Digital Health Meets APIE: Advancing Recreational Therapy through Ecological Momentary Assessment and Real-Time Predictive Modeling: Implications for Practice

Abstract

Certified Therapeutic Recreation Specialists (CTRSs) are increasingly turning to emerging technologies to enhance personalized care for individuals with disabilities and chronic conditions. This paper presents a conceptual outline for integrating Ecological Momentary Assessment (EMA) philosophy utilizing machine learning (ML) and deep learning (DL) predictive modeling to refine the APIE process (Assessment, Planning, Implementation, and Evaluation) in Recreational Therapy (RT). We discuss how real-time data from wearables, mobile apps, and passive sensing tools could allow CTRSs to better understand clients’ moment-to-moment emotional and behavioral responses, enhancing the precision and responsiveness of care. We introduce Synthetic Minority Oversampling Technique–Few Shot Learning (SMOTE-FSL), a ML method optimized for small-sample, personalized predictions. Practical applications across each APIE stage are illustrated through clinical examples, highlighting how digital tools could improve decision-making and streamline documentation. The described methods are not yet ubiquitous as their implementation remains limited due to cost, access, and privacy concerns, as well as the need for practitioner training. We advocate for foundational exposure to EMA and ML/DL in higher education, followed by ongoing, practice-based training to ensure ethical, informed use. Future research must focus on validating and standardizing these approaches, with interdisciplinary collaboration being the key in developing clinically relevant, ethically sound systems. CTRSs are well-positioned to lead this evolution in care, using EMA and ML/DL not only to improve outcomes but to model inclusive, person-centered, and data-informed practice in a rapidly evolving digital health landscape.