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: Volume 1, Issue 1

Prevalence and Associated Factors of Internet Gaming Disorder in Africa: A Web-Based Cross-Sectional Survey

Author(s) : Valerie Munyeti 1 and Samuel Ojuade 2

1 Department of Clinical Psychology , Daystar University , Kenya

2 Department of Clinical Psychology , African International University , Kenya

J Addict Psychiatr Ment Health

Article Type : Research Article

 

Abstract

Background: In today’s digital world, internet use across ages is increasingly becoming worrisome. Internet Gaming Disorder (IGD) was an emerging potential psychiatric diagnosis, as American Psychological Association recommended further studies on the phenomenon so as to include in the fourth coming DSM-6 as a full diagnostic condition. The purpose of this study was to establish the prevalence and associated factors of IGD in Africa.

Methods: This study was a web-based cross-sectional survey of 728 eligible respondents who assented to participate via google form. The samples were drawn from four major regions in Africa; Eastern Africa (416, 57.1%), Western Africa (129, 17.7%), Southern Africa (93, 12.8%) and Northern Africa (90, 12.4%). Researcher-generated socio-demographic questionnaire and Internet Gaming Disorder Scale-Short Form (IGDS9-SF) were used in English language to collect data from the participants.

Results: Result from this study indicated that the general prevalence of IGD among African respondents aged 10 to 50 years was 22.4%. However, the prevalence of IGD was found to be higher among participants aged 10-22 years at 14.3%, male participants at 13.5%, East Africa at 8.2%, and among participants whose family was perceived to be averagely functional at 9.2%. In addition, Binary Logistic Regression analysis showed that participants aged 10-22 years (AOR: 2.35; 95% CI: 0.881-6.294), respondents who came from Western Africa (AOR: 3.26; 95% CI: 1.963-5.411), respondents whose family economic status was considered to be very poor (AOR: 1.68; 95% CI: 0.465-6.072), and respondents who perceived their family functionality chaotic/dysfunctional (AOR: 2.83; 95% CI: 1.051-7.602) were at risk of internet gaming disorder. Also, being female (AOR: .93; 95% CI: 0.631-1.368), Average family economic status (AOR: 0.80; 95% CI: 0.276-2.328) and rich family economic status (AOR: 0.79; 95% CI: 0.240-2.652) were seen to be protective factors in this present study.

Conclusion: This study concluded that IGD is indeed a public phenomenon especially in Africa, and a call to mental health providers to consider preventive measures and that interventions should particularly target those with increased risk of developing the newly emerged disorder.

Keywords: Prevalence; Risk and Protective Factors; Internet Gaming Disorders; Africa

Description

 

Introduction

Internet use and especially internet gaming has become one of the most popular online activities. The term internet gaming disorder (IGD) was introduced in 2013 to the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) by the American Psychological Association (APA) as repetitive use of internet-based games that leads to significant issues with functioning [1,2]. IGD was labelled as a condition warranting further research, and sequel to this need, several studies have been conducted to unravel this new emerged phenomenon. For example, study among college students in USA by Narusk found the prevalence of IGD at 1.25%. A comparative prevalence study in Europe and USA found the prevalence of IGD to range between 7.9% to 25.2% among adolescents, while the Middle East and Africa had rates from 17.3 to 23.6% [3,4].

Müller et al., reported the prevalence of IGD among adolescents from seven European countries at 1.6% and a similar study among primary school students in Europe found the prevalence of IGD at 5.9% [5,6]. Additionally, a meta-analysis on the prevalence of IGD in Southeast Asia for example showed the prevalence rate of IGD at 20% [7]. Another prevalence study on IGD in Bangladesh, South Asia a high rate of 28.6%. Prevalence among Chinese adolescents found the proportion of IGD at 17% [8], in South Korea, it was found to be at 21.6% [9].

Further, statistics on African IGD are noted to be on the rise. A study among 9 African countries found the prevalence of IGD at 30%. A similar study in Tunisia showed that 37.4% of Tunisian adolescents had IGD [10]. Likewise, Nigeria, which was ranked the first in internet connectivity on African continent with 123 million subscribers, showed the prevalence of IGD at 51% [11]. Another study in Nigeria has a prevalence of 26.9% for IGD [12]. However, there are limited or no available data on prevalence of IGD among Eastern Africa population.

Several attempts have been made to study factors associated with IGD both in general population and specifically among adolescents. For instance, Severo et al., in a study to unravel the risk factors for IGD in Brazil found that increased depressive symptoms, poor sleeping, male gender, greater time spent gaming, and total free-time gaming are key risk factors for IGD [13]. Another longitudinal research reported that IGD is a maladaptive response leading to poorer psychosocial wellbeing and that IGD was found to be negatively associated with self-esteem and social support [14]. A multivariate logistic regression in a recent study found IGD to be significantly associated with male gender and lower age at first gameplay [15].

A cross-sectional study in Thailand similarly found male gender, not living with both parents, use of online dating sites, being bullied at school, depression, anxiety and stress to be significantly associated with IGD among secondary school students in rural community [16]. Also, a study by Beard, Haas, Wickham, and Stavropoulos, reported age of initiation, poorly functioning family to be associated with IGD. Another studies found high levels of psychological distress, use of alcohol, and suicidal ideation to be associated with IGD [17,18]. Likewise, in a systematic review of logitudinal epidemiological studies of internet gaming, Mihara and Higuchi, argued that having distinct problematic thoughts about gaming, higher neuroticism, decreased conscientiousness and low extraversion were factors predisposed internet gamers to IGD [19].

Methods

This study was a web-based cross-sectional survey where a total of 728 eligible respondents who assented to participate was recruited into the study via google form. The sample size was calculated using Yamane (1967) sample size formula with the confidence level at 95% and margin error of 5%. The samples for this study were spread across four major regions in Africa such as Eastern Africa (416, 57.1%), Western Africa (129, 17.7%), Southern Africa (93, 12.8%) and Northern Africa (90, 12.4%). The method of data collection was both quantitative and qualitative using both numerical scores and open-ended questions. The respondents must be 10 years and above, must be an African from any of the main African regions and must give assent to informed consent to participate in the study.

This study utilized a socio-demographic questionnaire, opinion index on assumed reasons why prevalence of IGD was high in Africa and standardized instrument such as the nine-item Internet Gaming Disorder Scale-Short Form (IGDS9-SF) to collect data from the participants. IGDS9-SF is one of the most popular instruments developed based on DSM-5 to assess gaming addiction globally [20] and the instrument was developed to assess and diagnosed gaming disorders across cultures. IGD is discriminated against by the endorsement of at least five core symptoms out of nine over a period of 12 months [21]. Total scores can be obtained by summing up all responses to the 9 items of the instrument and it can range from a minimum of 9 to a minimum of 45 points, with higher scores being indicative of a higher degree of IGD. In order to differentiate disordered gamers from non-disordered gamers, the respondents must have endorsed at least 5 criteria out of the nine by answering 5 ‘Often’ or ‘Very Often’, which translates as endorsement of the 5 criterion. All submitted e-questionnaires were re-entered into SPSS version 23 for analysis using both descriptive and binary logistic regression of inferential statistics was used to analyze both the prevalence study and associated factors in this study.

Results

Response Rates

The response rate of this study indicates that out of 730 respondents who attempted the online questionnaire in English language, 1 respondent, constituted 0.1% declined to participate in the study and 1 (0.1%) respondent was disqualified from participating because he was underaged. The total eligible respondents were 728, constituted to 99.7 response rate. The age mean was 25.5 ± (SD: 8.55).

Variables Frequency Percent
Participant’s Age
10-22 363 49.9
23-30  209 28.7
31-40 113 15.5
41+ 43 5.9
Participant’s Gender
Male 464 63.7
Female 264 36.3
African Region
Northern Africa 90 12.4
Southern Africa 93 12.8
Western Africa 129 17.7
Eastern Africa 416 57.1
Self-perceived family economic status
Very poor 29 4
Poor 102 14
Average 524 72
Rich 54 7.4
Affluent  19 2.6
Self-perceived family functionality
Very functional 258 35.4
Averagely functional 352 48.4
Chaotic/dysfunctional 78 10.7
Can’t tell 40 5.5

Table 1: presents the background distribution of socio-demographic characteristics of the respondents.

As regards age categories, distribution of respondents aged 10-22 years was higher (N=363, 49.9%) compared to 23-30 years (N=209, 28.7%), 31-40 (N=113, 15.5%) and 41+ (N=43, 5.9%). Also, distribution of male respondents was higher (N=464, 63.7%) as opposed female counterpart (N= 264, 36.3%). Similarly, frequency of respondents from eastern Africa was higher (N=416, 57.1%) compared to Western Africa (N=129, 17.7%), Southern Africa (N=93, 12.8%) and Northern Africa (N=90, 12.4%).

As regards, the respondent’s self-perceived family economic status, distribution of respondents who perceived their family economic status to be average was higher (524, 72%) compared to Poor (102, 14%), rich (54, 7.4%) very poor (29, 4% and affluent (19, 2.6%). In addition, distribution of respondent’s self-perceived family functionality shows that the frequency of respondents who perceived their family averagely functional was higher (352, 48.4%) compared to very functional (258, 35.4%), chaotic/dysfunctional (78, 10.7%) and those who can’t tell (40, 5.5%).

Variables Frequency Percent
Non disordered gamers 516 70.9
Non-internet gaming users 49 6.7
Internet gaming disorder 163 22.4
Total 728 100

Table 2: The prevalence of internet gaming disorder among the respondents.

The respondent’s scores on IGDS9-SF indicated that the frequency of non-disordered gamers was higher (516, 70.9%) as opposed to non-internet gaming users (49, 6.7%) and internet gaming disorder (163, 22.4%). Therefore, the prevalence of IGD among African respondents was 22.4%.

Variables Total IGDS9-SF Scores Chi-Square
Non disordered gamers Internet gaming disorder Value df Sig
Participant’s age
22-10 363 (49.9) 259 (35.6) 104 (14.3) 18.129 3 0
23-30 209 (28.7) 170 (23.4) 39 (5.4)
31-40 113 (15.5) 98 (13.5) 15 (2.1)
41+ 42 (5.9) 38 (5.2) 5 (0.7)
Participant’s Gender
Male 464 (63.7) 366 (50.3) 98 (13.5) 1.187 1 0.276
Female 264 (36.3) 199 (27.3) 65 (8.9)
African Region
Northern Africa 90 (12.4) 71 (9.8) 19 (2.6) 49.846 3 0
Southern Africa 93 (12.8) 64 (8.8) 29 (4.0)
Western Africa 129 (17.7) 74 (10.2) 55 (7.6)
Eastern Africa 416 (57.1) 356 (48.9) 60 (8.2)
Self-perceived family economic status
Very poor 29 (4.0) 16 (2.2) 13 (1.8) 16.809 4 0.002
Poor 102 (14.0) 72 (9.9) 30 (4.1)
Average 524 (72.0) 425 (58.4) 99 (13.6)
Rich 54 (7.4) 39 (5.4) 15 (2.1)
Affluent 19 (2.6) 13 (1.8) 6 (0.8)
Self-perceived family functionality
Very functional 258 (35.4) 200 (27.5) 58 (8.0) 16.351 3 0.001
Averagely functional 352 (48.4) 285 (39.1) 67 (9.2)
Chaotic/dysfunctional 78 (10.7) 47 (6.5) 31 (4.3)
Can’t tell 40 (5.5) 33 (4.5) 7 (1.0)

Table 3: Distribution of Socio-demographic characteristics and participant’s scores on IGDS9-SF.

Table presents the distribution of socio-demographic characteristics and participant’s scores on IGDS9-SF. Concerning age distribution, the Table indicated that the frequency of internet gaming disorder was higher among the respondents aged 10-22 years at 14.3% compared to aged 23-30 years at 5.4%, aged 31-40 years at 2.1% and aged 41 years and above at 0.7%. Chi-square test indicated that there was a significant difference in the distribution of respondent age and internet gaming disorder (p=0.000). Also, the frequency of IGD was higher among male respondents at 13.5% as opposed female counterparts at 8.9% The difference in the distribution of respondent’s gender and scores on IGDS9-SF was significiant (p=0.276). As regards the African region the respondents came from, frequency of IGD was slightly higher among the respondents from Eastern Africa at 8.2% compared to Western African at 7.6%, Southern Africa at 4% and Northern Africa at 2.6%. The statistical test shows there was a significant difference in the distribution of African region the respondents came from and the distribution of IGDS9-SF scores (p=0.000).

As regards self-perceive family economic status, the frequency of IGD was higher among the respondents who perceived their family to be average at 13.6%, compared to Poor at 4.1%, Rich at 2.1%, very poor at 1.8% and Affluent at 0.8%. Chi-square test indicates that the difference on the distribution of respondent’s scores on IGDS9-SF and self-perceived family economic status was significant (p=0.002). In addition, in terms of respondent’s self-perceived family functionality, the Table indicated that frequency of IGD was slightly higher among respondents whose family functionality was averagely functional at 9.2% and very functional at 8%, also chaotic/dysfunctional at 4.3% and those who can’t tell at 1%. There was a significant difference in the distribution of respondent’s scores on IGDS9-SF and self-perceived family functionality (p=0.001).

Factors Associated with IGD in Africa

  95% C.I. for EXP(B)
  B S.E. Wald Df Sig. Exp(B) Lower Upper
Step 1a Age Recode     7.257 3 0.064      
  10-22 (1) 0.857 0.502 2.916 1 0.088 2.355 0.881 6.294
23- 30 (2) 0.485 0.518 0.874 1 0.35 1.624 0.588 4.485
31-40 (3) 0.222 0.563 0.156 1 0.693 1.249 0.414 3.768
Gender                 
Female (2) -0.073 0.197 0.137 1 0.711 0.929 0.631 1.368
African_region     23.362 3 0      
NorthernAfrica (1) 0.214 0.318 0.452 1 0.501 1.238 0.664 2.31
SouthernAfrica (2) 0.755 0.288 6.884 1 0.009 2.128 1.211 3.742
Western Africa (3) 1.182 0.259 20.877 1 0 3.259 1.963 5.411
Family_status     3.816 4 0.431      
Very poor (1) 0.519 0.655 0.628 1 0.428 1.681 0.465 6.072
Poor (2) 0.096 0.575 0.028 1 0.867 1.101 0.357 3.397
Average (3) -0.222 0.544 0.166 1 0.683 0.801 0.276 2.328
Rich (4) -0.226 0.613 0.136 1 0.713 0.798 0.24 2.652
Family_func     5.865 3 0.118      
Very functional (1) 0.889 0.477 3.478 1 0.062 2.433 0.956 6.196
Averagely functional (2) 0.628 0.468 1.797 1 0.18 1.873 0.748 4.69
Chotic/dysfunctional (3) 1.039 0.505 4.239 1 0.04 2.827 1.051 7.602
Constant -2.844 0.847 11.26 1 0.001 0.058    

Table 4: Binary Logistic Regression variables in the equation assessing factors associated with IGD in Africa.

Table presents the binary logistic regression estimating the factors associated with emergence of IGD among the respondents. Adjusted odds ratio is an odds ratio that has been adjusted to account for other predictor variables in a binary response variable. As indicated in the Table, respondents aged 10-22 years (AOR: 2.35; 95% CI:0 0.881-6.294), respondents who come from Western Africa (AOR: 3.26; 95% CI: 1.963-5.411), respondents whose family economic status was considered to be very poor (AOR: 1.68; 95% CI: 0.465-6.072), and respondents who perceived their family functionality chaotic/dysfunctional (AOR: 2.83; 95% CI: 1.051-7.602) were indicated to be at risk of developing internet gaming disorder. In addition, certain factors in the Table were indicated to be a protecting factor of IGD among the respondents. For example, being female (AOR:0 .93; 95% CI: 0.631-1.368), Average family economic status (AOR: 0.80; 95% CI: 0.276-2.328) and rich family economic status (AOR: 79; 95% CI: 0.240-2.652) were seen to be protective factors in this present study.

Discussion

Findings from this study showed that the general prevalence of IGD among African respondents aged 10 to 50 years was 22.4%. Finding from this study was close to a similar study among 9 African countries where the researchers found the prevalence of IGD to be 30%, and closer to another prevalence study in Nigeria, where 26.9% of Nigerian adolescents were found to present with IGD [12]. Although, this data in Africa seemed to be relatively high, but there seem to be no significant different compared to the rest of the world. For example, a comparative prevalence study in Europe and USA found the prevalence of IGD to range between 7.9% to 25.2% among adolescents, while the Middle East and Africa had rates from 17.3 to 23.6% [4]. A meta-analysis on the prevalence of IGD in Southeast Asia also showed the prevalence rate of IGD at 20% [6]. Another prevalence study on IGD in Bangladesh, South Asia a high rate of 28.6%. Prevalence among Chinese adolescents found the proportion of IGD at 17% [8], in South Korea, it was found to be at 21.6% [9].

Additionally, results from binary logistic regression in this study showed that participants aged 10-22 years, (AOR: 2.35; 95% CI: .881-6.294), respondents who came from Western Africa (AOR: 3.26; 95% CI: 1.963-5.411), respondents whose family economic status was considered to be very poor (AOR: 1.68; 95% CI: .465-6.072), and respondents who perceived their family functionality chaotic/dysfunctional (AOR: 2.83; 95% CI: 1.051 – 7.602) were at risk of internet gaming disorder. These findings were similar to several other studies where male gender and lower age were found to be risk factors of IGD [15], and poorer psychosocial wellbeing and that IGD was found to be negatively associated with self-esteem and social support [14]. A systematic review of predisposing factors of online gaming addiction also found dysfunctional impulsivity, male gender, lack of attention, stress, anxiety, depression, low self-esteem, low self-control, low self-efficacy and family dysfunctionality to predispose online gamers to addicted internet gaming [22].

Also, there seems to be limited study on protective factors of IGD. Few studies found different factors such as Ji, Yin, Zhang, and Wong who found self-control as the only protective factor that was strongly correlated with IGD. Also, extraversion and conscientiousness appeared in another study as protective factors of IGD [23,24]. However, this study only concentrated into factors that put the participants at risk of IGD.

Conclusion

This research demonstrated the most recent estimated prevalence and associated factors of internet gaming disorder in Africa. The results showed that the new emerging IGD is a public concern especially in Africa where the proportion was found to be high. Psychological distress, hardship and family dysfunctionality were found to play major roles in disposing gamers to this new phenomenon. It is therefore a call for clinicians, mental health providers and African leaders to work among the population at risk of developing this disorder to provide preventive measures and more especially, interventions should target those with risk of developing this disorder.

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Correspondence & Copyright

Corresponding author: Valerie Munyeti, PhD student in Clinical Psychology, Daystar University, Kenya.

Copyright: © 2022 All copyrights are reserved by Valerie Munyeti, published by Coalesce Research Group. This work is licensed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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