Gates Peter, Ridgel Angela L
Motor Control Lab, Exercise Science and Exercise Physiology, School of Health Sciences, Kent State University, Kent, OH, United States.
Brain Health Research Institute, Kent State University, Kent, OH, United States.
Front Rehabil Sci. 2022 Apr 15;3:858401. doi: 10.3389/fresc.2022.858401. eCollection 2022.
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 = 0.81, < 0.001) explained its variance, with pre UPDRS as the main predictor ( < 0.0001). The binomial logistic model of mean effort did not significantly (R = 0.36, = 0.14) explain the variance. analysis found a significant (β = 0.28, = 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.
高踏频动态骑行可改善帕金森病（PD）的运动症状，如震颤和运动迟缓。然而，一些参与者比其他参与者受益更多。为了深入了解个体特征和骑行表现如何影响功能变化，我们使用了之前两项研究的数据来构建几个初步的预测模型。目的是研究哪些变量有助于在高踏频动态骑行后症状得到更大改善。我们假设体重指数（BMI）较高、年龄较大、症状较严重以及帕金森病药物剂量较高的个体在骑行时付出努力的可能性较小。每次训练前后均评估统一帕金森病评定量表第三部分（UPDRS-III），并每秒记录踏频和功率。计算踏频的熵，并使用方差分析和多元线性回归对数据进行分析。UPDRS后的多元线性回归模型显著（R = 0.81，<0.001）解释了其方差，其中UPDRS前作为主要预测因子（<0.0001）。平均努力程度的二项逻辑模型未显著（R = 0.36，= 0.14）解释方差。分析发现不同BMI水平对平均努力程度与UPDRS后之间的关联具有显著（β = 0.28，= 0.03）的调节作用。这些结果表明，BMI、努力程度和基线UPDRS水平可能预测个体对高踏频动态骑行的反应。
