




































Humanitas: Indonesian Psychological Journal  

Vol. 19 (2), August 2022, 161-173 
ISSN:  2598-6368(online); 1693-7236(print)                                                                 

        humanitas@psy.uad.ac.id     https://doi.org/10.26555/humanitas.v19i2.46 

The well-being of Indonesian university students during the 

pandemic: Smartphone use and sleep quality 

Bianca Marella, Grace Eugenia Sameve  

Faculty of Psychology, Universitas Tarumanagara, Indonesia 
Corresponding author: biancam@fpsi.untar.ac.id  
 

 

Introduction 

The COVID-19 control endeavors have required unexpected and broadly ordered physical 
separation, eliminating possible sources of social interaction from people's life. The impacts 
of physical and social separation may be especially substantial for young people (10–24 years 
old) who went through a phase of social interaction sensitivity (Orben et al., 2020). The 
prevention measures have had an unanticipated and unprecedented impact on higher 
education, not only on university administration and classes or exam delivery but also on 
the interactions between students and professors (Capone et al., 2020). These adaptations 
have exerted new pressure on students in the form of increased exam-related anxiety due to 
the unprecedented exam procedures and new and unexpected academic workloads, all of 
which have been cited as major sources of stress for students (Bedewy & Gabriel, 2015).  

During the COVID-19 pandemic, several studies found university students' mental 
health and well-being problems. Students have been reported with increased depressive and 
anxiety symptoms (Cao et al., 2020; Li et al., 2020; Odriozola-González et al., 2020; Savage 
et al., 2020); increased suicidal thoughts, and poor sleep quality (Kaparounaki et al., 2020); 
as well as increased perceived stress and time spent on sedentary lifestyle (Savage et al., 
2020). Nonetheless, another study has reported a relatively normal score of mental well-being 
and academic stress among Italian students (Capone et al., 2020), while studies from 
Indonesia have similarly reported only mild levels of depression and anxiety (Soetisna et al., 
2021; Sujarwoto et al., 2021). Changes in education policy have demanded certain 

ART ICLE  INFO  

 

AB ST R ACT  

 

Article history 

Received December 26, 2021 

Revised July 21, 2022 

Accepted July 31, 2022 

 Online learning and the lack of social interactions during lockdown 
pushed numerous college students to live employing the internet and 
social media. This study investigates students' well-being and its 
associated factors in relation to smartphone use and sleep quality 
among Indonesian university students during the pandemic. A sample 
of 327 undergraduate students (68 males and 259 females) 
anonymously completed the Smartphone Addiction Scale – Short 
Version (SAS-SV), WHO-Five Wellbeing Index (WHO-5), and 
Pittsburgh Sleep Quality Index (PSQI), along with several socio-
demographic data via an online survey. The data was then analyzed 
with hierarchical linear regression. The results indicate that gender, 
perceived physical health, smartphone use, and sleep quality were 
associated with well-being in university students. The final model 
predicted 23.4 percent of the WHO-5 scores with a significant 
increase in predictive value by adding perceived physical health and 
sleep quality. Therefore to enhance well-being among Indonesian 
university students, suitable physical activities, good sleeping habits, 
and controlled smartphone use is needed. 

    

 
Keywords 

mental health; 

sleep quality; 

smartphone addiction; 

university student;  

well-being. 

 

 

mailto:humanitas@psy.uad.ac.id
https://doi.org/10.26555/humanitas.v19i2.31
mailto:biancam@fpsi.untar.ac.id


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adaptations on the part of university students that presumably placed mental health burdens 
on students. Yet, findings regarding the mental health status are still unclear. Failure to 
identify and manage mental health issues in young people is a major public health issue in 
low and middle income countries, with important implications for meeting primary 
development goals (Kieling et al., 2011). 

However, most COVID-19 mental health research has been conducted with the adult 
population as the focus of attention. However, there is a growing body of evidence of the 
potential adverse impact of the pandemic on youth's mental health (Chen et al., 2020; Zhou 
et al., 2020). Therefore, effective strategies must be identified to promote mental health and 
well-being to protect adolescents against the negative effects of COVID-19. 

The other adjustment regarding educational demand and social restrictions that should 
be made in the COVID-19 pandemic is the rigorous use of the internet and social media 
among college students. With the limitations in outdoor activities, there is an increase in 
smartphone use at home to accommodate work, study, leisure, and social contact (King et al., 
2020; Király et al., 2020; McKay et al., 2020). The widespread use of smartphones raises 
concerns about their inherent risks. The overuse of smartphones has a negative effect on 
psychological well-being (Adams & Kisler, 2013; Elhai et al., 2017). According to the 
studies, adolescents with a high prevalence of social media use may utilize it as an unhealthy 
outlet for negative emotions and daily difficulties (Lerma et al., 2021; Sujarwoto et al., 2021). 
Such coping mechanisms could progress into excessive use and increase the risk of 
smartphone addiction if left unattended (Islam et al., 2021). Excessive internet use and screen 
time, particularly in children, adolescents, and young people, can have a negative impact on 
cognition and development (Neophytou et al., 2021). 

Studies have also linked smartphone use and sleep difficulties among young people. 
According to a cross-national study of undergraduate students, 10.4% of them had significant 
nocturnal sleep issues in the previous month, with Indonesia (32.9%) having the highest 
percentage and Thailand (3.0%) having the lowest (Peltzer & Pengpid, 2015). Sleep problems 
have been linked to smartphone addiction and numerous indicators of problematic 
smartphone usage among university students and teenagers, including prolonged smartphone 
use, late-night smartphone use, using a smartphone right before bedtime, and 
severe smartphone use (Demirci et al., 2015; Huang et al., 2020; Liu et al., 2019; Sahin et al., 
2013; Shoval et al., 2020).  

Indonesians spend more than 8 hours online daily, with around 3 hours and 26 minutes 
on social platforms mainly accessed through smartphones (Wong, 2019). Despite 
smartphones' high consumption and ownership, research about problematic smartphone use 
in Indonesia is still limited. Previous research reported an association between problematic 
smartphone use and temperament profile in Indonesian medical students (Hanafi et al., 2019). 
Another study reported an Indonesian adaptation of the smartphone addiction scale for 
Indonesian junior high school students (Arthy et al., 2019). These studies are yet to provide 
a prevalence and a description of problematic smartphone use among university students. 
Similar research conducted in other Asian countries has reported the high prevalence of 
problematic smartphone use, such as Taiwan (Wang et al., 2019), China (Huang et al., 2020), 
the Philippines (Buctot et al., 2020), and South Korea (Kim et al., 2018).  

This study aimed to assess the mental well-being of Indonesian college students a few 
months well after the school closure. This study explores the role of smartphone use and sleep 
quality on Indonesian college students' well-being during the ongoing COVID-19 pandemic. 
A further aim was to conduct exploratory analyses to investigate whether socio-demographic 
characteristics would be associated with well-being, smartphone usage, and sleep quality. It 
is critical to establish a clear picture of the severity of smartphone use and its associated 
factors in the adolescent population, given the time they spend on smartphones. Furthermore, 
there has been no study on the prevalence of problematic smartphone use in young people 
during the pandemic COVID-19 on Indonesia's population. Knowing the risks may provide 



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ways to introduce wise smartphone usage with minimum health risks, especially related to 
sleep quality, to maintain well-being.  

Method 

This study used an online cross-sectional research approach among university students in 
Jakarta. Three hundred forty-five university students were selected through convenience 
sampling. Eighteen students were excluded due to their incomplete scales. Thus, 327 students 
(68 males and 259 females; mean age = 19.2 ± 1.65) were included in the study. All 
participants owned and used smartphones regularly. Participants consented to participate in 
the study and were allowed to withdraw at any time. Data collection took place between 
August and September 2020. The study was reviewed and approved by the Universitas 
Tarumanagara's Ethics Committee (PPZ20202077). The confidentiality of participant 
responses was protected. Therefore, the university received the study's overall conclusions 
but not the participants' personal information. 

Instruments 

All participants completed an online questionnaire generated by Google form that consisted 
of two sections. The first part of this data included primary socio-demographic data of 
individual and family characteristics. Questions for individual characteristics include age, 
birth year, gender, place of origin, smoking exposure, and perceived health (feeling physically 
healthy in the past week).  

The second part of the questionnaire assessed the psychological attributes of the 
participants. The Smartphone Addiction Scale – Short Version (SAS-SV), developed by 
Kwon et al. (2013), measures smartphone use in university students. The SAS-SV contains 
ten items. Participants were asked to respond to statements like “Constantly checking my 
smartphone so as not to miss conversations between other people on WhatsApp, Facebook, 
or WeChat” and “The people around me tell me that I use my smartphone too much.” Each 
item scored on a Likert scale of 1 (strongly disagree) to 6 (strongly agree). The questionnaire 
gives an overall SAS-SV score from 10 to 60, with higher scores representing problematic 
use. SAS-SV is not intended to provide a pathological diagnosis of smartphone addiction but 
rather to assess the level of smartphone addiction risk and to identify the high-risk group 
Kwon et al. (2013). Arthy et al. (2019) translated and adapted the Indonesian version of SAS-
SV. The Cronbach's alpha of the SAS-SV in this study is .864. 

The Pittsburgh Sleep Quality Index (Buysse et al., 1989) was used to assess sleep 
problems. This index measures subjective sleep quality over one month. The Pittsburgh Sleep 
Quality Index, abbreviated PSQI, is a set of 19 self-reported questions that are scored on the 
following components: sleep latency, subjective sleep quality, sleep duration, sleep 
efficiency, sleep disruptions, usage of sleep medicine, and daytime dysfunction. These 
component values were combined to provide a global PSQI score ranging from 0 to 21, with 
greater scores indicating lower sleep quality. The Indonesian version of the PSQI was 
previously validated and had a reliability of .79, content validity of .89, and a specificity of 
81% (Alim & Elvira, 2015). The Cronbach's alpha of the PSQI in this study is .68. 

Subjective psychological well-being was assessed using the five-item World Health 
Organization Well Being Index (WHO-5). The WHO-5 has been translated into more than 
30 languages and is used in research projects worldwide (Topp et al., 2015). It consists of five 
items with positive connotations that reflect the presence or absence of well-being. On a 6-
point scale ranging from always (5 points) to never (0 points), participants are asked to 
indicate the presence of these happy sensations in the preceding two weeks. Higher scores 
indicate better-perceived well-being or quality of life. The Indonesian version for WHO-5 



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Marella & Sameve (The well-being of Indonesian university students during the pandemic: Smartphone …) 

was used in previous research with good reliability of .83 (Sasmito & Lopez, 2020; Soewondo 
et al., 2010), while the Cronbach's alpha of the WHO-5 in this study is .827. 

Data Analysis 

All data were subjected to descriptive analysis, and major socio-demographic variables were 
tested for correlation to well-being using linear regression. All variables were examined 
in several steps using hierarchical linear regression analysis. The following procedures were 
taken to generate the multiple regression model: The first step was to enter the participants' 
age and gender (demographic characteristics); the second step was to enter health-related 
variables, specifically perceived physical health and exposure to smoking behavior; the third 
step was to enter the severity of smartphone use, and the fourth and final step was to enter 
sleep quality. Each phase included reporting all standardized coefficients for each variable 
and the collinearity for the final mode. 

Chi-square and Pearson correlations were used to identify the correlation between 
socio-demographic characteristics and smartphone use severity.  Data analysis was performed 
using version 15 of Stata for Windows at a significant of .05 (StataCorp, 2017). 

Results 

Table 1 shows a hierarchical multiple linear regression analysis investigating the association 
between predictors and the WHO-5 score. Demographic variables explained 1.8% of the 
variance in the WHO-5, while health-related variables added 13.5% to the estimate. The 
smartphone use severity predicted an additional 2.5% of the variance, and the final model 
incorporating sleep quality predicted 23.4% of the WHO-5 variance. Each model has a 
significant level of less than .05. Being male students with healthy perceived physical health 
predicted increased well-being scores. In contrast, smartphone usage and poor sleep quality 
increase predict lower well-being scores in Indonesian university students.  
 

Table 1 
Hierarchical Regression Analysis Predicting Student’s Well-being 

Predictor Step 1 

β 

Step 2 

β 

Step 3 

β 

Step 4 

β 

ba SEa Tolerancea 

Demographic        

 Gender (male) 5.730*   4.630*   3.410   4.450*  .111 2.200 .77 

 Age  -.905    -.846    -.787    -.718 -.073 .484 .96 

Health-related        

 Perceived health 

(healthy) 

 11.910*** 10.980***    8.360***  .244 1.750 .88 

 Smoking exposure     -.819     .888      .712  .015 2.480 .79 

Smartphone use      -.267**     -.191* -.121 .081 .89 

Sleep quality (poor)    -10.110*** -.291 1.780 .88 

Adjusted R2   .018*     .135***     .160***      .234***    

ΔR2      .117     .025      .074    

*p<.05, il**p<.01, il***p<.001; ila Measure iof ilthe illast imodel 

 

Unadjusted and adjusted regression analyses stratified by participants’ perceived health 
with sleep quality and smartphone use as the main exposure variables were conducted. In 
Table 2, the models showed a significant effect of perceived physical health on the association 
between sleep quality, smartphone use, and the well-being of university students. Model 2 
was adjusted for gender and age. Both sleep quality and smartphone use were significantly 
negatively associated with mental well-being among university students with good 



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perceptions of their physical health (β = -8.92 and β = .303, respectively). Sleep quality affects 
students’ well-being, regardless of their health perceptions, while smartphone use did not 
influence students with poor perceived physical health (β =.051, p>.05).  
 

Table 2 
Associations between Main Predictors and Student’s Well-being 

 
Table 3 presented the general characteristics of the study participants stratified by their 

smartphone usage. Smartphone use severity was categorized into low and high problematic 
use, using a cutting score of 32 for males and 34 for females from the previous Indonesian 
study (Arthy et al., 2019). Most of the participants in this study were female students (79.2%) 
with a mean age of 19.2 years (SD = 1.65). The participants primarily used their smartphones 
for social networking applications (48.6%) and playing games (38.5%). Most students in this 
study had not been exposed to smoking behavior (85.3%), felt lonely for at least one or two 
days in a week (79.2%), and had a healthy perception of their physical health (66.1%). 

The average well-being score in this study is 60.6 (SD=16.2), with higher scores in 
participants in the low smartphone usage group (69.63, SD=15.19), whereas the average sleep 
quality score in this study is 7.03 (SD=2.85). There was a significant difference in smartphone 
use and sleep quality. Among 222 participants with poor sleep quality, the high smartphone 
usage group had a higher percentage than the low group (75% and 62.3%, respectively). On 
the other hand, most participants with good sleep had low smartphone use. There was no 
significant difference in the distribution of gender and age between the two groups of 
smartphone use severity. However, female participants have higher problematic smartphone 
use than their male counterparts. 
 
Table 3 
The Characteristics of the Participants by Smartphone Usage 

Variables Model 1 Model 2 

 Perceived health Perceived health 

 poor good poor good 

Sleep quality -13.360** -8.570*** -14.290*** -8.920*** 

Smartphone use score    .014   -.327**   .051 -.303** 

*p<.05, il**p<.01, il***p<.001. il 

 

 N = 327 

Smartphone usage Effect size 

Cohen's d p  Low 

(n=183, 55%)  

High 

(n=144, 44%) 

Gender      

 Male  68 (20.8) 42 (22.9) 26 (18.1)  
.279 

 Female 259 (79.2) 141 (77.1) 118 (81.9)  

Smoking exposure      

 Yes 48 (14.7) 20 (10.9) 28 (19.4)  
.031 

 No 279 (85.3) 163 (89.1) 116 (80.6)  

Perceived health      

 Healthy 216 (66.1) 134 (73.2) 62 (43.1)  
.002 

 Unhealthy 111 (33.9) 49 (26.78) 82 (56.9)  

Sleep quality      

 Good 105 (32.1) 69 (37.7) 36 (25)  
.015 

 Poor 222 (67.9) 114 (62.3) 108 (75)  

Well-being Index 60.6 ± 16.20 69.63 ± 15.19 56.34 ± 14.90 .883 < .001 

Age 19.20 ± 1.65 19.10 ± 1.49 19.25 ± 1.72 .091 .451 



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Discussion 

This study investigated factors associated with the well-being of Indonesian university 
students, mainly in Jabodetabek (Jakarta, Bogor, Depok, Tangerang, Bekasi) areas, a few 
months after the school closure due to the COVID-19 pandemic. Based on the cutoff score 
presented in previous studies, approximately 25% of the total participants had a low well-
being score, with a mean score lower than 50 (Dodd et al., 2021; Topp et al., 2015). This 
finding strengthened previous findings from Indonesian studies that revealed modest levels 
of depression and anxiety among university students in Indonesia during the pandemic 
(Soetisna et al., 2021; Sujarwoto et al., 2021).  

The results from the hierarchical regression show a positive correlation between sleep 
quality and well-being in Indonesian college students. This study's average global sleep 
quality score was higher than the suggested score in prior literature (Buysse et al., 1989; 
Herawati & Gayatri, 2019; Wang et al., 2019). Poor sleep quality was found in more than half 
of the students in this study. Similarly, a previous study found that inadequate sleep was more 
widespread and severely impacted mental health during the pandemic lockdown 
(Franceschini et al., 2020). Poor sleep quality affects well-being in both psychosocial and 
physical aspects. Insufficient sleep among adolescents has been associated with mood 
disturbances (Moore et al., 2011), somatic and psychosocial health, school performance, and 
risk-taking behavior (Shochat et al., 2014).  

This study established a 44% prevalence of suspected severe smartphone use, meaning 
those with a high risk of smartphone addiction, among Indonesian college students during the 
COVID-19 outbreak. Smartphone use was also a predictor of university students' well-being, 
with the well-being score significantly lower in the high smartphone use group than in their 
counterparts. This association is related to adolescents' social media use since past evidence 
showed that adolescents mostly use their smartphones to access the internet and social 
networking sites (Buctot et al., 2020; Pratama & Scarlatos, 2020). Previous evidence has 
established the linkage between social media and internet addiction to mental health status in 
adolescents. Adolescents who spend more time online and on social media platforms are more 
likely to suffer from anxiety and despair (Banjanin et al., 2015; Sujarwoto et al., 2021). 
Prolonged social media and internet use may cause adolescents to lose sleep as they linger on 
their smartphones. 

On the other hand, sleeping problems may result from their extensive internet use. 
Additionally, other research suggests that sleep disorders may occur due to emotional and 
behavioral concerns and may contribute directly to the development of internet addiction 
during adolescence (Siste et al., 2021). Additionally, a systematic study discovered that 
individuals who engage in problematic smartphone use face considerably higher risks of poor 
sleep quality, anxiety, and depression (Yang et al., 2020), and limited social media use might 
cause a small improvement in well-being (Graham et al., 2021).  

Age and gender were not significantly related to smartphone use in this study. Several 
studies reported no significant gender difference in smartphone usage (Chen et al., 2017; 
Long et al., 2016). Others showed significant differences with female adolescents as more 
vulnerable to problematic smartphone use (Emirtekin et al., 2019; Randler et al., 2016). 
Nevertheless, it is important to note that female students in this study had higher proportions 
of severe smartphone use than male students. This result is consistent with similar previous 
research in Indonesia (Dewi et al., 2018; Dhamayanti et al., 2019). Female adolescents often 
see smartphones as a means of social contact in which messaging and social network apps 
play prominent roles (Arthy et al., 2019; De-Sola et al., 2016). Voice calls, text messages, 
and social networks were reported as the most complex applications (Roberts et al., 2014). 
This condition might explain the higher score of severe smartphone use in female students. 
Nevertheless, studies still need to explore the inconsistent prevalence of gender differences 
in smartphone use severity among studies.  



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Additionally, the finding shows students with specific characteristics, including poor 
sleep quality, being exposed to smoking behavior, and having an unhealthy perception of 
their physical health, are significantly associated with severe smartphone usage. On the other 
hand, students with good sleep quality who have never been exposed to smoking behavior 
and had good health perception are associated with low smartphone use. Previous research 
on college students revealed that sleep quality is related to health-related behaviors, including 
smartphone dependence and nutritional intake, and thus affects physical and mental health 
(Wang et al., 2019). It is worth noting that a few past research found associations between 
increased smoking risk and vaping behavior with social media exposure through smartphones 
in college students (Massey et al., 2021; Shadel et al., 2013).  

The COVID-19 pandemic did cause significant disruptions in higher education. This 
study's findings have identified several factors to maintain a good level of well-being for 
university students: how they perceive their physical health, smartphone use, and sleep 
quality. This finding is expected because the relationship between health and well-being is 
interchangeable. Poor physical health or illness can influence one’s well-being, just as it can 
influence them (Diener et al., 2018). A study of Italian adolescents reported a significant 
increase in average daily smartphone use during the pandemic (Serra et al., 2021). People's 
behavior, particularly that of children and teenagers, may be altered by the COVID-19 
pandemic to mitigate some of the impacts of the crisis (Dong et al., 2020). During social 
isolation, adolescents may have depended more on smartphones as a source of 
communication, information, and entertainment than they would have under normal 
conditions (Serra et al., 2021).  

Nevertheless, university students should manage the use of smartphones, regulate and 
manage their sleep, and improve or maintain physical health (Faulkner et al., 2021; Serra et 
al., 2021). Students need to pay attention to the length of their screen time and learn to do 
activities apart from their smartphones or other electronic gadgets. This study invites 
university officials and lecturers to team up and think up alternative ways of teaching with a 
suitable balance of proportions between digital and physical aspects of learning. Such 
methods could be even more relevant under the notion of a hybrid learning program as a new 
way of learning in these ongoing new-normal circumstances. 

It is important to acknowledge the limitations of the present study that can be used to 
guide future research. First, this study involved a single demographic, university students; 
additional research is needed to corroborate our findings in other populations to develop a 
more comprehensive understanding of youth well-being. This study was conducted during a 
specific situation, the COVID-19 pandemic; therefore should be considered when interpreting 
the study results. Second, it is essential to acknowledge that our sample size was modest, 
resulting in weaker conclusions. Future studies should employ a bigger sample size to 
increase statistical power. Third, it is important to stress that our data were correlational; 
therefore, no causal implications can be made. Fourth, our research did not investigate social 
network addiction or maladaptive use, which could be a helpful indicator of the possibility 
that excessive social network use results in problematic behavior. As a result, comparative 
research in the future should incorporate scales to assess social media addiction or 
maladaptive use.  

Despite these limitations, this study’s results can be used to formulate a 
recommendation on how to improve the well-being of young people despite the inescapable 
use of technology. Regulated and controlled technology use is necessary to maintain 
prolonged usage, especially in young people. Knowing the risks of smartphone use and low 
sleep quality to psychological well-being may raise awareness of wise smartphone usage with 
minimum health adversity.  



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Conclusion 

This study found the risk factors for Indonesian students' well-being during the COVID-19 
pandemic consist of gender, poor physical health perceptions, sleep problems, and excessive 
smartphone use. These findings confirmed that physical and mental aspects are essential in 
youth well-being. Suitable physical activities, good sleeping habits, and controlled 
smartphone use are needed to nurture mental health during home confinement. 

Acknowledgment 

Authors would like to thank Lembaga Penelitian dan Pengembangan kepada Masyarakat 
(LPPM) Universitas Tarumangara for supporting this research. The authors also want to 
express gratitude to Tuan Hung Ngo (Academia Sinica) for his willingness to lend his 
knowledge during the research. Most importantly, the authors would like to thank the 
participants who have agreed to participate in this study and provided generous insight that 
made this study possible. 

Declarations  

Author contribution. BM designed the study, collected the data, performed the statistical 
analysis, and wrote the article. GES designed the study and wrote the article. The authors read 
and approved the final manuscript.  
Funding statement. The funding for this research received from Lembaga Penelitian dan 
Pengembangan kepada Masyarakat (LPPM) Universitas Tarumangara [Grant number: 922-
Int-KLPPM/UNTAR/VI/2021] 
Conflict of interest. The authors declare no conflict of interest with respect to the research, 
authorship, and publication of this article. 
Additional information. No additional information is available for this paper. 

 
 

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