Psychological and technological predictors of the physical activity intention-behavior gap: an explainable machine learning analysis.

Objective: Intention is widely regarded as the most proximal predictor of behavior. But, physical activity (PA) intentions do not invariably translate into actual exercise behavior, leaving a intention-behavior (I-B) gap. The study integrates psychological and technological frameworks to examine the mechanisms that moderate the PA I-B gap. Methods: Unlike traditional dichotomous measures of the PA I-B gap, this study employs baseline correction to derive a standardized continuous measure that quantifies the magnitude of the gap. Using survey data from 1,334 Chinese adults, we combined the Health Belief Model and the Unified Theory of Acceptance and Use of Technology within an explainable machine-learning framework to identify important predictors and their non-linear interactions. Results: The machine learning based optimal XGBoost model (R Conclusion: Psychological cognition and smart sports tools jointly predict the PA I-B gap. The study's conclusions are constrained by its reliance on self-reported measures and its cross-sectional design. Future research should adopt longitudinal or experimental protocols, supplemented by objective data from wearable devices, to delineate causal pathways and illuminate the finer mechanisms underlying the gap.