The effect of a game training intervention on cognitive functioning and depression symptoms in the elderly with mild cognitive impairment: A randomized controlled trial
Bibliographic record
Abstract
OBJECTIVES: This study aimed to explore whether game training could improve cognitive functioning and depression symptoms in the elderly affected by mild cognitive impairment (MCI). METHODS: A non-blinded randomized controlled trial was conducted. Participants were 72 patients with MCI and depression from a nursing home in Wuhan. Participants were randomized to either the intervention group or the control group (n = 36 each). The intervention group received regular nursing care plus game training for 50 min, three times per week for 8 weeks, whereas the control group received only regular nursing care during the same research period. Cognitive functioning and depression symptoms were tested in both groups at baseline and at the end of the 8-week intervention. We used the Montreal Cognitive Assessment and the 15-item Geriatric Depression Scale to assess cognitive functioning and depression symptoms, respectively. RESULTS: The 8-week game training intervention significantly improved the cognitive and depression scores when compared with the control group and baseline scores (p < 0.05). No significant difference was observed in the control group (p > 0.05). CONCLUSIONS: Our results suggest that the implementation of game training can improve the cognitive functioning and depression symptoms of the elderly with MCI, indicated that can be widely used.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| 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 | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".