Beyond Pre-Post Comparisons: A Comprehensive Effect Size Framework for Intensive Longitudinal Interventions via Time-Varying Modeling
Year: 2026
Type: preprint
Abstract: Intensive longitudinal interventions (ILIs), which deliver repeated interventions in people’s daily lives while intensively tracking their psychological states, offer unique opportunities for ecologically valid, real-time, and personalized support. However, there is a striking mismatch between the richness of the data these studies generate and the simplicity of the methods used to evaluate them: evaluations still rely largely on pre-post mean comparisons that collapse dozens or even hundreds of observations per person into a few summary statistics. To resolve this mismatch, we introduce a comprehensive effect size framework based on Bayesian multilevel time-varying autoregressive (TV-AR) models within the generalized additive mixed model (GAMM) approach. The framework organizes effect sizes along four dimensions: subject level (population-average, individual-specific, and between-group), parameter type (mean level, autoregressive effect, and variance), temporal scale (integrated and time-specific), and study phase (active intervention and post-intervention decay). These metrics move researchers beyond whether an intervention works to when effects emerge, plateau, or fade, how the intervention reshapes the dynamics and stability of a process, and for whom it is effective. We demonstrate the framework with data from a mindfulness-based ILI targeting negative affect, in which an intervention and a control group were assessed before and during the intervention, and provide openly available annotated R code and a tutorial. Overall, the framework provides a practical and flexible toolkit for evaluating ILIs, supporting the broader shift toward a more dynamic, process-oriented, and personalized intervention science.
Source: PsyArXiv (OSF Preprints)
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7213425350