Exergaming Compared to Conventional Physical Exercise Interventions on Health Status in Older People with Parkinson’s Disease: A Systematic Review with Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Background and Objectives: This systematic review aimed to analyze published peer-reviewed studies on the effects of exergaming (EXG) compared to conventional physical exercise (CPE) interventions on health status in older people with Parkinson’s disease (PD) according to training dose. Materials and Methods: Using six generic databases: PubMed, EBSCO, Medline, CINAHL Complete, Scopus, and Web of Science, the PRISMA, TESTEX, RoB 2, and GRADE tools assessed methodological quality and certainty. The protocol was registered in PROSPERO (code: CRD42024575969). Results: Out of 805 records, 14 randomized controlled trials with 406 older people with PD were included. Seven overall meta-analyses showed significant improvements (p < 0.01) in favor of EXG in the Berg Balance Scale (BBS, ES = 0.90), Dynamic Gait Index (DGI, ES = 0.77) and quality of life questionnaire (PDQ-39, ES = 0.52), without significant improvements (p > 0.05) in the Unified PD Rating Scale, Montreal Cognitive Assessment, Timed Up-and-Go and Falls Efficacy Scale-International. Four subgroup meta-analyses, according to training schedules, showed that there were significant improvements (p < 0.05) in BBS in favor of EXG at >8 weeks of training (ES = 1.38), >3 weeks per week (ES = 1.18), <45 min duration (ES = 0.99), and with >20 total sessions (ES = 1.31). Both weeks and total sessions were predictors of BBS performance in EXG interventions in older people with PD. Conclusions: EXG is an innovative alternative to improve the health status in balance, gait, and quality of life variables in older people with PD, with a high potential for clinical practice in this population. The training dose is a determinant (weeks and total sessions) that varies the response to intervention in the BBS.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.030 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".