Effects of Exergaming on Balance of Healthy Older Adults: A Systematic Review and Meta-analysis of Randomized Controlled Trials
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). Meta-analysis 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.029 |
| 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) | 0.005 | 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".