High-cadence dynamic cycling is an effective therapy for improving motor symptoms in individuals with Parkinson’s Disease (PD), as measured by the Unified Disease Parkinson’s Rating Scale-Motor III (UPDRS-III). However, there is significant variability in individual responses to this therapy. Our lab developed a patient-specific adaptive dynamic cycling (PSADC) paradigm that manipulates entropy of cadence to optimize exercise prescriptions for individuals at various stages of disease progression. The purpose of this study is to evaluate the effectiveness of 12 sessions of PSADC on motor symptom improvement (UPDRS-III score) in individuals with PD.


Twenty-three individuals with idiopathic PD (were randomized into two groups: PSADC (n=13) or active control (n=10). All individuals completed 12 sessions (3 sessions per week for 4 weeks) of dynamic cycling on a SMART (Speed Manipulated Adaptive Rehabilitation Therapy) bicycle. Each session consisted of a 5-minute warm-up at 60 revolutions per minute (rpm), 30-minute exercise session (80 rpm), and 5-minute cool-down (60 rpm). Individuals in the PSADC group followed an adaptive exercise prescription in which resistance level was optimized on a weekly basis, based on the individual’s entropy of cadence, and cycling effort. Individuals in the active control group remained at a constant resistance level for the entirety of the intervention. UPDRS-III was assessed in all participants prior to and following the 12-session intervention. Two-way analysis of variance (ANOVA) and paired samples t-tests were performed to detect statistical differences in UPDRS-III score between the groups.


There was a significant group by time interaction (F= 18.746, p <0.001). The PSADC group showed a significant reduction (improvement) in UPDRS-III score (Pre: 32.8, Post: 27.5; p<0.001), while the active control group showed no significant change in UPDRS-III score (Pre: 28.2, Post: 32.4, p=0.08).


12 sessions of PSADC significantly improved UPDRS-III score, compared to non-adaptive high-cadence dynamic cycling. These results suggest that optimizing entropy of cadence is valuable for motor symptom improvement. Future studies will develop machine learning algorithms designed to predict appropriate clinical exercise prescriptions for individuals with PD.

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