Baldwin DA. Further evidence of associations of type A personality scores and driving-related

attitudes and behaviors. Percept Mot Skills. 2000; 91(1):147–154. https://doi.org/10.2466/pms.2000.

91.1.147 PMID: 11011886

35. West R, Elander J, French D. Mild social deviance, Type-A behaviour pattern and decision-making

style as predictors of self-reported driving style and traffic accident risk. Br J Psychol. 1993; 84(2):207–

219. https://doi.org/10.1111/j.2044-8295.1993.tb02474.x PMID: 8319056

36. Friedman M, Rosenman RH. Association of specific overt behavior pattern with blood and cardiovascu-

lar findings: blood cholesterol level, blood clotting time, incidence of arcus senilis, and clinical coronary

artery disease. JAMA. 1959; 169(12):1286–1296.

37. Friedman M, Rosenman RH. Overt behavior pattern in coronary disease: Detection of overt behavior

pattern A in patients with coronary disease by a new psychophysiological procedure. JAMA. 1960; 173

(12):1320–1325. https://doi.org/10.1001/jama.1960.03020300032011 PMID: 13825133

38. Perry AR. Type A behavior pattern and motor vehicle drivers’ behavior. Percept Mot Skills. 1986; 63

(2):875–878. https://doi.org/10.2466/pms.1986.63.2.875 PMID: 3808868

39. Evans GW, Palsane MN, Carrère S. Type A behavior and occupational stress: A cross-cultural study of blue-collar workers. J Pers Soc Psychol. 1987; 52(5):1002–1033. https://doi.org/10.1037//0022-3514.

52.5.1002 PMID: 3585696

40. Bortner RW. A short rating scale as a potential measure of pattern A behavior. J Chronic Dis. 1969; 22

(2):87–91. https://doi.org/10.1016/0021-9681(69)90061-7 PMID: 5795891

PLOS ONE Mobile phone applications use while driving in Ukraine

PLOS ONE | https://doi.org/10.1371/journal.pone.0247006 February 17, 2021 14 / 15

41. Cohen J, Cohen P, West SG, Aiken LS. Applied multiple regression/correlation analysis for the behav-

ioral sciences. Routledge; 2013.

42. Prat F, Planes M, Gras ME, Sullman MJM. An observational study of driving distractions on urban roads

in Spain. Accid Anal Prev. 2015; 74:8–16. https://doi.org/10.1016/j.aap.2014.10.003 PMID: 25463939

43. Hosking SG, Young KL, Regan MA. The effects of text messaging on young drivers. Hum Factors.

2009; 51(4):582–592. https://doi.org/10.1177/0018720809341575 PMID: 19899366

44. Porter G, Kakabadse NK. HRM perspectives on addiction to technology and work. Journal of Manage-

ment Development. 2006; 25(6):535–560.

45. Royal Automobile Club [RAC]. Royal Automobile Club Foundation. Half of motorists txt+drv [Internet].

2008 Nov [cited 2021 Jan 20]. Available from: http://www.racfoundation.org/media-centre/half-britains-

motorists-text-drive.

46. Takao M, Takahashi S, Kitamura M. Addictive personality and problematic mobile phone use. Cyberp-

sychol Behav. 2009; 12(5): 501–507. https://doi.org/10.1089/cpb.2009.0022 PMID: 19817562

47. Park WK. Mobile phone addiction. In: Mobile communications. Springer, London; 2017. pp. 253–272.

48. Salehan M, Negahban A. Social Networking on Smartphones: When Mobile Phones Become Addictive.

Comput Human Behav. 2013; 29:2632–2639.

49. Douglas AC, Mills JE, Niang N, Stepchenkova S, Byun Set al. Internet addiction: Meta-synthesis of

qualitative research for the decade 1996–2006. Comput Human Behav. 2006; 24(6):3027–3044.

50. Stephens AN, Trawley SL, Ohtsuka K. Venting anger in cyberspace: Self-entitlement versus self-pres-

ervation in# roadrage tweets. Transp Res Part F Traffic Psychol Behav. 2006; 42:400–410.

51. Petrow S. Your digital life: How many selfies are too many? [Internet]. 2015 Oct [cited 2021 Jan 20].

Available from: http://experience.usatoday.com/weekend/story/lifestyle/2014/06/19/your-digital-life-

how-many-selfies-are-too-many/10936079/.

52. Miller JD, Hoffman BJ, Gaughan ET, Gentile B, Maples J, Campbell WK. Grandiose and vulnerable nar-

cissism: A nomological network analysis. J Pers. 2011; 79:1013–1042. https://doi.org/10.1111/j.1467-

6494.2010.00711.x PMID: 21204843

53. Barry CT, Doucette H, Loflin DC, Rivera-Hudson N, Herrington LL. “Let me take a selfie”: Associations

between self-photography, narcissism, and self-esteem. Psychol Pop Media Cult. 2017; 6(1):48–57.

54. Bazargan-Hejazi S, Teruya S, Pan D, Lin J, Gordon D, Krochalk PCet al. The theory of planned behav-

ior (TPB) and texting while driving behavior in college students. Traffic Inj Prev. 2017; 18(1):56–62.

https://doi.org/10.1080/15389588.2016.1172703 PMID: 27088534

55. Przepiorka AM, Błachnio AP, Sullman MJM. Factors influencing intentions to text while driving among

Polish drivers. Transp Res Part F Traffic Psychol Behav. 2018; 55:306–313.

56. White MP, Eiser JR, Harris PR, Pahl S. Who reaps the benefits, who bears the risks? Comparative opti-

mism, comparative utility, and regulatory preferences for mobile phone technology. Risk Anal. 2017; 27

(3):741–753.

57. Nelson E, Atchley P, Little TD. The effects of perception of risk and importance of answering and initiat-

ing a cellular phone call while driving. Accid Anal Prev. 2009; 41(3):438–444. https://doi.org/10.1016/j.

aap.2009.01.006 PMID: 19393790

58. White MP, Eiser JR, Harris PR. Risk perceptions of mobile phone use while driving. Risk Anal. 2004; 24

(2):323–334. https://doi.org/10.1111/j.0272-4332.2004.00434.x PMID: 15078303

59. Akter T. Social media addiction, resistance, and influence of awareness: Measurement of psychology

students’ resistance to Facebook addiction. Mediterr J Soc Sci. 2014; 5(8): 456–460.

60. De Craen S, Twisk DAM, Hagenzieker MP, Elffers H, Brookhuis KA. Do young novice drivers overesti-

mate their driving skills more than experienced drivers? Different methods lead to different conclusions.

Accid Anal Prev. 2007; 43(5):1660–1665.

61. Sullman MJM, Taylor JE. Social desirability and self-reported driving behaviours: Should we be wor-

ried? Transp Res Part F Traffic Psychol Behav. 2010; 13(3):215–221.

62. TNS global. Marketing index TNS Global annual report [Internet]. 2016 Dec [cited 2021 Jan 20]. Avail-

able from http://www.tnsglobal.com/news-centre.

PLOS ONE Mobile phone applications use while driving in Ukraine

PLOS ONE | https://doi.org/10.1371/journal.pone.0247006 February 17, 2021 15 / 15

Copyright of PLoS ONE is the property of Public Library of Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.