Effects of Exergaming on Musculoskeletal Pain in Older Adults: Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Exercise is effective for musculoskeletal pain. However, physical, social, and environmental factors make it difficult for older adults to persist in exercising. Exergaming is a new pathway that combines exercise with gameplay and may be helpful for older adults to overcome these difficulties and engage in regular exercise. OBJECTIVE: This systematic review aimed to determine the efficacy of exergaming to improve musculoskeletal pain in older adults. METHODS: The search was performed in 5 databases (PubMed, Embase, CINAHL, Web of Science, and Cochrane Library). The risk of bias for randomized controlled studies was assessed using the revised Cochrane Risk of Bias tool in randomized trials (RoB 2), and the methodological quality was assessed using the Physiotherapy Evidence-Based Database scale. Standardized mean difference and 95% CI were calculated using fixed-effects model meta-analyses in the Review Manager version 5.3 (RevMan 5.3). RESULTS: Seven randomized controlled studies were included, which contained 264 older adults. Three of the 7 studies reported significant improvements in pain after the exergaming intervention, but only 1 reported a significant difference between groups after adjustment for baseline (P<.05), and another reported a significant improvement in thermal pain between the 2 groups (P<.001). The results of the meta-analysis of the 7 studies showed no statistically significant improvement in pain compared to the control group (standardized mean difference -0.22; 95% CI -0.47 to 0.02; P=.07). CONCLUSIONS: Although the effects of exergames on musculoskeletal pain in older adults are unknown, exergame training is generally safe, fun, and appealing to older adults. Unsupervised exercise at home is feasible and cost-effective. However, most of the current studies have used commercial exergames, and it is recommended that there should be more cooperation between industries in the future to develop professional rehabilitation exergames that are more suitable for older adults. The sample sizes of the studies included are small, the risk of bias is high, and the results should be interpreted with caution. Further randomized controlled studies with large sample sizes, high quality, and rigor are needed in the future. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42022342325; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=342325.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".