Application of the random-intercept cross-lagged panel model to behavioral intervention outcomes: a methodological...

The Random-Intercept Cross-Lagged Panel Model (RICLPM) has gained popularity in longitudinal research due to its ability to disaggregate within- and between-subjects variance. This approach more accurately depicts processes over time compared to traditional Cross-Lagged Panel Models (CLPMs). While RICLPMs are increasingly used, their application to data from behavioral interventions still is underexplored. This study aims to address this gap by demonstrating the application of RICLPM using data from a clinical trial of the digital Unified Protocol (iUP), a transdiagnostic cognitive-behavioral intervention applicable to mental and physical health comorbidities. We focus specifically on how RICLPM can be used to examine dynamic psychological processes during treatment, a central yet under-addressed question in behavioral medicine. We provide a methodological tutorial on adapting the model to intervention outcomes data, compare model fit statistics from an RICLPM and a traditional CLPM, and interpret results specifically in the context of psychological processes during a cognitive-behavioral intervention. Our findings show that RICLPM offers superior fit and more precise estimates of within-subject processes, underscoring its value in clinical research. We argue that adopting RICLPM in behavioral medicine research can help accurately identify psychological mechanisms and processes during behavioral interventions in health settings, aiding intervention personalization. The tutorial offers a resource for researchers interested in using RICLPM for more robust longitudinal analyses of behavioral intervention outcomes.