Effects of active videogames added to conventional exercise on cognitive function in older adults: a randomized trial
Bibliographic record
Abstract
Introduction: Cognitive decline in older adults negatively affects independence and health-related quality of life (HRQoL). Integrating physical and cognitive stimuli through active videogames (AVGs) may enhance the benefits of conventional physical exercise (CPE). Objective: To evaluate the effects of adding AVGs to a CPE program on cognitive function and HRQoL in community-dwelling older adults. Methodology: A controlled trial with two parallel groups was conducted. Fifty participants aged 60–84 years were randomly assigned to an intervention group (IG: CPE+AVG) or a control group (CG: CPE alone) for eight weeks (two sessions/week). Cognitive function (primary outcome) was assessed with the Montreal Cognitive Assessment (MoCA), and HRQoL (secondary outcome) with the Short Form Health Survey Version 2 (SF-12v2). Results: Both groups led to improvements across several cognitive domains; however, the IG showed significantly greater gains in language, delayed recall, and global cognitive function (all p<0.05). The proportion of participants with normal cognitive function increased by 36% in the IG (p=0.003) versus 12% in the CG (p>0.05). Compared with the CG, the IG showed significant improvements in both the physical and mental health dimensions of HRQoL (all p<0.05). Conclusions: The findings suggest that adding AVGs to CPE may enhance cognitive function and HRQoL more effectively than CPE alone. AVGs appear to be a safe, engaging, and promising adjunct to promote cognitive health and well-being in older adults.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".