Risk and protective factors for (internet) gaming disorder: A meta-analysis of pre-COVID studies.

This large-scale meta-analysis aimed to provide the most comprehensive synthesis to date of the available evidence from the pre-COVID period on risk and protective factors for (internet) gaming disorder (as defined in the DSM-5 or ICD-11) across all studied populations. The risk/protective factors included demographic characteristics, psychological, psychopathological, social, and gaming-related factors. In total, we have included 1,586 effects from 253 different studies, summarizing data from 210,557 participants. Apart from estimating these predictive associations and relevant moderating effects, we implemented state-of-the-art adjustments for publication bias, psychometric artifacts, and other forms of bias arising from the publication process. Additionally, we carried out an in-depth assessment of the quality of underlying evidence by examining indications of selective reporting, statistical inconsistencies, the typical power of utilized study designs to detect theoretically relevant effects, and performed various sensitivity analyses. The available evidence suggests the existence of numerous moderately strong and highly heterogeneous risk factors (e.g., male gender, depression, impulsivity, anxiety, stress, gaming time, escape motivation, or excessive use of social networks) but only a few empirically robust protective factors (self-esteem, intelligence, life satisfaction, and education; all having markedly smaller effect sizes). We discuss the theoretical implications of our results for prominent theoretical models of gaming disorder and for the existing and future prevention strategies. The impact of various examined biasing factors on the available evidence seemed to be modest, yet we identified shortcomings in the measurement and reporting practices.

Contextual factors predicting compliance behavior during the COVID-19 pandemic: A machine learning analysis on survey data from 16 countries.

Voluntary isolation is one of the most effective methods for individuals to help prevent the transmission of diseases such as COVID-19. Understanding why people leave their homes when advised not to do so and identifying what contextual factors predict this non-compliant behavior is essential for policymakers and public health officials. To provide insight on these factors, we collected data from 42,169 individuals across 16 countries. Participants responded to items inquiring about their socio-cultural environment, such as the adherence of fellow citizens, as well as their mental states, such as their level of loneliness and boredom. We trained random forest models to predict whether someone had left their home during a one week period during which they were asked to voluntarily isolate themselves. The analyses indicated that overall, an increase in the feeling of being caged leads to an increased probability of leaving home. In addition, an increased feeling of responsibility and an increased fear of getting infected decreased the probability of leaving home. The models predicted compliance behavior with between 54% and 91% accuracy within each country's sample. In addition, we modeled factors leading to risky behavior in the pandemic context. We observed an increased probability of visiting risky places as both the anticipated number of people and the importance of the activity increased. Conversely, the probability of visiting risky places increased as the perceived putative effectiveness of social distancing decreased. The variance explained in our models predicting risk ranged from < .01 to .54 by country. Together, our findings can inform behavioral interventions to increase adherence to lockdown recommendations in pandemic conditions.