Available online at www.sciencedirect.com Problematic use of the Internet in low- and middle- income countries before and during the COVID-19 pandemic: a scoping review Biljana Gjoneska1, Marc N Potenza2,3,4,5, Julia Jones6,*, Célia MD Sales7,8, Georgi Hranov9 and Zsolt Demetrovics10,11,* People from low- and middle-income countries (LMICs) represent large portions of the world population, often occupy less favorable living conditions, and typically suffer greater health risks, yet frequently receive little research and global health attention. The present study reviews emerging evidence on problematic use of the Internet (PUI) in LMICs prior/during the COVID-19 pandemic. Analyzed studies mainly focused on general properties of PUI in university students, problematic gaming in youth, or problematic use of social media in adults, registering higher prevalence estimates, as compared with earlier reports. Research mainly focused on initially affected regions and COVID-exposed populations. Overall, unfavorable circumstances, including poor social support, family relationships, and lifestyle tendencies/habits, may present potential risk for PUI in LMICs, likely exacerbated during the pandemic. Addresses 1 Macedonian Academy of Sciences and Arts, Krste Misirkov 2, 1000 Skopje, North Macedonia 2 Department of Psychiatry and Child Study Center, Yale University School of Medicine, New Haven, CT 06511, United States 3 Department of Neuroscience and Wu Tsai Institute, Yale University, New Haven, CT 06510, United States 4 Connecticut Mental Health Centre, New Haven, CT 06519, United States 5 Connecticut Council on Problem Gambling, Wethersfield, CT 06109, United States 6 Centre for Research in Public Health and Community Care, University of Hertfordshire, Hatfield AL10 9AB, United Kingdom 7 Centre for Psychology, University of Porto, R. Alfredo Allen, 4200-135 Porto, Portugal 8 Faculty of Psychology and Education Sciences, University of Porto, R. Alfredo Allen, 4200-135 Porto, Portugal 9 Military Medical Academy, Sofia, Bulgaria 10 Centre of Excellence in Responsible Gaming, University of Gibraltar, Gibraltar, Gibraltar 11 Institute of Psychology, ELTE Eötvös Loránd University, Budapest, Hungary Corresponding authors: Biljana Gjoneska (biljanagjoneska@manu.edu.mk), Zsolt Demetrovics (zsolt.demetrovics@unigib.edu.gi) * Twitter account: @JJonesatherts, @Demetrovics Current Opinion in Behavioral Sciences 2022, 48:101208 This review comes from a themed issue on Internet Addiction Edited by Naomi Fineberg and Marc Potenza Available online 29 July 2022 https://doi.org/10.1016/j.cobeha.2022.101208 2352-1546/© 2022 The Authors. Published by Elsevier Ltd. Introduction The largest [1] and fastest-growing [2] portion of the world population currently comprises 84.3% of all people and resides in low- and middle-income countries (LMICs) [3]. In comparison with high-income countries, people in LMICs typically occupy less favorable living conditions and live in societies with lower levels of wealth, health, and education [4]. As a result, they are more likely to experience mental health problems during a global health crisis, yet they receive relatively few global health resources [5•]. The risk for mental health concerns and increased use of the Internet during the COVID-19 pandemic may be more pronounced in vulnerable populations and manifested as excessive, maladaptive, or problematic use of the Internet (PUI). Disease-related anxieties and fears, economic in- securities, and financial losses, as well the desire to re- duce emotional distress during the pandemic, may all contribute to increased risk for PUI in vulnerable po- pulations, regardless of the country or world re- gion [6••,7]. To date, comparatively little is known about the mental health of people in LMICs as most psychological re- search has been conducted on narrow populations from ]]]] ]]]]]] www.sciencedirect.com Current Opinion in Behavioral Sciences 48( 2022) 101208 countries with established research infrastructures and abundant resources, often referred to as Western, edu- cated, industrialized, rich, and democratic countries and populations [8]. This potentially generates an im- balanced global perspective that lacks sufficient insight into the circumstances of the less-developed countries. The present article aims to contribute to fill this knowledge gap and reviews recent data on PUI in LMICs during the period that preceded or coincided with the COVID-19 pandemic, summarizing studies published between 2018 and 2021. Specifically, we aim to provide a broad overview on PUI-related areas of in- vestigation, frequently employed measures and explored populations, and countries or regions in LMICs for the appointed periods. The findings presented in this review stem from original research articles and are considered with respect to more comprehensive articles (reviews and meta-analyses) on more general topics (such as mental health, PUI, COVID-19, LMICs, and regions), thus providing more complete coverage. Methods A broader collection of related studies was retrieved with a search strategy (see Figure 1) that was designed to include articles in accordance with the following criteria: a) The search was conducted via specialized academic databases, covering literature in the matching areas of interest from both biomedical and psychological do- mains (PubMed and APA PsycInfo). Figure 1 Current Opinion in Behavioral Sciences Conceptual framework of the search strategy and criteria for selection of relevant articles on PUI in LMICs in the period preceding or coinciding with the COVID-19 pandemic (2018–2021). The search strategy included original research studies, published two years prior and two years into the pandemic. The search criteria included a combination of terms or phrases pertaining to the topics of interest: problematic use, Internet activity, and low- or middle-income countries. The search procedure was conducted via two academic databases, covering literature in the matching areas of interest from both the biomedical and the psychological domain (PubMed and APA PsycInfo). The list of LMICs (low-income, lower–middle-, and upper–middle-income countries) is based upon the latest criteria and classifications by the World Bank. Data Source: World Bank Data Help Desk; URL: https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and- lending-groups. 2 Internet Addiction www.sciencedirect.com Current Opinion in Behavioral Sciences 48( 2022) 101208 b) The period of publication spanned between 2018 and 2021, covering studies from two years prior and two years into the COVID-19 pandemic. c) The studies of interest originated from low-income, lower–middle-income, and upper–middle-income countries (in accordance with the latest classifications by the World Bank). d) The searched keywords were terms and phrases that pertain to the topics of interest: problematic use, Internet activity, and low-income or middle-income countries. The search was performed by the title of the original research articles. The broader collection of retrieved studies was then reduced to the most relevant studies (see Figure 2), after exclusion of articles in accordance with the following criteria: a) Duplicates, or articles with similar reports (regarding used samples and methods) in different academic outlets. b) Studies on topics that were outside the specific scope of interest. c) Studies from countries that were outside the target list. d) Studies conducted outside the target period and/or studies published in languages other than EN. e) Studies with insufficient data regarding the study period and the methodologies used. The organization of work throughout the selection pro- cess was conducted in two phases. In Phase 1, the initial selection was performed by the first author (BG) and supervised by the last author (ZD) on the basis of search criteria that were previously agreed upon by all authors (Figure 1). In Phase 2, the prefinalized selection, in- formed by international standards for review studies and meta-analyses (Figure 2), was reviewed separately by the remainder of the authors (MNP, JJ, CMDS, and GH). The individual evaluations sought to promote unbiased feedback and objective reporting of the results. Four additional studies were identified in this process, and included in the final selection as relevant for the current review (Figure 2). Ultimately, 69 studies were reviewed, and findings were organized according to most fre- quently researched topics (PUI in general, problematic gaming, or problematic use of social media), investigated populations, frequently employed measures, reported prevalence estimates, potential risk factors (see Table 1 for a summary of studies and findings), and geographical regions (see Table 2 for the global distribution of stu- dies). Reports on problematic gambling in LMICs were excluded from the final review since they predominantly explored on-site, rather than online, gambling. Results and discussion PUI is a relatively recent phenomenon, and many LMICs still lack resources or policies to properly un- derstand or address PUI [9]. The need for a broader outlook and more general understanding of PUI in Figure 2 Current Opinion in Behavioral Sciences A flow diagram depicting selection of relevant studies on PUI in LMICs in the period preceding or coinciding with the COVID-19 pandemic (2018–2021). Reports on problematic gambling in LMICs were excluded from the final review since they predominantly explored on-site, rather than online gambling. The diagram was informed by the standards for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRIS. Data Source: PRISMA, 2020. URL: http://www.prisma-statement.org/. A review on PUI in LIMCs prior and during COVID-19 Gjoneska et al. 3 www.sciencedirect.com Current Opinion in Behavioral Sciences 48( 2022) 101208 Table 1 Summary of reviewed studies and reported findings on PUI in LMICs in the period preceding or coinciding with the pandemic [10- 14,16,19-28,30-91] . GPIUS2= Generalized Problematic Internet Use Scale 2; IGD = Internet Gaming Disorder Test; SMUQ = Social Media Use Questionnaire; YDQ = Young Diagnostic Questionnaire; CIAS = Chinese Internet Addiction Scale; IGCS = Internet Gaming Cognition Scale. Referencing styles: Alphabetical and numerical. Referencing order: Where applicable, the studies are ordered by the year of publication (primary criteria), the al- phabetic order of the first author’s name (secondary criteria), and the sequential order in the bibliography (tertiary criteria). 1The population categories reflect a combination of age and the manner in which the cohorts were defined in each study. In general, children are youth attending elementary school (aged approximately 7–10 years), adolescents are youth attending middle or high school (aged approximately 4 Internet Addiction www.sciencedirect.com Current Opinion in Behavioral Sciences 48( 2022) 101208 LMICs is reflected in the fact that most studies focused on exploring the general properties and correlates of PUI (n = 46). A smaller number of studies explored specific characteristics of problematic use of social media (n = 14) and problematic gaming (n = 9) in LMICs (see Table 1: ‘Gaming’ and ‘Social media’ columns, 1–8 rows). With one notable exception that provided qualitative evidence [10], the remainder of the reviewed studies were quantitative, reporting findings that were based on survey methodologies and statistical analyses. Also, three longitudinal studies [11–13•] presented exceptions to the overwhelming body of cross-sectional research. The sample sizes varied considerably across studies, ranging between 200 and 20 000 participants, with an average size of around 2000 and a median size of approximately 750 participants per study. The most frequently re- presented populations also differed across research to- pics, depending on whether studies explored PUI in general, problematic gaming, or problematic use of social media. For more information regarding the study topics and types, methodologies, populations, and findings, please see the following sections of this paper. An overview of problematic use of the Internet in low- and middle-income countries Generalized PUI was mainly assessed using convenience samples, with half of the studies (23 of 46 publications) surveying young adults attending universities or colleges (participants aged approximately 18–25 years). Approximately half of the studies investigating gen- eralized PUI (22 of 46 studies) utilized the Internet Addiction Test (IAT) [14], a 20-item survey with 0–5- point Likert-type responses and 0–100 score range. The IAT was used to quantify self-reported preoccupation and compulsive use of the Internet, as well as behavioral problems, emotional changes, and diminished function- ality due to Internet use. The measure has been re- ported to have relatively “high internal consistency reliability within homogenous samples (α = 0.90–0.93), test–retest reliability (ρ = 0.83), and a relatively simple factor structure of between one and two dimensions” [15•]. However, lately, the IAT has been subject to academic criticism regarding its psychometric properties. Some of the identified issues pertain to potentially re- dundant or outdated items, an unstable factor structure, arbitrary cutoff scores, and possible lack of universal validity [15•], so research may shift toward newer scales with better psychometric properties, such as the Com- pulsive Internet Use Scale (CIUS) [16]. However, this trend is still not evident in the latest research on PUI across LMICs. A considerable number of studies relied on IAT, while others relied on the average number of daily hours spent on the Internet as a rough estimation of PUI. Only a small group of studies relied on more targeted instruments (see Table 1 for the lists of as- sessment instruments that were used most frequently). A frequently used cutoff score (≥50) for the IAT was considered for PUI in the present review (even though cutoff scores often differed across studies and the pre- valence rates varied accordingly). Wherever applicable, the prevalence rate for the conventional cutoff score in healthy (control) individuals was extracted from the ori- ginal report, to calculate an average prevalence estimate for PUI among the general population in LMICs. The final average rates (34.6%) and median prevalence esti- mates (31.0%) were retrieved on the basis of reports from 19 studies. The average prevalence rate in particular was considerably higher than earlier estimates, obtained from large samples with 89 281 participants [17] and 693 306 participants [18••] in 31 nations (6.0% and 7.0% accord- ingly). Such a discrepancy may reflect contextual factors, such as the time period and region. Namely, earlier meta- analyses relied on studies that were published in earlier time periods, considerably before the onset of the COVID-19 pandemic (1996–2012 and 1996–2018, re- spectively). On the other hand, the present review scopes evidence for the period shortly preceding and coinciding with the COVID-19 pandemic (2018–2021), which is marked by a global expansion of Internet use. Regarding the regional analysis, earlier studies have indicated that the prevalence estimates are likely higher in Eastern re- gions (10.9% and 8.9%, respectively) [17,18••] and socie- ties with disadvantaged living conditions or dissatisfied populations [17]. Considerable [17,18••] differences in prevalence estimates between the present and the two referenced studies may also be technical in nature and attributable to the frequently used conventional cutoff score (IAT ≥50) being more inclusive than a stricter one (IAT ≥60) [18••]. In addition, several articles in the pre- sent review utilized the IAT to assess generalized PUI in children and adolescents [19,20], despite the IAT having been developed for assessing PUI in young and healthy 11–17 years), young adults attending universities or colleges (aged approximately 18–25 years), while other adults are aged approximately 26 years or higher. 2The before/during period refers to the years that preceded (2018–2019) or coincided (2020–2021) with the COVID-19 pandemic. 3The time spent online was measured as an average number of hours per day for the corresponding PUI activity. 4The prevalence estimates were measured with frequently used scales, while the conventional cutoff scores pertain to frequently used criteria. In studies relying on different criteria, the prevalence rates for the conventional cutoff scores among healthy individuals were extracted from the information provided in the articles. 5The summaries highlight frequently reported risk factors across reviewed studies for each of the PUI types. A review on PUI in LIMCs prior and during COVID-19 Gjoneska et al. 5 www.sciencedirect.com Current Opinion in Behavioral Sciences 48( 2022) 101208 adults. Younger and more vulnerable populations may be more susceptible to PUI behaviors, and this may in part explain the higher scores. In this regard, research on problematic gaming has ex- plored almost exclusively effects on younger populations, comprised of youth attending elementary school (aged approximately 7–10 years) or middle or high school (aged approximately 11–17 years). Eight (of 10) studies focused on problematic gaming, and prevalence rates were fre- quently estimated using a nine-item checklist by the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) [21]. Overall, prevalence estimates of problematic gaming across five studies, ranged between 11.7% and 40.0%, while the average prevalence was estimated at 18.4%. This value is higher than an earlier estimate of Table 2 Cumulative number of original research articles on PUI in LMICs across different world regions, in the period preceding or coinciding with the COVID-19 pandemic [10-13,19,20,22-24,26-28,30-47,49-57,59-61,63-69,71-86•]. Data source: World Bank Data Help Desk; URL: https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and- lending-groups. aThe list of world regions is based upon the latest classifications by the World Bank. 6 Internet Addiction www.sciencedirect.com Current Opinion in Behavioral Sciences 48( 2022) 101208 2.47% from a comprehensive meta-analysis [18••]. More- over, studies on problematic gaming have largely focused on parent–child relationships, examining roles of parenting styles or Internet mediation strategies on gaming behaviors in children and adolescents. Poor family relations and poor parental education, dysfunctional families, lack of parental supervision, and overly permissive maternal mediation strategies for Internet use of children were recurring de- terminants associated with problematic gaming [22–24]. Problematic use of social media has been explored in dif- ferent populations (mainly adults and young adults), in multiple ways (mainly via quantity of social media use and the Bergen Social Media Addiction Scale (BSMAS) [25]), and in different contexts (mainly the COVID-19 pan- demic). Hence, it is difficult to identify common patterns and draw general conclusions (see Table 1). Nonetheless, the use of social media may have been beneficial during the COVID-19 pandemic, possibly serving as a corrective force that enabled more efficient health communication with safe and timely delivery of information that was provided by close and reliable sources [26]. Protective behaviors and self-efficacy of people may have increased as a result [27], while feelings of impending threat, anxiety, and depression decreased in some instances [28]. However, a larger body of research conducted during the same period describes the opposite (positive) relationship between the increased use of social media (usually more than 2–3 hours/ day) and associated concerns among youth [12•,13•] and adults (see the next section for more details). Problematic use of the Internet in low- and middle-income countries during the COVID- 19 pandemic Research conducted during the COVID-19 pandemic mainly stems from initially affected regions, with most studies (21 of 35) conducted in East Asia. In fact, the in- tensity of research of PUI in East-Asian countries nearly doubled in the years coinciding with the pandemic (2020–2021), as compared with the years that preceded the pandemic (2018–2019). This was not the case with the rest-of-the-world regions (see Table 2). China was a re- gional leader in research on the subject, exploring multiple PUI behaviors in different contextual settings and popu- lations during the pandemic. Overall, prevalence estimates of PUI types in Eastern countries were higher than those previously reported. There is recent evidence to suggest that the prevalence estimates in Southeast Asia are higher than in other jurisdictions, but the findings stem from a single meta-analysis performed on nonrepresentative po- pulations [29]. Hence, the present review may provide a more nuanced and better understanding of the situation in regions that were initially affected by the pandemic. In addition to citizens from affected regions, other po- pulations exposed early to the virus also received considerable scholarly attention. These included med- ical and nursing students [11,30–34], medical residents, and doctors and nurses, among others [35–37]. However, the list of comorbidities frequently associated with PUI during the pandemic appears similar for medical and general populations. The problems ranged from ampli- fied levels of stress and pronounced traumatic experi- ences, including depression [19,36–43], anxiety [12•,31,37,44–47], or post-traumatic stress disorder [48] (in which case, the link with PUI was established due to increased exposure to distressing content and disin- formation on the Internet), to problems associated with instant gratification and stimulation such as substance use [49] and attention-deficit/hyperactivity [50] dis- orders. Across different research topics and contexts, findings suggest that PUI behaviors link to various potential risk factors, broadly categorized as demographic character- istics, personality features, coping styles, parenting strategies, social surroundings, and lifestyle tendencies/ habits (see Table 1, section ‘Findings’). Importantly, poor lifestyle tendencies/habits, living conditions, and negative coping styles appear implicated across different types of PUI and LMICs. For instance, poor quantity and quality of sleep (characterized by insufficient sleep hours or disorganized sleeping patterns with inadequate or irregular sleeping periods, and manifested as daytime sleepiness or even insomnia) was repeatedly described as a possible cause or a consequence of PUI during the pandemic [35,51,52]. Lack of physical activities (e.g. exercise and outdoor recreation) [53] and physical dis- comfort (e.g. headaches, back pains, and finger numb- ness) were also associated with PUI [54]. Prolonged exposure to inaccurate or distressing content on the In- ternet was also associated with PUI [30,55]. Regarding negative coping styles, feelings of boredom, isolation, and loneliness, coupled with a lack of social or emotional support from family and friends during long periods of quarantine and lockdown, were often associated with general and the specific forms of PUI [24,44,55]. Limitations In line with journal aims, the present review focused on recent studies (conducted in the period around the COVID-19 pandemic) and aimed to present findings in a condensed format (offering a snapshot of PUI in LMICs). To achieve this end, the authors performed targeted searches by article titles in bibliographic databases with matching areas of interest. Future studies could benefit from expanded searches covering longer periods (e.g. last five or ten years of research), and extending across dif- ferent article fields (e.g. keywords, title, abstract, body of text, or combinations thereof), as well as additional aca- demic databases (e.g. Web of Science or Scopus). In es- sence, the present review scopes the existing evidence A review on PUI in LIMCs prior and during COVID-19 Gjoneska et al. 7 www.sciencedirect.com Current Opinion in Behavioral Sciences 48( 2022) 101208 and synthesizes recent findings, thus serving as a useful precursor for future reviews that could systematically as- sess the quality and quantity of accumulated knowledge and propose viable solutions. Conclusions The present study provides evidence on PUI in LMICs shortly before, and during, the COVID-19 pandemic. The articles reviewed mainly focused on the generalized PUI in university students, problematic gaming among children and adolescents, or problematic use of social media in adults, with most reporting higher-than-average prevalence estimates, as compared with earlier studies. Research covering PUI during the COVID-19 pandemic nearly doubled in the initially affected geographical re- gions and populations that were first exposed to the novel coronavirus. Overall, unfavorable conditions asso- ciated with poor lifestyle tendencies/habits, social sup- port, and family relationships may represent risk factors for PUI in LMICs before and during the pandemic. This paper reviews a modest body of knowledge from less-represented countries, thus contributing to a more comprehensive and balanced view of PUI across dif- ferent geopolitical, social, and cultural contexts. The summary of findings may inform and inspire future re- search and policy strategies across concerned regions, countries, or populations, to mitigate PUI. Editorial disclosure statement Given his role as Guest Editor, Marc Potenza had no involvement in the peer-review of this article and has no access to information regarding its peer-review. Full re- sponsibility for the editorial process for this article was delegated to Naomi Fineberg. Conflict of interest statement MNP reports no conflicts of interest with respect to the content of this manuscript. MNP has consulted for and advised Game Day Data, the Addiction Policy Forum, AXA, Idorsia and Opiant/Lakelight Therapeutics; has been involved in a patent application with Yale University and Novartis; received research support from the Veteran’s Administration, Mohegan Sun Casino and the National Center for Responsible Gaming (now the International Center for Responsible Gaming); partici- pated in surveys, mailings, or telephone consultations related to drug addiction, impulse-control disorders, or other health topics; consulted for law offices, the federal public defender’s office and gambling entities on issues related to impulse-control and addictive disorders; pro- vided clinical care in the Connecticut Department of Mental Health and Addiction Services Problem Gambling Services Program; performed grant reviews for the National Institutes of Health and other agencies; edited journals and journal sections; given academic lectures in grand rounds, CME events, and other clin- ical/scientific venues; and generated books or chapters for publishers of mental health texts. ZD reports no conflicts of interest with respect to the content of this manuscript. ZD’s contribution was sup- ported by the Hungarian National Research, Development and Innovation Office (KKP126835; K128614; K134807). The ELTE Eötvös Loránd University receives funding from the Szerencsejáték Ltd. to maintain a telephone helpline service for pro- blematic gambling. ZD has also been involved in re- search on responsible gambling funded by Szerencsejáték Ltd. and the Gambling Supervision Board and provided educational materials for the Szerencsejáték Ltd’s responsible gambling program. The University of Gibraltar receives funding from the Gibraltar Gambling Care Foundation. ZD has been member of a WHO advisory group on the public health consequences of addictive behaviors. In this capacity he has been eligible for travel support from WHO or the host center to attend advisory group meetings but have not been remunerated for their work. However, these funding aren’t related to this study and the funding in- stitution had no role or any influence on this publication. The other authors (BG, JJ, CMDS and GH) report no disclosures. The views presented in this manuscript re- present those of the authors and not necessarily those of the funding agencies. Data Availability No data were used for the research described in the ar- ticle. Acknowledgements The publication of this work is kindly supported by the Hungarian National Consortium (Electronic Information Service National Programme, EISZ). ZD’s contribution was supported by the Hungarian National Research, Development and Innovation Office (KKP126835; K128614; K134807). MNP was supported by the Connecticut Council on Problem Gambling, Children and Screens, and the National Institute of Mental Health RF1 MH128614. 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