Using a dynamic network approach to examine the effect of daily experiences on gambling and gaming symptoms

The overarching aim of this project is to move beyond traditional variable-centered and linear models and instead adopt a systems-based, dynamic perspective on addictive behaviors. This methodological and analytical approach represents an innovative framework that is being applied for the first time to the study of biopsychosocial risk factors in gambling and gaming

Study 1: Cross-sectional Network Analysis

The first study is designed as a cross-sectional investigation aimed at identifying shared and disorder-specific biopsychosocial risk factors for gambling and gaming. A broad set of biological, psychological, and social variables is assessed, and Gaussian Graphical Models (GGMs) are employed to estimate networks based on partial correlations.

This approach allows us to:

• Identify risk factors that are uniquely associated with gaming,
• Identify risk factors that are uniquely associated with gambling,
• And determine which risk factors are shared across both behaviors.

Network analysis enables the estimation of direct associations between variables while controlling for all others in the network, thereby providing a more precise representation of the underlying structure of risk. 

Study 2: Daily Diary Study on Gambling

The second study adopts a highly innovative longitudinal design using a 40-day diary (experience sampling) methodology. Daily fluctuations in biopsychosocial risk factors related to gambling are assessed at the within-person level, allowing us to investigate how these variables dynamically interact over time.

To analyze these data, we apply longitudinal network modeling using a mlVAR (Multilevel Vector Autoregressive Model) approach, which makes it possible to disentangle:

• Temporal networks (lagged, directional associations),
• Contemporaneous networks (same-day associations),
• And between-person networks (stable individual differences).

This analytic framework allows us to capture the dynamic processes through which changes in one risk factor may predict changes in others across days, offering a fine-grained understanding of gambling-related vulnerability as it unfolds in daily life.

Study 3: Daily Diary Study on Gaming

The third study mirrors the design and methodology of Study 2 but focuses on gaming behavior. Using the same 40-day diary approach, daily changes in biopsychosocial risk factors related to gaming are modeled through longitudinal network analysis.

Taken together, this doctoral project integrates cross-sectional and longitudinal network approaches, daily diary data, and a biopsychosocial framework to provide a comprehensive and novel understanding of gambling and gaming. The findings are expected to have important implications for theory development, precision prevention, and targeted interventions, by identifying central and dynamic risk factors that may serve as optimal points for clinical and public health action.