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EP-04 Biomechanics and Neural Control of Movement

Optimization Of A SMART Dynamic Bike To Improve Motor Function In Parkinson’s Disease


Ridgel, Angela L. FACSM; Gates, Peter; Melczak, Robert

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Medicine & Science in Sports & Exercise: August 2021 - Volume 53 - Issue 8S - p 169
doi: 10.1249/01.mss.0000761052.47405.f3
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High-cadence dynamic cycling improves functional motor scores in individuals with Parkinson's disease (PD). However, variability in responses among individuals suggests that an adaptive controller could help to decrease that variability.

PURPOSE: To develop a Speed Manipulated Adaptive Rehabilitation Therapy (SMART) model for control of a custom designed motorized dynamic cycle and to evaluate the model accuracy for predicting motor function changes and physical effort.

METHODS: Individuals with PD (N = 17) completed three sessions of dynamic cycling under three different bike acceleration and velocity settings. Outcome measures included the pre and post UPDRS scores, cadence, and effort. Effort was defined as percent positive power over the length of the session, indicating the ability to keep up with the motor. Demographic data included BMI, age, gender, disease duration, medication dosage, most-affected side, and exercise history. Multiple linear regression models were built to examined how baseline UPDRS III scores, medication dosage, cycling cadence, entropy of cycling cadence (variability) and rider effort (power) affected the change in UPDRS III scores after a single bout of high-cadence cycling.

RESULTS: This model showed that daily total medication explains 68% of the variance in the change of UPDRS III after high-cadence dynamic cycling (p = 0.046). This finding is not surprising, but it is important to note because the testing and exercise were done with individuals 'on' medication. The second model showed that effort during high-cadence cycling explained 64% of the variance and that BMI (p = 0.01) and age (p = 0.05) were significant contributors.

CONCLUSIONS: These findings suggest that medication, rider effort, age and BMI are important predictors of UPDRS III improvement after high-cadence dynamic cycling. These models will be further tested in a broad range of individuals with PD (different ages, BMI, baseline UPDRS, medication dosage) to optimize the SMART controller. Supported by the Davis Phinney Foundation

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