Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Internet addiction among young Bangladeshi adults: critical commentary on Hassan et al. (2020)

Internet addiction among young Bangladeshi adults: critical commentary on Hassan et al. (2020) Griffiths and Mamun Journal of the Egyptian Public Health Association (2020) 95:26 Journal of the Egyptian https://doi.org/10.1186/s42506-020-00054-x Public Health Association LETTER TO THE EDITOR Open Access Internet addiction among young Bangladeshi adults: critical commentary on Hassan et al. (2020) 1* 1,2 Mark D. Griffiths and Mohammed A. Mamun Dear Editor, One of the major omissions in the paper is that in We were very interested to read the paper by Hassan their Introduction, not a single previous Bangladeshi et al. [1] examining the prevalence and associated factors study was discussed even though there are many (e.g., of internet addiction (IA) among young Bangladeshi [8–21] - see Table 1 for details), and only one was men- adults. However, there are a number of issues of concern tioned in the Discussion [10]. Hasan also claimed that: that we would like to raise. “Most of the studies conducted previously evaluated the In the first paragraph, Hassan et al. claimed that prevalence of internet addiction and its predictors in “Kimberly Young was the first to introduce the concept of adolescent samples, within the age range of 12 to 18 internet addiction disorder (IAD) in 1996” (p. 1). The years” (p. 2). There was no reference for this assertion, paper that was cited [2] was published in December and furthermore, it is simply not true. For instance, a 1996. However, it was actually Griffiths who systematic review by Kuss et al. [22] of 68 large-scale published the first paper on internet addiction in studies of IA (i.e., studies with over 1000 participants) November 1996 [3] having already published a more reported that 24 of the studies had purely adult samples, general paper on “technological addictions” in and a further 15 studies had age ranges that included February 1995 [4] as well as populist article on inter- participants over the age of 18 years. In sum, this sys- net addiction in April 1995 [5]. tematic review showed that in the large-scale studies in They then cited a paper by Xin et al. [6] to provide the period they examined, 39 of the 68 studies were not worldwide prevalence figures (who simply reported carried out on 12–18-year age group samples. prevalence data from other studies) but did not make Another major criticism of Hassan et al.’s study is their any reference to the largest meta-analysis in the IA field scoring on the scale used for IA assessment. The authors by Cheng and Li [7] who examined prevalence figures said they used the 20-item Bangla version of the Internet comprising 164 studies (N = 89,281) from 31 nations in Addiction Test (IAT) with a cutoff score of 50 (out of seven world regions, and producing a global estimate of 100) to categorize the participants as internet addicts. 6% for IA. Hassan et al. mentioned the wide variations However, the Bangla IAT is an 18-item scale (whereas in IA prevalence across different world regions but pro- the original English language IAT has 20 items) with vided no explanation as to why. Such variation in global total scores ranging from 20 to 90 [15]. The scoring for IA rates can be due to a number of factors, the most im- Bangla IAT is 18–35 for minimal use, 36–62 for moder- portant of which are arguably methodological (e.g., there ate use, and 63–90 for excessive use. However, Hassan are many different psychometric instruments used to as- et al. did not use the appropriate scoring for the sess IA, and different researchers use different cutoff validated Bangla IAT, and given they used a 20-item points even when the same instrument is being used) scale, it is not even clear if they used the Bangla IAT at [8]. These important factors were not mentioned. all (because there is no 20-item validated version). Add- itionally, they cited two papers [15, 23] who they claimed had used a cutoff score of 50 to class individuals as * Correspondence: mark.griffiths@ntu.ac.uk Psychology Department, Nottingham Trent University, 50 Shakespeare internet addicts but neither of these two psychometric Street, Nottingham NG1 4FQ, UK studies suggested 50 as a cutoff score. Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Griffiths and Mamun Journal of the Egyptian Public Health Association (2020) 95:26 Page 2 of 4 Table 1 Comparison of Bangladeshi studies examining internet addiction prevalence (in alphabetical order) Authors Study location Study Sample size Assessment tool Scale response Total scale Cutoff scores used Main findings (year published) population (age range) items Afrin et al. Chittagong High school 279 Internet Yes/no 9 < 3 = normal internet user; 2.5% severely addicted to the internet, 64.9% (2017) [19] students (14–17 years) Addiction Survey 4 to 6 = moderate internet moderately addicted to the internet user; ≤ 7 = severe user Hassan et al. Chittagong, Adults 454 Internet 5-point Likert-type 20* 20–49 points = average 27.1% prevalence of internet addiction (2020) [1] Dhaka, Sylhet (19–35 years) Addiction Test scale ranging from 1 internet user; ≥ 50 (rarely) to 5 (always) points = internet addicted Islam and Dhaka University 573 Internet 5-point Likert-type 20 ≥ 50 = moderate, excessive, 24% problematic internet users Hossin students (20–30 years) Addiction Test scale ranging from 1 or problematic internet user (2016) [10] (rarely) to 5 (always) Jahan et al. Dhaka University 390 Internet Yes/no 9 < 3 = normal internet user; 31.5% normal users, 49.2% moderately (2019) [20] medical (18–26 years) Addiction Survey 4 to 6 = moderate internet addicted users, and 19.3% severely internet students user; ≤ 7 = severe user addicted users Karim and Dhaka University 177 Bangla Internet 5-point Likert-type 18 < 36 = minimal internet 63.95% minimal internet users, 34.3% Nigar students (18–25 years) Addiction Test scale ranging from 1 user; 36–62 = moderate moderate internet users, 1.7% excessive internet (2014) [15] (rarely) to 5 (always) internet user; > 62 = users excessive internet user Khan Dhaka High school 797 Internet 5-point Likert-type 20 Not reported 20.20% reported as having “internet addiction (2012) [18] students (mean age = Addiction Test scale ranging from 1 disorder” 16.5 years) (rarely) to 5 (always) Mamun and Dhaka University 300 Bergen Facebook 5-point Likert-type 6 ≥ 18 = at risk of being 39.7% at risk of being addicted to Facebook Griffiths students (mean age = Addiction Scale scale ranging from 1 addicted to Facebook (2019) [11] 20.7 years) (very rarely) to 5 (very often) Mamun et al. Dhaka University 405 Internet 5-point Likert-type 20 ≥ 50 = moderate to high 32.6% problematic internet users (2019) [16] students (mean age = Addiction Test scale ranging from 1 or problematic internet user 20.2 years) (rarely) to 5 (always) Mamun et al. Rajshahi Graduated 284 Internet 5-point Likert-type 20 < 60 = non-excessive 0% internet addicts, but 3.9% classed as (2019) [14] students (mean age = Addiction Test scale ranging from 1 internet users; ≥ 60 = excessive users 21.1 years) (rarely) to 5 (always) excessive internet users Mostafa et al. Chittagong Medical and 379 Internet 5-point Likert-type 20 < 20 = normal internet user; Majority of the participants were mild (2019) [21] university (18–30 years) Addiction Test scale ranging from 1 20–49 = mild internet user; problematic users (54.88%). The prevalence of students (rarely) to 5 (always) 50–79 = moderate internet internet addiction was 1.06% (severe users) user; 80–100 = severe internet user Siddiqi et al. Dhaka High school 376 Bergen Facebook 5-point Likert-type 6** ≥ 20 = at risk of being 49.8%, 41.0%, and 1.6% mild, moderate, and (2018) [12] students (13–19 years) Addiction Scale scale ranging from 1 addicted to Facebook severe Facebook addicted and 7.6% non- (rarely) to 5 (always) problematic users Uddin et al. Dhaka University 475 Internet 5-point Likert-type 20 ≤ 30 = normal internet 47.7% male and 44.5% female students severely (2016) [13] students (18–25 years) Addiction Test scale ranging from 1 user; 31–49 = mild internet addicted to the internet, 7.1% male and 33.9% (rarely) to 5 (always) user; 50–79 = moderate female students moderately addicted to the internet user; ≥ 80 = severe internet, 20.7% male and 7.7% female students or excessive internet user mildly addicted to the internet *Paper said the Bangla Internet Addiction Test was used, but based on the description, it appears the Internet Addiction Test was used **Paper claimed there were 18 items, but the Bergen Facebook Addiction Scale only has six items Griffiths and Mamun Journal of the Egyptian Public Health Association (2020) 95:26 Page 3 of 4 Hassan et al. also wrote that the IAT “has been scien- regions, utilizing a “high number of sociodemographic tifically analyzed to state an ambiguous psychometric variables as well as variables related to internet use be- factor structure” (p. 2). On reading this, we were unsure havior and regular activity” [p. 6]) without referring to whether the authors deliberately meant to say the factor any previous Bangladeshi papers as a benchmark. Con- structure was “ambiguous” (as opposed to “unambigu- fusingly, the authors used a survey to collect the data, ous”) given that they were putting forward the rationale but then in the 'Strengths and limitations' section, they for using the IAT in the first place. However, we would said the participants were “conveniently selected for the agree that there is no consensus on the psychometric interview” (p. 6). properties of the IAT, because previous studies have re- Based on these aforementioned criticisms, it can be ported markedly different factor structures [23–28]. Fur- concluded that the paper by Hassan et al. [1] has many thermore, the items for the IAT were developed in 1998 methodological and conceptual weaknesses as well as in- and a number of the items are now very out of date cluding a number of assertions that were just simply and given the rise of smartphones and social media. These factually incorrect. are reasons that would weaken the rationale for using Abbreviations the IAT rather than strengthen it. IA: Internet addiction; IAD: Internet addiction disorder; IAT: Internet Addiction Hassan et al.’s study reported that over a quarter of Test their Bangladeshi sample are addicted to the internet Acknowledgements (27.1%). Given a cutoff score of 50 was used to classify None individuals as being “addicted” to the internet, it is not surprising the percentage of internet addiction was so Authors’ contributions MG wrote the initial draft of the paper based on ideas from MAM. Both high because the vast majority will not have been authors contributed to subsequent drafts. The author(s) read and approved addicted with such a low cutoff score. There is simply the final manuscript. no face validity to the claim that over one quarter of Bangladeshi adults are addicted to the internet. The ori- Funding None ginal developer of the IAT initially suggested a score of above 80 to be classed as an internet addict [29]. Hassan Availability of data and materials et al. then compared their Bangladeshi prevalence rate Not applicable to a single previous Bangladeshi study. To our know- Ethics approval and consent to participate ledge, at the time of writing, there were at least 14 previ- Not applicable ous papers specifically examining problematic internet use in Bangladesh (i.e., 10 papers on internet addiction, Consent for publication two on social media addiction, and two commentaries), Not applicable and of these, 11 papers examined online addiction Competing interests prevalence in Bangladesh, yet apart from one study [10], None none of these was referred to. Hassan et al. then com- Author details pared their IA prevalence rates to just four other seem- Psychology Department, Nottingham Trent University, 50 Shakespeare ingly arbitrary studies in four different countries (i.e., Street, Nottingham NG1 4FQ, UK. Undergraduate Research Organization, Jordan, Iran, UK, and Taiwan) out of the hundreds that Savar, Dhaka, Bangladesh. have now been published. The lack of comparison with Received: 16 May 2020 Accepted: 27 August 2020 studies that are clearly relevant (particularly previous Bangladeshi studies) is of concern. Put simply, the “Dis- cussion” did not contextualize the findings in relation to References 1. Hassan T, Alam MM, Wahab A, Hawlader MD. Prevalence and associated the most relevant studies. factors of internet addiction among young adults in Bangladesh. J Egypt Additionally, Hassan et al. claimed in their conclu- Public Health Assoc. 2020;95(1):3. sion that “[t]he prevalence of excessive internet use is 2. Young KS. Internet addiction: the emergence of a new clinical disorder. CyberPsychology Behav. 1996;1(3):237–44. significant among young adults in Bangladesh, which 3. Griffiths MD. Internet addiction: an issue for clinical psychology? Clin is conforming with the global trend” (p. 7). However, Psychol Forum. 1996;97:32–6. few studies were cited in the paper on which to base 4. Griffiths MD. Technological addictions. Clin Psychol Forum. 1995;76:14–9. 5. Griffiths MD. Netties anonymous: Are some people addicted to the such a comparison. Furthermore, “excessive internet internet? Times Higher Education Supplement; 1995. p. 17. use” is not the same as internet addiction given that 6. Xin M, Xing J, Pengfei W, Houru L, Mengcheng W, Hong Z. Online activities, many excessive internet users are not addicted to the prevalence of internet addiction and risk factors related to family and school among adolescents in China. Addict Behav Rep. 2017;7:14–8. internet (14). 7. Cheng C, Li AYL. Internet addiction prevalence and quality of (real) life: a Hassan et al. also provided what they claimed to be meta-analysis of 31 nations across seven world regions. Cyberpsychol Behav study strengths (i.e., sample from various Bangladeshi Soc Netw. 2014;17(12):755–60. Griffiths and Mamun Journal of the Egyptian Public Health Association (2020) 95:26 Page 4 of 4 8. Mamun MA, Griffiths MD. The assessment of internet addiction in Bangladesh: why are prevalence rates so different? Asian J Psychiatr. 2019; 40:46–7. 9. Mamun MA, Griffiths MD. Internet addiction and sleep quality: a response to Jahan et al. Sleep Biol Rhythms. 2019;17(4):463–4. 10. 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;6:1–11. 11. Mamun MAA, Griffiths MD. The association between Facebook addiction and depression: a pilot survey study among Bangladeshi students. Psychiatry Res. 2019;271:628–33. 12. Siddiqi SA, Islam MZ, Siddiqi KA, Haider MR. Is Facebook addiction leading adolescents to depression? Evidence from Dhaka city, Bangladesh. Value Health. 2018;21:S191. 13. Uddin MS, Mamun A, Iqbal M, Nasrullah M, Asaduzzaman M, Sarwar S, et al. Internet addiction disorder and its pathogenicity to psychological distress and depression among university students: a cross-sectional pilot study in Bangladesh. Psychology. 2016;7:1126–37. 14. Mamun MA, Rafi MA, Al Mamun AS, Hasan MZ, Akter K, Hsan K, et al. Prevalence and psychiatric risk factors of excessive internet use among Northern Bangladeshi job-seeking graduate students: a pilot study. Int J Ment Heal Addict. 2019. https://doi.org/10.1007/s11469-019-00066-5. 15. Karim AKMR, Nigar N. The Internet Addiction Test: assessing its psychometric properties in Bangladeshi culture. Asian J Psychiatr. 2014;10: 75–83. 16. Mamun MA, Hossain MS, Sikder MS, Arafat SMY, Kuss DJ, Griffiths MD. Problematic internet use in Bangladeshi students: the role of socio- demographics, depression, anxiety and stress. 2019. Asian J Psychiatr. 2014; 44:48–54. 17. Shahnaz I, Karim AR. The impact of Internet addiction on life satisfaction and life engagement in young adults. Univ J Psychol. 2014;2:273–84. 18. Khan SA. P-303-Internet addiction disorder among adolescent students. Eur Psychiatr. 2012;27(Suppl 1). Abstracts of the 20th European Congress of Psychiatry. 19. Afrin D, Islam MU, Rabbiand F, Hossain A. The school-level factors associated with internet addiction among adolescents: a cross-sectional study in Bangladesh. J Addic Depend. 2017;3(2):170–4. 20. Jahan SM, Hossain SR, Sayeed UB, Wahab A, Rahman T, Hossain A. Association between internet addiction and sleep quality among students: a cross-sectional study in Bangladesh. Sleep Biol Rhythms. 2019;17(3):323–9. 21. Mostafa A, Hoque R, Chakraborty R, Haque M, Munna MS. Internet use and addiction: a cross-sectional study to ascertain internet utilization level for academic & non-academic purpose among medical and university students of Bangladesh. Konuralp Tıp Dergisi. 2019;11(3):404–15. 22. Kuss DJ, Griffiths MD, Karila L, Billieux J. Internet addiction: a systematic review of epidemiological research for the last decade. Curr Pharm Des. 2014;20:4026–52. 23. Widyanto L, McMurran M. The psychometric properties of the Internet Addiction Test. CyberPsychol Behav. 2004;7(4):443–50. 24. Faraci P, Craparo G, Messina R, Severino S. Internet Addiction Test (IAT): which is the best factorial solution? J Med Internet Res. 2013;15(10):e225. 25. Ferraro G, Caci B, D'Amico A, Di Blasi M. Internet addiction disorder: an Italian study. CyberPsychol Behav. 2007;10(2):170–5. 26. Khazaal Y, Billieux J, Thorens G, Khan R, Louati Y, Scarlatti E, et al. French validation of the internet addiction test. CyberPsychol Behav. 2008;11(6): 703–6. 27. Watters CA, Keefer CV, Kloosterman PH, Summerfeldt LJ, Parker JDA. Examining the structure of the Internet Addiction Test in adolescents: a bifactor approach. Comput Hum Behav. 2013;29(6):2294–302. 28. Hawi NS. Arabic validation of the Internet addiction test. Cyberpsychol Behav Soc Netw. 2013;16(3):200–4. 29. Clinical Psychology and Mental Health. Internet Addiction Test. http:// huibee.com/wordpress/wp-content/uploads/2013/11/GLOBALADDICTION- Scales-InternetAddictionTest.pdf. Publisher’sNote Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the Egyptian Public Health Association Springer Journals

Internet addiction among young Bangladeshi adults: critical commentary on Hassan et al. (2020)

Loading next page...
 
/lp/springer-journals/internet-addiction-among-young-bangladeshi-adults-critical-commentary-soT0VTv0FX
Publisher
Springer Journals
Copyright
Copyright © The Author(s) 2020
eISSN
2090-262X
DOI
10.1186/s42506-020-00054-x
Publisher site
See Article on Publisher Site

Abstract

Griffiths and Mamun Journal of the Egyptian Public Health Association (2020) 95:26 Journal of the Egyptian https://doi.org/10.1186/s42506-020-00054-x Public Health Association LETTER TO THE EDITOR Open Access Internet addiction among young Bangladeshi adults: critical commentary on Hassan et al. (2020) 1* 1,2 Mark D. Griffiths and Mohammed A. Mamun Dear Editor, One of the major omissions in the paper is that in We were very interested to read the paper by Hassan their Introduction, not a single previous Bangladeshi et al. [1] examining the prevalence and associated factors study was discussed even though there are many (e.g., of internet addiction (IA) among young Bangladeshi [8–21] - see Table 1 for details), and only one was men- adults. However, there are a number of issues of concern tioned in the Discussion [10]. Hasan also claimed that: that we would like to raise. “Most of the studies conducted previously evaluated the In the first paragraph, Hassan et al. claimed that prevalence of internet addiction and its predictors in “Kimberly Young was the first to introduce the concept of adolescent samples, within the age range of 12 to 18 internet addiction disorder (IAD) in 1996” (p. 1). The years” (p. 2). There was no reference for this assertion, paper that was cited [2] was published in December and furthermore, it is simply not true. For instance, a 1996. However, it was actually Griffiths who systematic review by Kuss et al. [22] of 68 large-scale published the first paper on internet addiction in studies of IA (i.e., studies with over 1000 participants) November 1996 [3] having already published a more reported that 24 of the studies had purely adult samples, general paper on “technological addictions” in and a further 15 studies had age ranges that included February 1995 [4] as well as populist article on inter- participants over the age of 18 years. In sum, this sys- net addiction in April 1995 [5]. tematic review showed that in the large-scale studies in They then cited a paper by Xin et al. [6] to provide the period they examined, 39 of the 68 studies were not worldwide prevalence figures (who simply reported carried out on 12–18-year age group samples. prevalence data from other studies) but did not make Another major criticism of Hassan et al.’s study is their any reference to the largest meta-analysis in the IA field scoring on the scale used for IA assessment. The authors by Cheng and Li [7] who examined prevalence figures said they used the 20-item Bangla version of the Internet comprising 164 studies (N = 89,281) from 31 nations in Addiction Test (IAT) with a cutoff score of 50 (out of seven world regions, and producing a global estimate of 100) to categorize the participants as internet addicts. 6% for IA. Hassan et al. mentioned the wide variations However, the Bangla IAT is an 18-item scale (whereas in IA prevalence across different world regions but pro- the original English language IAT has 20 items) with vided no explanation as to why. Such variation in global total scores ranging from 20 to 90 [15]. The scoring for IA rates can be due to a number of factors, the most im- Bangla IAT is 18–35 for minimal use, 36–62 for moder- portant of which are arguably methodological (e.g., there ate use, and 63–90 for excessive use. However, Hassan are many different psychometric instruments used to as- et al. did not use the appropriate scoring for the sess IA, and different researchers use different cutoff validated Bangla IAT, and given they used a 20-item points even when the same instrument is being used) scale, it is not even clear if they used the Bangla IAT at [8]. These important factors were not mentioned. all (because there is no 20-item validated version). Add- itionally, they cited two papers [15, 23] who they claimed had used a cutoff score of 50 to class individuals as * Correspondence: mark.griffiths@ntu.ac.uk Psychology Department, Nottingham Trent University, 50 Shakespeare internet addicts but neither of these two psychometric Street, Nottingham NG1 4FQ, UK studies suggested 50 as a cutoff score. Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Griffiths and Mamun Journal of the Egyptian Public Health Association (2020) 95:26 Page 2 of 4 Table 1 Comparison of Bangladeshi studies examining internet addiction prevalence (in alphabetical order) Authors Study location Study Sample size Assessment tool Scale response Total scale Cutoff scores used Main findings (year published) population (age range) items Afrin et al. Chittagong High school 279 Internet Yes/no 9 < 3 = normal internet user; 2.5% severely addicted to the internet, 64.9% (2017) [19] students (14–17 years) Addiction Survey 4 to 6 = moderate internet moderately addicted to the internet user; ≤ 7 = severe user Hassan et al. Chittagong, Adults 454 Internet 5-point Likert-type 20* 20–49 points = average 27.1% prevalence of internet addiction (2020) [1] Dhaka, Sylhet (19–35 years) Addiction Test scale ranging from 1 internet user; ≥ 50 (rarely) to 5 (always) points = internet addicted Islam and Dhaka University 573 Internet 5-point Likert-type 20 ≥ 50 = moderate, excessive, 24% problematic internet users Hossin students (20–30 years) Addiction Test scale ranging from 1 or problematic internet user (2016) [10] (rarely) to 5 (always) Jahan et al. Dhaka University 390 Internet Yes/no 9 < 3 = normal internet user; 31.5% normal users, 49.2% moderately (2019) [20] medical (18–26 years) Addiction Survey 4 to 6 = moderate internet addicted users, and 19.3% severely internet students user; ≤ 7 = severe user addicted users Karim and Dhaka University 177 Bangla Internet 5-point Likert-type 18 < 36 = minimal internet 63.95% minimal internet users, 34.3% Nigar students (18–25 years) Addiction Test scale ranging from 1 user; 36–62 = moderate moderate internet users, 1.7% excessive internet (2014) [15] (rarely) to 5 (always) internet user; > 62 = users excessive internet user Khan Dhaka High school 797 Internet 5-point Likert-type 20 Not reported 20.20% reported as having “internet addiction (2012) [18] students (mean age = Addiction Test scale ranging from 1 disorder” 16.5 years) (rarely) to 5 (always) Mamun and Dhaka University 300 Bergen Facebook 5-point Likert-type 6 ≥ 18 = at risk of being 39.7% at risk of being addicted to Facebook Griffiths students (mean age = Addiction Scale scale ranging from 1 addicted to Facebook (2019) [11] 20.7 years) (very rarely) to 5 (very often) Mamun et al. Dhaka University 405 Internet 5-point Likert-type 20 ≥ 50 = moderate to high 32.6% problematic internet users (2019) [16] students (mean age = Addiction Test scale ranging from 1 or problematic internet user 20.2 years) (rarely) to 5 (always) Mamun et al. Rajshahi Graduated 284 Internet 5-point Likert-type 20 < 60 = non-excessive 0% internet addicts, but 3.9% classed as (2019) [14] students (mean age = Addiction Test scale ranging from 1 internet users; ≥ 60 = excessive users 21.1 years) (rarely) to 5 (always) excessive internet users Mostafa et al. Chittagong Medical and 379 Internet 5-point Likert-type 20 < 20 = normal internet user; Majority of the participants were mild (2019) [21] university (18–30 years) Addiction Test scale ranging from 1 20–49 = mild internet user; problematic users (54.88%). The prevalence of students (rarely) to 5 (always) 50–79 = moderate internet internet addiction was 1.06% (severe users) user; 80–100 = severe internet user Siddiqi et al. Dhaka High school 376 Bergen Facebook 5-point Likert-type 6** ≥ 20 = at risk of being 49.8%, 41.0%, and 1.6% mild, moderate, and (2018) [12] students (13–19 years) Addiction Scale scale ranging from 1 addicted to Facebook severe Facebook addicted and 7.6% non- (rarely) to 5 (always) problematic users Uddin et al. Dhaka University 475 Internet 5-point Likert-type 20 ≤ 30 = normal internet 47.7% male and 44.5% female students severely (2016) [13] students (18–25 years) Addiction Test scale ranging from 1 user; 31–49 = mild internet addicted to the internet, 7.1% male and 33.9% (rarely) to 5 (always) user; 50–79 = moderate female students moderately addicted to the internet user; ≥ 80 = severe internet, 20.7% male and 7.7% female students or excessive internet user mildly addicted to the internet *Paper said the Bangla Internet Addiction Test was used, but based on the description, it appears the Internet Addiction Test was used **Paper claimed there were 18 items, but the Bergen Facebook Addiction Scale only has six items Griffiths and Mamun Journal of the Egyptian Public Health Association (2020) 95:26 Page 3 of 4 Hassan et al. also wrote that the IAT “has been scien- regions, utilizing a “high number of sociodemographic tifically analyzed to state an ambiguous psychometric variables as well as variables related to internet use be- factor structure” (p. 2). On reading this, we were unsure havior and regular activity” [p. 6]) without referring to whether the authors deliberately meant to say the factor any previous Bangladeshi papers as a benchmark. Con- structure was “ambiguous” (as opposed to “unambigu- fusingly, the authors used a survey to collect the data, ous”) given that they were putting forward the rationale but then in the 'Strengths and limitations' section, they for using the IAT in the first place. However, we would said the participants were “conveniently selected for the agree that there is no consensus on the psychometric interview” (p. 6). properties of the IAT, because previous studies have re- Based on these aforementioned criticisms, it can be ported markedly different factor structures [23–28]. Fur- concluded that the paper by Hassan et al. [1] has many thermore, the items for the IAT were developed in 1998 methodological and conceptual weaknesses as well as in- and a number of the items are now very out of date cluding a number of assertions that were just simply and given the rise of smartphones and social media. These factually incorrect. are reasons that would weaken the rationale for using Abbreviations the IAT rather than strengthen it. IA: Internet addiction; IAD: Internet addiction disorder; IAT: Internet Addiction Hassan et al.’s study reported that over a quarter of Test their Bangladeshi sample are addicted to the internet Acknowledgements (27.1%). Given a cutoff score of 50 was used to classify None individuals as being “addicted” to the internet, it is not surprising the percentage of internet addiction was so Authors’ contributions MG wrote the initial draft of the paper based on ideas from MAM. Both high because the vast majority will not have been authors contributed to subsequent drafts. The author(s) read and approved addicted with such a low cutoff score. There is simply the final manuscript. no face validity to the claim that over one quarter of Bangladeshi adults are addicted to the internet. The ori- Funding None ginal developer of the IAT initially suggested a score of above 80 to be classed as an internet addict [29]. Hassan Availability of data and materials et al. then compared their Bangladeshi prevalence rate Not applicable to a single previous Bangladeshi study. To our know- Ethics approval and consent to participate ledge, at the time of writing, there were at least 14 previ- Not applicable ous papers specifically examining problematic internet use in Bangladesh (i.e., 10 papers on internet addiction, Consent for publication two on social media addiction, and two commentaries), Not applicable and of these, 11 papers examined online addiction Competing interests prevalence in Bangladesh, yet apart from one study [10], None none of these was referred to. Hassan et al. then com- Author details pared their IA prevalence rates to just four other seem- Psychology Department, Nottingham Trent University, 50 Shakespeare ingly arbitrary studies in four different countries (i.e., Street, Nottingham NG1 4FQ, UK. Undergraduate Research Organization, Jordan, Iran, UK, and Taiwan) out of the hundreds that Savar, Dhaka, Bangladesh. have now been published. The lack of comparison with Received: 16 May 2020 Accepted: 27 August 2020 studies that are clearly relevant (particularly previous Bangladeshi studies) is of concern. Put simply, the “Dis- cussion” did not contextualize the findings in relation to References 1. Hassan T, Alam MM, Wahab A, Hawlader MD. Prevalence and associated the most relevant studies. factors of internet addiction among young adults in Bangladesh. J Egypt Additionally, Hassan et al. claimed in their conclu- Public Health Assoc. 2020;95(1):3. sion that “[t]he prevalence of excessive internet use is 2. Young KS. Internet addiction: the emergence of a new clinical disorder. CyberPsychology Behav. 1996;1(3):237–44. significant among young adults in Bangladesh, which 3. Griffiths MD. Internet addiction: an issue for clinical psychology? Clin is conforming with the global trend” (p. 7). However, Psychol Forum. 1996;97:32–6. few studies were cited in the paper on which to base 4. Griffiths MD. Technological addictions. Clin Psychol Forum. 1995;76:14–9. 5. Griffiths MD. Netties anonymous: Are some people addicted to the such a comparison. Furthermore, “excessive internet internet? Times Higher Education Supplement; 1995. p. 17. use” is not the same as internet addiction given that 6. Xin M, Xing J, Pengfei W, Houru L, Mengcheng W, Hong Z. Online activities, many excessive internet users are not addicted to the prevalence of internet addiction and risk factors related to family and school among adolescents in China. Addict Behav Rep. 2017;7:14–8. internet (14). 7. Cheng C, Li AYL. Internet addiction prevalence and quality of (real) life: a Hassan et al. also provided what they claimed to be meta-analysis of 31 nations across seven world regions. Cyberpsychol Behav study strengths (i.e., sample from various Bangladeshi Soc Netw. 2014;17(12):755–60. Griffiths and Mamun Journal of the Egyptian Public Health Association (2020) 95:26 Page 4 of 4 8. Mamun MA, Griffiths MD. The assessment of internet addiction in Bangladesh: why are prevalence rates so different? Asian J Psychiatr. 2019; 40:46–7. 9. Mamun MA, Griffiths MD. Internet addiction and sleep quality: a response to Jahan et al. Sleep Biol Rhythms. 2019;17(4):463–4. 10. 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;6:1–11. 11. Mamun MAA, Griffiths MD. The association between Facebook addiction and depression: a pilot survey study among Bangladeshi students. Psychiatry Res. 2019;271:628–33. 12. Siddiqi SA, Islam MZ, Siddiqi KA, Haider MR. Is Facebook addiction leading adolescents to depression? Evidence from Dhaka city, Bangladesh. Value Health. 2018;21:S191. 13. Uddin MS, Mamun A, Iqbal M, Nasrullah M, Asaduzzaman M, Sarwar S, et al. Internet addiction disorder and its pathogenicity to psychological distress and depression among university students: a cross-sectional pilot study in Bangladesh. Psychology. 2016;7:1126–37. 14. Mamun MA, Rafi MA, Al Mamun AS, Hasan MZ, Akter K, Hsan K, et al. Prevalence and psychiatric risk factors of excessive internet use among Northern Bangladeshi job-seeking graduate students: a pilot study. Int J Ment Heal Addict. 2019. https://doi.org/10.1007/s11469-019-00066-5. 15. Karim AKMR, Nigar N. The Internet Addiction Test: assessing its psychometric properties in Bangladeshi culture. Asian J Psychiatr. 2014;10: 75–83. 16. Mamun MA, Hossain MS, Sikder MS, Arafat SMY, Kuss DJ, Griffiths MD. Problematic internet use in Bangladeshi students: the role of socio- demographics, depression, anxiety and stress. 2019. Asian J Psychiatr. 2014; 44:48–54. 17. Shahnaz I, Karim AR. The impact of Internet addiction on life satisfaction and life engagement in young adults. Univ J Psychol. 2014;2:273–84. 18. Khan SA. P-303-Internet addiction disorder among adolescent students. Eur Psychiatr. 2012;27(Suppl 1). Abstracts of the 20th European Congress of Psychiatry. 19. Afrin D, Islam MU, Rabbiand F, Hossain A. The school-level factors associated with internet addiction among adolescents: a cross-sectional study in Bangladesh. J Addic Depend. 2017;3(2):170–4. 20. Jahan SM, Hossain SR, Sayeed UB, Wahab A, Rahman T, Hossain A. Association between internet addiction and sleep quality among students: a cross-sectional study in Bangladesh. Sleep Biol Rhythms. 2019;17(3):323–9. 21. Mostafa A, Hoque R, Chakraborty R, Haque M, Munna MS. Internet use and addiction: a cross-sectional study to ascertain internet utilization level for academic & non-academic purpose among medical and university students of Bangladesh. Konuralp Tıp Dergisi. 2019;11(3):404–15. 22. Kuss DJ, Griffiths MD, Karila L, Billieux J. Internet addiction: a systematic review of epidemiological research for the last decade. Curr Pharm Des. 2014;20:4026–52. 23. Widyanto L, McMurran M. The psychometric properties of the Internet Addiction Test. CyberPsychol Behav. 2004;7(4):443–50. 24. Faraci P, Craparo G, Messina R, Severino S. Internet Addiction Test (IAT): which is the best factorial solution? J Med Internet Res. 2013;15(10):e225. 25. Ferraro G, Caci B, D'Amico A, Di Blasi M. Internet addiction disorder: an Italian study. CyberPsychol Behav. 2007;10(2):170–5. 26. Khazaal Y, Billieux J, Thorens G, Khan R, Louati Y, Scarlatti E, et al. French validation of the internet addiction test. CyberPsychol Behav. 2008;11(6): 703–6. 27. Watters CA, Keefer CV, Kloosterman PH, Summerfeldt LJ, Parker JDA. Examining the structure of the Internet Addiction Test in adolescents: a bifactor approach. Comput Hum Behav. 2013;29(6):2294–302. 28. Hawi NS. Arabic validation of the Internet addiction test. Cyberpsychol Behav Soc Netw. 2013;16(3):200–4. 29. Clinical Psychology and Mental Health. Internet Addiction Test. http:// huibee.com/wordpress/wp-content/uploads/2013/11/GLOBALADDICTION- Scales-InternetAddictionTest.pdf. Publisher’sNote Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Journal

Journal of the Egyptian Public Health AssociationSpringer Journals

Published: Sep 29, 2020

There are no references for this article.