Investigating the Role of Health Factors and Psychological Well-Being in Gaming Disorder

Abstract

The recent recognition of Gaming Disorder (GD) as a mental health issue has provided a unique opportunity for researchers to advance our current understanding of the intricate relationships between GD and specific health-related factors and well-being. This study sought to investigate the role of key physical and psychological health and well-being factors in GD. To achieve this, three goals were explored. First, we tested whether GD can be predicted by health and well-being factors such as depression, anxiety, loneliness, attention problems, physical health problems (PHP), and psychological well-being (PWB). Second, we assessed the role of distress tolerance (DT) as a moderator in the relationship between PWB and GD. Third, we examined whether PWB would mediate the relationship between PHP and GD. A sample of 474 participants (Mean_age = 28.86 years; SD = 9.23; range: 18–66 years; 47 percent female) were recruited. The results of the multiple linear regression analysis indicated that age, attention problems, and PHP significantly predicted GD (R² = 0.15). In addition, the mediation findings uncovered an important direct association between PHP and GD despite the lack of mediation effects through PWB and the absence of moderating effects from DT. The present findings signify important health-related implications related to GD that are further discussed here in terms diagnosis, treatment, and prevention efforts.

Introduction

Electronic gaming is increasingly becoming one of the largest forms of leisure and entertainment activity worldwide. The increased availability and accessibility to the expanding genres of video games in recent years has led to the wider interest and growing popularity of gaming as a leisure activity. There are currently numerous video games on the market, some of which contain hundreds of hours of playable content. Furthermore, streaming platforms such as Twitch, Facebook, and YouTube have also helped increase the popularity of video games,^1,2 since for some gamers, watching other players play is more enjoyable than playing the game itself.³ Consequently, the growing popularity of video games has led to greater interest within psychological research on the potential positive and negative effects of gaming on psychological health and well-being.

Despite the many advantageous and beneficial effects gaming can have,^4–6 excessive and dysregulated engagement have been shown to be associated with poor psychological well-being (PWB), social, and physical well-being.^7–9 As a result, in 2019, the World Health Organization (WHO) officially recognized gaming disorder (GD) as a mental health condition in the 11th revision of the International Classification of Diseases (ICD-11).¹⁰ The ICD-11 characterizes GD as excessive video gaming behavior marked by patterns of impaired control over gaming, increased priority given to gaming over other life priorities, and persistent engagement with video games despite knowledge of its negative effects on PWB, social, and physical well-being.¹¹

Previously, Internet Gaming Disorder (IGD) was added to the fifth revision of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) in 2013 as an area for further research.¹² IGD and GD differ in terms of number of diagnostic criteria, with the DSM-5 proposing nine core criteria defining IGD, whereas the WHO suggested three main criteria to diagnose GD, in addition to the experience of significant functional impairment.¹³

Although previous research has identified health factors for wide and narrow measures of disordered gaming, research on the predictors of GD using the more recent WHO framework remains scarce. Thus, there is a clear need to identify predictors of GD to develop effective diagnostic tools for GD and aid the development of effective treatment and prevention strategies.

In terms of prevalence rates, a recent meta-analytic study reviewing the evidence from 53 studies conducted in 17 different countries reported a worldwide prevalence of GD of 3.05 percent, a figure that was adjusted to 1.96 percent when considering only studies using robust sampling techniques.¹⁴ In Australia, GD has been shown to affect ∼1.8 percent of adolescents,¹⁵ coinciding with prevalence rates of other countries of up to 5 percent.¹⁵ Prevalence of GD also appears to be moderated by age, with studies consistently demonstrating that adolescents experience greater symptomology of disordered gaming than older adults.^8,16

The relatively low prevalence rates of GD reported in most Western populations should not deemphasize the need for GD as a clinical diagnosis since past research has shown that GD is related to poorer levels of health and well-being cross-sectionally and longitudinally.^16,17 Specifically, GD has been associated with greater levels of stress¹⁸; fatigue⁹; attention problems¹⁹; medical illnesses²⁰; lower life satisfaction²¹; and increased levels of depression, anxiety, loneliness, and poorer psychological health.^7,8,17,22 It is worth noting though that such relationships have been shown to be bidirectional under specific contexts,^23–25 therefore, the investigation of the nature of the associations proposed in this study is exploratory although needed since there are growing concerns about the potential detrimental effects that GD may have on health.²⁶

The current study

Research focusing on the potential associated risk factors of GD is paramount for effective understanding and diagnosis of the disorder.²⁷ Owing to the relative recency of the acceptance of GD by the WHO, relationships between GD and negative health outcomes (both mental and physical) are currently under-researched when considering the WHO framework defining GD.¹⁰ Therefore, the main aim of this study was to investigate the role of key physical and psychological health measures alongside well-being factors in GD, so that clinicians may better identify those who are at greater risk of developing GD, and to assist the development of future preventative strategies for GD.

Therefore, three key exploratory goals were devised for this study. First, we tested whether GD can be predicted by health and well-being factors including depression, anxiety, loneliness, attention problems, physical health problems (PHP), and PWB. Second, given that distress tolerance (DT) has been identified as an important protective factor for mental health disorders, including addictive disorders,^28–30 this study examined the role of DT as a moderator in the relationship between PWB and GD. Third, we assessed whether PWB mediated the relationship between PHP and GD.

Methods

Participants and procedure

An a priori power analysis for testing linear models was conducted using G*Power (v.3.1.9.6).³¹ The a priori test was estimated with the six main predictor variables of the study, and had a preset power (1 − β = 0.95), medium effect size (f² = 0.15), and α = 0.05. The choice of the effect size was informed by previous research reporting medium effects regarding the association between disordered gaming and health-related outcomes (see Refs.^7,32,33). The results of the power analysis indicated that a minimum sample size of 146 participants would be required to obtain a power of 0.95.

A total of 515 participants were recruited online. However, data from 41 individuals were excluded due to failing at least one attention check. Therefore, data from a total of 474 participants were analyzed in this study. To be eligible for the study, participants had to (a) be at least 18 years old, (b) be either an Australian citizen or resident, and (c) be a fluent English speaker. Participants' ages ranged from 18 to 66 years, with a mean age of 28.86 years (SD = 9.23) and 47 percent (n = 224) reporting being female.

The online survey was created on SurveyMonkey, with data being collected from June to September, 2020. Participants were recruited online through social media platforms (i.e., Facebook, Reddit, and Instagram) and Prolific. The first page of the survey provided information about the study complying with plain language information statement guidelines and ensuring participants of their anonymity, confidentiality, and right to withdraw. Participants recruited through Prolific received ∼$2.22 AUD (£1.13 GBP) as monetary compensation, whereas those recruited through social media were not compensated for their participation. This study was granted ethics approval by the Tasmania Social Sciences Human Research Ethics Committee (Project ID: 20602).

Measures

Sociodemographics and video game use

Participants provided data on their basic sociodemographic features (e.g., age, gender) and gaming behaviors (see Table 1 for a full description).

Table 1.

Sociodemographic Characteristics and Gaming Behaviors (N = 474)

Variable
Age, mean (SD)	28.82 (9.22)
Gender, n (%)
Female	224 (47)
Male	247 (52)
Other	3 (1)
Relationship status, n (%)
Single	226 (48)
Widowed	0 (0)
Divorced	7 (1)
Separated	8 (2)
Married	107 (23)
De facto relationship	126 (27)
Household arrangement, n (%)
Single person	119 (25)
Single-parent family with children	70 (14)
Couple with children	31 (7)
Group/share household	135 (28)
Other	119 (25)
Employment status, n (%)
Employed, working full time	178 (38)
Employed, working part time	99 (21)
Unemployed	56 (12)
Home duties/full-time carer	17 (4)
Retired	3 (1)
Student	121 (26)
Played video games in past 12 months, n (%)
Yes	474 (100)
Gaming behavior, mean (SD)
Hours gaming per day	2.36 (1.76)
Hours gaming per week	13.07 (13.16)
Gaming disorder, n (%)
Yes	6 (1.27)
No	468 (98.75)

SD, standard deviation.

Disordered gaming

The Gaming Disorder Test (GDT)³⁴ was used to assess GD as defined in the ICD-11.¹¹ The GDT examines both online and offline video game use/behavior across a 12-month timeframe. All items are answered using a 5-point Likert scale (from 1: never to 5: very often). Total scores are obtained by summing all responses and can range from 4 to 20, with higher scores indicating greater levels of disordered gaming behavior. The GDT has been previously shown to be psychometrically sound.³⁴ In this study, the GDT had adequate internal consistency (α = 0.82).

Psychological health

The depression and anxiety subscales from the depression anxiety and stress scales-21³⁵ were utilized to measure symptoms and severity of depression and anxiety. Each subscale consists of seven items, with responses given using a 4-point Likert scale (from 1: never to 4: always). Total scores for depression and anxiety range from 7 to 49, with higher scores being indicative of greater levels of depression or anxiety. In this study, this tool had adequate internal consistency (depression α = 0.93 and anxiety α = 0.86).

Physical health and PWB

The two-item PROMIS Global Health scale (v 1.20)³⁶ consists of two subscales containing two items each, with one scale measuring PHP and the other measuring PWB. Responses are given using a 5-point Likert scale for items 1–3 (from 1: excellent to 5: poor) and for item 4 (from 1: completely to 5: not at all). Total scores for PHP and PWB can range from 2 to 10, with lower scores indicating greater presence of PHP and poorer PWB. In this study, this tool had adequate internal consistency (α = 0.71).

Loneliness

The three-item loneliness scale³⁷ utilizes three items to assess symptoms of loneliness. Responses are given using a 4-point Likert scale (from 1: never to 4: always). Total scores can range from 3 to 12 with higher scores being indicative of higher levels of loneliness. In this study, the three-item loneliness scale had adequate internal consistency (α = 0.85).

Attention problems

The attention problem scale³⁸ consists of three items designed to measure attentional difficulties. Responses are given using a 5-point Likert scale (from 1: strongly disagree to 5: strongly agree). Scores range from 3 to 15 with higher scores being indicative of greater levels of attention problems. In this study, this tool had adequate internal consistency (α = 0.75).

Distress tolerance

The distress tolerance scale-short form (DTS-SF)³⁹ is a four-item measure of DT.⁴⁰ Responses are given using a 5-point Likert scale (from 1: strongly disagree to 5: strongly agree). Total scores range from 5 to 20 with higher scores being indicative of greater ability to tolerate distress. In this study, the DTS-SF had adequate internal consistency (α = 0.87).

Statistical analysis

Before conducting the formal statistical analyses, parametric assumptions were checked to ascertain the appropriateness of the analyses. Shapiro–Wilk tests of normality were significant (p < 0.001), suggesting non-normal distribution of scores for all variables. Further inspection of the Skewness and Kurtosis levels for each main variable indicated that depression, anxiety, loneliness, attention problems, PHP, PWB, and DT scores met the univariate normal distribution assumption (Skewness <3.0 and Kurtosis <8.0).⁴¹ Potential multicollinearity issues were assessed, and values for variance inflation factors were <2.5, whereas tolerance levels were >0.4, thus not indicating multicollinearity.⁴²

To investigate the study's first goal, a multiple linear regression model was estimated using R (4.0.2)⁴³ to test a predictive model for GD based on the following health and well-being predictors: depression, anxiety, loneliness, attention problems, PHP, and PWB, including age as a control variable. Both the Breusch Pagan test of heteroscedasticity and Bartlett's test of homogeneity were significant (p < 0.001), suggesting heteroscedasticity within the multiple linear regression model. Thus, the model was estimated using robust standard errors to account for this issue.⁴⁴ To investigate the remaining goals of the study, a moderated mediation analysis was conducted using Jamovi (1.2.5.0)⁴⁵ to examine the potential mediating effect of PWB, between PHP and GD, while also accounting for the potential moderating effect of DT.

Results

Multiple linear regression

Overall, the model accounted for ∼15 percent of the total variance in GD (R² = 0.15, ΔR² = .14, F(7, 466) = 11.86, p < 0.001). In terms of specific predictors, age (β = −0.04, t = −2.55, p = 0.011), attention problems (β = 0.24, t = 3.69, p < 0.001), and PHP (β = 0.26, t = 2.31, p = 0.021) emerged as statistically significant predictors. Conversely, depression (β = 0.09, t = 1.76, p = 0.079), anxiety (β = 0.04, t = 0.67, p = 0.503), loneliness (β = 0.11, t = 1.24, p = 0.215), and PWB (β = 0.16, t = 1.42, p = 0.157) did not emerge as significant predictors of GD (Table 2).

Table 2.

Multiple Linear Regression Analysis Testing the Role of Health and Well-Being Factors in Predicting Gaming Disorder

Predictors	β	SE	t	p
Intercept	3.01	1.52	1.99	0.048
Age	−0.04	0.01	−2.55	0.011
Depression	0.09	0.05	1.76	0.079
Anxiety	0.04	0.06	0.67	0.503
Loneliness	0.11	0.09	1.24	0.215
Attention problems	0.24	0.06	3.69	<0.001
Physical health problems	0.26	0.11	2.31	0.021
Psychological well-being	0.16	0.12	1.42	0.157

Model summary: R² = 0.15, ΔR² = 0.14, F(7, 466) = 11.86, p < 0.001.

Outcome: gaming disorder. Age was included as a control variable.

β, standardized regression coefficient; SE, standard error.

Moderated mediation

A moderated mediation model with 1,000 bootstrapped samples was estimated to test for mediation effects of PWB on the relationship between PHP and GD, conditional of DT. It was found that DT had no moderation effects in the model (B = 0.00, SE = 0.02, 95% CI [−0.04 to 0.05], β = 0.02, p = 0.905). Moreover, the direct and indirect pathways of the model revealed a statistically significant negative effect of PHP on PWB (a path) (B = −0.49, SE = 0.06, 95% CI [−0.59 to −0.37], β = −0.36, p < 0.001), suggesting that PHP worsen PWB.

However, PWB was not shown to have a significant effect on GD (b path) (B = −0.08, SE = 0.09, 95% CI [−0.27 to 0.10], β = −0.05, p = 0.386). The indirect effect (ab path) was found to be nonsignificant (B = 0.04, SE = 0.05, 95% CI [−0.05 to 0.13], β = 0.02, p = 0.389), indicating that PWB does not fully, nor partially mediate the relationship between PHP and GD in this model. Finally, the direct effect (c′) was found to be significant (B = 0.26, SE = 0.12, 95% CI [0.04 to 048], β = 0.12, p = 0.020), indicating that higher levels of PHP are associated with higher levels of GD (Fig. 1).

FIG. 1.

Moderated mediation analysis further exploring the relationship between physical health problems and gaming disorder through psychological well-being when moderated by distress tolerance. ***p < 0.001, **p < 0.01, *p < 0.05. Unstandardized coefficients are being shown.

Discussion

The main aim of this study was to explore the role of health and well-being factors in predicting GD. In this study, particular focus was given to physical health, considering additional psychological factors. With the WHO acknowledging GD as a behavioral addiction, there is a need for identifying predictors of GD to assist with the future diagnoses of GD and the development of prevention strategies and treatment for this condition. Previous research on disordered gaming using the APA framework for IGD identified depression, anxiety, loneliness, attention problems, PHP, and PWB, as important correlates of IGD.^7,8

With regard to the first goal of this study, the multiple linear regression model investigating whether GD could be predicted by following health and well-being factors (i.e., depression, anxiety, loneliness, attention problems, PHP, and PWB) was able to account for ∼15 percent of the total variance in GD scores, with age, attention problems, and PHP being the most relevant predictors. Interestingly, PHP was the strongest predictor of GD in this study. Nevertheless, the findings that depression, anxiety, loneliness, and PWB were not significant predictors of GD are not consistent with previous empirical literature reporting associations between GD and these factors.^7,17,46–48 After this, a moderated mediation analysis was conducted to explore the second and third aims of this study.

This study found that attention problems predicted GD successfully, a finding that converges with previous research reporting positive associations between attention deficit hyperactivity disorder (ADHD) and GD, and between subclinical attention issues and GD.^19,49–51 Although causality cannot be inferred here, previous literature has provided theoretical perspectives on the relationship between attention and gaming.^19,37,43 For instance, the engaging and exciting nature of video games may be what draws people with attention problems to gaming.⁵² Video games are enticing, challenging, and have attention grabbing features, and although these features may attract gamers, they may also make it more difficult for individuals with attention problems to divert their attention elsewhere and stop playing.^19,52 Furthermore, video games may also be used as a coping or escape method for such attention problems.⁴⁶ In light of previous research,⁵³ it can be concluded that individuals with attention problems may be more prone to impulsive decision making, which may lead to GD.

The results obtained in this study showing that PHP is associated with increases in GD mirror those of previous studies finding positive associations between PHP and excessive gaming.^9,20 More specifically, PHP was shown to meaningfully predict changes in GD, within the regression model, suggesting that PHP is a unique correlate of GD, and that individuals who experience general physical impairments (e.g., overweight, physical injuries, and illness) are at greater risk of developing GD.

There are a few potential reasons underpinning the relationship between PHP and GD. Previous research has suggested that time spent gaming may reduce one's availability for physical activity.⁵⁴ Another plausible explanation is that physically unwell individuals may use gaming as a method of coping and escaping from their health issues.¹³ This finding aligns with previous reports suggesting that gaming can constitute a coping mechanism for nonphysical illnesses such as schizophrenia.⁵⁵ Furthermore, GD has been associated with other addictive disorders such as smoking and alcohol use, which can also negatively impact physical health.^20,56

It is known that the etiology of addiction in the context of illness has been hypothesized as a form of self-medication.⁵⁷ The self-medication hypothesis suggests that when an individual is experiencing poor health, they are more likely to develop addictive disorders that provide relief from their symptoms, rendering them more vulnerable to addictions.⁵⁷ Although the self-medication hypothesis is more commonly used to explain the development of substance abuse, a recent study found escapism to be the greatest predictor of GD among common motivations for gaming, thus providing indirect evidence of video games acting as a form of self-medication.¹³ At the theoretical level, the current findings provide support for the self-medication hypothesis.

Despite previous literature finding moderating effects of DT between relationships of poor psychological health and addictions,^28,30 this study found that DT did not play a protective role against the development of GD for those with greater symptomology of depression, anxiety, and/or poorer PWB. This analysis also found a significant main effect of PHP on PWB, however, no indirect effect of PHP on GD was found through PWB, and DT did not significantly moderate the pathway between PWB and GD. This indicates that PWB did not mediate the relationship between PHP and GD when accounting for the moderating effects of DT.

There are several important implications that can be drawn from this study. For example, health practitioners should assess for GD alongside the related health factors identified in this study. In doing so, health practitioners may pinpoint underlying health issues potentially causing, or being caused by, GD, or otherwise identify comorbidities of mental illness experienced by clients. This will aid the development of effective treatment plans for individuals who experience GD. An additional implication of the present findings is that they may help identify populations more susceptible to developing GD and, as such, prevention strategies targeting these at-risk populations should be implemented. More specifically, video game use should be monitored in individuals who experience attention problems, so that gaming does not reach unhealthy levels. As previously suggested,²⁷ this onus of responsibility is not only on the individual, nor on health practitioners, but also on the gaming industry itself.⁵⁸ The findings reported in this study pave the way to future individual differences research examining potential gender effects in GD. Given the existing mixed findings regarding GD prevalence rates in terms of gender, it is paramount to test the present findings to further elucidate specific gender-related vulnerability and impairments.

This study was not without potential limitations. First, the sampling strategy that was used limited recruitment to a population of Internet users, and thus, sampling bias may be present. Moreover, the sample was not recruited from a clinical setting, therefore, the results should be interpreted within a dimensional perspective based on higher and lower GD symptom severity and how these fluctuations may affect other variables proportionally. The use of a cross-sectional design also presents with limitations and renders the findings reported not causal in nature. Another potential limitation of this study relates to the current COVID-19 pandemic, which may interfere with the present results given the potentially increased level of mental health issues experienced globally,^59,60 particularly in the context of technology use.⁶¹ Furthermore, results pertaining to video game use may have been affected by increased engagement in Internet and gaming activities as a coping response to pandemic-induced stress and global restrictions (e.g., lockdowns).^62–65 Finally, this study did not test for specific illness or injuries (e.g., fatigue, body mass index, broken limbs, and cancer), so the relationship between PHP and GD may be limited in nature.

Footnotes

Authors' Contributions

S.M. was involved in statistical analyses, data collection, study design, drafting, and reviewing the article. J.S. contributed to study design, supervision, drafting, and reviewing the article. H.M.P. was involved in study design, supervision, data collection, drafting, and reviewing the article.

Author Disclosure Statement

No competing financial interests exist.

Funding Information

No funding was received for this article.

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