Analysis of Movement Entropy during Community Dance Programs for People with Parkinson's Disease and Older Adults: A Cohort Study.

Dance therapy can improve motor skills, balance, posture, and gait in people diagnosed with Parkinson's disease (PD) and healthy older adults (OA). It is not clear how specific movement patterns during dance promote these benefits. The purpose of this cohort study was to identify differences and complexity in dance movement patterns among different dance styles for PD and OA participants in community dance programs using approximate entropy (ApEn) analysis. The hypothesis was that PD participants will show greater ApEn during dance than OA participants and that the unique dance style of tango with more pronounced foot technique and sharp direction changes will show greater ApEn than smoother dance types such as foxtrot and waltz characterized by gradual changes in direction and gliding movement with rise and fall. Individuals participated in one-hour community dance classes. Movement data were captured using porTable 3D motion capture sensors attached to the arms, torso and legs. Classes were also video recorded to assist in analyzing the dance steps. Movement patterns were captured and ApEn was calculated to quantify the complexity of movements. Participants with PD had greater ApEn in right knee flexion during dance movements than left knee flexion (p = 0.02), greater ApEn of right than left hip flexion (p = 0.05), and greater left hip rotation than right (p = 0.03). There was no significant difference in ApEn of body movements (p > 0.4) or mean body movements (p > 0.3) at any body-segment in OA. ApEn analysis is valuable for quantifying the degree of control and predictability of dance movements and could be used as another tool to assess the movement control of dancers and aid in the development of dance therapies.

Design and Development of a Smart Exercise Bike for Motor Rehabilitation in Individuals with Parkinson's Disease.

Recent studies in rehabilitation of Parkinson's disease (PD) have shown that cycling on a tandem bike at a high pedaling rate can reduce the symptoms of the disease. In this research, a smart motorized bicycle has been designed and built for assisting Parkinson's patients with exercise to improve motor function. The exercise bike can accurately control the rider's experience at an accelerated pedaling rate while capturing real-time test data. Here, the design and development of the electronics and hardware as well as the software and control algorithms are presented. Two control algorithms have been developed for the bike; one that implements an inertia load (static mode) and one that implements a speed reference (dynamic mode). In static mode the bike operates as a regular exercise bike with programmable resistance (load) that captures and records the required signals such as heart rate, cadence and power. In dynamic mode the bike operates at a user-selected speed (cadence) with programmable variability in speed that has been shown to be essential to achieving the desired motor performance benefits for PD patients. In addition, the flexible and extensible design of the bike permits readily changing the control algorithm and incorporating additional I/O as needed to provide a wide range of riding experiences. Furthermore, the network-enabled controller provides remote access to bike data during a riding session.

Test and Validation of a Smart Exercise Bike for Motor Rehabilitation in Individuals With Parkinson's Disease.

To assess and validate the Smart Exercise Bike designed for Parkinson's Disease (PD) rehabilitation, 47 individuals with PD were randomly assigned to either the static or dynamic cycling group, and completed three sessions of exercise. Heart rate, cadence and power data were captured and recorded for each patient during exercise. Motor function for each subject was assessed with the UPDRS Motor III test before and after the three exercise sessions to evaluate the effect of exercise on functional abilities. Individuals who completed three sessions of dynamic cycling showed an average of 13.8% improvement in the UPDRS, while individuals in the static cycling group worsened by 1.6% in UPDRS. To distinguish the static and dynamic cycling groups by biomechanical and physiological features, the complexity of the recorded signals (cadence, power, and heart rate) was examined using approximate entropy (ApEn), sample entropy (SaEn) and spectral entropy (SpEn) as measures of variability. A multiple linear regression (MLR) model was used to relate these features to changes in motor function as measured by the UPDRS Motor III scale. Pattern variability in cadence was greater in the dynamic group when compared to the static group. In contrast, variability in power was greater for the static group. UPDRS Motor III scores predicted from the pattern variability data were correlated to measured scores in both groups. These results support our previous study which explained how variability analysis results for biomechanical and physiological parameters of exercise can be used to predict improvements in motor function.

Dynamic High-Cadence Cycling Improves Motor Symptoms in Parkinson's Disease.

RATIONALE: Individuals with Parkinson's disease (PD) often have deficits in kinesthesia. There is a need for rehabilitation interventions that improve these kinesthetic deficits. Forced (tandem) cycling at a high cadence improves motor function. However, tandem cycling is difficult to implement in a rehabilitation setting. OBJECTIVE: To construct an instrumented, motored cycle and to examine if high cadence dynamic cycling promotes improvements in motor function. METHOD: This motored cycle had two different modes: dynamic and static cycling. In dynamic mode, the motor maintained 75-85 rpm. In static mode, the rider determined the pedaling cadence. UPDRS Motor III and Timed Up and Go (TUG) were used to assess changes in motor function after three cycling sessions. RESULTS: Individuals in the static group showed a lower cadence but a higher power, torque and heart rate than the dynamic group. UPDRS score showed a significant 13.9% improvement in the dynamic group and only a 0.9% improvement in the static group. There was also a 16.5% improvement in TUG time in the dynamic group but only an 8% improvement in the static group. CONCLUSION: These findings show that dynamic cycling can improve PD motor function and that activation of proprioceptors with a high cadence but variable pattern may be important for motor improvements in PD.

Variability in cadence during forced cycling predicts motor improvement in individuals with Parkinson's disease.

Variability in severity and progression of Parkinson's disease symptoms makes it challenging to design therapy interventions that provide maximal benefit. Previous studies showed that forced cycling, at greater pedaling rates, results in greater improvements in motor function than voluntary cycling. The precise mechanism for differences in function following exercise is unknown. We examined the complexity of biomechanical and physiological features of forced and voluntary cycling and correlated these features to improvements in motor function as measured by the Unified Parkinson's Disease Rating Scale (UPDRS). Heart rate, cadence, and power were analyzed using entropy signal processing techniques. Pattern variability in heart rate and power were greater in the voluntary group when compared to forced group. In contrast, variability in cadence was higher during forced cycling. UPDRS Motor III scores predicted from the pattern variability data were highly correlated to measured scores in the forced group. This study shows how time series analysis methods of biomechanical and physiological parameters of exercise can be used to predict improvements in motor function. This knowledge will be important in the development of optimal exercise-based rehabilitation programs for Parkinson's disease.