Effects of exergaming on cognitive functions and loneliness of older adults with cognitive frailty
Bibliographic record
Abstract
OBJECTIVES: Cognitive frailty combines physical frailty and cognitive impairment in the absence of dementia. The prompt detection of cognitive frailty and early implementation of preventive interventions may reduce the incidence of dementia. However, intervention studies of exergaming in older adults with cognitive frailty are scant. Therefore, we aim to investigate the effectiveness of exergaming on cognitive functions and loneliness among older adults with cognitive frailty. DESIGN: Quasi-experimental design. METHODS: Participants were recruited from four community settings. The experimental group participated in two 40-min group exergaming sessions weekly for eight weeks; the control group received usual care. The outcome measures were the Montreal Cognitive Assessment (MoCA) and the Chinese Version of the Loneliness Scale. Analyses of covariance were conducted to analyze whether exergaming influenced participants' cognitive functions and loneliness. In addition, the effect size of the posttest of the experimental group relative to its baseline value was calculated to determine the effectiveness of the intervention. RESULT: 69 older adults with cognitive frailty were included, and 35 and 34 were assigned to the experimental and control groups, respectively. The exergaming effectively improved the cognitive function of older adults with cognitive frailty. CONCLUSIONS: Exergaming interventions can effectively improve the cognitive functions of older adults with cognitive frailty but do not positively affect loneliness. We provide evidence to healthcare workers to apply exergaming interventions for older adults with cognitive frailty to improve cognitive function.
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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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".