Effect of Exercise on Parkinson’s Disease Tremor: A Meta-analysis Study
Bibliographic record
Abstract
Background: Prior studies suggest that exercise may prevent movement disorders in Parkinson’s disease (PD) patients. In this meta-analysis, the pooled effect of exercise on PD-induced tremor was investigated. Method: Relevant published studies (n = 7) were retrieved by searching major databases, including Scopus, Web of Science and PubMed from 1985 to November 2020. Studies were eligible if the effect of exercise on PD-induced tremor was studied and sufficient information for calculating the effect size was reported. Furthermore, non-English manuscripts and studies related to non-human species were excluded. The quality of studies was evaluated using the improved Newcastle-Ottawa scale (NOS). In this study, variables such as participant’s age and gender, type of exercise, intervention duration and tremor indices were extracted for each study. Between-study heterogeneity and publication bias were calculated using I2 statistic and funnel plot, respectively. Results: Results showed that hand movement and cycling exercises were effective for reducing tremor amplitude or frequency. When all types of exercises (upper, lower or whole-body movement) were considered, an odds ratio (OR) greater than 1 was obtained (log(OR) = 2, 95% CI: 0.88–3.12), while between-study heterogeneity was high (I2 = 78%). By restricting the studies to hand-movement exercises, a lower odds ratio (log(OR) = 1, 95% CI: 0.24–1.77) with small between-study heterogeneity (I2 = 0.0%, p = 0.502) was obtained. Statistical analysis based on Egger’s and Begg’s tests revealed no significant publication bias. Conclusions: Outcomes of this study suggested that exercises as inexpensive, non-invasive and easy-to-implement strategies could be applied for PD patients alongside medical interventions for reducing tremors. Clinical Highlights Exercises reduce tremors in Parkinson’s disease patients significantly. Hand movement exercises are beneficial for reducing tremors in PD patients.
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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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.064 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".