3.1. Descriptive Statistics and Comparative Prevalence Rates Analyses
In terms of gender distribution, males represented 80% (n = 1229) of all participants. The mean age observed in the sample was 29.74 years (SD = 12.37 years, range = 12–82 years). Furthermore, about 52.41% (n = 749) of sample reported not being in a romantic relationship. With regards to employment status among the participants, 68.99% (n = 986) reported being unemployed at the time of the survey.
In terms of gaming-related behaviors, the average time spent gaming during the week was 19.15 h (SD = 14.69 h), with about 42% of this time being spent over the weekend alone. In terms of preferred mode of play, 55.84% (n = 798) of all participants reported playing mainly online games. Moreover, about 38.34% (n = 548) of the sample reported in-game social membership to a group (e.g., clan, guild). Although no participant self-identified as a professional gamer, a small minority (i.e., 5.74%, n = 82) demonstrated intentions to become a professional gamer in the future. Finally, about 10.5% (n = 150) of the respondents declared having experienced significant problems in their lives due to gaming.
Participants’ levels of psychopathological symptoms were also assessed in the sample, these included attention problems (mean = 6.43; SD = 2.52; min = 3, max = 15), depression (mean = 13.58; SD = 4.13; min = 8, max = 32), loneliness (mean = 6.12; SD = 2.98; min = 3, max = 15) and severity of GD across both diagnostic frameworks (APA mean = 19.36; SD = 6.62; min = 9, max = 44; WHO mean = 8.46; SD = 3.42; min = 4, max = 20). A complete summary of the sample’s main sociodemographic characteristics and severity of psychopathological symptoms is presented in Table 1
The comparative analyses of the sample’s prevalence rates of GD according to each diagnostic framework revealed that about 5.74% (n = 82) of all participants fulfilled the APA criteria for IGD and about 3.28% (n = 47) met the WHO diagnostic criteria for GD. Interestingly, further analysis suggested that these two prevalence rates were significantly different as per the choice of the diagnostic framework (χ2 = 798.32, df = 1, p ≤ 0.001; φ = 0.75). Moreover, the ratio of gamers afflicted with GD did not differ across genders in relation to the APA (χ2 = 2.15, df = 1, p = 0.14) and the WHO frameworks (χ2 = 2.34, df = 1, p = 0.13). Age-related effects were found in the APA (t(1427) = 3.84, p < 0.001) but not in the WHO framework (t(1427) = 0.58, p = 0.56).
3.2. Examining the Mediational Role of Gaming Motives in the Relationship between Psychopathological Symptoms and GD
In order to further investigate the potential psychometric differences in the APA and WHO frameworks, a complex mediation analysis was conducted to compare how in-game motives may mediate the relationship between psychopathological symptoms and GD. All latent variables were included in two single structural equation models in which GD was predicted by depression, loneliness and attention problems through specific gaming motives namely social, escape, competition, coping, skill development, fantasy and recreation motives. To ensure the robustness of the findings, the computation of the two mediation models accounted for potential confounding effects stemming from time spent playing video games during the week (βGDT
= 0.25, p
< 0.001; βIGDS9-SF
= 0.22, p
< 0.001), age (βGDT
= −0.08, p
< 0.001; βIGDS9-SF
= −0.09, p
< 0.001) and gender (βGDTref:female
= −0.06, p
= 0.01; βIGDS9-SFref:female
= 0.11). Furthermore, the two mediation models were estimated using the ML estimator [49
] with 5000 bootstrap samples to enhance the quality of the findings. The results of this analysis indicated that both mediation models presented an adequate fit to the data as evidenced by the following fit indices: ML(GDT)
= 5488.32, CFI = 0.91, TLI = 0.90 and RMSEA = 0.05 (90% CI (0.05–0.05)); and ML(IGDS9-SF)
= 6941.63, CFI = 0.90, TLI = 0.90 and RMSEA = 0.05 (90% CI (0.05–0.06)).
A more fine-grained analysis of the mediation results obtained (see Table 2
for a complete summary) suggested that across both diagnostic frameworks, depression had significant direct effects on the following gaming motives: social, escape, competition, coping, skill development, fantasy and recreation (β ranging from 0.30 to 0.81, p
< 0.001). Additionally, loneliness (β ranging from 0.08 to 0.52, p
≤ 0.01) and attention problems (β ranging from 0.51 to 0.78, p
< 0.001) also yielded statistically significant direct effects on all seven gaming motives. Overall, the results pertaining to the direct effects in the two mediation models were highly comparable across both diagnostic frameworks.
In relation to the mediational role of gaming motives on GD, the escape motive positively influenced GD with results closely matching across both diagnostic frameworks (βGDT = 0.44, βIGDS9-SF = 0.47, p < 0.001). Although the competition motive had a statistically significant effect on GD (βGDT = 0.16, βIGDS9-SF = 0.21, p < 0.001), this effect was rather weak. Moreover, negative direct effects on GD were found for both skill development (βGDT = −0.09, βIGDS9-SF = −0.08, p ≤ 0.006) and recreation motives (βGDT = −0.14, βIGDS9-SF = −0.09, p ≤ 0.004) across both diagnostic frameworks. Non-significant direct effects included the effects from social and fantasy motives on GD (p ≥ 0.16) across both diagnostic frameworks. Interestingly, the main difference observed across the two diagnostic frameworks was in relation to the predictive role of coping on GD as it had a weak influence on GD according to the APA (β = 0.08, p = 0.03) but not to the WHO framework (p = 0.08).
Overall, the results related to the indirect effects on the association between depression, loneliness and attention problems on GD through gaming motives yielded highly comparable results across the two diagnostic frameworks. More specifically, positive indirect effects of depression on GD through gaming motives were statistically significant for both escape and competition motives (ind. β ranging from 0.04 to 0.39, p < 0.001), whereas negative indirect effects were found through skill development and recreation motives (ind. β ranging from −0.02 to −0.04, p ≤ 0.01). Conversely, non-significant indirect paths were found across both diagnostic frameworks for social and fantasy motives as mediators (p ≥ 0.16). The only pronounced discrepancies found between the two diagnostic frameworks in terms of indirect effects occurred in the relationship between depression on GD through coping motive as it had a weak influence on GD according to the APA (ind.β = 0.05, p = 0.03) but not to the WHO framework (p = 0.08).
Positive indirect effects of loneliness on GD through gaming motives were statistically significant for escape, competition and coping motives only (ind.β ranging from 0.02 to 0.23, p ≤ 0.01). Additionally, weak statistically significant negative effects were found for skill development and recreation motives (ind.β = −0.01, p ≤ 0.04), while social motive had no mediational effect on the relationship between loneliness and GD (p ≥ 15). The indirect effects based on loneliness as a predictor also yielded highly consistent findings across both diagnostic frameworks. Furthermore, the only main discrepancy on the indirect effect of loneliness on GD emerged in relation to fantasy motive which produced a weak influence on GD according to the APA (ind.β = 0.02, p ≤ 0.04) but not to the WHO framework (p = 0.57).
As per the indirect effects in the relationship between attention problems and GD through all seven gaming motives, the results indicated positive and statistically significant indirect effects for escape, competition and coping motives (ind.β ranging from 0.08 to 0.30, p ≤ 0.02). Furthermore, negative indirect effects were found for skill development and recreation motives (ind.β ranging from −0.07 to −0.11, p ≤ 0.01). Interestingly, only two mediators related to social and fantasy motives had no mediational effects on the relationship between attention problems and GD (p ≥ 0.12). In short, the examination of the indirect effects in the two models indicated similar and highly consistent findings across both diagnostic frameworks.
Finally, the analysis of the total effects across both diagnostic frameworks identified slightly stronger effects for the APA framework in comparison to the WHO framework with depression (βGDT = 0.39, p < 0.001; βIGDS9-SF = 0.49, p < 0.001), loneliness (βGDT = 0.24, p < 0.001; βIGDS9-SF = 0.31, p < 0.001) and attention problems (βGDT = 0.28, p < 0.001; βIGDS9-SF = 0.41, p < 0.001) all positively influencing GD. Despite those minor differences at the level of total effects, these findings are highly comparable across both diagnostic frameworks.