=Paper=
{{Paper
|id=Vol-2662/BCSS2020_paper1
|storemode=property
|title=Preliminary Study on the Smartphone Zombie Phenomenon by Utilising a Monitoring Application
|pdfUrl=https://ceur-ws.org/Vol-2662/BCSS2020_paper1.pdf
|volume=Vol-2662
|authors=Yukitoshi Kashimoto,Jaakko Hyry,Pasi Karppinen,Harri Oinas-Kukkonen,Masato Taya,Chihiro Ono
|dblpUrl=https://dblp.org/rec/conf/persuasive/KashimotoHKOTO20
}}
==Preliminary Study on the Smartphone Zombie Phenomenon by Utilising a Monitoring Application==
https://ceur-ws.org/Vol-2662/BCSS2020_paper1.pdf
Preliminary study on the smartphone zombie
phenomenon by utilising a monitoring
application
Yukitoshi Kashimoto1 , Jaakko Hyry1 , Pasi Karppinen2 , Harri
Oinas-Kukkonen2 , Masato Taya1 , and Chihiro Ono1
1
KDDI Research Inc., Fujimino, Japan
2
University of Oulu, Oulu, Finland
{yu-kashimoto, ja-hyry, ma-taya, ono}@kddi-research.jp,
{pasi.karppinen, harri.oinas-kukkonen}@oulu.fi
Abstract. Several studies exist on the dangers of using a smartphone
while walking. Unfortunately, pedestrians often disregard the warnings
either intentionally or by accident as their focus is on the phone and
not fully on the surrounding environment or situation. In this paper, we
present our preliminary work to study the correlation between smart-
phone zombie behaviour and individual’s psychological features, to re-
duce walking use. At first, in order to collect smartphone zombie be-
haviour from actual users, we have developed a monitoring application
for smartphones. We asked seven subjects to install this application and
continue living their lives normally for 15 days. For collecting the psy-
chological features, we asked them to answer a profiling questionnaire.
Keywords: Smartphone zombie · Smartphone addiction · Problematic
smartphone usage · Trans-Theoretical-Model
1 Introduction
The dangers related to being distracted while a smartphone during walking ex-
ist. This behaviour is often called smartphone zombie or sometimes shortened
to smombie. The term can be understood broadly as zombie phone use can re-
fer to a person focusing on their phone in any situation, be it during a dinner
or while driving a car. In this study we focus only on the walking phone use.
Walking smartphone zombie users often disregard warnings related to distracted
use either by accident as their focus is concentrated on the phone, or by choice
as they might feel necessary to reply a message or want to watch a video. This
results in them not fully concentrating to the surrounding environment or situa-
tion at hand properly. A study from Australia found that from 4129 pedestrians
observed crossing the street, on average 20% were using their phones and would
also have a bigger likelihood for critical events, such as crossing at the wrong
time or not checking both ways before crossing [10]. The number of users is also
steadily growing as shown by the increase in smartphones prevalence numbers
Copyright © 2020 for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
2 Y. Kashimoto et al.
from 2010 to 2019. In Norway and Romania the prevalence rose from 31% to
86% and 2% to 86% respectively [19].
In Tokyo, from 2010 to 2014, 152 were injured for smartphone zombie [6]
and between 2014 to 2018 this number rose to 201 individuals [7]. A growing
number of studies are trying to understand the problematic phenomenon and
create solutions that would reduce walking use. Some cities are even trying to
accommodate phone users by creating separate walking lanes [9]. In Japan, a
telecommunications service company created a screen blocker application3 that
forced users to stop using the smartphone while walking. However, as this feature
completely prevented any phone use, it saw very little use in the general public.
Using a smartphone is also often shown to be tied to some forms of addiction
and several new applications have been introduced which let users track and
reduce their own use. However, these applications are not designed especially for
reducing smartphone zombie behaviour, but instead for overuse of a smartphone
in general. They commonly use time limits, application blocking, self-assigned
use goals and use statistics of the phone, as presented in [13]. Therefore, we
think looking into additional approaches for changing the underlying behaviour
of people are needed, as well as natural and e↵ective smartphone features that
assist an individual in breaking the walking phone use habits specifically.
In this paper, we present our preliminary work to study the correlation be-
tween smartphone zombie behaviour and psychological features, to achieve the
above-mentioned behavioural change. At first, in order to collect smartphone
zombie behaviour from actual users, we have developed a monitoring applica-
tion for smartphones. We have asked seven subjects to install this application
and spend their daily lives normally for 15 days. For collecting the psychological
features, we have asked them to answer a questionnaire.
Our major contribution is that we propose suppressing smartphone zombie
behaviour by utilizing Persuasive Technology approach. Specifically, we collected
realistic data on smartphone zombie behaviour through our monitoring applica-
tion and compared it with the questionnaire data. Second, we looked into the
correlation between the psychological features: Stage in the Trans-Theoretical-
Model, Risk/Benefit of smartphone zombie, Dickman’s Impulsivity Inventory,
Self-efficacy, and Big Five scores, and smartphone zombie behaviour.
2 Literature review
2.1 Study on smartphone zombie
Smartphone zombie behaviour has been recently gathered more focus but is
still an understudied area of research. A study on phone use while crossing the
street showed that it takes more time to cross if a phone is used and it also
shows more tendency to do unsafe behaviour such as not looking or crossing
at the wrong point. [22]. The slower walk gait was also a result in a study by
[8] in this pilot study. The e↵ect might be due to individuals focusing their
3
https://www.au.com/mobile/service/aruki-sumaho/
Study on smartphone zombie 3
attention on their phones. The e↵ects on the cognitive load, gaze and general
awareness has been studied and showed that general awareness is reduced when
using a phone, and reading is more disruptive than texting [12]. The walking
phone use also was found to be due to people having a feeling of missing out of
their social interaction. This fear of missing out (FoMo) increased the likelihood
of smartphone zombie behaviour and results in similar dangerous behaviour as
shown in other studies. In addition, this behaviour true for both genders and
older or younger age groups when the social desirability score was the same, [1]
indicating that personality traits have a link in zombie behaviour. Some research
try to tackle the problem by o↵ering the smartphone zombie users with a radar-
like assistance to avoid collisions with other people or obstacles[11]. However,
we argue that it would be more beneficial to also focus on the phone user’s
behaviour, instead of trying to only alleviate the problematic behaviour be it
unintentional or intended.
2.2 Study on smartphone addiction
While smartphone zombie as a behavioural problem requires research, an under-
standing the underlying problem is essential. The growth in smart device use and
the link to excessive and problematic use has been studied for years, but com-
mon terms, criteria and unified terminology are needed so that cross-cultural
and comparative studies and be made. Phone use has steadily grown and ac-
cording to a recent EU study on 19 countries, around 80% of 9-16 years old use
the internet on their phones [19]. In addition, the use of various smart devices
starts at an increasingly younger age, which might also develop into smartphone
addiction as the adolescents are more vulnerable for mental health issues [20].
Often overuse related symptoms are anxiety, depression, stress and poor sleep.
Many studies have also looked into smartphone addiction in relation to DSM-5
criteria on substance and gambling [2] perspective as similarities seem to occur
for both addictions. Compared to DSM-5, addicted smartphone users often use
their phones in 1) problematic or dangerous situations, 2) lose interest in other
social situations like family, 3) continue use even with negative e↵ects, 4) have
difficulty in controlling or stopping use, 5) constant need to check the phone,
6)increase phone use to get satisfaction or relaxation, 7) urgency and need to
be always connected, responding immediately, and 8) anxiety if the phone is not
available [5]. However, as of now there is no clear consensus on the terms or on
the exact definition on what amounts to a person being addicted to their smart-
phone and how the level of smartphone addiction can be e↵ectively measured.
It is likely that smartphone addiction and the use of a phone while walking is
somewhat connected. As stated above, the need to constantly be connected, the
need to check one’s phone, anxiety from non-use and the need to respond to
messages immediately, a↵ects a person’s ability to focus on their surroundings
e↵ectively while walking. In various studies personality and gender play a role on
what type of smartphone use occurs, and which types of personalities are more
vulnerable for smartphone addiction. In multiple studies [3, 18] For example, fe-
males are more likely to focus on social networking applications and a need to
4 Y. Kashimoto et al.
maintain or create new relationships and have a higher dependency and prob-
lematic use levels than males. Phone use by males is more reflected in gaming
applications, voice and texting as well as having a tendency for using phones in
risky situations. The correlation between the smartphone zombie behaviour and
the psychological characteristics of phone users might shed more light on how
reduction in phone use can be achieved.
3 Method
3.1 Theoretical background and policy
Since we are in the initial stage of the study, we selected two theoretical frame-
works for the experiments and investigate the correlation between the smart-
zombie behaviour and the frameworks through a field study.
Trans-Theoretical-Model: Trans-Theoretical-Model (TTM)[17] is where in-
dividuals achieve a targeted behaviour change by progressing through several
stages: Pre-contemplation, Contemplation, Preparation, Action, and Mainte-
nance. As an example, a person might be considering quitting smoking but is
currently in the contemplation stage and not yet committed for the behaviour
change. TTM is frequently used in clinical therapy for habitual behaviours such
as quitting smoking or drinking. In this study, we assume that smartphone zom-
bie behaviour is also similar to these habitual behaviours. TTM also shows that
we need to select suitable intervention strategies for individuals in various stages
of change for a better likelihood of success. As a starting point for this study, we
focus on the subjects who are either in the Contemplation or in the Preparation
stages, because achieving change in their behaviours should be easier as they are
more willing participants.
Pathway model of problematic mobile phone use: Bullieux et al. proposed
a framework to describe the correlation between the dysfunctional mobile phone
use and specificity of the factors based on the related studies[4]. They claimed
that there are four pathways as follows: the impulsive pathway, the relationship
maintenance pathway, the extraversion pathway, and cyber addiction pathway, to
reach dysfunctional use1. Here, we measure the correlation between smartphone
zombie behaviour and psychological features based on this model.
3.2 The smartphone zombie behaviour monitoring application
In order to collect realistic smartphone zombie behaviour, we developed a mon-
itoring application for smartphones. It collects smartphone’s operational data
while a user is walking, including the foreground application used and whether
the display is on or o↵. The application also collects activity recognition data
for the subject such as “walking”, “still” or “tilting”. The collected data is then
stored into cloud storage from Amazon Web Services.
Study on smartphone zombie 5
Fig. 1. Pathway model of problematic mobile phone use
3.3 Questionnaire
In order to collect the psychological features of the subjects, we distributed a
questionnaire.
In the smartphone use part, we collected data on which smartphones func-
tions did the subjects use in their daily lives, such as calling, web browsing,
messaging applications.
In the TTM part, we inquired where in the TTM model stage would the
subject consider themselves to be in and excluded everyone who was not in
either the Contemplation or Preparation stages.
The smartphone zombie behaviour part focused on collecting data on the
phone functions the subjects used while walking and the reasons why they used
those functions.
In “Risk/Benefit of being a smartphone zombie” part, we collected subjects’
opinions towards smartphone zombie behaviour with 14 questions. Seven ques-
tions were about the risks related to smartphone zombie, which corresponded to
“Negative A↵ect” in the “Problematic mobile phone use” model. The other seven
questions are about the benefits, which corresponded to the “Positive A↵ect” in
the same model.
6 Y. Kashimoto et al.
Fig. 2. Intervention methods
In the intervention part, we collected the subjects’ preferences for smartphone
applications which could encourage users to stop smartphone zombie behaviour.
We asked subject to choose three of their favourite and one least-favourite inter-
vention applications they would be willing to installs on their own smartphone.
Figure 2 shows the intervention applications shown in the questionnaire. Table 1
shows the description of each App. We have created the intervention applications
by a behaviour change support systems (BCSS) and persuasive systems design
(PSD) model with guides on how to change people’s attitudes or behaviour [15].
In the psychological measurement part, we collected the subject’s psycholog-
ical features with the following: Dickman’s Impulsivity Inventory, Self-efficacy,
and Big five scores. Dickman’s Impulsivity corresponds to “IMPULSIVITY” in
Fig. 1. Self-efficacy corresponds to “Poor self-esteem”. Big five scores correspond
to “Neuroticism” and “Extraversion”.
Study on smartphone zombie 7
Table 1. Description on the Interventions
# Intervention Name PSD’s Category description
(a) Screen blocker Primary Task App. blocks any user’s operation if they
Support try to use their smartphone.
(b) Notification silencer Primary Task App. blocks notifications while walking.
Support
(c) Zombie impression System Credibility App. occasionally shows news on
Support the impressions of smartphone zombie
behaviour from others.
(d) Risk news System Credibility App. occasionally shows news on the risks of
Support smartphone zombie.
(e) Feedback Primary Task App. shows the total time of smartphone
Support zombie per week. It also provides advice on
how to stop zombie.
(f) Gaming Primary Task App. gives exp. points for a game character,
Support if the user does not use smartphone
while walking.
(g) Reward Dialogue Support App. gives some coupons: drink tickets,
if a user does not use a smartphone
while walking.
(h) Praise SNS Social Support App. reports a user’s smartphone zombie time
to others.
The user receives “Like!” praise,
if the user does not use a smartphone
while walking.
(i) Competition SNS Social Support App. reports a user’s smartphone zombie
time in a ranking SNS. Users compete
with each other.
(j) Self-monitoring Primary Task The user sets a goal score in the App.
Support Users can monitor how much of the goal
they have achieved.
3.4 Data collection
We conducted the data collection between 25.12.2019 – 24.01.2020. First, we
recruited subjects from Lancers4 , which is a crowd sourcing service in Japan,
willing to install the smartphone monitoring application. Through the service,
we recruited seven subjects who used an Android smartphone. They were re-
quested to install the monitoring application and spend their daily lives nor-
mally. This monitoring period lasted between 06.01.2020 – 17.01.2020. Starting
from 17.01.2020, we asked subjects to answer the questionnaire and the reply
deadline was 24.01.2020.
8 Y. Kashimoto et al.
Table 2. Monitoring application and Questionnaire results
# 1 2 3 4 5
Age 20 – 29 30 – 39 30 – 39 40 – 49 40 – 49
Gender Female Male Female Male Female
Zombie frequency a day 16 3 6 10 3
App.
Zombie second a day 433 48 190 1316 432
once once over three over three less than
Frequency of zombie
a week a week times a day times a day a month
When considering
quitting zombie In six months Tomorrow In a month No In a month
behaviour?
Functions Mail/Message X X X
while walking Internet X X X X
Take photo X
SNS X X X
Games X
Questionnaire
Movies X
Map X X X
Healthcare X
News apps X
Risk score 2 7 3 3 4
Benefit score 0 1 3 12 -1
Impulsivity Dysfunctional 4 -3 -10 4 -14
Functional -8 -6 -5 -14 5
Self-esteem 33 23 37 16 42
Big Five Openness 26 24 25 21 33
Conscientious -21 -12 -4 -22 6
Extraversion 11 7 16 -3 13
Agreeableness 3 9 2 -1 16
Neuroticism 28 23 22 33 6
Preferred App. (b)(f)(g) (b)(e)(g) (f)(g)(i) (b)(e)(g) (b)(e)(g)
Not-pref. App. (a) (h) (a) (f) (c)
4 Results
Table 2 shows the App. and questionnaire results for the five subjects. We did
not collect questionnaire data from two participants, because they answered
that they do not use smartphones while walking. However, we did analyse the
monitoring application data to verify the accuracy of their statements. The five
subjects were in their 20s, 30s, and 40s with a female and male in each group
except for the 20s.
There is a significant di↵erence on the frequency of using smartphones while
walking between App-measured times and questionnaire-answered ones except
for subjects #3 and #4. Among this small sample we cannot make definitive
statements, but the subjects who were in 20s and 30s group tended to use more
functions while walking compared with subjects in their 40s except for subject
#2. The subjects who had higher scores in “Risk score” used less functions while
walking. There was no significant correlation between the number of “functions
while walking” and “Frequency of zombie / When will quit zombie”, “Impulsiv-
4
Lancers: https://www.lancers.jp/
Study on smartphone zombie 9
Subject #1 Subect #2 Subject #3
80 64 8.0 20 17
Used Ɵme (min.)
5.7 5.7
Used Ɵme (min.)
Used Ɵme (min.)
60 52 6.0
15
40 4.0 2.3
15 2.1 1.6 9
20 11 9 2.0 10
6
0 0.0 5 3 3
0
Camera Google Google LINE Archero
Chrome Maps heros
Subject #4 Subject #5
120 110 100 88
Used Ɵme (min.)
100 87
Used Ɵme (min.)
80 80
58 56
60 41 60
40 39
20 40 27
0 13 10
20
0
LINE Google Slack Google Google
Search drive Chrome
Fig. 3. Functions and time while walking
ity”, “Self-esteem” or“Bigfive”. For the intervention methods, (b) Notification
silencer is mostly selected as preferred app., while (a) Screen blocker is selected
as the most unpopular intervention app.
Figure 3 shows the top five functions and time while walking for each subject.
We extracted the functions and time while walking from the monitoring applica-
tion. In order to calculate the time, we first used walking time from Google Ac-
tivity Recognition and Screen On/O↵ recordings. Then, we calculated the time
of Google Activity Recognition output if “walk” and Screen On/O↵ recording
was “On”. Surprisingly, among most subjects there was a significant di↵erence
between the self-reported answers in the questionnaire and “Functions and time
while walking” measurements. For instance, Subject#1 answered that she does
not use Game applications while walking. However, the monitoring application
data showed that she used Pokémon Go5 , a location-based game, the most while
walking.
5 Discussion
5.1 Comparison between the app’s data and questionnaire
The comparison between the app’s data and questionnaire results demonstrates
that there is a cognitive di↵erence between the two for some subjects.
First di↵erence was the frequency of using smartphone while walking. The all
subjects use smartphones while walking more than three times a day. However,
the only Subject #3,4 answer correctly. From this di↵erence, we consider that
it is difficult to ask subjects to recall the frequency accurately with the current
questionnaire. For our preliminary nature of this field study, we would study the
correlation with more subjects.
5
https://www.pokemongo.com/en-gb/
10 Y. Kashimoto et al.
The other one is the di↵erence between the realistic app use while walking
under the monitored data and those which subjects answered in the question-
naire. This di↵erence illustrates that it is difficult for the subjects to recall the
exact app they use while walking, since they mistakenly recall using other apps.
Similar discrepancy between recorded and self-reported use times for subjects
has been shown to be problematic in other self-reported research results [14, 21,
16]. Users often over- or underestimate their own use, so caution in self-reporting
questionnaires is advised.
From another point of view, this might give some hints towards suitable
behaviour change. Seeing the actual smartphone zombie time and related ap-
plications name might e↵ectively reduce zombie behaviour. Specifically, subjects
#1,2,5 might recognize that they are using their phones more while walking than
they had conceived.
5.2 Correlation between psychological features and zombie
behaviour
We discuss each subject for their psychological features with mapping them to
(i) Impulsivity , (ii) Relation maintenance, (iii) Extraversion, and (iv) Cyber
addiction in “Pathway model of problematic mobile phone use”.
Subject #4 is considered to be an (i) Impulsivity user, since he has higher
dysfunctional impulsivity score and Poor self-esteem score. Subject #1 is consid-
ered to be a (ii) Relation maintenance user, since she has a higher Neuroticism
score. In addition, she answered that she uses SNS functions to communicate
with others, so that she would not make them feel uncomfortable. Subject #5
is considered to be an (iii) Extraversion user, since she has the highest score in
Big Five’s Extraversion score.
Subject #2 and #3 are difficult to clearly categorize to (i)–(iv), since they do
not have any significant scores. Subject #2 is considered to be a light smartphone
zombie user, since their “zombie seconds a day” is significantly smaller than
others. In addition, he stated willingness to quit zombie behaviour by answering
“Tomorrow” in the TTM question on “When quitting zombie?”. Therefore, his
behaviour fits for a person that is in the preparation stage of change. Subject
#3 might be considered to be a (iv) Cyber addiction user, since she answered
using game application while walking. However, the monitoring data indicated
that she only plays games just for three minutes while walking . If anything, she
utilizes the camera application more while walking, which is not her answer in
the questionnaire.
For the intervention methods, we could not find any significant di↵erence
between the subjects. However, the questionnaire implies that the subjects who
play games while walking prefer to select (f) Gaming and (i) Competition SNS
intervention methods, so that they can stop smartphone zombie while playing
with others.
On the other hand, the amount of data we have collected is not enough to
find further implications toward the target behaviour change. Therefore, we plan
to conduct an experiment with more subjects. In parallel, we plan to collect more
Study on smartphone zombie 11
than 500 participants for an updated questionnaire study based on the current
study.
References
1. Appel, M., Krisch, N., Stein, J.P., Weber, S.: Smartphone zombies! pedestrians’
distracted walking as a function of their fear of missing out. Journal of Environ-
mental Psychology 63, 130–133 (2019)
2. Association, A.P., et al.: Diagnostic and statistical manual of mental disorders
(DSM-5 R ). American Psychiatric Pub (2013)
3. Bianchi, A., Phillips, J.G.: Psychological predictors of problem mobile phone use.
CyberPsychology & Behavior 8(1), 39–51 (2005)
4. Billieux, J.: Problematic Use of the Mobile Phone: A Literature Review and
a Pathways Model. Current Psychiatry Reviews 8(4), 299–307 (10 2012).
https://doi.org/10.2174/157340012803520522
5. De-Sola Gutiérrez, J., Rodrı́guez de Fonseca, F., Rubio, G.: Cell-phone addiction:
A review. Frontiers in psychiatry 7, 175 (2016)
6. Department, T.F.: Statistics on emergency transports related to mobile phone use
2010-2014. http://www.tfd.metro.tokyo.jp/lfe/topics/201503/mobile.html (2015)
7. Department, T.F.: Statistics on emergency transports related to mobile phone
use 2014-2018. https://www.tfd.metro.tokyo.lg.jp/lfe/topics/201602/mobile.html
(2018)
8. Di Giulio, I., McFadyen, B.J., Blanchet, S., Reeves, N.D., Baltzopoulos, V., Mag-
anaris, C.N.: Mobile phone use impairs stair gait: A pilot study on young adults.
Applied Ergonomics 84, 103009 (2020)
9. Guardian, T.: Chinese city opens ’phone lane’ for texting pedestri-
ans. https://www.theguardian.com/world/shortcuts/2014/sep/15/china-mobile-
phone-lane-distracted-walking-pedestrians (2014)
10. Horberry, T., Osborne, R., Young, K.: Pedestrian smartphone distraction: Preva-
lence and potential severity. Transportation research part F: traffic psychology and
behaviour 60, 515–523 (2019)
11. Kim, D., Han, K., Sim, J.S., Noh, Y.: Smombie guardian: We watch for potential
obstacles while you are walking and conducting smartphone activities. PLoS one
13(6) (2018)
12. Lin, M.I.B., Huang, Y.P.: The impact of walking while using a smartphone on
pedestrians’ awareness of roadside events. Accident Analysis & Prevention 101,
87–96 (2017)
13. Moment: Moment - less phone, more real life. https://inthemoment.io (Feb 2020)
14. Montag, C., Blaszkiewicz, K., Lachmann, B., Sariyska, R., Andone, I., Trendafilov,
B., Markowetz, A.: Recorded behavior as a valuable resource for diagnostics in mo-
bile phone addiction: evidence from psychoinformatics. Behavioral Sciences 5(4),
434–442 (2015)
15. Oinas-Kukkonen, H.: A foundation for the study of behavior change support sys-
tems. Personal and ubiquitous computing 17(6), 1223–1235 (2013)
16. Podsako↵, P.M., MacKenzie, S.B., Lee, J.Y., Podsako↵, N.P.: Common method
biases in behavioral research: a critical review of the literature and recommended
remedies. Journal of applied psychology 88(5), 879 (2003)
17. Prochaska, J.O., Velicer, W.F.: The transtheoretical model of
health behavior change. American Journal of Health Promotion
12 Y. Kashimoto et al.
12(1), 38–48 (9 1997). https://doi.org/10.4278/0890-1171-12.1.38,
http://journals.sagepub.com/doi/10.4278/0890-1171-12.1.38
18. Roberts, J., Yaya, L., Manolis, C.: The invisible addiction: Cell-phone activities and
addiction among male and female college students. Journal of behavioral addictions
3(4), 254–265 (2014)
19. Smahel, D., MacHackova, H., Mascheroni, G., Dedkova, L., Staksrud, E., Olafsson,
K., Livingstone, S., Hasebrink, U.: Eu kids online 2020: survey results from 19
countries (2020)
20. Sohn, S., Rees, P., Wildridge, B., Kalk, N.J., Carter, B.: Prevalence of problematic
smartphone usage and associated mental health outcomes amongst children and
young people: a systematic review, meta-analysis and grade of the evidence. BMC
psychiatry 19(1), 1–10 (2019)
21. Tossell, C., Kortum, P., Shepard, C., Rahmati, A., Zhong, L.: Exploring smart-
phone addiction: insights from long-term telemetric behavioral measures. Interna-
tional Journal of Interactive Mobile Technologies (iJIM) 9(2), 37–43 (2015)
22. Zhang, H., Zhang, C., Chen, F., Wei, Y.: E↵ects of mobile phone use on pedestrian
crossing behavior and safety at unsignalized intersections. Canadian Journal of
Civil Engineering 46(5), 381–388 (2019)