Evaluating Cross-Study Generalization of Receptivity Models for Just-in-Time Adaptive Interventions

Just-in-Time Adaptive Interventions (JITAIs) offer a promising paradigm for delivering personalized mobile health support. Receptivity, i.e., the user's ability to receive the intervention plays a critical role in the effectiveness of JITAIs. While prior work has shown that receptivity can be inferred from mobile sensing data, the generalizability of these models across interventions, populations, and sensing configurations remains largely unexplored. Developing a new model for each intervention is burdensome and costly, whereas transferring models across studies is complicated by heterogeneity in populations, sensors, and data collection protocols. This paper presents the first systematic evaluation of cross-study generalization in receptivity detection. Using data from four diverse studies (yielding seven distinct datasets), we establish within-study performance benchmarks (median AUCs = 0.687-0.844) and quantify the performance drop when models transfer using only shared features (mean generalization AUCs of up to 0.676). We evaluate strategies that leverage the full feature union, employing imputation-based methods, correlation alignment (Deep CORAL), and a missingness-aware neural network. These approaches close the generalization gap, improving mean generalization AUCs of up to 0.722. In two target datasets, cross-study models matched or exceeded within-study baselines. These findings demonstrate the feasibility of robust, generalizable ``warm-start'' receptivity models, offering a practical pathway for deploying effective JITAIs without a costly ``cold-start'' learning phase.