The Predictive Role of Tolerance and Health Problems in Problem Gambling: A Cross-Sectional and Cross-Lagged Network Analyses.

The existing symptomatic networks of problem gambling are all based on cross-sectional data. Thus, there is a need to explore longitudinal symptom networks of problem gambling. Moreover, the replicability of cross-sectional symptom networks can be limited; therefore, further research should assess the convergence between cross-sectional networks of problem gambling symptoms. The present study aimed (i) to examine cross-sectional networks of problem gambling symptoms and evaluate their replicability and (ii) to examine a longitudinal cross-lagged network of problem gambling symptoms. The study included a representative sample of young adult gamblers (born between 1984 and 2000) from the first two waves of the Budapest Longitudinal Study (original sample: N = 2777; final sample: N = 335). The Problem Gambling Severity Index was used to assess symptoms of problem gambling. Cross-sectional symptom networks showed differences in the centrality of nodes. Correlations between the two cross-sectional networks were low in the presence vs. absence of edges, rank order of edge weights, and centrality estimates. However, network invariance tests indicated non-significant differences between them. The cross-lagged network revealed that the symptoms of tolerance and health problems could predict the subsequent presence of multiple problem gambling symptoms. Overall, limited evidence demonstrated the replicability of cross-sectional symptom networks of problem gambling. Future research needs to explore the utility of cross-sectional networks of problem gambling and assess more precisely causal relationships between problem gambling symptoms by distinguishing within- and between-subject effects.