Smartphone Usage Behavior Between Depressed and Non- Depressed Students: An Exploratory Study in the Context of Bangladesh

Abstract

Increasing smartphone usage has received much scholarly attention to investigate its impact on mental health. To our best knowledge, none of the previous studies have explored smartphone usage behavior of depressed students. In this study, using 7 days' actual smartphone usage data of 44 students, we present the smartphone usage behavior that varies between depressed and non-depressed students. Our findings show that in terms of aggregated smartphone usage data, these two groups of students use similar number of apps. However, depressed students' frequency of launch per app is significantly higher. Moreover, they use Communication category apps more and their diurnal usage pattern is also significantly different. Therefore, our findings show the possibility to differentiate depressed and non-depressed students based on their smartphone usage data.

Full text

Smartphone Usage Behavior Between Depressed and Non-
Depressed Students
An Exploratory Study in the Context of Bangladesh
Md. Sabbir Ahmed
Dept. of CSE
Eastern University
Dhaka, Bangladesh
sabbir.eu.bd@gmail.com
Rahat Jahangir Rony
Dept. of ECE
North South University
Dhaka, Bangladesh
rahat.rony@northsouth.edu
Tanvir Hasan
Dept. of CSE
East West University
Dhaka, Bangladesh
2017-1-60-102@std.ewubd.edu
Nova Ahmed
Dept. of ECE
North South University
Dhaka, Bangladesh
nova.ahmed@northsouth.edu
ABSTRACT
Increasing smartphone usage has received much scholarly
attention to investigate its impact on mental health. To our
best knowledge, none of the previous studies have explored
smartphone usage behavior of depressed students. In this
study, using 7 days’ actual smartphone usage data of 44
students, we present the smartphone usage behavior that
varies between depressed and non-depressed students.
Our
findings show that in terms of aggregated smartphone
usage data, these two groups of students use similar
number of apps. However, depressed students’ frequency
of launch per app is significantly higher. Moreover, they
use
Communication
category apps more and their diurnal
usage pa"ern is also significantly different. $erefore, our
findings show the possibility to differentiate depressed and
non-depressed students based on their smartphone usage
data.
CCS CONCEPTS
•
Human-centered computing → Human computer
interaction (HCI) → Empirical studies in HCI
KEYWORDS
Smartphone; Depression; Students; Communication; Social Media
ACM Reference Format:
Md. Sabbir Ahmed, Rahat Jahangir Rony, Tanvir Hasan, Nova Ahmed.
2020. Smartphone Usage Behavior Between Depressed and Non-
Depressed Students. In Adjunct Proceedings of the 2020 ACM International
Joint Conference on Pervasive and Ubiquitous Computing and the 2020
International Symposium on Wearable Computers (UbiComp/ISWC ’20
Adjunct), September 12–16, 2020, Virtual Event, Mexico. ACM, New York,
NY, USA, 4 pages. https://doi.org/10.1145/3410530.3414441
1 INTRODUCTION
Depression is a type of mental health problem which may
lead to an unexpected incident in worst cases [1].
According to an estimation of WHO, 4.1% of the total
population of Bangladesh has depression [1]. Many studies
have been conducted in the area of depression. Previous
studies present the factors associated with depression [6],
problematic smartphone usage relation with depression
[10, 11], etc. However, none of these studies have explored
the smartphone usage behavior difference between
depressed and non-depressed students. On the other hand,
during COVID-19, smartphone usage has been increased to
a higher level [13] which make this research crucial at this
moment.
In this paper, we have used PHQ-9 questionnaire [4] to
identify the depressed and non-depressed students, and
then their actual smartphone usage data has been collected
through the development of an Android app. Following a
previous study [2], we have used a cut-off score of 10 for
detecting the depressed and non-depressed students. Our
findings show that in terms of frequency of launch per app,
depressed students use smartphone significantly more.
Moreover, they use Communication category apps
significantly more, and their diurnal usage pattern
regarding this category also differs significantly. However,
we do not find notable difference in case of Social
Media category.
In this study, we contribute by presenting the difference of
smartphone usage between depressed and non-depressed
students. This study can be helpful for technology
designers to develop smartphone as better humanitarian
technology.
2 RELATED WORK
In previous studies, Facebook usage difference between
depressed and non-depressed students [7], risk and non-
risk users’ smartphone usage difference [12], factors
associated with depression [6], impact on well-being
through limiting Social Media use [9] etc. have been shown.
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https://doi.org/10.1145/3410530.3414441
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UbiComp/ISWC '20 Adjunct, September 12–16, 2020, Virtual Event, Mexico
Ahmed et al.
However, none of these studies have investigated how
depressed students use smartphone or different app
categories.
2.1 Problematic Smartphone Use and Depression
Younger users have significantly higher addiction to
smartphones [11]. They (risk users) spend more time on
smartphones and have different diurnal usage pattern than
the non-risk smartphone users [12]. Using self-reported
data, Hossain et al. [5] conducted a large scale study on
Bangladeshi undergraduate students and find high and
excessive recreational screen time as the critical risk factors
behind students’ depression and anxiety. Moreover, Wang
et al. [6] find that higher depressed students use their
smartphones more, particularly at study places. There are
many factors associated with depression such as depressed
students were found to have fewer conversations with
others [6]. Impact of the smartphone on depression can
vary depending on smartphone usage data as shown by a
previous study [10].
2.2 App Categories Usage and Depression
Depressed and non-depressed students Facebook usage
differ. For example: Park et al. [7] find that depressed
students’ peak Facebook use occurs at 5:00 pm whereas non-
depressed students’ peak activity was found at 11:00 pm.
Using self-reported data, investigation of another study [8]
shows that 86% of the highly depressed students do not chat
on Facebook or chat less frequently. On the other hand,
depressed participants make phone calls frequently and the
conversation continues for a long time [8]. Apart from that,
Lee et al. [12] find that high-risk smartphone users browse
the web more, and also search more frequently for content
updates. It is proved that limiting Facebook, Instagram,
and Snapchat app reduces depression, loneliness among the
students of the experimental group [9].
3 METHODOLOGY
We have conducted a quantitative study over 44 students
to understand their mental state and smartphone usage
behavior during COVID-19.
3.1 Participants
The data were collected from 44 students of 10 different
universities in Bangladesh. They were from 8 different
faculties and the majority of them were studying in
Computer Science and Engineering department. The data
has been collected at the beginning of the Summer – 2020
semester and none of the participants had mid-term or
semester final exam near to data collection time.
3.2 Data Collection
Data Collection Tool: We developed an Android app to
collect actual smartphone usage data and self-reported
psychological data. The app retrieves past 7 days’ usage
data using a Java Package once it is installed. It uses the
IMEI (International Mobile Equipment Identity) number to
prevent data donation more than once. The app can
calculate app usage data (e.g. duration) very accurately.
Survey Instrument: To understand depression among the
students, we have used the PHQ-9 questionnaire [4] whose
score range is 0 to 27. We include this scale in the developed
app in English as well as in native language. Three of the
researchers and three undergraduate students have
checked the translation. We removed the bold words (e.g.
dead) to make the questions comfortable for the
participants.
Data Collection Procedure: We have collected data during
COVID-19 pandemic. Data were collected by arranging
group meetings and one to one meetings through a virtual
platform (e.g. Google Meet). Beside these, some participants’
data were collected through a face-to-face meeting.
3.3 Data Analysis
To understand the difference between depressed and non-
depressed students, we have used Standard T-test or Mann-
Whitney U test depending on normality test. The analysis
regarding diurnal usage was conducted following a
previous study [12]. We have divided a day into four equal
time ranges such as Night [0, 6), Morning [6, 12), Afternoon
[12, 18), and Evening [18, 24). We use one-tailed hypothesis
test only for diurnal usage data difference when total usage
of that data was significantly different (using two tailed
test) between depressed and non-depressed students.
3.4 Research Ethics
This research was according to ethics; informed consent
was provided to every participant to let them know the
research purposes. On the other hand, we stored IMEI
number as encrypted value and it was deleted after the data
collection phase which means all of the participants are
anonymous.
4 FINDINGS
Following a previous study [2], we use cut-off score 10 to
categorize the depressed (=>10) and non-depressed
students (<10), based on PHQ-9 scale which score range is
0 to 27. In our study, 26 students were non-depressed (Mean
score=5.85, SD=2.56) and 18 students were depressed (Mean
score=15.11, SD=3.83). Later on, we tried to understand
their smartphone usage scenarios.
In 7 days, all the 44 students have used 695 (M=52.05,
SD=20.54) different apps. Excluding launcher like apps (e.g.
Xperia Home), we have grouped the remaining apps into 26
different categories. Our statistical analysis shows that
frequency of launch per app is significantly higher for
depressed students in terms of aggregated smartphone
data. Moreover, we find significant differences between
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depressed and non-depressed students regarding
Communication category (e.g. Gmail, Messenger app) usage
data. However, there was no notable difference in case
Social Media app category (e.g. Facebook, Instagram app).
We present analysis regarding Communication and Social
Media categories (in 4.2 and 4.3 subsections) which are two
of the most used app categories, in terms of usage duration.
4.1 Aggregated Smartphone Usage Data
We sum up all apps’ usage data to calculate aggregated
smartphone usage data (e.g. usage duration, freq. of
launch).
We find that depressed students’ frequency of launch per
app is significantly higher than the non-depressed students
(Depressed: 47.6 vs Non-depressed: 35.6, p=0.01) which is
presented in Table 1. Though depressed and non-depressed
students use mostly similar number of apps, depressed
students spend much time on apps and launch the apps
more (Table 1). However, these differences are not
statistically significant.
Table 1: Difference of smartphone usage data (7 days)
between depressed and non-depressed students. Duration is
in minutes.
Usage Data
Depressed
Students
Non-Depressed
Students
Test Stat.
p value
M
SD
M
SD
Usage Duration
2853.9
1145.6
2431.2
1127.9
t(42)=1.21
0.23
Freq. of Launch
2464.4
1225.1
1911.8
1301.8
U=308.5
0.08
# of Unique Apps
51.9
18.4
52.1
22.2
U=250
0.71
Duration Per App
60.1
25.7
49.7
25
t(42)=1.35
0.18
Launch Per App
47.6
18.1
35.6
20.2
U=337
0.01
Duration Per Launch
1.5
1.4
1.6
1.2
U=209
0.56
Depressed students’ diurnal usage pattern is different in terms
of freq. of launch per app. Though the number of used
unique apps remains almost same in each time range
(Figure 1(a)), frequency of launch per app between
depressed and non-depressed students vary significantly (p
< 0.05) in night, afternoon, and evening time range (Figure
1 (b)). After adjusting p values using FDR [3], we find that
difference during afternoon and evening time remains
significant.
a) No. of Used Unique Apps
b) Freq. of Launch Per App
Figure 1: Difference between depressed and non-depressed
students’ diurnal (a) usage number of unique apps and (b)
frequency of launch per app.
4.2 Communication Category Usage Data
Depressed students use Communication apps more. Our
findings show that depressed students spend time, launch
Communication category apps significantly more number
of times (Table 2). Beside these, usage duration per app and
frequency of launch per app of this category is also
significantly higher in case depressed students (Table 2).
Table 2: Difference between depressed and non-depressed
students in using Communication apps. Duration is in
minutes.
Usage Data
Depressed
Students
Non-Depressed
Students
Test Stat.
p value
M
SD
M
SD
Usage Duration
664
499.9
401.7
307.1
U=324
0.03
Freq. of Launch
561
365.1
379.8
366.5
U=329.5
0.02
# of Unique Apps
7.2
2
8
3
U=213.5
0.63
Duration Per App
92.1
61.7
54
44.3
U=337
0.01
Launch Per App
76
45.8
49.5
51.9
U=353
0.005
Duration Per
Launch
1.4
0.9
1.2
0.5
U=247
0.77
Depressed students’ diurnal pattern of Communication
category is different. Depressed students’ usage duration is
significantly higher in morning, afternoon, and evening
time (Figure 2). Their frequency of launching
Communication apps is also significantly higher in
afternoon and evening time. In case of duration per app and
frequency of launch per app, we also find significant (p<
0.05) difference (Figure 2). Moreover, we find these
differences regarding usage duration per app and frequency
of launch per app remain significant on adjusting
familywise error rate using FDR [3].
a) Usage Duration
b) Usage Duration Per App
c) Freq. of Launch
d) Freq. of Launch Per App
Figure 2: Depressed and non-depressed students’
Communication category diurnal (a) usage duration (b)
usage duration per app (c) frequency of launch (d)
frequency of launch per app.
4.3 Social Media Category Usage Data
Depressed students use Social Media same as non-depressed
students. Our findings show that depressed students use
Social Media category apps more than the non-depressed
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Ahmed et al.
students in terms of most of the usage data (e.g. duration)
presented in Table 3. However, all of these differences are
statistically insignificant (p>0.05).
Table 3: Difference between depressed and non-depressed
students in using Social category apps. Duration is in
minutes.
Usage Data
Depressed
Students
Non-Depressed
Students
Test
Stat.
p value
M
SD
M
SD
Usage Duration
837
616.9
619.5
380.6
U=274
0.35
Freq. of Launch
267.9
198.7
200.4
244.1
U=311
0.07
# of Unique Apps
2.8
1.7
2.5
1.4
U=257
0.58
Duration Per App
344.6
288
310.9
246.4
U=258
0.57
Launch Per App
93.8
51
109
221.4
U=301
0.11
Duration Per
Launch
4.1
3.2
4.8
3.5
U=190
0.3
In time range based analysis, we find that depressed
students’ Social Media usage duration also remain same as
non-depressed students (Figure 3). Though they launch this
category apps significantly more in evening, this difference
does not remain significant after adjusting p values using
FDR [3]. On the other hand, non-depressed students'
evening time frequency of launch per app is significantly
higher which remains significant after adjusting p values
also.
a) Usage Duration
b) Usage Duration Per App
c) Freq. of Launch
d) Freq. of Launch Per App
Figure 3: Depressed and non-depressed students’ Social
Media category diurnal (a) usage duration (b) usage
duration per app (c) frequency of launch (d) frequency of
launch per app.
5 DISCUSSION
In this study, we have tried to understand the mental health
of the students and their smartphone usage. Using PHQ-9
scale score, we find 18 students as depressed. In terms of
aggregated smartphone usage data, depressed students’
frequency of launch per app is significantly higher.
However, we do not find significant difference in case of
other smartphone usage data. Previous study [10] also finds
that impact on depression, anxiety can vary depending on
smartphone usage data which supports our findings.
Depressed students use Communication category apps
more, rather than Social Media category apps. This reveals
that they want to talk with others virtually more through
Communication category apps. These findings contrast
with findings of a previous study [6] who find a negative
relation between depression score and conversation. The
discrepancy can be due to the consideration of different
types of data in our study. Wang et al. [6] considered
conversation that occurs around participants, whereas we
considered conversations with the use
of Communication category apps. A previous study [7]
remarks that depressed participants do not have enough
energy to involve in real social interactions which might
cause to increase their social network (e.g. Facebook)
activities. Similarly, depressed students of our study may
use Communication apps more to communicate with their
peers for their wellbeing.
6 LIMITATIONS
The sample size (N=44) of this study was short and the data
collection length was only 7 days. Furthermore, we
collected data during COVID-19 which may act as a
confounding variable affecting students’ depression.
Therefore, further studies are needed to affirm the
smartphone usage behavior of the depressed and non-
depressed students. We hope to come up with more
outcomes in future submissions.
7 CONCLUSION
We have presented a quantitative study to explore
depressed and non-depressed students’ smartphone usage
difference. We collect actual smartphone usage data and
self-reported depression scores using our developed app. In
terms of aggregated usage data, our findings show that
depressed students’ frequency of launch per app is
significantly higher. Moreover, we find that they spend and
launch Communication category apps more. Their diurnal
usage pattern regarding this category is also significantly
different. Interestingly, we do not find a remarkable
difference in Social Media category. Our findings reveal that
smartphone usage behavior vary between depressed and
non-depressed students which may help to come up with
possible support for the depressed students.
ACKNOWLEDGEMENTS
We thank to CRD, Eastern University, Dhaka for funding
us.
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