Quantifying metabolic cost during short transitional movements is challenging because conventional metabolic measurements have limited temporal resolution. This proof-of-concept study examined whether model-derived metabolic cost during the sit-to-walk (STW) transition could be exploratorily stratified using interpretable ensemble learning. Forty-nine healthy adults completed the STW phase of the Timed Up and Go task with synchronized three-dimensional kinematics, ground reaction forces, and eight-channel surface electromyography. Individually scaled OpenSim gait2392 models and the Umberger metabolic model were used to estimate metabolic power from seat-off to the end of the first complete gait cycle. Window-averaged metabolic power was stratified into low-, medium-, and high-cost levels. Window-level biomechanical features were extracted from kinematic, kinetic, and muscle-state time series. Seven classifiers were trained using a subject-level 7:3 train–test split and stratified five-fold cross-validation within the training set, and their probability outputs were integrated through TOPSIS-weighted classifier fusion. SHapley Additive exPlanations were used for class-specific feature attribution. The fused ensemble achieved an AUC of 0.870, F1 score of 0.703, accuracy of 0.705, and specificity of 0.853 on the independent test set. Discrimination was stronger for the low- and high-cost levels than for the medium-cost level. SHAP-based attribution highlighted force-related changes and knee-angle variability and amplitude measures as prediction-relevant biomechanical features. These findings support a model-derived, interpretable workflow for extending STW assessment from task performance to task cost, while indicating the need for further validation in larger and clinical datasets.

Objective: To evaluate the effects of exercise-based interventions on self-reported sleep outcomes in patients with Parkinson’s disease (PD) and to assess the certainty of the available evidence. Methods: PubMed, Web of Science, Scopus, the Cochrane Central Register of Controlled Trials (CENTRAL), and China National Knowledge Infrastructure (CNKI) were searched from inception to 17 August 2026. The review protocol was registered with INPLASY (INPLASY202480029). Risk of bias was assessed using the Cochrane Risk of Bias tool (RoB 1). Random-effects meta-analyses were performed using Hedges’ g, with between-study variance estimated by restricted maximum likelihood. Exploratory subgroup analyses and univariable meta-regression were used to investigate heterogeneity, and sensitivity analyses were conducted to assess the robustness of the findings. Certainty of evidence was assessed using Grading of Recommendations Assessment, Development and Evaluation (GRADE). Results: Fifteen randomized controlled trials involving 696 participants were included. Exercise-based interventions were associated with improved self-reported sleep outcomes (standardized mean difference [SMD] = −0.75, 95% confidence interval [CI] −1.28 to −0.23; p = 0.0084), although between-study heterogeneity was substantial (I² = 82.4%). Instrument-stratified analyses showed no statistically significant differences across sleep questionnaires. Sensitivity analyses restricted to studies using the two most frequently reported sleep questionnaires and leave-one-out analyses yielded estimates consistent in direction with the primary analysis. Subgroup analyses showed no statistically significant differences across exercise intensity, session duration, weekly frequency, intervention period, or exercise type. None of the continuous moderators examined in univariable meta-regression was statistically significant. Conclusions: Exercise-based interventions may improve self-reported sleep outcomes in patients with PD, but the certainty of evidence is very low, and substantial unexplained heterogeneity limits the precision and clinical interpretability of the pooled estimate. Current evidence is insufficient to identify an optimal exercise prescription. Future adequately powered trials should incorporate standardized exercise reporting and objective sleep assessments.