2026-02-16
Larissa Lobo, Meghann Lloyd, Robert Balogh, Kristina Sobolewski, Serene Kerpan
Swimming and spending time outdoors near lakes, rivers, and oceans provides therapeutic benefits for children with autism spectrum disorders (ASD). However, engaging in aquatic environments is not without risks for children with ASD. Drowning is a top cause of mortality for children with ASD. We investigated the experiences of parents of children with ASD around aquatic environments to understand: the benefits of swimming and being in aquatic environments for children with ASD and their families, features of aquatic environments that serve as facilitators to access, and the barriers to accessing aquatic environments. Guided by phenomenology, semi-structured interviews were conducted with parents of children with ASD ( N =12). Four themes emerged from the findings of this study: safety is the priority, attraction to water, acceptance of children with ASD in aquatic environments, and therapeutic benefits of water. While there are many positives to children with ASD engaging in water activities, families with ASD experience several barriers that limit access to water environments. Recommendations for therapeutic recreation practice and future research are provided. These findings support stakeholders of aquatic environments, therapeutic recreation professionals, and others seeking to increase safety for children with ASD to create reduced-risk aquatic environments.
2026-02-16
Cedomir Stanojevic, Nikki Abbott, Casey Bennett
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.