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Telehealth utilization has increased since the coronavirus disease 2019 pandemic, reducing barriers to healthcare and, potentially, reducing participation in group health-promotion interventions. However, preferences for telehealth versus in-person formats have not been established. To examine preferences for telehealth and in-person format for primary care and group health-promotion interventions among Black and White women and men aged 20-39. We hypothesized that respondents would report a higher preference for telehealth than in-person appointments. This cross-sectional survey study recruited participants to answer questions about access to technology and preferences for telehealth and in-person formats of primary care and group health promotion. Respondents (n = 404) included similar proportions of White women (24.3%, 26.4 ± 4.3 years), Black women (25.0%, 29.0 ± 6.1 years), White men (25.9%, 32.8 ± 4.5 years), and Black men (24.8%, 30.6 ± 5.2 years). About 98.5% reported having a smartphone, and 80.4% had access to a computer with a camera. Preference ratings were higher for in-person visits, compared to telehealth visits, for both primary care (M = 3.86 ± 1.13 vs. M = 2.87 ± 1.18) and group health promotion (M = 3.72 ± 1.12 vs. M = 3.04 ± 1.20) F's(1,400) > 59.0, P's < .001. Most young adults have access to technology, supporting the feasibility of telehealth interventions. However, telehealth preference ratings were lower than in-person appointments. Preferences for delivery formats should be considered when designing behavioral interventions to promote health and prevent disease., (© Society of Behavioral Medicine 2024. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background and Purpose: This pilot randomized controlled trial evaluated the effects of 12 sessions of patient-specific adaptive dynamic cycling (PSADC) versus non-adaptive cycling (NA) on motor function and mobility in individuals with Parkinson's disease (PD), using inertial measurement unit (IMU) sensors for objective assessment., Methods: Twenty-three participants with PD (13 in the PSADC group and 10 in the NA group) completed the study over a 4-week period. Motor function was measured using the Kinesia™ sensors and the MDS-UPDRS Motor III, while mobility was assessed with the TUG test using OPAL IMU sensors., Results: The PSADC group showed significant improvements in MDS-UPDRS Motor III scores ( t = 5.165, p < 0.001) and dopamine-sensitive symptoms ( t = 4.629, p = 0.001), whereas the NA group did not improve. Both groups showed non-significant improvements in TUG time. IMU sensors provided continuous, quantitative, and unbiased measurements of motor function and mobility, offering a more precise and objective tracking of improvements over time., Conclusions: PSADC demonstrated enhanced treatment effects on PD motor function compared to NA while also reducing variability in individual responses. The integration of IMU sensors was essential for precise monitoring, supporting the potential of a data-driven, individualized exercise approach to optimize treatment outcomes for individuals with PD.

Background: Previous studies have established that increased Sample Entropy (SampEn) of cadence, a measure of non-linear variability, during dynamic cycling leads to greater improvements in motor function for individuals with Parkinson's disease (PD). However, there is significant variability in responses among individuals with PD due to symptoms and disease progression., Objectives: The aim of this study was to develop and test a paradigm for adapting a cycling exercise intervention using SampEn of cadence and rider effort to improve motor function., Methods: Twenty-two participants were randomized into either patient-specific adaptive dynamic cycling (PSADC) or non-adaptive (NA) group. SampEn of cadence was calculated after each of the 12 sessions, and motor function was evaluated using the Kinesia test. Pearson's correlation coefficient was used to analyze the relationship between SampEn of cadence and motor function improvement. Multiple linear regression (MLR) was used to identify the strongest predictors of motor function improvement., Results: Pearson's correlation coefficient revealed a significant correlation between SampEn of cadence and motor function improvements ( R 2  = -.545, P  = .009), suggesting that higher SampEn of cadence led to greater motor function improvement. MLR demonstrated that SampEn of cadence was the strongest predictor of motor function improvement (β = -8.923, t  = -2.632, P  = .018) over the BMI, Levodopa equivalent daily dose, and effort., Conclusions: The findings show that PSADC paradigm promoted a greater improvement in motor function than NA dynamic cycling. These data will be used to develop a predictive model to optimize motor function improvement after cycling in individuals with PD., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Angela Ridgel and Kenneth Loparo are co-inventors on two patents which are related to the device used in this study: “Bike System for Use in Rehabilitation of a Patient,” US 10,058,736. No royalties have been distributed from this patent.

High-cadence dynamic cycling improves motor symptoms of Parkinson's disease (PD), such as tremor and bradykinesia. However, some participants experience greater benefits than others. To gain insight into how individual characteristics and cycling performance affects functional changes, data from two previous studies were used to build several preliminary predictive models. The purpose was to examine which variables contribute to greater improvement in symptoms after high-cadence dynamic cycling. We hypothesized that individuals with higher body mass index (BMI), increased age, more severe symptoms, and higher PD medication dosages were less likely to contribute effort during cycling. UPDRS-III was assessed before and after each session, and cadence and power were recorded every second. Entropy of cadence was calculated, and data were analyzed using analysis of variance and multiple linear regression. The multiple linear regression model of post UPDRS significantly (R 2 = 0.81, p < 0.001) explained its variance, with pre UPDRS as the main predictor ( p < 0.0001). The binomial logistic model of mean effort did not significantly (R 2 = 0.36, p = 0.14) explain the variance. Post-hoc analysis found a significant (β = 0.28, p = 0.03) moderating effect of different levels of BMI on the association between mean effort and post UPDRS. These results suggest that BMI, effort, and baseline UPDRS levels can potentially predict individual responses to high-cadence dynamic cycling., Competing Interests: AR is a co-inventor on two patents which are related to the device used in this study: “Bike System for Use in Rehabilitation of a Patient,” US 10,058,736 and US 9,802,081. No royalties have been distributed from this patent. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Gates and Ridgel.)

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.