Examination of the Relationship Between Smartphone Dependency

and Driving Behaviour in Young Drivers: Preliminary Analysis

Won Sun Chen

, James Boylan

and Denny Meyer

Department of Health Sciences and Biostatistics, Swinburne University of Technology, John Street, Hawthorn, Australia

Keywords: Smartphone Use, Driving Behaviour, Young Drivers.

Abstract: The smartphone has emerged as one of the important necessities in our daily lives. However, smartphone

dependency can have negative as well as positive impacts on our overall well-being. Young adults are likely

to demonstrate particularly problematic dependency on smartphone use. This is also the age group with a

disproportionate contribution to road deaths in Australia (approximately 25% for 17-25 year olds), for reasons

such as lack of experience and road awareness, resulting in bad choices or poor assessment of a road situation.

The current study aimed to examine the relationship between smartphone dependency and driving behaviour

in young people provided with the basic (control group) and extended (intervention group) features of an in-

car telematics device. Participants aged between 18-30 were invited to complete the self-reported

questionnaires, and an in-car telematics device with basic features was then activated over a 30-day period in

their vehicles. At the start of the second 30-day period, half of the participants had their telematics installation

extended. A linear mixed model analysis was conducted to allow for the hierarchical structure of the telematics

data, with trips nested within drivers. The results suggest that in-car telematics devices can be adopted to

improve the driving behaviour of young drivers.

1 INTRODUCTION

1.1 Background

In 2020 about 12% of the Australian population

(approximately 3.2 million) consisted of young adults

aged 15-24 years and by 2026 this number is

projected to reach 5.1 million (AIHW, 2021). In

2020-21, the reported number of road hospitalisations

and deaths was dominated by those aged 15-24 years,

accounting for 430 hospitalisations and 6.9 deaths per

100,000 young people in this age group (AIHW,

2022). Globally, young drivers have emerged to be

over-represented in accident deaths partly due to lack

of driving experience and developmental factors

(Arnett, 2022).

Smartphone ownership is gaining popularity in

Australia. In 2019, almost all Australians aged 18 and

above owned a smartphone (Granwal, 2022a). The

number of smartphone users in Australia is estimated

to reach 23.6 million by 2026, which translates to an

https://orcid.org/0000-0001-9077-3530

https://orcid.org/0000-0003-3089-8408

https://orcid.org/0000-0002-9902-0858

87% smartphone penetration rate in 2026 compared

to just under 75% in 2022 (Granwal, 2022b). A study

conducted by White et al. (2010) revealed that young

adults are disproportionately represented amongst the

most frequent mobile phone users in Australia

compared to other advanced countries. These young

adults tended to engage in excessive phone use and

demonstrated indications of phone addiction, such as

checking their phone continuously and thinking about

their mobile phone constantly. With the advancement

of technology, drivers have tended to broaden their

mobile phone use from traditional usage (receiving or

making call and texting) to also include using social

media applications, reading emails, taking

photographs and videos, as well as navigation

guidance. If conducted while driving these activities

are likely to put these young drivers as well as other

drivers in danger (Kaviani et al., 2020a).

Furthermore, more screen time has been found to be

significantly associated with increasingly

problematic smartphone dependency in younger

428

Chen, W., Boylan, J. and Meyer, D.

Examination of the Relationship Between Smartphone Dependency and Driving Behaviour in Young Drivers: Preliminary Analysis.

DOI: 10.5220/0012028500003479

In Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2023), pages 428-435

ISBN: 978-989-758-652-1; ISSN: 2184-495X

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

people (Kaviani et al., 2020b). Interestingly, a recent

Canadian study has shed some light on the reasons for

high mobile device usage during the COVID-19

pandemic. This was associated with improved social

connectedness, productivity and mental well-being

(Jonnatan et al., 2022).

In 2021, the World Health Organisation reported

that drivers engaging in mobile phone use while

driving were found to be four times more likely to be

involved in an accident than those drivers without

such engagement (WHO, 2021). Additionally, mobile

phone use while driving was found to affect drivers’

response time to braking and traffic signals (WHO,

2021; Strayer & Johnston, 2001), also influencing

their ability to maintain lateral vehicle control (Caird,

et al., 2014). Existing literature has revealed that the

main factors associated with young driver deaths on

the roads included mobile phone use while behind the

wheel (Li et al., 2016), internal factors (e.g. lack of

concentration), driver’s behaviour and driver’s

gender (Koppel et al., 2022). Arvin & Khattak (2020)

highlighted the alarming finding that a one-second

delay due to dialling or texting elevated the chance of

an accident by 5.6% and 3.6%, respectively.

1.2 In-Car Telematics

In-car telematics is defined as a system capable of

measuring and capturing real-time car usage. These

systems can easily be installed in any car and they

generally collect data for variables such as

longitudinal acceleration (forwards and backwards

movement), lateral acceleration (sideways

movement), yaw (turning speed), global positioning

system (GPS) coordinates, timestamps, vehicle

speed, speed zone, revolutions per minute (RPM),

engine load, mass airflow intake, and carbon dioxide

(CO2) intake (SIRA, 2019).

Over the last decade, the use of in-car telematics

has gained popularity because of evolving

information and communications technology (SIRA,

2019). The existing literature has found contrasting

results about the use of in-car telematics along with

the effect of feedback from these devices for

influencing driving behaviour. For example, the use

of in-car telematics along with feedback (Wijnands et

al., 2018) and related incentives for good driving

(Peer et al., 2020), tended to improve driving

behaviour. However, no such improvement was

found in a study conducted by Stevenson et al. (2021).

Findings from a Young Drivers Telematics Trial

(YDTT) conducted in Australia revealed that

telematics use led to positive impacts for young driver

behaviour (SIRA, 2019). However, the degree of

behaviour and sociodemographic characteristics of

positive impact depends on the previous driving

behaviour and the sociodemographic characteristics

of the driver, as well as the surrounding traffic

environment. Interestingly, the young drivers

reflected that the telematics devices they experienced

in the study had constantly reminded them to be more

aware of their driving behaviour (SIRA, 2019).

1.3 Study Objective

The present study aimed to compare the relationship

between smartphone dependency and driving

behaviour for participants allocated with the basic and

extended features of an in-car telematics device. Both

smartphone dependency and driving behaviour were

assessed using self-reported measures, while the

braking behaviour was investigated through

telematics data.

2 METHODOLOGY

This section provides an overview of the participant

characteristics, the self-reported measures used, the

telematics data considered and the statistical methods

used for analysis.

2.1 Participants

Participants aged between 18 and 30 years, residing

in the state of Victoria in Australia and with a valid

Victorian driver license were invited to join this study

between January and December 2022. This

naturalistic study collected data through in-car

telematics devices for a 60-day driving period, with

telematics data collected as described in section 2.2,

and a research questionnaire as explained in section

2.3. All participants completed questionnaires before

the start of the study (baseline), at the end of the first

30-day driving period (Time 1) and at the end of the

second 30-day driving period (Time 2).

This study was approved by the Swinburne

University Human Research Ethics Committee (SHR

Project 20225945-9779).

2.2 Telematics Data

The GOFAR in-car telematics devices shown in

Figure 1 were adopted for this study. All participants

were asked to install an adapter to their car’s

diagnostic port. Next, they were required to download

a GOFAR app available at the Google Play or the

Apple App store on their smartphone. After that,

Examination of the Relationship Between Smartphone Dependency and Driving Behaviour in Young Drivers: Preliminary Analysis

429

participants needed to sync their smartphone with this

adapter. The GOFAR app is capable of monitoring a

car’s efficiency, state of repair and performance. The

combination of the GOFAR app and adapter were

regarded as the basic feature of the telematics devices.

The extended feature of this device, known as the ray,

aimed to provide feedback to help participants to

become safer and more efficient in their driving.

Figure 1: GOFAR devices.

The telematics device recorded and transferred

vehicle real-time data, such as speed, braking score,

GPS coordinates, timestamp, RPM, engine load, fuel

consumption and emission, in two second intervals

when the ignition was engaged and the driver’s phone

had Bluetooth switched on.

2.3 Research Questionnaire

The research questionnaire consisted of three

components: 1) questions related to participant

demographic characteristics and driving

characteristics; 2) nomophobia severity questionnaire

(NMP-Q) developed by Yildirim & Correia (2015);

and 3) driving behaviour questionnaire (DBQ)

established as well as validated by Lawton et al.

(1997), Parker et al. (1998) and Lajunen et al. (2004).

2.3.1 Nomophobia Severity Questionnaire

(NMP-Q)

Nomophobia, which is an abbreviation for “no mobile

phone phobia”, is defined as a collection of symptoms

experienced when without a phone including 1) being

unable to communicate, 2) losing connectedness, 3)

not being able to access information and 4)

inconvenience (Yildirim & Correia, 2015).

The Nomophobia severity questionnaire (NMP-

Q) comprises 20 items across the above four

symptom domains. Each item is rated using a seven-

point Likert scale (1=strongly disagree and

7=strongly agree). The total score is calculated by

summing item responses to produce a score ranging

from 20 to 140, where higher scores indicate higher

levels of nomophobia. The total score has further

been categorised as “absence of nomophobia” (score

less than 20), “mild level of nomophobia” (score of

20 or more to less than 60), “moderate level of

nomophobia” (score of 60 or more to less than 100),

and “severe level of nomophobia” (score of 100 or

more) (Yildirim & Correia, 2015). In this preliminary

study this categorical measure was converted into a

binary measure identifying drivers with moderate to

severe nomophobia levels.

2.3.2 Driving Behaviour Questionnaire

(DBQ)

Globally, the Manchester Driver Behaviour

Questionnaire (DBQ) can be regarded as one of the

most well accepted self-reported measures of aberrant

driver behaviour for the last 20 years. The extended

27-item DBQ includes items pertaining to aggressive

violations, ordinary violations, errors and lapses

(Lawton et al., 1997; Parker et al., 1998; Lajunen et

al., 2004).

Participants were asked to respond to the 27

driving behaviour items using a six-point Likert scale

(0=never and 5=nearly all the time), based on the

vehicle they most frequently drove (Reason et al.,

1990). The total score is obtained by summing item

responses to produce a score ranging from 0 to 135,

where higher scores show more aggressive driving

behaviour (Ang et al., 2019).

2.4 Data Preparation

2.4.1 Data Collection

According to Figure 2, all participants were provided

with a basic configuration of the device during Time

1. Half of these participants were randomly allocated

to enjoy additional features of the device such as real-

time driver feedback and an alert system during Time

2 (intervention group). The remaining half of the

participants continued their driving during Time 2

with the basic configuration of the device (control

group). The main outcome measure captured from the

telematics device for each of these periods was the

braking score, with higher scores indicating less

aggressive braking behaviour. Nomophobia and DBQ

Scores were collected at the start of Time 1 and Time

2 while average Braking Scores were computed using

the Braking data collected within each of these 30-

day periods. At present, the data collection for Time

2 is still ongoing for most of the participants.

2.4.2 Data Cleaning

There were challenges associated with the cleaning of

the telematics data due to the volume of data.

VEHITS 2023 - 9th International Conference on Vehicle Technology and Intelligent Transport Systems

430

Figure 2: Data collection process.

Furthermore, the quality of data captured varied

between individual participants as did the frequency

of their driving. All trips without GPS coordinates,

with a zero braking score and less than 1km in

travelling distance were excluded from the analyses.

Furthermore, zero DBQ total score were treated as

missing values. Range checks were performed on the

responses collected for both the NMP-Q and DBQ,

and only participants with responses for all items

were included in this study.

2.5 Statistical Analysis

Descriptive statistics (such as mean, standard

deviation, median and range) were presented for

continuous data whilst frequencies and percentages

were reported for categorical data. Boxplots were

used to compare the nomophobia total scores, DBQ

total scores and braking scores for the control and

intervention groups for Time 1 and Time 2.

Pearson’s correlation coefficients were used to

examine the strength of the linear relationship

between nomophobia total scores and DBQ total

scores, between nomophobia total scores and average

braking scores, as well as between DBQ total scores

and average braking scores.

Linear mixed model analyses were conducted

allowing for the hierarchical structure of the

telematics data, with trips nested within drivers.

Separate analyses used the braking scores and the

DBQ total scores as the dependent variables whilst

testing the significance of the Nomophobia by Time

interaction effect separately for each Group. These

analyses were adjusted for age, gender and distance

travelled when comparing the relationship between

driving behaviour and nomophobia severity in the

intervention and control groups.

Diagnostic tests were conducted for normality,

linearity and multicollinearity to validate the results.

A p-value < 0.05 was deemed statistically significant

for all tests. The analyses were conducted using

STATA Intercool version 16 (Stata Corp, College

Station, TX).

3 RESULTS

3.1 Demographic Characteristics

A total of 42 participants joined this naturalistic

study, but only 32 (76%) and 9 (21%) completed data

collection for Time 1 and Time 2 respectively. These

participants were aged between 19 to 29 years with an

average age of 24 years (SD of 2.6 years). The

majority of these participants were female (51%),

with full driver licenses (67%), mostly residing in a

major city (65%).

There was a total of 3,134 and 905 driving trips

recorded for Time 1 and Time 2 respectively. The

average travelled distance per trip for participants in

Time 1 was found to be 13 km (SD of 19 km), with

individual distances travelled per trip ranging

between 1km and 499 km. On the other hand,

participants in Time 2 travelled between 1 to 228 km

per trip with an average travelled distance per trip of

16 km (SD of 25 km).

3.2 Parameter Characteristics

3.2.1 Nomophobia Total Score

Figure 3a showed participants from the control group

had higher average nomophobia total scores (M=65,

SD=0.50) compared to participants in the

intervention group (Ray) (M=51, SD=1.0),

demonstrating higher smartphone dependency. On

the other hand, the average nomophobia total score

for participants at the start of Time 1 was only slightly

lower (M=63, SD=0.5) in comparison to the

beginning of Time 2 (M=64, SD=1.0), showing no

significant difference for smartphone dependency

(Figure 3b).

Examination of the Relationship Between Smartphone Dependency and Driving Behaviour in Young Drivers: Preliminary Analysis

431

Figure 3a: Boxplot of nomophobia total score at the start of

each period by Group.

Figure 3b: Boxplot of nomophobia total score at the start of

each period by Time.

3.2.2 DBQ Total Score

It was indicated in Figure 4a that the average DBQ

total score for the control group (M=23, SD=0.3) was

only slightly higher than the intervention (Ray) group

(M=22, SD=0.6). Additionally, the average DBQ

Figure 4a: Boxplot of DBQ total score at the start of each

period by Group.

total score at the start of time 1 (M=23, SD=0.2) was

similar at the beginning of time 2 (M=23, SD=0.6)

(Figure 4b).

Figure 4b: Boxplot of DBQ total score at the start of each

period by Time.

3.2.3 Braking Scores

Figure 5a revealed that the average braking score per

trip for participants from the control group (M=76,

SD=0.1) was significantly lower than for participants

from the intervention (Ray) group (M=77, SD=0.4),

demonstrating more aggressive braking behaviour.

Furthermore, the average braking score per trip for

participants for time 1 (M=77, SD=0.1) was higher

than for time 2 (75, SD=0.3) (Figure 5b).

3.2.4 Correlations Between Parameters

Pearson correlation coefficients were computed to

assess the strength of the linear relationship between

nomophobia total scores, DBQ total scores and

average braking scores. There was a weak positive

correlation between the nomophobia total scores and

DBQ total scores (r(3541)=0.15, p<.001), indicating

Figure 5a: Boxplot of average braking score per trip by

Group.

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432

Figure 5b: Boxplot of average braking score per trip by

Time.

that participants with high smartphone dependency

tended to rate their driving more aggressively.

There was a weak negative correlation between

nomophobia total scores and the average braking

scores (r(3513)=-0.09, p<.001), showing that

participants with low levels of smartphone

dependency were likely to drive with less aggressive

braking behaviour. On the other hand, DBQ total

scores were found to have a weak negative

association with the average braking score (r(3626)=-

0.04, p=.031), indicating that participants who rated

their behaviour more favourably tended to have less

aggressive braking behaviour.

3.3 Linear Mixed Model

The linear mixed model analyses were conducted

based on the complete data collected for 32

participants at Time 1 and 9 participants at Time 2.

Time 2 included four (46%) and five (56%)

participants allocated to the control group and

intervention (Ray) group, respectively.

3.3.1 Braking Score

This preliminary analysis indicated that there was a

significant Nomophobia by Time interaction effect

(Z=-2.74, p=.006) for braking behaviour for

participants from the intervention group, but no such

finding was observed for participants from the control

group (Z=-1.61, p=.107). This outcome suggested

that participants who were moderately to severely

reliant on their smartphone were able to improve their

braking behaviour in the presence of real-time driver

feedback (via the Ray) (Figure 6).

Figure 6: Daily marginal means for braking scores by

nomophobia level for the intervention group.

3.3.2 DBQ Total Score

In contrast, when using the self-report DBQ total

score as an indicator of driving behaviour, no

significant Time by Nomophobia interaction effect

was found for participants from the intervention

(Ray) group (Z=1.52, p=.130) nor for those in the

control group (Z=0.49, p=.624). This suggests that

there was no significant change in self-assessed

driving behaviour that was related to smartphone

dependency for either group.

4 CONCLUSIONS

This preliminary study explored the effect of

smartphone dependency on driving behaviour over a

period of time in a naturalistic setting.

This study showed that participants exhibited a

mild to moderate level of nomophobia, meaning that

they relied on their smartphones to some extent. This

finding was consistent with other studies (Kaviani et

al., 2020a; Yildrim & Correia, 2015). However, since

nomophobia was gauged through a self-reported

questionnaire, the actual prevalence of nomophobia

remains unknown.

Both braking score and DBQ total score were

found to be important indicators of driving behaviour.

However, braking score could be regarded as the

better indicator of driving behaviour because the

braking score was estimated using real-time

telematics data for each participant, whilst the DBQ

total score was derived from self-reported responses

from the participant.

Participants who received feedback from the in-

car telematics device (Ray) have shown improvement

in their self-reported driving behaviour over time.

Examination of the Relationship Between Smartphone Dependency and Driving Behaviour in Young Drivers: Preliminary Analysis

433

This finding was consistent with the results of

Wijnands et al. (2018).

Despite only a small pool of participants included

in this preliminary study for Time 2, the results

suggest that in-car telematics use has a positive

impact on young driver behaviour. This finding aligns

with a previous study conducted by SIRA (2019).

There were a few limitations in this study. Firstly,

the use of self-reported questionnaires may have

caused bias. Participants might not have provided

accurate responses to the questionnaire designed to

gauge their smartphone dependency, due to the fear

that their behaviour might be judged to be socially

unacceptable. Secondly, only a small sample of

participants were included in this preliminary

analysis for Time 2. The research team expect to

show more reliable findings from this study after all

42 participants have completed their Time 2 driving

period. Thirdly, most of the participants captured all

their daily driving trips using the in-car telematics

devices. However, a handful of participants logged a

much lower driving frequency than the average,

making their data less reliable. Lastly, this study

commenced during the COVID-19 pandemic and the

effect of the pandemic on the study findings remains

unknown.

In conclusion, this study suggests that in-car

telematics feedback and alarm systems have the

potential to improve the braking behaviour of young

drivers who exhibit moderate/severe dependency on

their smartphones, reminding them to behave more

responsibly when behind the wheel.

ACKNOWLEDGEMENTS

The research team would like to thank Erwin

Muharemovic, who was an honours student at

Swinburne University of Technology, for his

assistance with the data collection. Furthermore, the

research team was grateful for the significant

contributions from all participants. This work was

supported by the Australian Government Department

of Infrastructure, Transport, Regional Development

and Communications (RSIF2-59).

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