Effects of Exergaming on Physical and Cognitive Outcomes of Older Adults Living in Long-Term Care Homes: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Aging is often associated with increasing functional decline as measured by deterioration in mobility and activities of daily living. Older adults (OAs) living in residential long-term care (LTC) homes in particular may not engage in regular physical exercise, significantly increasing their risk of further cognitive and functional decline. Exergaming may hold promise for OAs by combining exercise and technology-based gaming systems, but evidence for its use in LTC is unknown. METHODS: A systematic review was conducted to summarize the effects of exergaming interventions on physical, cognitive, and quality of life (QoL) outcomes for OAs (>65 years of age) living in LTC. RESULTS: Twenty-one studies involving 657 OAs living in LTC met the inclusion criteria. Most studies were associated with a high risk of bias and many used uncontrolled designs and small samples. Across studies, exergame interventions were associated with preliminary benefits relative to control conditions on standardized measures of physical outcomes (e.g., Timed Up & Go, 5-meter gait speed). No consistent effects were found for cognitive and QoL outcomes. CONCLUSIONS: Exergames might be a promising intervention to benefit the physical health of OAs (>65 years) living in LTC, but more research is required to determine the effects of exergaming on physical health, as well as cognitive and QoL outcomes. More specifically, larger and more methodologically robust evaluations are needed.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".