EFFECT OF KINECT GAMES ON COGNITION AND QUALITY OF LIFE OF ELDERLY: RANDOMIZED CLINICAL TRIAL
Bibliographic record
Abstract
Objective: To analyze the effect of Kinect Adventures games on cognition and quality of life of community dwelling elderly people. Method: This is randomized clinical trial. 36 elderlies were selected with mean age 69.68 (5.60) were randomized into control group (CG) and experimental group (GE), 18 in each group. The subjects underwent 14 training sessions of one hour, twice a week. The sessions of the CG were composed by warming up, balance training, aerobic exercises, muscular strengthening and cool-down. EG played four Kinect Adventures games. Participants were assessed before, after and 30 days after the training (follow up). Cognition was assessed by the Montreal Cognitive Assessment (MoCA) and the quality of life by the World Health Organization Quality of Life-Older Adults (WHOQOL-OLD). The Study was registered in the Brazilian Registry of Clinical Trials (RBR-4z4f48). Statistical analysis was performed using ANOVA of repeated measures and the post hoc test of Tukey, adopting alfa of 0.05. Results: CG showed improvement on MoCA after training (the mean difference between before and after training was 3.5; 95% Confidence Interval 1.11 to 5.99; P<0.01). GE showed improvement on MoCA on follou up (the mean difference between before and follow up was 3.66; 95% Confidence Interval 1.18 to 6.14; P<0.01). There was no difference between the groups. Regarding Quality of Life, both groups showed no improvements. Conclusion: Both training improved cognition and did not interfere on quality of life of community dwelling elderly people. This result is attributed to the short intervention time and small sample.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".