Abstract
OBJECTIVE: Physical therapists are well-positioned to prescribe exercise outside of a clinical setting to promote positive health behaviors in people with Parkinson disease (PD). Traditionally, a barrier to precise exercise prescription has been reliance on participant self-reported exercise adherence and intensity. Home-based, commercially available exercise platforms offer an opportunity to remotely monitor exercise behavior and facilitate adherence based on objective performance metrics. The primary aim of this project was to characterize the feasibility and processes of remote aerobic exercise data monitoring from a home-based, commercially available platform in individuals participating in the 12-month Cyclical Lower Extremity Exercise for PD II (CYCLE-II) randomized clinical trial. Secondary aims focused on using exercise behavior to classify the cohort into exercise archetypes and describing a shared decision-making process to facilitate exercise adherence. METHODS: Data from each exercise session were extracted, visualized, and filtered to ensure ride integrity. Weekly exercise frequency was used to determine exercise archetypes: Adherent (2-4 exercise sessions per week), Over-adherent (>4 exercise sessions per week), and Under-adherent (<2 exercise sessions per week). RESULTS: A total of 123 people with PD completed 22,000+ exercise sessions. Analysis of exercise frequency indicated that 79% of participants were adherent; 8% were over-adherent; and 13% were under-adherent. Three case reports illustrate how shared decision-making with the use of exercise performance data points guided exercise prescription. CONCLUSIONS: The number of exercise sessions and completeness of the data indicate that people with PD were able to utilize a commercial, home-based exercise platform to successfully engage in long-term aerobic exercise. Physical therapists can use objective data as a part of a shared decision-making process to facilitate exercise adherence. IMPACT: Commercially available exercise platforms offer a unique approach for physical therapists to monitor exercise behavior outside of a clinical setting. The methods used in this project can serve as a roadmap to utilizing data from consumer-based platforms.