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. 2023 Jan 24;13:1307. doi: 10.1038/s41598-023-27394-w

Investigating problematic uses of Facebook and other Internet activities among university students in Bangladesh during the COVID-19 pandemic

Abu Sayeed 1,#, Md Saiful Islam 2,3,✉,#, Enryka Christopher 4, Abdullah Al Zubayer 5, Satyajit Kundu 6,13, Mohammad Raihan Tariq 8, Mst Sadia Sultana 2, Md Hasan Al Banna 8, Md Hafizur Rahman 7,9, Md Shafiqul Islam Khan 8, M Tasdik Hasan 10,11,12, Kamrun Nahar Koly 9
PMCID: PMC9871428  PMID: 36693878

Abstract

Problematic use of Internet (PUI) and problematic use of Facebook (PUF) has been linked to escalating behavioral health issues among university students and has increased during the COVID-19 pandemic. This study estimated the prevalence of and explored associated factors for PUI and PUF among Bangladeshi university students during the COVID-19 pandemic. A cross-sectional online survey was undertaken among 1101 Bangladeshi university students between November and December 2020. The Internet Addiction Test and Facebook Addiction Scale were used to assess PIU and PUF, respectively. A multiple linear regression analysis was performed to adjust for confounders. Among the participants, PUI and PUF were found in 39.3% and 37.1%, respectively. The multiple linear regression model indicated PUI was significantly associated with participants residing in a village, arts majors, those unsatisfied with their major, having mediocre parental relationships, failure in romantic relationships, physical comorbidities, longer use of the Internet, using the Internet for purposes other than education, using social media, and downloading movies/TV series. PUF was significantly associated with village residence, lower income, arts majors, failure in romantic relationships, longer use of the Internet, using the Internet for purposes other than education, and downloading movies/TV series. Both PUI and PUF have been prevalent among Bangladeshi university students during the COVID-19 pandemic. Longitudinal & exploratory studies are warranted in the future to identify causal factors for PUI and PUF and appropriate interventions should be designed quickly for this population.

Subject terms: Human behaviour, Psychology, Risk factors

Introduction

The COVID-19 pandemic has led to increased usage of the Internet and social media1. Confinement and isolation due to social distancing measures have facilitated this increase in the usage of the Internet2. In addition, many individuals lost jobs or decided to make a career change due to the pandemic, and they are even more engaged with digital platforms to search for opportunities2. Facebook, one of the most accessed Internet applications, is useful for networking3. However, there is potential to misuse Facebook, leading to harms in psychological health, interpersonal connections, and academic or job performance4. Problematic use of Facebook (PUF), which is part of the more general problematic use of Internet (PUI), comprises an important area of research worldwide, as it explores negative social, financial, academic, work-related, physical, and psychological health impacts that are associated5,6.

Social media and Internet usage were relatively high even prior to the pandemic7. However, COVID-19 may have spurred on problematic or addictive usage for some people8. A meta-analysis conducted before the pandemic reported prevalence rates of PUI ranging from 0.8% to 26.7% globally9. Low and middle-income countries (LIMCs) had higher than average prevalence estimates, ranging from 14 to 55%10. Asian regions report the highest prevalence of PUI along with significant problems10,11. Previous studies have found smartphone addiction, Facebook addiction, depression, anxiety, making new friendships online, and getting into romantic relationships online to be factors associated with PUI 12,13. On the other hand, the prevalence of PUF specifically has been studied in the Turkey, Philippines, Malaysia, Peru, and Norway, and estimates vary from 4 to 43.2% 14–16. Previous studies have identified relationship issues, a history of domestic violence, stressful life events, sleep disturbances, and depression as potential risk factors for PUF17.

PUI and PUF are prevalent among young adults in Bangladesh. A recent study reported that about half of students surveyed had moderate to severe levels of PUI12. Another study suggested that 36.9% of students were at risk for PUF in Bangladesh17. As addictive behaviors formed during young adulthood are more likely to persist into adulthood, and appropriate interventions are needed now more than ever18,19.

The move to virtual education from in-person learning as a result of the pandemic has also increased other online activities among young adults, such as social media usage and online gaming18,20. Circumstances brought on by the COVID-19 pandemic has led to increases in stress, anxiety, depression, and post-traumatic stress21,22. Students, already using online spaces with more frequency, may turn to unhealthy virtual coping mechanisms to deal with their psychosocial health. Gambling, video gaming, binge-watching TV, utilizing social media, watching pornography, or surfing the Internet may temporarily ease stress, anxiety, or depression, yet can easily lead to more problems, including PUI and PUF18. In addition to that, PUI was predicted by gender, failure in love history, and stressful life event23, whereas PUF was predicted by having history of domestic violence, failure in love, and having stressful life events17.

Problematic behavior related to digital technology usage is exponentially increasing in developing countries, mirroring usage patterns of some developed countries24. Despite being a resource-poor setting, the government of Bangladesh is providing enormous support in the digitalization of the country25–27. As of July 2021, 46 million people in Bangladesh use Facebook, 46% of whom are aged between 18 and 24 years28. One study reported that one out of every two young Facebook users suffers from PUF25. Several studies investigated PUI and PUF in pre-COVID-19 Bangladesh25–27, but few studies on PUI have been conducted after the pandemic hit, and no study has yet focused on PUF in this new era. Many of these existing studies were limited by small sample sizes from non-representative populations, such as students of a specific university or medical college25,29,30. This study was conducted during a period which was entirely uncertain & a shock to everyone where the university students faced added challenges. The study aimed to find out the actual Internet and Facebook using behaviors of the university students to provide a context to the policy makers to design appropriate interventions to support the university students to address their problematic Internet and Facebook using behaviors during public health emergencies like this. Therefore, the gravity of this study is immense.

In Bangladesh, limited evidence are available on PUI and PUF, so contemporary findings are critically important to fill this research gap & informing the policy makers about the significance of the issue. Therefore, the current study aimed to investigate the prevalence of and factors associated with PUI and PUF among university students in Bangladesh.

Materials and methods

Study setting and participants

An online-based cross-sectional study was conducted among undergraduate and graduate university students from November 2020 to December 2020, around eight months after the onset of the COVID-19 pandemic in Bangladesh. Because a community-based, face-to-face survey was not feasible at that critical time, data were collected online via snowball sampling. A self-reported questionnaire was made accessible through an online survey link, and distributed it over several social media platforms (e.g., Facebook, WhatsApp, etc.). Inclusion criteria were Bangladeshi adults (≥ 18 years old), living in Bangladesh during the government-imposed lockdown, university students, and able to read and interpret Bengali.

Sampling procedure

Sample size was estimated based on a single population proportion formula with the assumption of 95% confidence level (1.96) and 3% margin of error (0.03)31 to account for the two primary outcomes, PUI and PUF. Two different sample sizes for two outcomes were calculated for this study. For PUI, a 44% prevalence of PUI in a recent study among university students23 was used to calculate the needed sample size of 1,052. For PUF, a 36.9% prevalence of PUF in a recent study among university students17 was used to calculate the needed sample size of 994. A total of 1127 people took part in the survey. Twenty-six participants were excluded during data cleaning due to incomplete and inconsistent responses, leaving 1101 participants for the analysis.

Survey procedure

Two bilingual researchers translated the questionnaire into Bengali from English. Another bilingual researcher back translated the questionnaire to ensure consistency and eliminate errors. The questionnaire was first piloted among 50 participants to check for clarity and consistency. Test participants used the comment feature to flag wordings that were not clear, which helped us to amend questions and answers clearer and easier to comprehend. Links to the electronic questionnaire were then distributed in Bengali among various social media platforms, aiming to reach all nine administrative divisions of Bangladesh. Upon clicking on the link, participants were led to the first page of the study questionnaire, which included a study summary, procedures, a confidentiality agreement, and an informed consent form. Participants answered anonymously, and electronic data were kept secure, using encrypted data collection platforms and password-protected computers. The survey was conducted following the human involving guidelines (i.e., Declaration of Helsinki). The research protocol was reviewed and approved by the Research Ethical Committee (REC) of the Department of Food Microbiology, Patuakhali Science and Technology University, Bangladesh (Approval No: FMB: October 29, 2020:16).

Measures

A total of 51 items were included in the questionnaire, which was divided into four sections: (i) sociodemographic characteristics, (ii) health, behavioral, and Internet-use variables, (iii) Internet Addiction Test (IAT), and (iv) Bergen Facebook Addiction Scale.

Sociodemographic characteristics

This section included fourteen questions about age, sex, residence (town, city, or village area), family type (nuclear or joint), monthly household income, study year, major subject (Engineering, Business, Arts, Science), satisfaction with major subject (yes, no, moderate), relationship status with parents (poor, moderate, good, not applicable), experience of domestic violence (yes or no), relationship status (single, in a relationship, married), failure in a romantic relationship (yes or no), previous history of physical or sexual abuse (yes or no).

The term “failure in a romantic relationship” refers to a circumstance in which a person's relationship with his/her partner has failed or ended. This term is quite culturally distinctive, and it most closely refers to, in Bangladeshi culture, a failed attempt to get engaged in a meaningful romantic relationship that would eventually lead to marriage. If they respond “yes,” they had a breakup, it means they had previously been in a mutual romantic relationship that had ended. A “no” response suggests that the person had never been in a relationship that ended. Therefore, they are still in their first relationship. The N/A choice included people who were not interested in dating or being in a relationship because of their age or did not believe in the traditional relationship paradigm. Failure to maintain a relationship in a society with high importance on marriage can lead to stress and other mental health difficulties17.

Health, behavioral, and Internet-use variables

Health-related data was obtained from questions about physical comorbidity (yes or no). Questions about tobacco smoking (yes or no) and history of substance use (yes or no) yielded behavioral and lifestyle-related data. To avoid response bias, explicit questions about substance usage were not asked as it is a stigmatized issue. Engagement in Internet use for educational purposes, online gaming, browsing YouTube, utilizing a chatroom, using social media (such as Twitter, Instagram), movie/ TV series download, online shopping, and frequency of Internet use were among the Internet-use variables.

Internet Addiction Test (IAT)

Young (1998) developed the Internet Addiction Test (IAT) as a self-report tool to assess PUI32. There are 20 Likert-type questions on this scale, ranging from 0 (‘never’) to 5 (‘always’). Individual answer scores of questions were added together to create total scores, ranging from 0 to 100. Participants were divided into two “internet-usage” categories based on their IAT scores, with those scoring less than 50 being classified as “non-PUI” and those scoring 50 or more as “PUI.” Methodology from previous studies among Bangladeshi university students was used to determine this cutoff23,27. Cronbach's alpha coefficient for the entire IAT score was 0.83 in this study, indicating good reliability.

Bergen Facebook addiction scale (BFAS)

The Bergen Facebook Addiction Scale (BFAS) was used to measure PUF4. This instrument is comprised of six items (e.g., “Tried to cut down on the use of Facebook without success?”), each of which is assessed on a 5-point Likert scale ranging from 1 (‘very rarely’) to (‘very often’). The total score ranged from 6 to 30. Although cutoff values were not employed in the original study, the authors used a cutoff score of ≥ 18 in the current study, using a conservative monothetic scoring scheme of 3 or above for each question. This cutoff score is similar to a previously conducted study on this topic among Bangladeshi university students17,33. The current study's Cronbach's alpha for this scale was 0.79.

Statistical analysis

Statistical analyses were performed using two statistical software packages: SPSS (version 25.0) and STATA (version 13.0). Descriptive statistics (e.g., frequencies, percentages, means, standard deviations) were computed using the SPSS. Finally, bivariate and multiple linear regressions were performed using the STATA. While constructing multiple linear regression models, confounders were selected from the bivariate regression analysis, applying a p-value of less than 0.2534,35. The conventional threshold (p-value less than 0.05) was considered statistically significant in the multiple regression models.

Ethical approval

The research protocol was reviewed and approved by the Research Ethical Committee (REC) of the Department of Food Microbiology, Patuakhali Science and Technology University, Bangladesh (Approval No: FMB: October 29, 2020:16).

Results

General profile of participants

A total of 1101 responses from university students (58.4% male; mean age: 22.63 ± 2.03 years) comprised the study sample. Of them, the majority were single (76.9%), resided in cities (48.0%), belonged to nuclear families (77.6%), and reported their monthly family income between 25,000–50,000 BDT (43.6%) (Table 1). There was a fairly equal distribution of students from 1st years to master’s level (13.3%–29.8%), the majority studied science (39.6%), and a sizeable minority were not satisfied with their study major (8.1%).

Table 1.

Distribution of variables and regression analysis for PUI.

Variables Overall PUI Unadjusted estimates Adjusted estimates
n (%) Mean (SD) B SE t β p-value B SE t β p-value
Age 22.63 ± 2.03 0.28 0.28 0.75 0.02 0.456 – – – – –
Sex
Male 643 (58.4) 44.13 (18.66) 1.15 1.15 1.09 0.03 0.275 – – – – –
Female 458 (41.6) 42.88 (18.99) Ref
Relationship status
In a Relationship 214 (19.4) 45.81 (19.23) 1.44 1.44 1.98 0.06 0.048 1.35 1.29 1.05 0.03 0.296
Married 40 (3.6) 45.53 (16.8) 3.04 3.04 0.84 0.03 0.399 0.88 2.74 0.32 0.01 0.748
Single 847 (76.9) 42.96 (18.75) Ref Ref
Residence
Town 257 (23.3) 47.05 (19.06) 1.57 1.57 2.40 0.08 0.017 0.80 1.46 0.55 0.02 0.585
City 529 (48.0) 42.14 (18.95) 1.33 1.33  − 0.86  − 0.03 0.392  − 4.38 1.33  − 3.29  − 0.12 0.001
Village 315 (28.6) 43.28 (18.03) Ref Ref
Family type
Nuclear 854 (77.6) 43.99 (18.75) 1.36 1.36 1.25 0.04 0.210  − 0.32 1.24  − 0.26  − 0.01 0.797
Joint 247 (22.4) 42.29 (18.97) Ref Ref
Monthly household income
25,000–50,000 BDT 480 (43.6) 44.75 (18.76) 1.28 1.28 1.45 0.05 0.147  − 0.01 1.19 0.00  < 0.01 0.997
 > 50,000 BDT 224 (20.3) 42.42 (18.73) 1.57 1.57  − 0.31  − 0.01 0.760  − 2.89 1.50  − 1.93  − 0.06 0.054
 < 25,000 BDT 397 (36.1) 42.9 (18.86) Ref Ref
Study year
2nd year 177 (16.1) 45.19 (18.51) 2.10 2.10 1.83 0.08 0.068  − 0.39 1.88  − 0.21  − 0.01 0.837
3rd year 230 (20.9) 42.68 (18.65) 1.99 1.99 0.67 0.03 0.503  − 1.73 1.80  − 0.96  − 0.04 0.337
4th year 328 (29.8) 44.2 (18.96) 1.87 1.87 1.53 0.07 0.127  − 0.12 1.72  − 0.07  <  − .01 0.947
Masters 220 (20) 43.91 (19.31) 2.01 2.01 1.28 0.05 0.202 1.10 1.82 0.61 0.02 0.544
1st year 146 (13.3) 41.35 (18.21) Ref Ref
Study major
Engineering 146 (13.3) 44.5 (18.84) 1.80 1.80 0.91 0.03 0.362 1.26 1.60 0.78 0.02 0.433
Business 235 (21.3) 42.85 (18.42) 1.52 1.52  − 0.01  <  − 0.01 0.995 1.75 1.36 1.28 0.04 0.199
Arts 284 (25.8) 44.93 (19.23) 1.43 1.43 1.44 0.05 0.150 2.83 1.30 2.17 0.07 0.030
Science 436 (39.6) 42.86 (18.71) Ref Ref
Satisfaction with major
Moderate 330 (30) 46.58 (17.81) 1.25 1.25 3.85 0.12  < 0.001 3.14 1.16 2.71 0.08 0.007
Poor 89 (8.1) 46.78 (19.45) 2.10 2.10 2.39 0.07 0.017 1.08 1.90 0.57 0.02 0.569
Good 682 (61.9) 41.76 (18.97) Ref Ref
Relationship with parents
Mediocre 126 (11.4) 49.23 (19.53) 1.76 1.76 3.80 0.11  < 0.001 4.15 1.64 2.53 0.07 0.012
Poor 25 (2.3) 58 (21.58) 3.76 3.76 4.11 0.12  < 0.001 6.76 3.45 1.96 0.05 0.054
Not applicable 15 (1.4) 39.4 (15.98) 4.83 4.83  − 0.65  − 0.02 0.517  − 5.29 4.35  − 1.22  − 0.03 0.224
Good 935 (84.9) 42.53 (18.39) Ref Ref
Domestic violence
Yes 123 (11.2) 46.22 (19.03) 1.80 1.80 1.64 0.05 0.102 0.55 1.67 0.33 0.01 0.742
No 978 (88.8) 43.28 (18.76) Ref Ref
Failure in a romantic relationship
Yes 417 (37.9) 46.77 (18.22) 1.16 1.16 4.39 0.13  < 0.001 2.36 1.07 2.21 0.06 0.027
No 684 (62.1) 41.68 (18.90) Ref Ref
Sexually abused
Yes 116 (10.5) 49.39 (19.07) 1.84 1.84 3.52 0.11  < 0.001 2.95 1.83 1.61 0.05 0.108
No 985 (89.5) 42.93 (18.66) Ref Ref
Physically abuses
Yes 153 (13.9) 47.54 (19.33) 1.63 1.63 2.79 0.08 0.005  − 0.48 1.68  − 0.29  − 0.01 0.775
No 948 (86.1) 42.97 (18.65) Ref Ref
Tobacco smoking
Yes 265 (24.1) 43.71 (18.48) 1.33 1.33 0.10  < 0.01 0.923 – – – – –
No 836 (75.9) 43.58 (18.91) Ref
Drug use
Yes 49 (4.5) 48.96 (19.78) 2.74 2.74 2.04 0.06 0.041 2.51 2.48 1.01 0.03 0.312
No 1052 (95.5) 43.36 (18.73) Ref Ref
Physical comorbidity
Yes 191 (17.3) 49.03 (17.44) 1.48 1.48 4.42 0.13  < 0.001 4.54 1.37 3.32 0.09 0.001
No 910 (82.7) 42.47 (18.89) Ref Ref
Time spent per day using Internet
2–3 h 253 (23) 36.06 (14.89) 1.90 1.90 3.28 0.13 0.001 5.29 1.83 2.89 0.11 0.004
4–5 h 293 (26.6) 44.76 (17.1) 1.85 1.85 8.05 0.35  < 0.001 13.05 1.81 7.21 0.31  < 0.001
 > 5 h 432 (39.2) 51.16 (18.66) 1.76 1.76 12.09 0.55  < 0.001 19.07 1.76 10.83 0.50  < 0.001
 < 2 h 123 (11.2) 29.85 (16.86) Ref Ref Ref
Educational or information purpose
No 199 (18.1) 51.26 (20.49) 1.45 1.45 6.46 0.19  < 0.001 7.19 1.34 5.37 0.15  < 0.001
Yes 902 (81.9) 41.92 (17.99) Ref Ref
Use chatting site like Messenger, WeChat, WhatsApp etc
Yes 1025 (93.1) 44.09 (18.49) 2.23 2.23 3.14 0.09 0.002 3.21 2.17 1.48 0.04 0.140
No 76 (6.9) 37.09 (21.72) Ref Ref
Online gaming
Yes 347 (31.5) 44.53 (19) 1.22 1.22 1.10 0.03 0.270 – – – – –
No 754 (68.5) 43.18 (18.71) Ref
YouTubing or watching videos
Yes 1039 (94.4) 43.88 (18.62) 2.46 2.46 1.98 0.06 0.048 1.53 2.32 0.66 0.02 0.509
No 62 (5.6) 39.03 (21.24) Ref Ref
Using social media like FB, Twitter, Instagram, etc
Yes 1037 (94.2) 44.16 (18.54) 2.41 2.41 3.97 0.12  < 0.001 4.80 2.35 2.05 0.06 0.041
No 64 (5.8) 34.61 (20.74) Ref Ref
Movie/ TV series downloading
Yes 708 (64.3) 45.59 (18.31) 1.17 1.17 4.74 0.14  < 0.001 3.13 1.08 2.90 0.08 0.004
No 393 (35.7) 40.04 (19.16) Ref Ref
Online shopping
Yes 632 (57.4) 43.74 (18.65) 1.15 1.15 0.27 0.01 0.787 – – – – –
No 469 (42.6) 43.43 (19.02) Ref
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SD Standard deviations; B unstandardized regression coefficient; SE Standard error; β standardized regression coefficient.

Most reported a good relationship with their parents (84.9%), and 11.2% reported experiencing domestic violence. A sizable number of participants reported failure in a romantic relationship (37.9%), sexual abuse (10.5%), and physical abuse (13.9%). One-fourth of participants smoked cigarettes (24.1%), and 4.5% used illicit drugs. Additionally, 17.3% of participants reported that they had comorbidities.

Many used the Internet for more than five hours daily (39.2%), and it was mostly used for educational purposes (81.9%), chatting (Messenger, WeChat, WhatsApp, etc.; 93.1%), YouTubing or watching videos (94.4%), social media (FB, Twitter, Instagram; 94.2%), movie/ TV series downloading (64.3%), and online shopping (57.4%).

Problematic use of Internet (PUI)

The prevalence of PUI was 39.3% among university students during the COVID-19 pandemic (Fig. 1). In a bivariate regression analysis the following were identified as candidate variables (p < 0.25) of the multivariable regression: relationship status, residence, family type, household income, study year, study major, satisfaction with major subject, relationship with parents, domestic violence, failure in romantic relationship, sexual abuse, physical abuse, drug addiction, physical comorbidities, frequency of Internet use, education or information Internet use, online chatting, YouTubing, social media, and video downloading. For the multiple linear regression model, the positively predicting factors of PUI included: (i) having arts major (β = 0.07, p = 0.030) in reference to ‘science’, (ii) having moderate satisfaction with their major (β = 0.08, p = 0.007) in reference to ‘good’, (iii) having mediocre relationship with parents (β = 0.07, p = 0.012) in reference to ‘good’, (iv) being failure in romantic relationship (β = 0.06, p = 0.027) in reference to ‘none’, (v) having physical comorbidities (β = 0.09, p = 0.001), (vi) having excessive Internet use (for 2–3 h [β = 0.11, p = 0.004], for 4–5 h [β = 0.31, p < 0.001], and for > 5 h [β = 0.50, p < 0.001]) in reference to ‘ < 2 h’, (vii) using Internet for purposes other than education (β = 0.15, p < 0.001), (viii) using social media (β = 0.06, p = 0.041), and (ix) downloading movies/TV series (β = 0.08, p = 0.004) (Table 1). The negatively predicting factors of PUI included: residing in city areas (β =  − 0.12, p = 0.001) in reference to ‘village’.

Figure 1.

Figure 1

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Prevalence of Problematic Use of the Internet (PUI) and Problematic Use of Facebook (PUF).

Problematic use of Facebook (PUF)

The prevalence of PUF was 37.1% among university students during the COVID-19 pandemic (Fig. 1). Relationship status, residence, family type, household income, study year, study major, satisfaction with major subject, relationship with parents, domestic violence, failure in love, sexual abuse, physical abuse, physical comorbidities, frequency of Internet use, Internet use for education or information purposes, online chatting, and downloading movies/TV series were identified as candidate variables (p < 0.25) for the multivariable regression. For the multiple linear regression model, the positively predicting factors of PUF included: (i) having arts major (β = 0.07, p = 0.021) in reference to ‘science’, (ii) being failure in romantic relationship (0.06 = 0.027) in reference to ‘none’, (iii) having excessive Internet use (for 4–5 h [β = 0.27, p < 0.001], and for > 5 h/day [β = 0.50, p < 0.001]), (iv) using Internet for purposes other than education (β = 0.15, p < 0.001), and (v) downloading movies/TV series (β = 0.06, p = 0.031) (Table 2). The negatively predicting factors of PUF included: (i) residing in city areas (β =  − 0.10, p = 0.007) in reference to ‘village’, and (ii) having household income of more than 50,000 BDT (β =  − 0.09, p = 0.006) in reference to ‘ < 25,000 BDT’.

Table 2.

Distribution of variables and regression analysis for PUF.

Variables PUF Unadjusted estimates Adjusted estimates
Mean (SD) B SE t β p − value B SE t β p − value
Age 0.09 0.08 1.10 0.03 0.272 – – – – –
Sex
Female 15.18 (5.69) 0.15 0.34 0.44 0.01 0.660 – – – – –
Male 15.33 (5.36) Ref
Relationship status
In a Relationship 15.73 (4.98) 0.61 0.42 1.46 0.04 0.144 0.37 0.40 0.93 0.03 0.355
Married 15.92 (5.21) 0.81 0.89 0.91 0.03 0.363 0.58 0.84 0.69 0.02 0.491
Single 15.11 (5.63) Ref Ref
Residence
Town 15.93 (5.56) 0.49 0.46 1.07 0.04 0.286  − 0.11 0.45  − 0.25  − 0.01 0.801
City 14.83 (5.76)  − 0.61 0.39  − 1.56  − 0.06 0.119  − 1.10 0.41  − 2.69  − 0.10 0.007
Village 15.44 (4.94) Ref Ref
Family type
Nuclear 15.39 (5.53) 0.56 0.40 1.42 0.04 0.156 0.19 0.38 0.50 0.01 0.618
Joint 14.83 (5.4) Ref Ref
Monthly household income
25,000 − 50,000 BDT 15.5 (5.4) 0.11 0.37 0.28 0.01 0.777  − 0.25 0.36  − 0.68  − 0.02 0.493
 > 50,000 BDT 14.54 (5.53)  − 0.85 0.46  − 1.85  − 0.06 0.064  − 1.26 0.46  − 2.75  − 0.09 0.006
 < 25,000 BDT 15.39 (5.58) Ref Ref
Study year
2nd year 15.81 (5.58) 1.16 0.61 1.88 0.08 0.060 0.34 0.58 0.59 0.02 0.557
3rd year 14.93 (5.23) 0.28 0.58 0.49 0.02 0.625  − 0.22 0.55  − 0.39  − 0.02 0.695
4th year 15.59 (5.93) 0.94 0.55 1.73 0.08 0.085 0.40 0.53 0.76 0.03 0.447
Masters 15.08 (5.15) 0.43 0.59 0.73 0.03 0.463 0.19 0.56 0.34 0.01 0.730
1st year 14.65 (5.27) Ref Ref
Study major
Engineering 15.14 (5.43) 0.05 0.53 0.09  < .01 0.931  − 0.09 0.49  − 0.19  − 0.01 0.852
Business 15.02 (5.33)  − 0.08 0.45  − 0.18  − 0.01 0.855 0.37 0.42 0.88 0.03 0.381
Arts 15.78 (5.53) 0.68 0.42 1.63 0.05 0.104 0.93 0.40 2.32 0.07 0.021
Science 15.1 (5.59) Ref Ref
Satisfaction with major
Moderate 15.81 (5.4) 0.92 0.37 2.51 0.08 0.012 0.61 0.36 1.72 0.05 0.085
No 16.16 (5.58) 1.27 0.62 2.06 0.06 0.040 0.23 0.58 0.39 0.01 0.699
Yes 14.88 (5.51) Ref Ref
Relationship with parents
Mediocre 16.36 (5.42) 1.33 0.52 2.57 0.08 0.010 0.63 0.50 1.25 0.04 0.212
Poor 18.84 (5.83) 3.81 1.11 3.44 0.10 0.001 1.62 1.06 1.53 0.04 0.126
Not applicable 14.93 (7.16)  − 0.09 1.42  − 0.06  <  − 0.01 0.948  − 0.43 1.34  − 0.32  − 0.01 0.747
Good 15.03 (5.43) Ref Ref
Domestic violence
Yes 16.15 (5.52) 0.99 0.53 1.89 0.06 0.059 0.63 0.51 1.24 0.04 0.216
No 15.15 (5.49) Ref Ref
Failure in romantic relationship
Yes 16.01 (5.6) 1.20 0.34 3.53 0.11  < 0.001 0.72 0.33 2.22 0.06 0.027
No 14.81 (5.39) Ref Ref
Sexually abuse
Yes 16.16 (5.76) 1.00 0.54 1.85 0.06 0.065 0.18 0.56 0.32 0.01 0.747
No 15.16 (5.46) Ref Ref
Physically abuse
Yes 15.8 (5.91) 0.63 0.48 1.31 0.04 0.190  − 0.33 0.51  − 0.64  − 0.02 0.523
No 15.18 (5.43) Ref Ref
Tobacco smoking
Yes 15.07 (5.15)  − 0.26 0.39  − 0.66  − 0.02 0.507 – – – – –
No 15.33 (5.61) Ref
Drug use
Yes 16.02 (5.64) 0.79 0.80 0.99 0.03 0.325 – – – – –
No 15.23 (5.49) Ref
Physical comorbidity
Yes 16.34 (5.63) 1.30 0.44 2.99 0.09 0.003 0.80 0.42 1.90 0.06 0.055
No 15.04 (5.45) Ref Ref
Time spent per day using Internet
2 − 3 h 13.25 (4.75) 1.07 0.57 1.87 0.08 0.062 0.89 0.56 1.59 0.07 0.112
4 − 5 h 15.69 (5.22) 3.51 0.56 6.28 0.28  < 0.001 3.31 0.55 5.97 0.27 < 0.001
 > 5 h 17.04 (5.57) 4.86 0.53 9.14 0.48  < 0.001 4.53 0.54 8.41 0.40  < 0.001
 < 2 h 12.18 (4.67) Ref Ref
Educational or information purpose
No 17.5 (5.66) 2.73 0.42 6.45 0.19  < 0.001 2.21 0.41 5.43 0.15  < 0.001
Yes 14.77 (5.34) Ref Ref
Use chatting site like Messenger, WeChat, WhatsApp etc
Yes 15.29 (5.47) 0.40 0.65 0.61 0.02 0.545 – – – – –
No 14.89 (5.97) Ref
Online gaming
Yes 15.05 (5.68)  − 0.31 0.36  − 0.88  − 0.03 0.381 – – – – –
No 15.36 (5.42) Ref
YouTubing or watching videos
Yes 15.31 (5.49) 0.79 0.72 1.10 0.03 0.271 – – – – –
No 14.52 (5.73) Ref
Using social media like FB, Twitter, Instagram
Yes 15.29 (5.46) 0.51 0.71 0.72 0.02 0.470 – – – – –
No 14.78 (6.12) Ref
Movie/TV series downloading
Yes 15.65 (5.54) 1.07 0.35 3.12 0.09 0.002 0.70 0.32 2.16 0.06 0.031
No 14.57 (5.36) Ref Ref
Online shopping
Yes 15.23 (5.44)  − 0.07 0.34  − 0.20  − 0.01 0.838 – – – – –
No 15.3 (5.59) Ref
Open in a new tab

SD Standard deviations; B unstandardized regression coefficient; SE Standard error; β standardized regression coefficient.

Discussion

A growing body of research suggests that PUI and PUF are alarmingly increasing among young adults, particularly university students33,36–39. This study estimated the prevalence of PUI and PUF, and identified associated risk factors among university students in Bangladesh during the COVID-19 pandemic. This study found that place of residence, major of the study, failure in romantic relationship, time (hours) spent per day using the Internet, purpose of Internet use, and movie/TV series downloading were factors associated with both PUI and PUF.

The overall prevalence of PUI was 39.3% in the present study, which is higher than previous Bangladeshi studies conducted in the same context using a similar tool27,40. This discrepancy could be due to home confinement during the COVID-19 pandemic. As public health measures (i.e., lockdown) taken by the government of Bangladesh bound students to their homes41, the opportunity to develop addictive behaviors with Internet use has increased. A previous study conducted by Sayeed et al. identified that 43.8% of participants used the Internet problematically23. This disparity could be related to the lack of diversity in that study – only two universities were involved, while the current study has participants from over 25 universities. On the other hand, the prevalence of PUF was 37.1% in the present study, which is similar to previous Bangladeshi studies by Sayeed et al. (36.9%)17 and Mamun et al. (39.7%)33. Outside of Bangladesh, a multi-country study reported that the prevalence rates of PUI were 14% in South Korea, 19% in China, 35% in Hong Kong, 37.5% in Malaysia, 48% in Japan, 51% in the Philippines, 43.4% in Egypt, 30.8% in Czech, and 33.1% Slovak42–44. Prevalence rates for Facebook addiction has been identified as 47% in Malaysia45, 5.1% in Turkey46, and 26% in India47. These variations may be explained by several factors such as study setting, geographical location, sample size, tools used for measurements, different cutoff points, etc. Additionally, fear, worries, and anxiety related to COVID-19 are also associated with compulsive Internet use and increased use of social media2.

In this study, when compared to their rural counterparts, urban residents were less likely to be problematic Internet and Facebook users. This finding does not match with the previous findings from the same context in Bangladesh, in which no association was found between place of residence and PUI or PUF17,23,33. This may in part be due to the rapid rise of Internet access in rural Bangladesh through the initial Aspire to Innovate (a2i) program undertaken by the government of Bangladesh41,48. Another study in Turkey suggests that rural families employ more autocratic child-rearing practices than families from urban areas49, the mental health implications of which could be another reason why PUI and PUF are more common among students from rural areas.

This study did not find any prominent sex differences in PUF and PUI, which aligns with other studies17,25. However, in some studies, sex differences were found for excess Facebook and Internet use37,50–52. As this is a less researched topic, future attempts should be made to conduct exploratory qualitative research to get gender specific insights on this issue. PUF and PUI were higher in students who experienced failure in a romantic relationship than those who did not. This finding is consistent with previous studies in Bangladesh17,23,53. Breakdown of a close relationship magnifies poor mental health conditions54,55, most notably depression and insomnia56. This in turn may lead to Internet addiction, as it is well-established that individuals with depression and insomnia are more prone to PUI and PUF17,23,27,33.

This study shows that art majors used both the Internet and Facebook more than science majors. There is some evidence to suggest that students in arts and humanities fields experience more depressive and anxiety symptoms than students in science57–59. One explanation for this could be that sometimes students settle on art as their major because they cannot meet the more rigorous academic requirements of a science major59. Perceptions of inadequacy stemming from this may cause loneliness, depression, and anxiety, all of which can propel addictive behavior40,60,61. Further exploratory research is needed to understand why art students are at higher risk for PUI and PUF.

Previous evidence has also found that several online behaviors and activities (i.e., browsing education, watching videos/movies) are associated with PUI and PUF17,23,27,33. Using Facebook and the Internet for more than 4 hours per day increases the risk of PUI and PUF; this finding corroborates some previous studies27,36. Young adults spend much time on the Internet, especially social media for maintaining interpersonal interaction with relatives and friends, which results in elevated risk for both PUI and PUF62,63.

In this study, PUI was significantly associated with participants who described themselves as having a mediocre relationship with their parents. Hassan et al. conducted a study among young Bangladeshi adults, of which findings suggested that participants who experienced detached family relationships were more likely to suffer from Internet addiction64. When personal relationships are not in an ideal state, an individual’s mental health suffers, including developing addictions54,55.

Limitations and strengths

This study has some critical limitations to consider when interpreting the results. The snowball sampling technique and the self-reported data are subjected to selection bias, reporting bias, and social desirability bias. Due to stigma, participants might not transparently answer sensitive questions related to failure in romantic relationships or history of substance use. The cross-sectional study design precludes extrapolation of causal factors for PUI and PUF, which warrants the necessity of further longitudinal research. Also, it has failed to capture data from other genders, transgenders which limits the inclusivity aspect of the study. Finally, this study did not include COVID-19 specific variables that might influence PUI and PUF. Despite these limitations, this study is among emerging research exploring PUI and PUF during the COVID-19 pandemic in Bangladesh and is critical to understanding the nature of Internet use in rapidly digitizing, low-resource settings like Bangladesh.

Conclusions

This study showed that a substantial proportion of students scored high for PUI and PUF during the COVID-19 pandemic. Rural residence, study major, longer use, Internet use for recreational purposes other than education, downloading movies/ TV series, and failure in a romantic relationship were identified as predictors of both PUI and PUF. Targeted awareness programs focused on these factors should be tested to see whether they would mitigate PUI and PUF among young students. Psychosocial education campaigns and other appropriate academic institution-led interventions need to quickly be disseminated to prevent further excessive Internet use as the impacts of the pandemic are long lasting.

Supplementary Information

Supplementary Information. (194.6KB, xlsx)

Acknowledgements

The authors would like to express the most profound gratitude to all of the respondents who participated in this study.

Author contributions

Conceptualization: A.S.; Analysis: M.S.I.; Interpretation of data: A.S., M.S.I., A.A.Z., M.R.T., M.H.A.B.; Drafting the work: A.S., M.S.I., S.K.M.R.T., M.S.S., Revised the manuscript: E.C., M.H.R., M.S.I.K., M.T.H., K.N.K.

Data availability

The datasets and questionnaire used and/or analyzed during the current study will be shared upon request to corresponding author.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Abu Sayeed and Md. Saiful Islam.

These authors jointly supervised this work: Md Shafiqul Islam Khan, M. Tasdik Hasan and Kamrun Nahar Koly.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-023-27394-w.

References

  • 1.Gupta K, DSilva M. Proliferation of social media during the COVID-19 pandemic: A statistical enquiry. J. Xi’an Univ. Archit. Technol. 2020;12:1752–1759. [Google Scholar]
  • 2.Fernandes B, Biswas UN, Mansukhani RT, Casarín AV, Essau CA. The impact of COVID-19 lockdown on internet use and escapism in adolescents. Revista de psicología clínica con niños y adolescentes. 2020;7(3):59–65. doi: 10.21134/rpcna.2020.mon.2056. [DOI] [Google Scholar]
  • 3.Brailovskaia J, Schillack H, Margraf J. Facebook Addiction Disorder in Germany. Cyberpsychol. Behav. Soc. Netw. 2018;21(7):450–456. doi: 10.1089/cyber.2018.0140. [DOI] [PubMed] [Google Scholar]
  • 4.Andreassen CS, Torsheim T, Brunborg GS, Pallesen S. Development of a facebook addiction scale. Psychol. Rep. 2012;110(2):501–517. doi: 10.2466/02.09.18.PR0.110.2.501-517. [DOI] [PubMed] [Google Scholar]
  • 5.Fox J, Moreland JJ. The dark side of social networking sites: An exploration of the relational and psychological stressors associated with Facebook use and affordances. Comput. Hum. Behav. 2015;45:168–176. doi: 10.1016/j.chb.2014.11.083. [DOI] [Google Scholar]
  • 6.Fineberg NA, Demetrovics Z, Stein DJ, Ioannidis K, Potenza MN, Grünblatt E, et al. Manifesto for a European research network into problematic usage of the internet. Eur. Neuropsychopharmacol. 2018;28(11):1232–1246. doi: 10.1016/j.euroneuro.2018.08.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Li YY, Sun Y, Meng SQ, Bao YP, Cheng JL, Chang XW, et al. Internet addiction increases in the general population during COVID-19: Evidence from China. The Am. J. Addict. 2021;30:389–397. doi: 10.1111/ajad.13156. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Chandrima RM, Kircaburun K, Kabir H, Riaz BK, Kuss DJ, Griffiths MD, et al. Adolescent problematic internet use and parental mediation: A Bangladeshi structured interview study. Addict. Behav. Rep. 2020;12:100288. doi: 10.1016/j.abrep.2020.100288. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Kuss DJ, Griffiths MD, Karila L, Billieux J. Internet addiction: A systematic review of epidemiological research for the last decade. Curr. Pharm. Design. 2014;20(25):4026–52. doi: 10.2174/13816128113199990617. [DOI] [PubMed] [Google Scholar]
  • 10.Laconi S, Kaliszewska-Czeremska K, Gnisci A, Sergi I, Barke A, Jeromin F, et al. Cross-cultural study of problematic internet use in nine European countries. Comput. Hum. Behav. 2018;84:430–440. doi: 10.1016/j.chb.2018.03.020. [DOI] [Google Scholar]
  • 11.Block JJ. Issues for DSM-V: Internet addiction. Am. Psychiatric Assoc. 2008;165:306–307. doi: 10.1176/appi.ajp.2007.07101556. [DOI] [PubMed] [Google Scholar]
  • 12.Jahan I, Hosen I, Al Mamun F, Kaggwa MM, Griffiths MD, Mamun MA. How has the COVID-19 pandemic impacted internet use behaviors and facilitated problematic internet use? A Bangladeshi study. Psychol. Res. Behav. Manag. 2021;14:1127. doi: 10.2147/PRBM.S323570. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Krishnamurthy S, Chetlapalli SK. Internet addiction: Prevalence and risk factors: A cross-sectional study among college students in Bengaluru, the Silicon Valley of India. Indian J. Public Health. 2015;59(2):115. doi: 10.4103/0019-557X.157531. [DOI] [PubMed] [Google Scholar]
  • 14.Balakrishnan V, Shamim A. Malaysian Facebookers: Motives and addictive behaviours unraveled. Comput. Hum. Behav. 2013;29(4):1342–1349. doi: 10.1016/j.chb.2013.01.010. [DOI] [Google Scholar]
  • 15.Çam E, Isbulan O. A new addiction for teacher candidates: Social networks. Turk. Online J. Educ. Technol. TOJET. 2012;11(3):14–19. [Google Scholar]
  • 16.Hanif A, Tariq S. Towards an effective strategy for implementation of technology enhanced learning in higher education in Pakistan. Int. Proc. Econ. Dev. Res. 2014;78:39. [Google Scholar]
  • 17.Sayeed A, Hassan MN, Rahman MH, El Hayek S, Al Banna MH, Mallick T, et al. Facebook addiction associated with, internet activity, depression and behavioral factors among university students of Bangladesh: A cross-sectional study. Child. Youth Serv. Rev. 2020;118:105424. doi: 10.1016/j.childyouth.2020.105424. [DOI] [Google Scholar]
  • 18.Király O, Potenza MN, Stein DJ, King DL, Hodgins DC, Saunders JB, et al. Preventing problematic internet use during the COVID-19 pandemic: Consensus guidance. Compr. Psychiatry. 2020;100:152180. doi: 10.1016/j.comppsych.2020.152180. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Coffey C, Carlin JB, Lynskey M, Li N, Patton GC. Adolescent precursors of cannabis dependence: Findings from the victorian adolescent health cohort study. Br. J. Psychiatry. 2003;182(4):330–336. doi: 10.1192/bjp.182.4.330. [DOI] [PubMed] [Google Scholar]
  • 20.Higuchi S, Mihara S, Kitayuguchi T, Miyakoshi H, Ohi M, Maezono M, et al. Prolonged use of internet and gaming among treatment seekers arising out of social restrictions related to COVID-19 pandemic. Psychiatry Clin. Neurosci. 2020;74(11):607–608. doi: 10.1111/pcn.13127. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Khan AH, Sultana MS, Hossain S, Hasan MT, Ahmed HU, Sikder MT. The impact of COVID-19 pandemic on mental health & wellbeing among home-quarantined Bangladeshi students: A cross-sectional pilot study. J. Affect. Disord. 2020;277:121–128. doi: 10.1016/j.jad.2020.07.135. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Sultana MS, Khan AH, Hossain S, Ansar A, Sikder MT, Hasan MT. Prevalence and predictors of post-traumatic stress symptoms and depressive symptoms among Bangladeshi students during COVID-19 mandatory home confinement: A nationwide cross-sectional survey. Child. Youth Serv. Rev. 2021;122:105880. doi: 10.1016/j.childyouth.2020.105880. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Sayeed A, Hafizur Rahman M, Nazmul Hassan M, Christopher E, Kundu S, Hasan Al Banna M, et al. Problematic internet use associated with depression, health, and internet-use behaviors among university students of Bangladesh: A cross-sectional study. Child. Youth Serv. Rev. 2020;120:105771. doi: 10.1016/j.childyouth.2020.105771. [DOI] [Google Scholar]
  • 24.Islam MA, Hossin MZ. Prevalence and risk factors of problematic internet use and the associated psychological distress among graduate students of Bangladesh. Asian J. Gambl. Issues Public Health. 2016;6(1):1–14. doi: 10.1186/s40405-016-0020-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Al Mamun M, Griffiths MD. The association between Facebook addiction and depression: A pilot survey study among Bangladeshi students. Psychiatry Res. 2019;271:628–633. doi: 10.1016/j.psychres.2018.12.039. [DOI] [PubMed] [Google Scholar]
  • 26.Mamun, M. A., Griffiths, M. D. The assessment of internet addiction in Bangladesh: Why are prevalence rates so different? Asian J. Psychiatry (2019). [DOI] [PubMed]
  • 27.Mamun MA, Hossain MS, Siddique AB, Sikder MT, Kuss DJ, Griffiths MD. Problematic internet use in Bangladeshi students: the role of socio-demographic factors, depression, anxiety, and stress. Asian J. Psychiatr. 2019;44:48–54. doi: 10.1016/j.ajp.2019.07.005. [DOI] [PubMed] [Google Scholar]
  • 28.Facebook users by country 2021 Statista [Internet]. 2021. Available from: https://www.statista.com/statistics/268136/top-15-countries-based-on-number-of-facebook-users/.
  • 29.Huq, L., Khondaker, S., Mahpara, P., Sultan, M., Khondoker, Z. A., Syed, S. new forms of adolescent voice and agency in Bangladesh through the use of mobile phones and ICT. (2021).
  • 30.Karim AR, Nigar N. The internet addiction test: Assessing its psychometric properties in Bangladeshi culture. Asian J. Psychiatry. 2014;10:75–83. doi: 10.1016/j.ajp.2013.10.011. [DOI] [PubMed] [Google Scholar]
  • 31.Lwanga, S. K., Lemeshow, S. World Health Organization. Sample size determination in health studies: a practical manual. World Health Organization. (1991).
  • 32.Young KS. Caught in the net: How to recognize the signs of internet addiction and a winning strategy for recovery. John Wiley & Sons; 1998. [Google Scholar]
  • 33.Mamun MA, Griffiths MD. The association between Facebook addiction and depression: A pilot survey study among Bangladeshi students. Psychiatry Res. 2019;271:628–633. doi: 10.1016/j.psychres.2018.12.039. [DOI] [PubMed] [Google Scholar]
  • 34.Fikreyesus M, Soboka M, Feyissa GT. Psychotic relapse and associated factors among patients attending health services in Southwest Ethiopia: A cross-sectional study. BMC Psychiatry. 2016;16(1):354. doi: 10.1186/s12888-016-1076-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Talie A, Taddele M, Alemayehu M. Magnitude of low birth weight and associated factors among newborns delivered in Dangla primary hospital, Amhara Regional State, Northwest Ethiopia, 2017. J. Pregnancy. 2019;2019:3587239. doi: 10.1155/2019/3587239. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Salehi M, Khalili MN, Hojjat SK, Salehi M, Danesh A. Prevalence of internet addiction and associated factors among medical students from Mashhad, Iran in 2013. Iran. Red Crescent Med. J. 2014;16(5):e17256. doi: 10.5812/ircmj.17256. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Soron T, Tarafder M. The relation between Facebook use pattern and demographic factors. J. Psychiatry. 2015;18(6):1–5. [Google Scholar]
  • 38.Kuss DJ, Griffiths MD, Binder JF. Internet addiction in students: Prevalence and risk factors. Comput. Hum. Behav. 2013;29(3):959–966. doi: 10.1016/j.chb.2012.12.024. [DOI] [Google Scholar]
  • 39.Sayeed A, Hassan MN, Rahman MH, El Hayek S, Al Banna MH, Mallick T, et al. Facebook addiction associated with internet activity, depression and behavioral factors among university students of Bangladesh: A cross-sectional study. Child Youth Serv. Rev. 2020;118:105424. doi: 10.1016/j.childyouth.2020.105424. [DOI] [Google Scholar]
  • 40.Mamun MA, Hossain MS, Moonajilin MS, Masud MT, Misti JM, Griffiths MD. Does loneliness, self-esteem and psychological distress correlate with problematic internet use? A Bangladeshi survey study. Asia Pac. Psychiatry. 2020;12(2):e12386. doi: 10.1111/appy.12386. [DOI] [PubMed] [Google Scholar]
  • 41.Islam MA, Hoq KMG. Community Internet access in rural areas: A study on community information centres in Bangladesh. Malays. J. Libr. Inf. Sci. 2010;15(2):109–124. [Google Scholar]
  • 42.Mak K-K, Lai C-M, Watanabe H, Kim D-I, Bahar N, Ramos M, et al. Epidemiology of internet behaviors and addiction among adolescents in six Asian countries. Cyberpsychol. Behav. Soc. Netw. 2014;17(11):720–728. doi: 10.1089/cyber.2014.0139. [DOI] [PubMed] [Google Scholar]
  • 43.Gavurova B, Ivankova V, Rigelsky M, Mudarri T. Internet addiction in socio-demographic, academic, and psychological profile of college students during the COVID-19 pandemic in the Czech Republic and Slovakia. Front. Public Health. 2022;10:944085. doi: 10.3389/fpubh.2022.944085. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Shehata WM, Abdeldaim DE. Internet addiction among medical and non-medical students during COVID-19 pandemic, Tanta University Egypt. Environ. Sci. Pollut. Res. 2021;28(42):59945–59952. doi: 10.1007/s11356-021-14961-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Jafarkarimi H, Sim ATH, Saadatdoost R, Hee JM. Facebook addiction among Malaysian students. Int. J. Inform. Educ. Technol. 2016;6(6):465. doi: 10.7763/IJIET.2016.V6.733. [DOI] [Google Scholar]
  • 46.Balci Ş, Gölcü A. Facebook addiction among university students in Turkey: “Selcuk university example”. Selçuk Üniversitesi Türkiyat Araştırmaları Dergisi. 2013;1(34):255–278. [Google Scholar]
  • 47.Shettar M, Karkal R, Kakunje A, Mendonsa RD, Chandran VM. Facebook addiction and loneliness in the post-graduate students of a university in southern India. Int. J. Soc. Psychiatry. 2017;63(4):325–329. doi: 10.1177/0020764017705895. [DOI] [PubMed] [Google Scholar]
  • 48.Bhuiyan SH. Modernizing Bangladesh public administration through e-governance: Benefits and challenges. Gov. Inf. Q. 2011;28(1):54–65. doi: 10.1016/j.giq.2010.04.006. [DOI] [Google Scholar]
  • 49.Saricam H, Halmatov M, Halmatov S, Celik I. The investigation of child rearing attitudes of families living in rural and urban areas (Turkish sample) Procedia Soc. Behav. Sci. 2012;46:2772–2776. doi: 10.1016/j.sbspro.2012.05.563. [DOI] [Google Scholar]
  • 50.MPh MM. Prevalence of facebook addiction and related factors among Thai high school students. J. Med. Assoc. Thai. 2015;98(3):S51–S60. [PubMed] [Google Scholar]
  • 51.Lam LT, Peng Z-W, Mai J-C, Jing J. Factors associated with Internet addiction among adolescents. Cyberpsychol. Behav. 2009;12(5):551–5. doi: 10.1089/cpb.2009.0036. [DOI] [PubMed] [Google Scholar]
  • 52.Cao F, Su L. Internet addiction among Chinese adolescents: Prevalence and psychological features. Child Care Health and Dev. 2018;33(3):275–81. doi: 10.1111/j.1365-2214.2006.00715.x. [DOI] [PubMed] [Google Scholar]
  • 53.Abbasi IS. Social media and committed relationships: What factors make our romantic relationship vulnerable? Soc. Sci. Comput. Rev. 2019;37(3):425–434. doi: 10.1177/0894439318770609. [DOI] [Google Scholar]
  • 54.Simon RW, Barrett AE. Nonmarital romantic relationships and mental health in early adulthood: Does the association differ for women and men? J. Health Soc. Behav. 2010;51(2):168–182. doi: 10.1177/0022146510372343. [DOI] [PubMed] [Google Scholar]
  • 55.Liu C-Y, Kuo F-Y. A study of Internet addiction through the lens of the interpersonal theory. Cyberpsychol. Behav. 2007;10(6):799–804. doi: 10.1089/cpb.2007.9951. [DOI] [PubMed] [Google Scholar]
  • 56.Michael Kerr. Dealing with depression after a breakup 2018 Available from: https://www.healthline.com/health/depression/after-break-up#Suicide-prevention. (2018).
  • 57.Panger, G., Tryon, J., Smith, A. The graduate assembly graduate student happiness & well-being report 2014. Grad Assem. (2014).
  • 58.Ajinkya S, Schaus JF, Deichen M. The relationship of undergraduate major and housing with depression in undergraduate students. Cureus. 2016;8(9):e786. doi: 10.7759/cureus.786. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Baviskar M, Phalke V, Phalke D. Depression, anxiety and stress: A comparative study in arts, commerce & science junior college students in rural area of India. Global Res. Analysis. 2013;2(11):183–185. [Google Scholar]
  • 60.Tokunaga RS. A meta-analysis of the relationships between psychosocial problems and internet habits: Synthesizing internet addiction, problematic internet use, and deficient self-regulation research. Commun. Monogr. 2017;84(4):423–446. doi: 10.1080/03637751.2017.1332419. [DOI] [Google Scholar]
  • 61.Islam MS, Sujan MSH, Tasnim R, Mohona RA, Ferdous MZ, Kamruzzaman S, et al. Problematic smartphone and social media use among Bangladeshi college and university students amid COVID-19: The role of psychological well-being and pandemic related factors. Front. Psychiatry. 2021;12:647386. doi: 10.3389/fpsyt.2021.647386. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Weinstein A, Lejoyeux M. Internet addiction or excessive internet use. Am. J. Drug Alcohol Abuse. 2010;36(5):277–283. doi: 10.3109/00952990.2010.491880. [DOI] [PubMed] [Google Scholar]
  • 63.Cerniglia L, Zoratto F, Cimino S, Laviola G, Ammaniti M, Adriani W. Internet Addiction in adolescence: Neurobiological, psychosocial and clinical issues. Neurosci. Biobehav. Rev. 2017;76:174–184. doi: 10.1016/j.neubiorev.2016.12.024. [DOI] [PubMed] [Google Scholar]
  • 64.Hassan T, Alam MM, Wahab A, Hawlader MD. Prevalence and associated factors of internet addiction among young adults in Bangladesh. J. Egypt. Public Health Assoc. 2020;95(1):1–8. doi: 10.1186/s42506-019-0032-7. [DOI] [PMC free article] [PubMed] [Google Scholar]

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Supplementary Materials

Supplementary Information. (194.6KB, xlsx)

Data Availability Statement

The datasets and questionnaire used and/or analyzed during the current study will be shared upon request to corresponding author.

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