An 8-Week Low-Intensity Progressive Cycling Training Improves Motor Functions in Patients with Early-Stage Parkinson's Disease
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The effects of high-intensity cycling as an adjuvant therapy for early-stage Parkinson's disease (PD) were highlighted recently. However, patients experience difficulties in maintaining these cycling training programs. The present study investigated the efficacy of cycling at a mild-to-moderate intensity in early-stage PD. METHODS: Thirteen PD patients were enrolled for 16 serial cycling sessions over a 2-month period. Motor function was assessed using the Unified Parkinson's Disease Rating Scale part III (UPDRS III) and Timed Up and Go (TUG) test as primary outcomes. The Montreal Cognitive Assessment (MoCA), modified Hoehn and Yahr Stage (mHYS), total UPDRS, Falls Efficacy Scale, New Freezing of Gait Questionnaire, Schwab and England Activities of Daily Living, 39-item Parkinson's Disease Questionnaire, Patient Global Impression of Change, and gait performance were assessed as secondary outcomes. RESULTS: The age and the age at onset were 59.67±7.24 and 53.23±10.26 years (mean±SD), respectively. The cycling cadence was 53.27±8.92 revolutions per minute. The UPDRS III score improved significantly after 8 training sessions (p=0.011) and 16 training sessions (T2) (p=0.001) in the off-state, and at T2 (p=0.004) in the on-state compared to pretraining (T0). The TUG duration was significantly shorter at T2 than at T0 (p<0.05). The findings of MoCA, total UPDRS, double limb support time, and mHYS (in both the off- and on-states) also improved significantly at T2. CONCLUSIONS: Our pioneer study has demonstrated that a low-intensity progressive cycling exercise can improve motor function in PD, especially akinesia. The beneficial effects were similar to those of high-intensity rehabilitation programs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".