Video gaming plays a major role in the daily lives of U.S. teenagers, with many spending over 100 minutes per day engaged in gaming. Many of these platforms have reward systems that have the potential to facilitate addictive behaviour, particularly in teenagers who are highly reward-sensitive and exhibit poor impulse control. Despite organizations and the health authorities globally having officially recognized gaming disorder (GD), the causal factors are under question, especially whether it should be classified as a mental illness. Certain professionals have deemed that psychological conditions such as anxiety, depression, and attention-deficit/hyperactivity disorder (ADHD) result from excessive gaming. However, others believe these conditions contribute to the development of GD.
This study investigates whether GD is a cause or consequence of psychological issues, using the interaction of person-affect-cognition-execution (I-PACE) model. This model investigates how personal traits, emotions, and environmental stressors interact to influence addictive behaviours. The cohort study used data from the Adolescent Brain Cognitive Development (ABCD) Study 5.1, covering the period between January 1, 2018, and December 31, 2022. Using NDA Tools, the data accessed and included 4,289 adolescents aged 11-14 years who reported playing video games, drawn from a larger subset of 4754 with partial 4-year follow-up data.
GD was assessed using a 6-item video game addiction questionnaire (ranging between 6 and 36). Mental health was assessed through caregiver-reported scales for depression (0–24), ADHD (0–14), anxiety (0–18), social problems (0–18), and conduct disorder (0–21), which together formed a latent factor called “psychopathology.” Other variables included impulsivity (20 to 77), bullying (9 to 45), family conflict (0 to 9), adverse events (0 to 51), and sex.
The modelling was implemented in R (v4.4.2) through the lavaan package, applying the structural equation modelling (SEM), such as a cross-lagged panel model (CLPM). The evaluation of model fit was evaluated using root mean square error of approximation (RMSEA) ≤ 0.06, comparative fit index (CFI) ≥ 0.95 or more, and standardized root mean square residual (SRMR) ≤ 0.08. The study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) and received ethical approval from UC San Diego.
Among the 4,289 adolescents, 56% were male, and 44% female. The racial/ethnic distribution included 2% Asian, 11% Black, 21% Hispanic, and 56% White participants. Around 35% of households had incomes between $100,000 and $199,000. Most variables were generally right-skewed and underwent log transformation, except for GD, ADHD, impulsivity, and family conflict, which followed a normal distribution.
Confirmatory factor analysis confirmed that ADHD, conduct disorder, depression, anxiety, and social problems formed a single psychopathology factor across all waves. The strongest loadings were those of social problems and depression. The results of the CLPM analyses indicated that psychopathology was a predictive measure of GD (Year 2 →3: β = 0.03, P = 0.003; Year 3 →4: β = 0.07, P < 0.001). Lateral mental health symptoms did not occur because of GD.
Including I-PACE factors such as impulsivity, conflict, bullying, adverse life events, and sex, the model fit increased (CFI = 0.95; RMSEA = 0.04; SRMR = 0.05). Year 3 psychopathology was the only significant predictor of GD in Year 4 (β = 0.04, P = 0.04). This association was validated using mixed-effects modelling, and it explained 21.2% of the variance in GD. The most robust causes were depression, anxiety, ADHD, and social problems; conduct disorder did not show a significant impact.
In conclusion, this cohort study found that pre-existing mental health problems significantly predicted the development of GD, whereas GD itself did not exacerbate mental health symptoms. These findings underscore the importance of early mental health intervention as a preventive strategy for GD. Future studies should incorporate longer follow-up periods, Â clinical interviews, Â and advanced tools for assessing media use and gaming motivations.
References: Falcione K, Weber R. Psychopathology and Gaming Disorder in Adolescents. JAMA Netw Open. 2025;8(7):e2528532. doi:10.1001/jamanetworkopen.2025.28532
