Effects of Exergaming on Balance of Healthy Older Adults: A Systematic Review and Meta-analysis of Randomized Controlled Trials
Why this work is in the frame
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Bibliographic record
Abstract
Balance is critical for older adults to perform daily activities. However, age-related declines in balance increase the risk of falls and severe injuries, such as bone fractures and head injuries. Exergames have been widely applied to improve health-related outcomes in older adults. This meta-analysis aims to quantify the effects of exergaming interventions on balance performance in healthy older adults. A literature search was performed using PubMed, ScienceDirect, SPORTDiscus, COCHRANE, EBSCO, and EMBASE. A total of 16 experimental studies met inclusion criteria for a full-text review. Data synthesis examined balance functions, including static, dynamic, proactive, and perceived balance abilities when performing daily activities. Intervention protocols of the reviewed studies included an average of two to three 40-minute exergaming sessions per week for 8 weeks. A random effects model identified significant effects in favor of the exergaming group, with moderate effect size in dynamic balance (Hedges' g = 0.36, 95% CI = 0.26-1.30, P < 0.001), and perceived balance (Hedges' g = 0.31, 95% CI = 0.04-0.58, P = 0.02); and considerable effect size in Chair Stand Test (Hedges' g = 0.78, 95% CI = 0.26-1.30, P = 0.003), and balance test batteries (Hedges' g = 0.72, 95% CI = 0.42-1.02, P < 0.001). No significant effect was found in the static balance (Hedges' g = 0.22, 95% CI = -0.31 to 0.76, P = 0.42), or proactive balance (Hedges' g = 0.54, 95% CI = -0.12 to 1.20, P = 0.11). Metaanalysis identified exergaming-associated benefits in older adults' balance function and confidence. This finding supports the feasibility of exergaming as a supplementary approach to improve balance for healthy older adults. Health professionals may optimize treatment effect by integrating exergaming sessions into a traditional balance exercise program.
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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 | 0.067 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.105 | 0.017 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 it