Quantifying the effect of Behaviour Self-regulation on well-being through causal analysis: A methodological framework for longitudinal health data

Understanding the drivers of well-being from longitudinal behavioral data is a fundamental challenge in biomedical informatics, where traditional analyses often conflate correlation with causation. This paper presents a rigorous application of causal inference to disentangle the drivers of well-being from complex longitudinal self-report data (N = 141 enrolled; N = 94 analyzed after a priori completeness threshold of ≥ 20 of 28 daily entries). We introduce a novel computational metric, the Behaviour Self-Regulation Score (BSRS), to
quantify both trait-like (long-term) and state-like (short-term) behavioral consistency from daily reports of physical activity and sleep. Employing causal graphical models and propensity score methods, we estimate the causal effects of these behavioral patterns, controlling for motivational and perceptual confounders. Our analysis uncovers distinct causal pathways: while long-term self-regulation (BSRS-L) has a stable positive causal effect, short-term behavioral consistency (BSRS-S) demonstrates a significantly stronger causal impact on daily
well-being, despite a near-zero correlation. Furthermore, we demonstrate that features selected via our causal framework significantly improve the predictive accuracy of well-being in machine learning models compared to conventional feature selection methods. This work contributes a robust methodological framework for causal analysis of longitudinal self-report data and provides evidence that causally-informed modeling can identify more potent targets
for digital health interventions.