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Article

Prevalence of Smartphone Addiction and Its Association with Sociodemographic, Physical and Mental Well-Being: A Cross-Sectional Study among the Young Adults of Bangladesh

by
Zubair Ahmed Ratan
1,2,
Anne-Maree Parrish
1,
Mohammad Saud Alotaibi
1,3 and
Hassan Hosseinzadeh
1,*
1
School of Health and Society, Faculty of the Arts, Social Sciences and Humanities, University of Wollongong, Northfields Ave., Wollongong, NSW 2522, Australia
2
Department of Biomedical Engineering, Khulna University of Engineering and Technology, Khulna 9203, Bangladesh
3
Department of Social Work, College of Social Sciences, Umm Al-Qura University, Mecca 24382, Saudi Arabia
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2022, 19(24), 16583; https://doi.org/10.3390/ijerph192416583
Submission received: 2 November 2022 / Revised: 27 November 2022 / Accepted: 5 December 2022 / Published: 9 December 2022
(This article belongs to the Special Issue Health Psychology Related to Addiction Behavior)
Versions Notes

Abstract

:
Smartphones have made our lives easier and have become indispensable for everyday life; however, their uncontrolled and excessive use can trigger “smartphone addiction” (SA). SA is a rising public health issue, particularly among young people around the world. There is a dearth of empirical research about SA and its impacts on young adults, particularly in developing countries such as Bangladesh. This cross-sectional study is an attempt to fill this gap. The data were collected from 440 eligible young adults in Bangladesh using an online survey between July 2021 and February 2022. Study results revealed that 61.4% of the young adults were addicted to their smartphone. Logistic regression analysis showed that being male, aged ≤25, unemployed and living with a large family size (≥8) were the significant sociodemographic predictors of SA. Smartphone-addicted participants were more likely to be less physically active, suffer from insomnia, be overweight or obese and use their phones while driving, walking and eating. In addition, the SA group were more likely to have physical and mental well-being problems. This study brings to light significant implications for policy makers and indicates a need for an SA community awareness programme which aims to reduce SA at the societal level.

1. Introduction

Smartphones offer entertainment and socialization opportunities such as playing online games, surfing the web and browsing social media [1], as well as make life easier with their convenient software applications [2]. According to Statista in 2022, about 83.72% of the world’s population, over 6 billion people, use a smartphone [3], with young people being the most common users [4]. Despite its advantages, the smartphone brings some new challenges; people look at their smartphones while walking or driving [5], and there are reports of “nomophobia”, which was explained by Bhattacharya, Bashar [6] as “a psychological condition when people have a fear of being detached from mobile phone connectivity”.
A recent systematic review indicates that “overuse”, “problematic use” and “excessive use” have been used alternatively to narrate SA [7]. According to Ching, Yee [8], SA is “mainly characterised by excessive or poorly controlled preoccupations, usage or behaviour regarding smartphone use; to the extent that individuals neglect other areas of life”. According to Alhazmi, Alzahrani [1], “the excessive use of smartphones to a level where it interferes with the daily lives of users is considered to be smartphone addiction”. Interestingly, until now, the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) has not recognized SA; however, it has recognized the “diagnostic criteria for Internet gaming addiction with the need for further research” [9]. We think SA is another form of digital addiction since, in line with Professor Griffiths [10], salience, mood modification, tolerance, withdrawal, conflicts and relapse are key components of SA.
Previous studies revealed that SA was associated with sociodemographic factors and found that young people were at higher risk of SA [11,12,13,14]. For an example, a recent study conducted in Saudi Arabia reported that youth was a strong predictor of SA [12]. Several studies also suggested that that SA was associated with different physical problems like insomnia [15], musculoskeletal problems [16] and low levels of physical activity [17]. A study conducted among Korean adults found that cervical repositioning errors were associated with SA [18]. Another recent study also revealed that SA can lead to chronic neck pain [19]. In addition, research has shown that smartphone-addicted drivers use their devices hazardously while driving [20]. Existing evidence also suggests that SA can lead to anxiety, stress and negative emotions [21,22].
Once considered a luxury, smartphones today are an essential device in every household in all countries of the world, and Bangladesh is no exception. Bangladesh has seen a high smartphone penetration rate and has become the fifth-largest mobile market in the Asia Pacific region. According to the Bangladesh Telecommunication Regulatory Commission (BTRC), there are more than 176 million people using smartphones in Bangladesh [23]. One recent study in Bangladesh reported that young people were more vulnerable to SA [24]. However, there are limited studies about SA among young adults in Bangladesh. To address this gap, this current study attempts to investigate (1) the prevalence of SA, (2) the association of sociodemographic factors with smartphone addiction and (3) the effect of SA on the physical and mental health of Bangladeshi young adults.

2. Methods

2.1. Study Design and Data Collection

This cross-sectional study was performed with young adults aged 18 to 32 years in Bangladesh from July 2021 to February 2022. Data were collected using an online survey administered via Qualtrics (Provo, UT, USA). The participants were recruited after the study was advertised via flyers, social media platforms and emails. A “Participant Information Sheet (PIS)” and “Participant Consent Form (PCF)” were included in the online survey. The online survey link was attached to the study advertisement. The survey took 15–25 min to complete. This current study was approved by the University of Wollongong Human Research Ethics Committee (HREC) in July 2021 (Reference number 2021/059).

2.2. Study Measures

Sociodemographic measures included age, gender, occupation status, family size, marital status, education level and family income.
Smartphone Addiction (SA) was measured using the “Smartphone addiction (SAS-SV) scale” [25]. This is an internationally validated scale [7] with 10 questions, each with a Likert scale from 1 to 6, indicating “strongly disagree” (1) to “strongly agree” (6). The overall SAS-SV score ranges from 10 to 60, and the cut-off value for addiction is different for male and female participants (31 for males and 33 for females). In this current study, the Cronbach’s α of the SAS-SV was 0.84.
Anxiety and depression were measured by the four-item “Patient Health Questionnaire (PHQ-4)”, which is composed of depression (PHQ-2) and generalized anxiety disorders (GAD) screener (GAD-2) items [26]. This four-item scale has two questions about anxiety, including “Feeling nervous, anxious or on edge?” and “Not being able to stop or control worrying?” It also includes two questions about depression, including “Feeling down, depressed or hopeless?” and “Little interest or pleasure in doing things?” The questions are scored from 0 (not at all) to 4 (nearly every day) [26]. Respondents with GAD-2 and PHQ-2 scores of ≥3 have been advised to seek further screening for anxiety and depression, respectively [27]. The Cronbach’s α of the PHQ-4 and GAD-2 scale were 0.78 and 0.77 respectively.
Physical health information included the following: physical activity status (“I do not currently exercise and I am not seriously thinking about changing in the next 6 months”, “I exercise sometimes but not regularly” and “I exercise regularly (most days of the week for at least 45 min each time) and have done so for longer than 6 months”); body mass index (BMI); recommended sleeping hours [28]; and pain or discomfort (shoulder, eyes, neck and hands).

2.3. Statistical Analysis

Statistical analysis was performed using IBM SPSS Statistics Version 27.0 (Chicago, IL, USA). Univariate statistical analyses were used to determine the prevalence of SA and describe the distribution of sociodemographic characteristics, as well as physical and mental well-being, among participants. A chi-square analysis was conducted to analyse the effect of SA on physical and mental health. In addition, logistic regression analyses were performed to identify the main predictors of SA. Statistical significance was set at a p value of <0.05.

3. Results

3.1. Sociodemographic Characteristics

In total, 440 young adults participated in the study. Over half of the participants were males (53.2%), and 62.7% of the participants were aged ≤25 years old and were students (56.1%). Three quarters of the participants were single (75.0%) and 53.6% of the participants were from a high-income family. The majority of the participants had obtained a college or a university qualification (89.3%) (Table 1).

3.2. Smartphone Addiction Prevalence

The overall prevalence of SA among the participants was 61.4%; however, male participants (68.4%) were more addicted compared to female participants (53.4%).

3.3. Health Characteristics of the Participants

According to Table 2, 66.6% of the participants reported that they were sometimes physically active as they do exercise sometimes but not regularly. More than one third (34.3%) slept less than 6 h per day. The majority of the participants (81.4%) admitted that their smartphone kept them awake and 63.7% were awake until 12 am because of smartphone use. About 38.4%, 32% and 13% of the participants used their smartphones while eating, walking and driving, respectively. Participants reported experiencing pain in their shoulders (37%), eyes (57.5%), neck (46.1%) and hands (43.6%) as a result of smartphone use.

3.4. Associations of Sociodemographics with Smartphone Addiction

Univariate Logistic Regression Analysis

Univariate logistic regression analyses were performed to determine the associations between sociodemographics and SA. The analyses found that gender, age, occupation status and family size were significantly associated with SA (Table 3). Male participants were 1.88 times more likely to be addicted to a smartphone compared to their female counterparts (OR = 1.88, 95% CI: 1.28–2.78). Similarly, participants aged ≤25 were 2.88 times more likely to be smartphone-addicted compared to those aged ≥31 (OR = 2.88, 95% CI: 1.50–5.53). In addition, unemployed participants were 2.06 times more likely to be smartphone-addicted in comparison to those who were employed (OR = 2.06, 95% CI: 1.09–3.84). Participants from a large family of ≥8 were 1.42 times more likely to be smartphone-addicted than those living with a small family of ≤4 (OR = 1.42, 95% CI: 1.45–10.72). In conclusion, those who were male, aged ≤25, unemployed, and living with a large family (≥8) were more likely to be smartphone-addicted.

3.5. Comparison of Physical Health and Mental Well-Being between Smartphone-Addicted and Non-Addicted Groups

As outlined in Table 4, smartphone-addicted participants were more likely to be physically inactive (26.7%), sleep ≤ 6 h per day (38.1%) and be overweight or obese (32.5%). Participants in the SA group were also more likely to stay awake at night due to smartphone use (88.9%). Participants who were addicted were more likely to use a smartphone while walking (39.3%), driving (31.3%) and eating (42.2%) when compared to the non-addicted group. Smartphone-addicted participants were more likely to feel pain in their shoulders (41.5%), eyes (64.4%), neck (53.0%) and hands (48.1%). Moreover, the chance of having anxiety (40.7%) and depression (40.0%) within the smartphone-addicted group was much higher than in the non-addicted group.

4. Discussion

The widespread adaptation of the internet and smartphones has been monumental all around the globe; it has brought profound change to culture and society in both positive and negative ways. Young people have become a more vulnerable group as they spend most of their time on smartphones. This study was an initial step to understanding the association between SA and the sociodemographics and physical and mental well-being of the young adults in Bangladesh. Our findings show that the prevalence of SA among young adults in Bangladesh is 61.4%, which is higher than the global prevalence (26.99%) [29], as well as the prevalence in other countries including Malaysia (46.9%) [8], Turkey (39.8%) [30], Lebanon (44.6%) [31], China (29.8%) [32] and Brazil (33.1%) [33]. Such a high prevalence of SA among the participants is in line with smartphone usage rates in South-East Asia compared to other regions of the world [29]. These rates might be due to the transition from a traditional culture to a “digital culture” in Bangladesh [34], which has seen a sharp increase in smartphone use especially among the younger generation [35]. In addition, young people of the country use smartphones for watching movies or listening to music [36], which could be another possible explanation for the high prevalence of SA among the youth of the country.
In line with the literature, our findings suggest that young adults (aged ≤25) are more likely to be addicted to smartphone use [37,38,39]. One reason for this could be the frequent use of social media via smartphone, which is common among young people [40]. Another probable reason is that young adults who were born and raised during the internet era might prefer online communication to face-to-face communication [41]. In line with previous internet addiction studies in Bangladesh [42,43], this study also revealed that males are more prone to SA. This could be explained by findings that show that males frequently use smartphones for playing online games [44,45]. In contrast to our findings, some studies have reported that females are more prone to SA than males [25,46,47]. This warrants further study to explore what role gender plays in smartphone addiction.
Our study also found that unemployment was associated with SA. Previous research indicates this is likely due to unemployed people having more free time to spend on a smartphone [48]. This current study also revealed that living in a large family (≥8 members) is associated with smartphone overuse, which is similar to the results of a recent study conducted among adolescents in southern China [49]. It is possible that young adults who belong to a large family may have less communication [50] but more conflicts with other family members [49], which may lead to a smartphone overuse. In contrast, other studies have shown that living in a small family is also linked to SA [51,52]. This requires further study to explore how family size impacts smartphone use.
Consistent with previous studies, this study found that SA was more likely to be associated with physical inactivity and being obese or overweight [17,53]. A recent study suggested that smartphone overuse was linked with weight gain [15]. In terms of sleep time, the current study reported that smartphone-addicted participants had less sleep in comparison to non-addicted participants, which is similar to results found in previous studies [30,54,55]. “Bedtime procrastination” [56] and “blue light” [41] emitted by a smartphone are some of the underlying triggering factors of insufficient sleep, as blue light inhibits the brain from releasing melatonin, an important sleep hormone [57,58]. Young adults who are addicted to smartphones may be unable to stop using their smartphones, causing “bedtime procrastination,” which may stimulate poor sleep quality [56]. Like previous studies, the current study also revealed that those who were addicted to smartphones were more likely to use a smartphone while walking and driving [20,59]. This has significant implications for policy makers, as smartphone use while walking and driving can cause serious accidents [20,60].
Our study suggests that smartphone-addicted young adults are more likely to experience physical pain in their shoulders, neck and hands, which is similar to other results in the literature [61,62]. Overuse of smartphones can lead to defective postures that initiate pain in different joints and muscles of the body [63]. In addition, one study reported that “De Quervain tenosynovitis”, or pain in the wrists, was associated with using different electronic devices including smartphones [64]; excessive chatting and texting through a smartphone was among the common risk factors for De Quervain tenosynovitis [65]. Notably, a previous study also revealed that SA can initiate carpal tunnel syndrome [66], which is in line with our findings. This study also reports that SA can lead to eye pain; previous research found that smartphone overuse can induce different eye problems such as redness and blurred vision [67,68], which support our results.
Our findings reveal that young adults those who are addicted to smartphones are more likely to suffer from anxiety and depression when compared to non-addicted groups, which is in line with previous research [7,69,70]. Overusing instant messaging or notifications in social media can lead to more online communication, rather than face-to-face communication, increasing the potential for social isolation [71], which may act as a trigger for anxiety and depression among the younger generations [72,73]. Smartphone-addicted people have a common tendency to check notifications on their mobile phone all the time, which may lead to a “reassurance seeking” pathway [69,74,75], which ultimately results in “Fear of Missing Out (FoMO)”. FOMO has been considered to be one of the main underlying factors of anxiety and depression related to SA [76]. Moreover, numerous studies indicate that SA can lead to nomophobia, which is associated with anxiety and depression [77,78].
The findings from this research are invaluable in identifying the effects of SA, its main drivers and its impact on young adults in Bangladesh. However, this study has some limitations. It was a cross-sectional study; therefore, a causal relationship cannot be determined. The data were self-reported, which is a potential source of bias. In addition, data were collected through convenience sampling methods, so the findings may not be generalizable for the Bangladesh-wide population of young adults.

5. Conclusions

This study found a high prevalence of SA among young adults in Bangladesh, which has significant implications for policy makers with respect to the health outcomes of young adults both at present and into the future. Our finding suggest that there is a need to find solutions to address the significant impact of SA at a national and international level. National and international public health organizations should develop policies and guidelines to reduce smartphone overuse. Furthermore, it is important to raise awareness of the potential long-term effects of SA on the health outcomes of young adults.

Author Contributions

Z.A.R. conceptualized and designed the study. H.H., A.-M.P. supervised the study. Z.A.R. collected, analyzed and drafted the manuscript. H.H., A.-M.P., M.S.A. and Z.A.R. critically assessed the data analysis and interpretation and reviewed the manuscript. H.H., A.-M.P. and M.S.A. reviewed and approved the final manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was assessed and approved by the University of Wollongong Human Research Ethics Committee (HREC) (Reference number- 2021/059).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Alhazmi, A.A.; Alzahrani, S.H.; Baig, M.; Salawati, E.M.; Alkatheri, A. Prevalence and factors associated with smartphone addiction among medical students at King Abdulaziz University, Jeddah. Pak. J. Med. Sci. 2018, 34, 984. [Google Scholar] [CrossRef] [PubMed]
  2. Imteaj, A.; Hossain, M.K. A smartphone based application to improve the health care system of Bangladesh. In Proceedings of the 2016 International Conference on Medical Engineering, Health Informatics and Technology (MediTec), Dhaka, Bangladesh, 17–18 December 2016; IEEE: New York, NY, USA, 2016. [Google Scholar]
  3. Turner, A. How Many Smartphones Are In The World? Available online: https://www.bankmycell.com/blog/how-many-phones-are-in-the-world (accessed on 18 May 2022).
  4. Deloitte. 2017 Global Mobile Consumer Survey: US Edition. Available online: https://www2.deloitte.com/tr/en/pages/technology-media-and-telecommunications/articles/global-mobile-consumer-survey-us-edition.html (accessed on 20 October 2021).
  5. Lee, M.; Han, M.; Pak, J. Analysis of behavioral characteristics of smartphone addiction using data mining. Appl. Sci. 2018, 8, 1191. [Google Scholar] [CrossRef] [Green Version]
  6. Bhattacharya, S.; Bashar, M.A.; Srivastava, A.; Singh, A. NOMOPHOBIA: NO MObile PHone PhoBIA. J. Fam. Med. Prim. Care 2019, 8, 1297–1300. [Google Scholar] [CrossRef] [PubMed]
  7. Ratan, Z.A.; Parrish, A.-M.; Zaman, S.B.; Alotaibi, M.S.; Hosseinzadeh, H. Smartphone Addiction and Associated Health Outcomes in Adult Populations: A Systematic Review. Int. J. Environ. Res. Public Health 2021, 18, 12257. [Google Scholar] [CrossRef] [PubMed]
  8. Ching, S.M.; Yee, A.; Ramachandran, V.; Sazlly Lim, S.M.; Wan Sulaiman, W.A.; Foo, Y.L.; Hoo, F.K. Validation of a Malay Version of the Smartphone Addiction Scale among Medical Students in Malaysia. PLoS ONE 2015, 10, e0139337. [Google Scholar] [CrossRef] [PubMed]
  9. Petry, N.M.; Rehbein, F.; Gentile, D.A.; Lemmens, J.S.; Rumpf, H.J.; Mößle, T.; Bischof, G.; Tao, R.; Fung, D.S.; Borges, G. An international consensus for assessing internet gaming disorder using the new DSM-5 approach. Addiction 2014, 109, 1399–1406. [Google Scholar] [CrossRef] [PubMed]
  10. Griffiths, M. A ‘components’ model of addiction within a biopsychosocial framework. J. Subst. Use 2005, 10, 191–197. [Google Scholar] [CrossRef]
  11. Aljomaa, S.S.; Al Qudah, M.F.; Albursan, I.S.; Bakhiet, S.F.; Abduljabbar, A.S. Smartphone addiction among university students in the light of some variables. Comput. Hum. Behav. 2016, 61, 155–164. [Google Scholar] [CrossRef]
  12. Augner, C.; Hacker, G.W. Associations between problematic mobile phone use and psychological parameters in young adults. Int. J. Public Health 2012, 57, 437–441. [Google Scholar] [CrossRef]
  13. Rozgonjuk, D.; Kattago, M.; Täht, K. Social media use in lectures mediates the relationship between procrastination and problematic smartphone use. Comput. Hum. Behav. 2018, 89, 191–198. [Google Scholar]
  14. Alhassan, A.A.; Alqadhib, E.M.; Taha, N.W.; Alahmari, R.A.; Salam, M.; Almutairi, A.F. The relationship between addiction to smartphone usage and depression among adults: A cross sectional study. BMC Psychiatry 2018, 18, 148. [Google Scholar]
  15. Alosaimi, F.D.; Alyahya, H.; Alshahwan, H.; Al Mahyijari, N.; Shaik, S.A. Smartphone addiction among university students in Riyadh, Saudi Arabia. Saudi Med. J. 2016, 37, 675–683. [Google Scholar] [CrossRef] [PubMed]
  16. Hanphitakphong, P.; Keeratisiroj, O.; Thawinchai, N. Smartphone addiction and its association with upper body musculoskeletal symptoms among university students classified by age and gender. J. Phys. Ther. Sci. 2021, 33, 394–400. [Google Scholar] [CrossRef] [PubMed]
  17. Kim, S.-E.; Kim, J.-W.; Jee, Y.-S. Relationship between smartphone addiction and physical activity in Chinese international students in Korea. J. Behav. Addict. 2015, 4, 200–205. [Google Scholar] [CrossRef]
  18. Lee, J.; Seo, K. The comparison of cervical repositioning errors according to smartphone addiction grades. J. Phys. Ther. Sci. 2014, 26, 595–598. [Google Scholar] [CrossRef] [Green Version]
  19. Al-Hadidi, F.; Bsisu, I.; AlRyalat, S.A.; Al-Zu’bi, B.; Bsisu, R.; Hamdan, M.; Kanaan, T.; Yasin, M.; Samarah, O. Association between mobile phone use and neck pain in university students: A cross-sectional study using numeric rating scale for evaluation of neck pain. PLoS ONE 2019, 14, e0217231. [Google Scholar] [CrossRef] [Green Version]
  20. Kita, E.; Luria, G. The mediating role of smartphone addiction on the relationship between personality and young drivers’ smartphone use while driving. Transp. Res. Part F Traffic Psychol. Behav. 2018, 59, 203–211. [Google Scholar] [CrossRef]
  21. Samaha, M.; Hawi, N.S. Relationships among smartphone addiction, stress, academic performance, and satisfaction with life. Comput. Hum. Behav. 2016, 57, 321–325. [Google Scholar] [CrossRef]
  22. Wan Ismail, W.S.; Sim, S.T.; Tan, K.-A.; Bahar, N.; Ibrahim, N.; Mahadevan, R.; Nik Jaafar, N.R.; Baharudin, A.; Abdul Aziz, M. The relations of internet and smartphone addictions to depression, anxiety, stress, and suicidality among public university students in Klang Valley, Malaysia. Perspect. Psychiatr. Care 2020, 56, 949–955. [Google Scholar] [CrossRef]
  23. BTRC. Mobile Phone Subscribers; BTRC, Ed.; BTRC: Dhaka, Bangladesh, 2019. [Google Scholar]
  24. Rokonuzzaman, M. Smart Phones—From Necessity to Addiction; The Financial Express: Dhaka, Bangladesh, 2018. [Google Scholar]
  25. Kwon, M.; Kim, D.J.; Cho, H.; Yang, S. The smartphone addiction scale: Development and validation of a short version for adolescents. PLoS ONE 2013, 8, e83558. [Google Scholar] [CrossRef] [Green Version]
  26. Kroenke, K.; Spitzer, R.L.; Williams, J.B.; Löwe, B. An ultra-brief screening scale for anxiety and depression: The PHQ-4. Psychosomatics 2009, 50, 613–621. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  27. Löwe, B.; Wahl, I.; Rose, M.; Spitzer, C.; Glaesmer, H.; Wingenfeld, K.; Schneider, A.; Brähler, E. A 4-item measure of depression and anxiety: Validation and standardization of the Patient Health Questionnaire-4 (PHQ-4) in the general population. J. Affect. Disord. 2010, 122, 86–95. [Google Scholar] [CrossRef] [PubMed]
  28. National Sleep Foundation. How Much Sleep Do We Really Need? 2011. Available online: https://www.sleepfoundation.org/how-sleep-works/how-much-sleep-do-we-really-need (accessed on 21 October 2022).
  29. Meng, S.-Q.; Cheng, J.-L.; Li, Y.-Y.; Yang, X.-Q.; Zheng, J.-W.; Chang, X.-W.; Shi, Y.; Chen, Y.; Lu, L.; Sun, Y.; et al. Global prevalence of digital addiction in general population: A systematic review and meta-analysis. Clin. Psychol. Rev. 2022, 92, 102128. [Google Scholar] [CrossRef] [PubMed]
  30. Demirci, K.; Akgonul, M.; Akpinar, A. Relationship of smartphone use severity with sleep quality, depression, and anxiety in university students. J. Behav. Addict. 2015, 4, 85–92. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  31. Hawi, N.S.; Samaha, M. To excel or not to excel: Strong evidence on the adverse effect of smartphone addiction on academic performance. Comput. Educ. 2016, 98, 81–89. [Google Scholar] [CrossRef]
  32. Chen, B.; Liu, F.; Ding, S.; Ying, X.; Wang, L.; Wen, Y. Gender differences in factors associated with smartphone addiction: A cross-sectional study among medical college students. BMC Psychiatry 2017, 17, 341. [Google Scholar] [CrossRef] [Green Version]
  33. Mescollotto, F.F.; de Castro, E.M.; Pelai, E.B.; Pertille, A.; Bigaton, D.R. Translation of the short version of the smartphone addiction scale into Brazilian Portuguese: Cross-cultural adaptation and testing of measurement properties. Braz. J. Phys. Ther. 2019, 23, 250–256. [Google Scholar]
  34. Hussain, R. The Emerging Digital Culture of Bangladesh: Problems and Prospects. J. Philos. Cult. Relig. 2015, 6, 18–24. [Google Scholar]
  35. Hoque, A.S.M.M. Digital device addiction effect on lifestyle of generation Z in Bangladesh. Asian People J. APJ 2018, 1, 21–44. [Google Scholar]
  36. Rashid, M.A.; Amin, M.N.; Arefin, S.; Rahaman, M.A. ICTs and Time Use Behavior of the Students: A Study of Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj, Bangladesh. Am. J. Educ. Res. 2018, 6, 1164–1171. [Google Scholar] [CrossRef] [Green Version]
  37. Olson, K.E.; O’Brien, M.A.; Rogers, W.A.; Charness, N. Diffusion of technology: Frequency of use for younger and older adults. Ageing Int. 2011, 36, 123–145. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  38. Van Deursen, A.J.; Bolle, C.L.; Hegner, S.M.; Kommers, P.A. Modeling habitual and addictive smartphone behavior: The role of smartphone usage types, emotional intelligence, social stress, self-regulation, age, and gender. Comput. Hum. Behav. 2015, 45, 411–420. [Google Scholar]
  39. Alotaibi, M.S.; Fox, M.; Coman, R.; Ratan, Z.A.; Hosseinzadeh, H. Smartphone Addiction Prevalence and Its Association on Academic Performance, Physical Health, and Mental Well-Being among University Students in Umm Al-Qura University (UQU), Saudi Arabia. Int. J. Environ. Res. Public Health 2022, 19, 3710. [Google Scholar] [PubMed]
  40. Khan, A.; Uddin, R.; Islam, S.M.S. Social media use is associated with sleep duration and disturbance among adolescents in Bangladesh. Health Policy Technol. 2019, 8, 313–315. [Google Scholar] [CrossRef]
  41. Tateno, M.; Teo, A.R.; Ukai, W.; Kanazawa, J.; Katsuki, R.; Kubo, H.; Kato, T.A. Internet addiction, smartphone addiction, and Hikikomori trait in Japanese young adult: Social isolation and social network. Front. Psychiatry 2019, 10, 455. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. Hassan, T.; Alam, M.M.; Wahab, A.; Hawlader, M.D. Prevalence and associated factors of internet addiction among young adults in Bangladesh. J. Egypt Public Health Assoc. 2020, 95, 3. [Google Scholar] [CrossRef] [Green Version]
  43. Uddin, M.S.; Al Mamun, A.; Iqbal, M.A.; Nasrullah, M.; Asaduzzaman, M.; Sarwar, M.S.; Amran, M.S. Internet addiction disorder and its pathogenicity to psychological distress and depression among university students: A cross-sectional pilot study in Bangladesh. Psychology 2016, 7, 1126. [Google Scholar] [CrossRef] [Green Version]
  44. Doornwaard, S.M.; van den Eijnden, R.J.; Baams, L.; Vanwesenbeeck, I.; ter Bogt, T.F. Lower Psychological Well-Being and Excessive Sexual Interest Predict Symptoms of Compulsive Use of Sexually Explicit Internet Material Among Adolescent Boys. J. Youth Adolesc. 2016, 45, 73–84. [Google Scholar] [CrossRef] [Green Version]
  45. Bruno, A.; Scimeca, G.; Cava, L.; Pandolfo, G.; Zoccali, R.A.; Muscatello, M.R.A. Prevalence of Internet Addiction in a Sample of Southern Italian High School Students. Int. J. Ment. Health Addict. 2014, 12, 708–715. [Google Scholar] [CrossRef]
  46. Demirci, K.; Orhan, H.; Demirdas, A.; Akpinar, A.; Sert, H. Validity and Reliability of the Turkish Version of the Smartphone Addiction Scale in a Younger Population. Klin. Psikofarmakol. Bülteni-Bull. Clin. Psychopharmacol. 2014, 24, 226–234. [Google Scholar] [CrossRef] [Green Version]
  47. Bagci, H.; Peksen, M.F. Investigating the Smart Phone Addictions of Vocational School Students from Different Variables. Malays. Online J. Educ. Technol. 2018, 6, 40–52. [Google Scholar] [CrossRef]
  48. Kim, H.J.; Min, J.Y.; Kim, H.J.; Min, K.B. Association between psychological and self-assessed health status and smartphone overuse among Korean college students. J. Ment. Health 2019, 28, 11–16. [Google Scholar] [CrossRef] [PubMed]
  49. Chi, X.; Hong, X.; Chen, X. Profiles and sociodemographic correlates of Internet addiction in early adolescents in southern China. Addict. Behav. 2020, 106, 106385. [Google Scholar] [CrossRef] [PubMed]
  50. Hogan, D.P.; Kitagawa, E.M. The impact of social status, family structure, and neighborhood on the fertility of black adolescents. Am. J. Sociol. 1985, 90, 825–855. [Google Scholar] [CrossRef]
  51. Karuppasamy, G.; Anwar, A.; Bhartiya, A.; Sajjad, S.; Rashid, M.; Mathew, E.; Saikh, R.; Al Sharbatti, S.; Sreedharan, J. Use of Social Networking Sites among University Students in Ajman, United Arab Emirates. Nepal J. Epidemiol. 2013, 3, 245–250. [Google Scholar] [CrossRef]
  52. Sul, S. Determinants of internet game addiction and therapeutic role of family leisure participation. J. Incl. Phenom. Macrocycl. Chem. 2015, 82, 271–278. [Google Scholar]
  53. Haripriya, S.; Samuel, S.; Megha, M. Correlation between Smartphone Addiction, Sleep Quality and Physical Activity among Young Adults. J. Clin. Diagn. Res. 2019, 13, 13212. [Google Scholar] [CrossRef]
  54. Gezgin, D.M. Understanding patterns for smartphone addiction: Age, sleep duration, social network use and fear of missing out. Cypriot J. Educ. Sci. 2018, 13, 166–177. [Google Scholar] [CrossRef] [Green Version]
  55. Nunes, P.P.B.; Abdon, A.P.V.; Brito, C.B.; Silva, F.V.M.; Santos, I.C.A.; Martins, D.Q.; Meira, P.M.F.; Frota, M.A. Factors related to smartphone addiction in adolescents from a region in Northeastern Brazil. Cien. Saude Colet. 2021, 26, 2749–2758. [Google Scholar] [CrossRef]
  56. Zhang, M.X.; Wu, A.M.S. Effects of smartphone addiction on sleep quality among Chinese university students: The mediating role of self-regulation and bedtime procrastination. Addict. Behav. 2020, 111, 106552. [Google Scholar] [CrossRef]
  57. West, K.E.; Jablonski, M.R.; Warfield, B.; Cecil, K.S.; James, M.; Ayers, M.A.; Maida, J.; Bowen, C.; Sliney, D.H.; Rollag, M.D. Blue light from light-emitting diodes elicits a dose-dependent suppression of melatonin in humans. J. Appl. Physiol. 2011, 110, 619–626. [Google Scholar] [PubMed] [Green Version]
  58. Nagare, R.; Plitnick, B.; Figueiro, M. Does the iPad Night Shift mode reduce melatonin suppression? Light. Res. Technol. 2019, 51, 373–383. [Google Scholar] [PubMed]
  59. Mourra, G.N.; Sénécal, S.; Fredette, M.; Lepore, F.; Faubert, J.; Bellavance, F.; Cameron, A.-F.; Labonté-LeMoyne, É.; Léger, P.-M. Using a smartphone while walking: The cost of smartphone-addiction proneness. Addict. Behav. 2020, 106, 106346. [Google Scholar] [CrossRef] [PubMed]
  60. Kim, H.-J.; Min, J.-Y.; Kim, H.-J.; Min, K.-B. Accident risk associated with smartphone addiction: A study on university students in Korea. J. Behav. Addict. 2017, 6, 699–707. [Google Scholar] [CrossRef]
  61. İnal, Ö.; Serel Arslan, S. Investigating the effect of smartphone addiction on musculoskeletal system problems and cognitive flexibility in university students. Work 2021, 68, 107–113. [Google Scholar] [CrossRef]
  62. Park, Y.-H.; An, C.-M.; Moon, S.-J. Effects of visual fatigue caused by smartphones on balance function in healthy adults. J. Phys. Sci. 2017, 29, 221–223. [Google Scholar] [CrossRef] [Green Version]
  63. Elserty, N.; Helmy, N.; Mounir, K.M. Smartphone addiction and its relation to musculoskeletal pain in Egyptian physical therapy students. Eur. J. Physiother. 2020, 22, 70–78. [Google Scholar]
  64. Baabdullah, A.; Bokhary, D.; Kabli, Y.; Saggaf, O.; Daiwali, M.; Hamdi, A. The association between smartphone addiction and thumb/wrist pain: A cross-sectional study. Medicine 2020, 99, e19124. [Google Scholar] [CrossRef]
  65. Bahathiq, M.; Almadaabgy, A.; Marzogi, K.A.; Alnahdi, A.; Mufti, H.; Alsharif, K. The association between smartphones and thumb/wrist pain among students at Umm Al-Qura University, Makkah, Saudi Arabia. Int. J. Med. Dev. Countrie 2020, 4, 1924–1937. [Google Scholar] [CrossRef]
  66. Karaçorlu, F.N.; Balgetir, F.; Pirinçci, E.; Deveci, S.E. The relationship between carpal tunnel syndrome, smartphone use, and addiction: A cross-sectional study. Turk. J. Phys. Med. Rehab. 2022, 68, 517–523. [Google Scholar] [CrossRef]
  67. Kim, J.; Hwang, Y.; Kang, S.; Kim, M.; Kim, T.-S.; Kim, J.; Seo, J.; Ahn, H.; Yoon, S.; Yun, J.P.; et al. Association between Exposure to Smartphones and Ocular Health in Adolescents. Ophthalmic Epidemiol. 2016, 23, 269–276. [Google Scholar] [CrossRef] [PubMed]
  68. Sekarrini, L. the Impact of Smartphone Usage in Adolescents 15–24 Years Old in Jabodetabek Region. Indones. J. Public Health 2020, 15, 103. [Google Scholar]
  69. Elhai, J.D.; Dvorak, R.D.; Levine, J.C.; Hall, B.J. Problematic smartphone use: A conceptual overview and systematic review of relations with anxiety and depression psychopathology. J. Affect. Disord. 2017, 207, 251–259. [Google Scholar] [CrossRef] [PubMed]
  70. De-Sola Gutierrez, J.; Rodriguez de Fonseca, F.; Rubio, G. Cell-Phone Addiction: A Review. Front. Psychiatry 2016, 7, 175. [Google Scholar] [CrossRef] [Green Version]
  71. Sepúlveda-Loyola, W.; Rodríguez-Sánchez, I.; Pérez-Rodríguez, P.; Ganz, F.; Torralba, R.; Oliveira, D.V.; Rodríguez-Mañas, L. Impact of Social Isolation Due to COVID-19 on Health in Older People: Mental and Physical Effects and Recommendations. J Nutr. Health Aging 2020, 24, 938–947. [Google Scholar] [CrossRef]
  72. George, M.J.; Odgers, C.L. Seven fears and the science of how mobile technologies may be influencing adolescents in the digital age. Perspect. Psychol. Sci. 2015, 10, 832–851. [Google Scholar] [CrossRef] [Green Version]
  73. Pantic, I.; Damjanovic, A.; Todorovic, J.; Topalovic, D.; Bojovic-Jovic, D.; Ristic, S.; Pantic, S. Association between online social networking and depression in high school students: Behavioral physiology viewpoint. Psychiatr. Danub. 2012, 24, 90–93. [Google Scholar]
  74. Ratan, Z.A.; Zaman, S.B.; Islam, S.M.S.; Hosseinzadeh, H. Smartphone overuse: A hidden crisis in COVID-19. Health Policy Technol. 2021, 10, 21–22. [Google Scholar] [CrossRef] [PubMed]
  75. Billieux, J.; Maurage, P.; Lopez-Fernandez, O.; Kuss, D.J.; Griffiths, M.D.J.C.A.R. Can Disordered Mobile Phone Use Be Considered a Behavioral Addiction? An Update on Current Evidence and a Comprehensive Model for Future Research. Curr. Addict. Rep. 2015, 2, 156–162. [Google Scholar] [CrossRef] [Green Version]
  76. Wolniewicz, C.A.; Rozgonjuk, D.; Elhai, J.D. Boredom proneness and fear of missing out mediate relations between depression and anxiety with problematic smartphone use. Hum. Behav. Emerg. Technol. 2020, 2, 61–70. [Google Scholar] [CrossRef]
  77. Sharma, M.; Amandeep; Mathur, D.M.; Jeenger, J. Nomophobia and its relationship with depression, anxiety, and quality of life in adolescents. Ind. Psychiatry J. 2019, 28, 231–236. [Google Scholar] [CrossRef] [PubMed]
  78. Farchakh, Y.; Hallit, R.; Akel, M.; Chalhoub, C.; Hachem, M.; Hallit, S.; Obeid, S. Nomophobia in Lebanon: Scale validation and association with psychological aspects. PLoS ONE 2021, 16, e0249890. [Google Scholar]
Table
Table 1. Distribution of sociodemographic characteristics, N = 440.
Table 1. Distribution of sociodemographic characteristics, N = 440.
n(%)
Gender
Male 234(53.2)
Female206(46.8)
Age a
≤25 276(62.7)
26–30120(27.3)
≥3144(10.0)
Occupation status b
Employed 127(20.9)
Unemployed 66(15.0)
Students247(56.1)
Family monthly income
<5000 TK 39(8.9)
5000–<10,000 TK56(12.7)
10,000–<20,000 TK109(24.8)
≥20,000 TK236(53.6)
Marital status b
Single 330(75.0)
Married103(23.4)
Others (Divorced and Widowed)7(1.6)
Family size a
Small (≤4) 204(46.4)
Average (5–7)206(46.8)
Large (≥8) 30(6.8)
Education level b
Primary or secondary47(10.7)
College or university qualification393(89.3)
a “Age and family size were continuous variables but they are categorized into groups for the purpose of this study”. b “Occupation status, marital status and education level categories are re categorized due to small numbers in some original categories”.
Table
Table 2. Distribution of health characteristics, N = 440.
Table 2. Distribution of health characteristics, N = 440.
n(%)
Physical activity
I do not currently exercise 98(22.3)
I exercise sometimes 293(66.6)
I exercise regularly 49(11.1)
Average of sleep hours a
≤6 h (Not Recommended)151(34.3)
7–9 h (Recommended)270(61.4)
≥10 (Not Recommended)19(4.3)
Is your smartphone keep you awake at night
Yes358(81.4)
No82(18.6)
If yes, up to
12 am 130(36.3)
After 12 am 228(63.7)
Do you use your smartphone while walking?
Yes141(32.0)
No299(68.0)
Do you use your smartphone while driving?
Yes57(13.0)
No193(43.2)
Not applicable190(43.2)
Do you eat while using smartphone?
Yes169(38.4)
No271(61.6)
Body Mass Index (BMI) b
Underweight (≤18.4)47(10.7)
Healthy weight (18.5–24.9)271(61.6)
Overweight (25.0–29.9)89(20.2)
Obese (≥30.0)33(7.5)
Experienced pain
Shoulder
Yes163(37.0)
No277(63.0)
Eyes
Yes253(57.5)
No187(42.5)
Neck
Yes203(46.1)
No237(53.9)
Hands
Yes192(43.6)
No248(56.4)
PHQ-4 c
Anxiety (GAD-2)
Anxiety 155(35.2)
Non-Anxiety285(64.8)
Depression (PHQ-2)
Depressed 147(33.4)
Non-Depressed 293(66.6)
a Average sleep time was a continuous variable but it is categorized into “recommended” and “not recommended” times according to the Sleep Foundation (https://www.sleepfoundation.org/press-release/national-sleep-foundation-recommends-new-sleep-times, accessed on 1 June 2022). b BMI was calculated by using participants’ weight and height data. c GAD-2 and PHQ-2 Scores of ≥3 indicate a positive screening for anxiety and depression symptoms.
Table
Table 3. Univariate logistic regression analysis of associations of sociodemographic variables with smartphone addiction, N = 440.
Table 3. Univariate logistic regression analysis of associations of sociodemographic variables with smartphone addiction, N = 440.
Non-Smartphone
Addicted		Smartphone AddictedOR95% CIp
n(%)n(%)LowerUpper
Gender
Male74(43.5)160(59.3) 1.881.282.780.001
Female96(56.5)110(40.7)Ref.
Age
≤25 92(54.1)184(68.1)2.881.505.530.001
26–3052(30.6)68(25.2)1.880.933.800.075
≥3126(15.3)18(6.7)Ref.
Occupation status
Employed60(35.3) 67 (24.8) Ref.
Unemployed 20(11.8)46(17.0)2.061.093.840.025
Students90(52.9)157(58.1)1.561.012.410.044
Family monthly income
<5000 TK14 (8.2)25 (9.3) Ref.
5000–<10,000 TK21(12.4)35(13.0)0.930.392.180.873
10,000–<20,000 TK39(22.9)70(25.9)1.000.462.150.990
≥20,000 TK96(56.5)140(51.9)0.810.401.650.573
Marital status
Single 120 (70.6) 210 (77.8) Ref.
Married 48(28.2)51(20.4)0.650.411.020.064
Divorced/Widowed/Others2(1.2)5(1.9)1.420.277.470.673
Family size
Small (≤4) 90 (52.9)114(42.2) Ref.
Average (5–7)75(44.1) 131(48.5)1.370.922.040.112
Large (≥8)5(2.9)25(9.3)1.421.4510.720.007
Education level
Primary or secondary 19(11.2) 28(10.4) Ref.
College or university qualification151(88.8)242(89.6)1.080.582.010.790
OR = odds ratio, CI = confidence interval, A p-Value ≤ 0.05 was considered significant.
Table
Table 4. Comparison between physical health and mental well-being information between smartphone-addicted and non-smartphone-addicted groups, N = 440.
Table 4. Comparison between physical health and mental well-being information between smartphone-addicted and non-smartphone-addicted groups, N = 440.
Non-Smartphone
Addicted		 Smartphone
Addicted		dfX2
(440)		p
Physical Activityn(%)n(%)29.7110.008
I do not currently exercise 26(15.3)72(26.7)
I exercise sometimes 119(70.0)174(64.4)
I exercise regularly 25(14.7)24(8.9)
Average of sleep hours a26.6570.036
≤6 h (Not Recommended)48(28.2)103(38.1)
7–9 h (Recommended)111(65.3)159(58.9)
≥10 (Not Recommended)11(6.5)8(3.0)
Is your smartphone keep you awake at night?126.090.001
Yes118(69.4)240(88.9)
No52(30.6)30(11.1)
If yes, up to 15.6320.018
12 am 53(44.9)77(32.1)
After 12 am65(55.1)163(67.9)
Do you use your smartphone while walking?116.700.001
Yes35(20.6)106(39.3)
No135(79.4)164(60.7)
Do you use your smartphone while driving? 114.620.001
Yes11(10.7)46(31.3)
No92(89.3)101(68.7)
Do you eat while using smartphone?14.2950.038
Yes55(32.4)114(42.2)
No115(67.6)156(57.8)
Body Mass Index (BMI) b39.2040.027
Underweight (≤18.4)22(12.9)25(9.3)
Healthy weight (18.5–24.9)114(67.1)157(58.1)
Overweight (25.0–29.9)23(13.5)66(24.4)
Obese (≥30.0)11(6.5)22(8.1)
Experienced pain
Shoulder15.8960.015
Yes51(30.0)112(41.5)
No119(70.0)158(58.5)
Eyes113.790.001
Yes79(46.5)174(64.4)
No91(53.5)96(35.6)
Neck 113.100.001
Yes60(35.3)143(53.0)
No110(64.7)127(47.0)
Hands 15.7840.016
Yes62(36.5)130(48.1)
No108(63.5)140(51.9)
PHQ-4
Anxiety 19.3100.002
Anxiety 45(26.5)110(40.7)
Non-Anxiety125(73.5)160(59.3)
Depression113.640.001
Depressed 39(22.9) 108(40.0)
Non-Depressed131(77.1)162(60.0)
a Average of sleep hours was a continuous variable but it is categorized into recommended and not recommended times according to Sleep Foundation (https://www.sleepfoundation.org/press-release/national-sleep-foundation-recommends-new-sleep-times, accessed on 1 June 2022). b BMI was calculated by using participation’s weight and height data.
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Ratan, Z.A.; Parrish, A.-M.; Alotaibi, M.S.; Hosseinzadeh, H. Prevalence of Smartphone Addiction and Its Association with Sociodemographic, Physical and Mental Well-Being: A Cross-Sectional Study among the Young Adults of Bangladesh. Int. J. Environ. Res. Public Health 2022, 19, 16583. https://doi.org/10.3390/ijerph192416583

AMA Style

Ratan ZA, Parrish A-M, Alotaibi MS, Hosseinzadeh H. Prevalence of Smartphone Addiction and Its Association with Sociodemographic, Physical and Mental Well-Being: A Cross-Sectional Study among the Young Adults of Bangladesh. International Journal of Environmental Research and Public Health. 2022; 19(24):16583. https://doi.org/10.3390/ijerph192416583

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Ratan, Zubair Ahmed, Anne-Maree Parrish, Mohammad Saud Alotaibi, and Hassan Hosseinzadeh. 2022. "Prevalence of Smartphone Addiction and Its Association with Sociodemographic, Physical and Mental Well-Being: A Cross-Sectional Study among the Young Adults of Bangladesh" International Journal of Environmental Research and Public Health 19, no. 24: 16583. https://doi.org/10.3390/ijerph192416583

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