ALL Metrics
-
Views
-
Downloads
Get PDF
Get XML
Cite
Export
Track
Research Article

Internet addiction during COVID-19 restricted movement period: A cross-sectional study from Bangladesh

[version 1; peer review: 3 approved with reservations]
PUBLISHED 13 May 2022
Author details Author details
OPEN PEER REVIEW
REVIEWER STATUS

This article is included in the Addiction and Related Behaviors gateway.

Abstract

Background: The restricted movement period related to COVID-19 has presumably contributed to the deterioration of the Internet addiction crisis. Therefore, this study aimed to determine the prevalence of Internet addiction and identify the factors associated with the increase in severity of Internet addiction among the general population of Bangladesh during the COVID-19 related restricted movement period.
Methods: We conducted a cross-sectional online survey in Bangladesh from September 20 to October 5, 2020, and 315 Bangladeshi adults were included in the study. We used Young’s Internet Addiction Scale to assess the prevalence of Internet addiction and identified the factors associated with the increase in severity of Internet addiction during the restricted movement period using multivariable logistic regression analysis.
Results: The overall prevalence of Internet addiction was 39.7% among the general population of Bangladesh during the restricted movement period. Almost 75% of the respondents reported increased time spent on recreational use of the Internet during the period of interest, and 48.5% of the respondents reported increases in the severity of Internet addiction. In logistic regression analyses, the increase in severity of Internet addiction was found to be significantly associated with social class, occupation, sleeping hours, and increased time spent on recreational use of the internet (p < 0.05). Watching movies/series was the main activity of the respondents during the restricted movement period.
Conclusion: Our study reported an increase in the prevalence of Internet addiction among the general population of Bangladesh during the restricted movement period. Social class, occupation, sleeping hours, and increased time spent on recreational use were the significant determinants of the increase in severity of Internet addiction. The policymakers should undertake tailored policies to prevent people from being victims of the consequences of psychological issues in the long run.

Keywords

Internet addiction, COVID-19 restricted movement period, Bangladesh, depression, sleep quality, social inequality

Introduction

The Internet has become an inseparable element of daily life, providing an integrated platform for communication and access to a broad range of information. In recent decades, the number of Internet users and their use hours has risen significantly.1 As of March 2021, 65.6% of the global population were using the Internet, with 1,331.9% growth between 2000-2021.2 However, excessive or limitless use can result in Internet addiction, also known as “pathological Internet use” or “problematic Internet use”. It can cause significant distress and functional impairments in daily life and comorbid psychiatric disorders such as depression, attention deficit and hyperactivity disorder and substance abuse.3,4

Mark D. Griffiths was the first to publish a scientific article on Internet addiction in November 1996.5 The term ‘Internet addiction’ is often defined as a situation in which a person has lost control of their Internet usage and continues to use it excessively to the point that they encounter negative consequences that adversely influence their lives.6 It has become a severe concern for mental health in different groups of people on different parts of the world, including South Korea (20.0%), China (16.4%), Vietnam (21.2%), Bangladesh (32.6%) and the Philippines (21.0%).710 Its prevalence is also increasing in the Western countries.11

The COVID-19 pandemic had unprecedented social, economic, and healthcare consequences, and it caused widespread psychological issues. During the COVID-19 pandemic, relatively high rates of anxiety (6.33%-50.9%), depression (14.6%-48.3%), and posttraumatic stress disorder (7.0%-53.8%) were reported in the general population.1215 These traumatic emotional reactions contribute to the development and relapse of addictive behaviours, including substance and behavioural addictions.16 Disasters, such as large-scale natural catastrophe and economic downturns, have boosted rates of Internet addiction.17,18 When individuals with disordered coping mechanisms are exposed to stressful or traumatic situations, they are more likely to develop internet addiction.19 Social distancing and isolation measures for pandemic containment may potentially lead to increased Internet usage, putting vulnerable groups in danger of becoming addicted to the Internet.20

The significant efforts from the Bangladesh government to deploy digital technology and transform life on a considerable level have resulted in more access to the Internet than ever before. As a consequence, there has been a substantial growth in the number of people using the Internet. By the end of January 2021, the total number of Internet customers had risen to 112.7 million and 103.2 million of them are mobile Internet users.21 Although the figure is attractive, this may also be regarded as a problem for the general population of Bangladesh as it is widely assumed that the hours spent on Internet usage increased during COVID-19 restricted movement period. Since identifying the first COVID case in Bangladesh, the government has imposed several restrictions on public transportation, businesses, schools, and all public and private offices except emergency services.22 These restrictions confined people to stay more at home and appeared to spend more time on the internet as the offline medium of accomplishing daily works and recreations was limited. This excessive usage has several adverse outcomes that are gradually becoming apparent.23

Considering the above facts, we have commenced an online survey to assess the prevalence of Internet addiction among the general population and identify the risk factors for increased severity in Internet addiction in Bangladesh during the restricted movement period.

Methods

Ethical approval

We obtained the ethical approval (2020/OR-NSU/IRB-No.0801) to conduct the study from the Institutional Review Board of North South University. We adequately disclosed the purpose of the research to the respondents. Participation in the study was entirely voluntary, and the respondents shared their informed consent electronically by a ticking a specific checkbox in the questionnaire before participating.

Participants and procedure

We invited general Bangladeshi people aged 18 years or above to participate in the survey by completing a self-administered questionnaire from September 20 to October 5, 2020. The invitations to participate in the survey were delivered via social media platforms like Facebook, WhatsApp and email using our personal, professional, and academic email groups. We have used a convenience sampling technique to collect data from all over Bangladesh.

Sample size

A single population proportion formula was employed to determine the sample size. Several prior research done on different Bangladesh based samples estimated the prevalence of internet addiction was around 24-28%.24,25 Considering 28% prevalence, the required sample size was n = 310, while the allowable error was 5%. Among the invitees, 355 individuals completed the questionnaire. By restricting each Gmail account to a single submission, we decreased the chance of the respondents to fulfil the questionnaire repeatedly. Moreover, we excluded 13 duplicate responses by cross-checking contact information and 27 questionnaires with answers that did not match the questions or seemed contradictory were discarded, leaving 315 responses to be included in the final analysis.

Measures

The respondents answered questions about socio-demographic characteristics, Internet use characteristics and degree of addiction in pre and during the restricted movement period.

The socio-demographic variables included age, gender, occupation, place of residence, and self-reported social class.

We used Young’s Internet Addiction Scale to assess the prevalence of internet addiction.26 The scale includes eight items with dichotomous responses for each item. The score ranges from 0 to 8, with the severity of addiction categorized as addiction with scores 5 to 8. We utilized the scores to assess the prevalence of Internet addictions. We also used the scale to measure changes in the severity of Internet addiction during the restricted movement period compared to the pre-COVID-19 period. Furthermore, we sought to identify the contributing factors for increased severity in Internet addiction during the restricted movement period. The Internet Addiction Scale showed a satisfactory internal consistency among the respondents of the study (During-restricted-movement period Cronbach’s alpha coefficient = 0.68). A value of Cronbach’s alpha greater than 0.6 is regarded as an acceptable index and very reliable.27,28

Besides, we included questions related to the type of internet service use, monthly Internet billing and purpose of Internet use to understand the using patterns of the respondents. We have also collected information on behavioural factors like sleeping hours, physical activities.

A copy of the questionnaire can be found in the Extended data.

Statistical analysis

We checked the data for consistency and completeness. STATA version 15 was used for data management and analysis. Various descriptive statistics like frequencies and percentages were calculated. The chi-square tests were used to determine the degrees of association between the response variable and predictor variables. We used multivariable logistic regression analyses to assess associated factors for increased severity in internet addiction during the restricted movement period. We adjusted the variables contained in Tables 1 and 2 using enter method (Table 3), and odds ratios were reported for each factor of the variables included in the model. Results with p < 0.05 were regarded to be statistically significant.

Table 1. Univariate analysis: Socio-demographic and personal characteristics of the study respondents with increased severity of internet addiction.

VariablesAddiction score increased n (%)Addiction score not increased n (%)Total (%) within categories n (%)p-value
Age (in years)
18-2469 (53.1)61 (46.9)130 (41.3)0.119
25-3469 (43.1)91 (56.9)160 (50.8)
≥3515 (60.0)10 (40.0)25 (7.9)
Gender
Female50 (49.0)52 (51.0)102 (32.4)0.912
Male103 (48.4)110 (51.6)213 (67.6)
Place of residence
Urban127 (49.4)130 (50.6)257 (81.6)0.528
Rural26 (44.8)32 (55.2)58 (18.4)
Social class
Higher33 (44.0)42 (56.0)75 (23.8)0.261
Middle87 (47.5)96 (52.5)183 (58.1)
Lower33 (57.9)24 (42.1)57 (18.1)
Occupation
Students74 (52.5)67 (47.5)141 (44.8)0.001
Housewives2 (15.4)11 (84.6)13 (4.1)
Online professionals3 (13.6)19 (86.4)22 (7.0)
Businesspersons and Service holders65 (54.2)55 (45.8)120 (38.1)
Unemployed9 (47.4)10 (52.6)19 (6.0)
Sleeping hours
<6 hours/day75 (59.5)51 (40.5)126 (40.0)0.001
≥6 hours/day78 (41.3)111 (58.7)189 (60.0)
Physical exercise
No physical exercise42 (51.2)40 (48.8)82 (26.0)0.473
≤1 hour/day81 (50.0)81 (50.0)162 (51.4)
>1 hour/day30 (42.2)41 (57.8)71 (22.6)

Table 2. Univariate analysis: Association between internet using activity related characteristics with increased severity of internet addiction.

VariablesAddiction score increased n (%)Addiction score not increased n (%)Total (%) within categories n (%)p-value
Main activity
Learning15 (50.0)15 (50.0)30 (9.6)0.554
Movie/Series72 (52.9)64 (47.1)136 (43.3)
Social media/News/Games56 (44.1)71 (55.9)127 (40.4)
Office work10 (47.6)11 (52.4)21 (6.7)
Monthly payment for internet
<1000 BDT104 (47.3)116 (52.7)220 (69.8)0.483
≥ 1000 BDT49 (51.6)46 (48.4)95 (30.2)
Satisfaction with internet speed
Poor62 (54.4)52 (45.6)114 (36.2)0.120
Satisfactory91 (45.3)110 (54.7)201 (63.8)
Recreational time
Not increased28 (35.9)50 (64.1)78 (24.8)0.010
Increased125 (52.7)112 (47.3)237 (75.2)

Table 3. Multivariable logistic regression model of variables associated with increased severity of internet addiction.

FactorsCategoriesOR (95% CI)AOR (95% CI)
Age (in years)
18-2411
25-340.67 (0.42 – 1.07)0.77 (0.40 – 1.47)
≥351.33 (0.55 – 3.17)2.41 (0.70 – 8.36)
Gender
Male11
Female1.03 (0.64 – 1.65)0.91 (0.53 – 1.57)
Place of residence
Urban11
Rural0.83 (0.47 – 1.47)0.74 (0.38 – 1.45)
Social class
Higher11
Middle1.15 (0.67 – 1.98)1.29 (0.67 – 2.47)
Lower1.75 (0.87 – 3.51)2.35 (1.01 – 5.47)
Occupation
Students11
Housewives0.16 (0.04 – 0.77)0.11 (0.02 – 0.68)
Online professionals0.14 (0.04 – 0.50)0.09 (0.02 – 0.38)
Businesspersons and Service holders1.07 (0.66 – 1.74)1.00 (0.49 – 2.01)
Unemployed0.81 (0.31 – 2.13)0.86 (0.30 – 2.50)
Sleeping duration
≥6 hours/day11
<6 hours/day2.09 (1.32 – 3.31)2.21 (1.33 – 3.67)
Physical exercise
>1 hour/day11
≤1 hour/day1.37 (0.78-2.40)1.20 (0.63 – 2.28)
No physical exercise1.44 (0.76 – 2.72)1.12 (0.54 – 2.31)
Main activity
Learning11
Movie/Series1.13 (0.51 – 2.48)0.57 (0.22 – 1.51)
Social media/News/Games0.79 (0.36. – 1.75)0.56 (0.22 – 1.43)
Office work0.91 (0.30 – 2.78)0.66 (0.18 – 2.40)
Monthly payment for internet
<1000 BDT11
≥1000 BDT1.19 (0.73 – 1.92)1.50 (0.85 – 2.67)
Satisfaction with internet speed
Poor11
Satisfactory0.69 (0.44 – 1.10)0.70 (0.41 – 1.18)
Recreational time
Not increased11
Increased1.99 (1.18 – 3.38)2.69 (1.46 – 4.99)

Results

A total of 315 respondents were included in the final analysis. We received participation from respondents of various socio-demographic backgrounds by social class and place of residence. Overall, 22.3% of the respondents (n =70) had Internet addiction prior to restricted movement period. On the other hand, 39.8% (n =125) reported having internet addiction during COVID-19 restricted movement period (Figure 1).

ad715567-12a1-4f5e-b34c-7a4af3a9d68c_figure1.gif

Figure 1. Prevalence of internet addiction among general population in Bangladesh: (A) prior to COVID-19 restricted movement period, (B) during COVID-19 restricted movement period.

Socio-demographic and personal characteristics

We included 315 valid completed questionnaires (male/female: 67.6%/32.4%) in our analysis, including 18.4% rural residents and 44.8% students. Nearly half of the participants (50.8%) were 25-34 years old, and 58.1% belonged to middle-class families. In terms of sleeping hours, nearly 40% of the respondents reported sleeping less than six hours per day during the restricted movement period. Almost 80% (77.4%) of respondents reported doing one hour or less physical exercise per day during the period of interest. Increased severity of Internet addiction during the restricted movement period was significantly associated with occupation (p = 0.001) and sleeping hours (p = 0.001) (Table 1).

Internet-using activity related characteristics

The respondents mostly used the Internet to watch movies/series (43.3%) and use social media/reading news/playing games (40.4%). Nearly 70% of the respondents (69.8%) reported paying less than 1000 Bangladeshi Taka per month as an Internet bill. More than 60% of the respondents (63.8%) were satisfied with Internet speed, and 54.4% of the respondents who were not satisfied with Internet speed reported to increase in severity of Internet addiction. An increase in recreational time spent on the Internet had a significant association with increased severity of internet addiction during the restricted movement period than that of the pre-COVID-19 situation (p = 0.010) (Table 2).

Risk factors for increased severity of internet addiction included lower social class (adjusted odds ratio [AOR] = 2.35, 95% confidence interval [CI] = 1.01 – 5.47), sleeping hours less than 6 hours per day (AOR = 2.21, 95% CI = 1.33 – 3.67), and increase in recreational time spent on Internet (AOR = 2.69, 95% CI = 1.46 – 4.99). Occupation housewives (OR = 0.11, 95% CI = 0.02 – 0.68) and online professionals (OR = 0.09, 95% CI = 0.02 – 0.38) were protective factors for increased severity of internet addiction compared to students.

Discussion

The study aimed to determine the prevalence of Internet addiction among the general population of Bangladesh during the COVID-19 restricted movement period. Furthermore, the study also sought to identify the factors associated with the increase in severity of Internet addiction.

Our study provides evidence of the remarkably high prevalence of Internet addiction during the period of interest. Nearly 40% of the respondents reported suffering from Internet addiction, close to a during-pandemic Internet addiction study conducted in China.29 We also observed that the prevalence during the restricted movement period was much higher than the pre-COVID-19 period (39.7%/22.3%). One probable explanation is that more individuals spend time on the Internet during this time. The fear caused by the COVID-19 virus, as well as the implications of lockdown, stress, and anxiety have modified people’s behaviour. Nearly three-fourths of the respondents reported increasing time spent on recreational use of the Internet during the period of interest, and about half of the respondents reported increases in the severity of Internet addiction. As people had to stay more at home and the range of leisure activities (e.g., socializing, outdoor activities, etc.) became limited due to COVID, they were severely seeking an alternative source of refreshment. The recreational use of Internet filled the vacant place with a greater possession.

We found economic status of the family was a significantly associated factor with Internet addiction. Respondents from the lower social class were 2.35 times more likely to report an increase in severity of Internet addiction than respondents from higher social class. In line with our findings, studies conducted in Turkey and Greece also observed the inverse relationship between social class and Internet addiction.30,31 This might be because low-income individuals frequently prefer the Internet in this direction as a substitute for hobbies that require monetary resources to release tension, spend time, and make fun.

Increased time spent on recreational use of the Internet was revealed as a positively associated factor with increased severity of Internet addiction during restricted movement period in our multivariable logistic regression model. Several previous studies reported that individuals who spend the most time on Facebook, Instagram and other recreational platforms have a much greater risk of reporting depression than those who spend the least time.32,33 And a study conducted in India reported a significant positive relationship between depression and internet addiction.34 Recreational platforms are designed to capture people’s interest, keep them online, and keep them monitoring the screen for updates. It is how businesses generate money. But at the same time, excessive use of the platforms contributes to developing several psychological issues, including internet addiction, our study is evident.

On the other hand, being a housewife or an online professional were identified as protective factors for increased severity of internet addiction compared to students. In accordance with us, Soule et al. also stated that students are more at risk of developing internet addiction.35 The possible reason could be that during the restricted movement period, students had more free time to spend on the internet as the educational institutes were remained closed while the other professionals were struggling to maintain their professional flow. That is why it is required to develop and implement occupation-specific intervention plans to reduce excessive addiction to the internet, especially during a pandemic, and prevent long-term negative psychological consequences.

We also identified that people with shortened sleep duration were at a higher risk for increased severity in Internet addiction during the restricted movement period. Although it is well-established that Internet addiction is associated with frequent sleep disturbance, shortened sleeping hours and poor sleep quality,36,37 in our case, we can explain the relationship from the other side of the mirror. A study conducted in Greece found that anxiety levels were much higher in persons who slept for a short period.38 In link with the finding, the famous psychologist Dr Christina Gregory stated that those who are already suffering from anxiety or depression often turn to the Internet to relieve their suffering. As it is emotionally rewarding, it transforms into Internet addiction eventually.39

Strength and limitations

Our target population was the general population from various regions of Bangladesh, whereas most of the previously conducted studies in Bangladesh targeted particular occupational group or a specific age group.4042 Also, our study gathered data from multiple relevant angles related to the Internet using behaviour and regular activity from pre-COVID-19 and during the restricted movement period. Moreover, this study is one of the few studies which compared the Internet addiction level of pre-COVID-19 and during the restricted movement period.

There were several drawbacks of this research. At first, since the survey was conducted online, people who regularly use the internet and are actively involved in browsing social media websites were more likely to be participated in it, resulting in response bias. As a result, the respondents may not be representative of the whole population. Second, data on Internet use patterns prior to the COVID-19 might be skewed by recall bias. Third, rather than clinical diagnosis, we used respondents’ self-reported information from the Internet Addiction Scale to assess severity of Internet addiction. Fourth, the Young’s Internet Addiction Scale measures the possibility of Internet addiction only; it is not a confirmatory tool. Finally, we could not establish causal relationship between independent variables and increased severity of Internet addiction due to the cross-sectional nature of the data. Additional longitudinal follow-up investigation is suggested to evaluate the restricted movement period’s long-term impact on Internet use and psychological health.

Conclusion

The prevalence of Internet addiction was significantly enhanced among the general population in Bangladesh during the restricted movement period, which is in line with other studies conducted in different parts of the world. We created a detailed profile of the consequences of restricted movement on the Internet addiction in Bangladesh’s general population. We also shared our concern about the influence of the restricted movement on vulnerable communities; lack of proper recognition of the crisis may penetrate the problem in a deeper stage. Evidence-based early preventions like cognitive behavioural therapy are required for vulnerable group. The study could be a good base for policymakers to understand the severity of the problem better and undertake policies to confront long-term impact.

Data availability

Underlying data

OSF: Internet addiction during COVID-19 restricted movement period: A study from Bangladesh. https://doi.org/10.17605/OSF.IO/FSN49.43

This project contains the following underlying data:

  • - COVID19_Restricted_Movement_Period_Internet_Addiction_Bangladesh.dta

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Extended data

OSF: Internet addiction during COVID-19 restricted movement period: A study from Bangladesh. https://doi.org/10.17605/OSF.IO/FSN49.43

This project contains the following extended data:

  • - A copy of the questionnaire

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Comments on this article Comments (0)

Version 1
VERSION 1 PUBLISHED 13 May 2022
Comment
Author details Author details
Competing interests
Grant information
Copyright
Download
 
Export To
metrics
Views Downloads
F1000Research - -
PubMed Central
Data from PMC are received and updated monthly.
- -
Citations
CITE
how to cite this article
Tasneem Chowdhury A, Siddiqua SR, Rahman L et al. Internet addiction during COVID-19 restricted movement period: A cross-sectional study from Bangladesh [version 1; peer review: 3 approved with reservations]. F1000Research 2022, 11:519 (https://doi.org/10.12688/f1000research.108664.1)
NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.
track
receive updates on this article
Track an article to receive email alerts on any updates to this article.

Open Peer Review

Current Reviewer Status: ?
Key to Reviewer Statuses VIEW
ApprovedThe paper is scientifically sound in its current form and only minor, if any, improvements are suggested
Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approvedFundamental flaws in the paper seriously undermine the findings and conclusions
Version 1
VERSION 1
PUBLISHED 13 May 2022
Views
2
Cite
Reviewer Report 22 Sep 2023
Shamala Ramasamy, International Medical University, Kuala Lumpur, Malaysia 
Approved with Reservations
VIEWS 2
  • No research design mentioned in the manuscript. 
     
  • The conclusion is far fetched, suggesting CBT etc.
     
  • RQ is not available.
     
  • A cross-sectional  study
... Continue reading
CITE
CITE
HOW TO CITE THIS REPORT
Ramasamy S. Reviewer Report For: Internet addiction during COVID-19 restricted movement period: A cross-sectional study from Bangladesh [version 1; peer review: 3 approved with reservations]. F1000Research 2022, 11:519 (https://doi.org/10.5256/f1000research.120071.r192691)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
Views
1
Cite
Reviewer Report 22 Sep 2023
Patrick Chin Hooi Soh, Multimedia University, Cyberjaya, Malaysia 
Approved with Reservations
VIEWS 1
Overall, it is a clear, concise and relevant article worthy of publication,  However, a minor defect is that the researcher did not explain how the sample size of 310 was adequate. 

One significant limitation of this research, ... Continue reading
CITE
CITE
HOW TO CITE THIS REPORT
Chin Hooi Soh P. Reviewer Report For: Internet addiction during COVID-19 restricted movement period: A cross-sectional study from Bangladesh [version 1; peer review: 3 approved with reservations]. F1000Research 2022, 11:519 (https://doi.org/10.5256/f1000research.120071.r180742)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
Views
2
Cite
Reviewer Report 15 Aug 2023
Xinyan Xie, Huazhong University of Science and Technology, Wuhan, Hubei, China 
Approved with Reservations
VIEWS 2
This study reported an increase in the prevalence of Internet addiction and identified some risk factors among the general population of Bangladesh during the restricted movement period. Some problems need to be revised.

Is the work clearly ... Continue reading
CITE
CITE
HOW TO CITE THIS REPORT
Xie X. Reviewer Report For: Internet addiction during COVID-19 restricted movement period: A cross-sectional study from Bangladesh [version 1; peer review: 3 approved with reservations]. F1000Research 2022, 11:519 (https://doi.org/10.5256/f1000research.120071.r188163)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.

Comments on this article Comments (0)

Version 1
VERSION 1 PUBLISHED 13 May 2022
Comment
Alongside their report, reviewers assign a status to the article:
Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested
Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions
Sign In
If you've forgotten your password, please enter your email address below and we'll send you instructions on how to reset your password.

The email address should be the one you originally registered with F1000.

Email address not valid, please try again

You registered with F1000 via Google, so we cannot reset your password.

To sign in, please click here.

If you still need help with your Google account password, please click here.

You registered with F1000 via Facebook, so we cannot reset your password.

To sign in, please click here.

If you still need help with your Facebook account password, please click here.

Code not correct, please try again
Email us for further assistance.
Server error, please try again.