Feasibility of home-based, low-intensity exergame on cognitive function of older adults with mild cognitive impairment
Bibliographic record
Abstract
Background Combined physical–cognitive training has shown beneficial effects on cognition in older adults with mild cognitive impairment (MCI). However, most interventions employ moderate-to-high intensity, which may limit accessibility for individuals with health concerns. Whether low-intensity exercise provides similar cognitive benefits remains inconclusive. Delivering such training through home-based exergame may improve adherence. This study evaluated the feasibility of a home-based, low-intensity exergame for older adults with MCI. Methods Older adults with MCI completed 50-min exergaming sessions, triweekly, for 4 weeks. Feasibility outcomes, including recruitment, adherence, adverse events, enjoyment, and cognitive and physical performance, were evaluated at baseline and post-intervention. Cognitive performance was assessed using the Montreal Cognitive Assessment (MoCA), 10-word recall test, digit span, and verbal fluency tests. Physical performance was assessed with the four-square step and five-times sit-to-stand tests. Results Fifteen older adults with MCI (mean age 66.07 ± 4.36 years; 75% recruitment rate) participated. The average adherence rate was 93.33% (11.2 sessions), with no adverse events or attrition reported. Enjoyment significantly increased from week 1 to week 4 ( p = 0.001). Significant cognitive improvements were demonstrated for the 10-word immediate and delayed recall ( p = 0.03, p = 0.002), MoCA total scores ( p = 0.002), and MoCA sub-domains: executive function ( p = 0.007), language ( p = 0.034), and delayed recall ( p = 0.001). No significant changes were observed in physical performance. Conclusion Home-based, low-intensity exergaming is a safe, feasible, and enjoyable approach for older adults with MCI. Preliminary findings suggest potential cognitive benefits of low-intensity exergaming in improving cognitive function, which warrant confirmation in a large-scale, rigorous randomized controlled trial.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".