Integrating Cognitive and Motor Dual-task Training to Prevent Fall Risk Among Community-dwelling Elderly in Thailand: A Randomized Controlled Study
Bibliographic record
Abstract
OBJECTIVES: Falls are a leading cause of morbidity and mortality among older adults, often resulting in severe injuries and a loss of independence. Dual-task training, which integrates cognitive and motor exercises, has emerged as a promising intervention for fall prevention. This study aimed to evaluate the effects of a structured cognitive and motor dual-task training program on fall risk, balance, and cognitive function in community-dwelling older adults. METHODS: Seventy-two participants aged 60 years or older were randomly assigned to either an intervention group (IG, n=36) or a control group (CG, n=36). The IG underwent 3 sessions per week for 8 weeks (totaling 24 sessions) that incorporated simultaneous cognitive and motor exercises, while the CG continued their usual total body stretching exercise. Assessments were conducted at baseline, week 4, and week 8, and included gait speed (10-meter walk test), functional performance (Timed Up and Go test), cognitive function (Montreal Cognitive Assessment), and quality of life (World Health Organization Quality of Life Assessment). RESULTS: Participants in the IG demonstrated significant improvements in functional performance (p<0.05) and enhanced cognitive function compared to the CG after both 4 weeks and 8 weeks of training. Functional performance and cognitive function significantly improved after 8 weeks of training (p<0.01). However, the intervention did not produce changes in gait speed or quality of life. CONCLUSIONS: Integrating cognitive and motor dual-task training into fall prevention programs may enhance functional stability and cognitive resilience in older adults. Future studies should investigate long-term adherence and determine the optimal training intensity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".