The impact of digitalized community-based square-stepping exercise program on cognitive and balance functions among older adults living in senior facilities: a pilot study
Bibliographic record
Abstract
Abstract Objectives: Older adults exhibit a high desire for active and healthy aging without physical or mental dysfunction, particularly those living independently in senior facilities. Preserving or improving cognitive function and minimizing fall risks are essential for older adults to live a happy and active lifestyle. The purpose of this pilot study was to examine the feasibility, safety, and preliminary effectiveness of the innovative digitalized community-based square-stepping exercise program (DC-SSEP) in improving cognitive and physical function among older adults residing in senior facilities. Methods: Guided by the Health Promotion Model and Social Cognitive Theory, this pilot study used a quasi-experiment design with one intervention group. A total of 17 older adults recruited from a senior facility in Southern Texas participated in 40 sessions of DC-SSEP over 20 weeks. Cognitive function was measured using the latest version (8.1) of Montreal Cognitive Assessment and the balance function focusing on balance and functional mobility was measured using Berg Balance Scale and Time to Up and Go. Results: Most participants were non-Hispanic white women. The DC-SSEP was a feasible and safe exercise program for older adults living in senior facilities; and the results showed the preliminary effectiveness of the DC-SSEP in improving cognitive and balance function ( P < 0.01) among older adults. Conclusion: This pilot study is distinctive as it is among the first to evaluate the multi-layered impacts of DC-SSEP using Internet of Things (IoT) technology and integrated operating software in the United States. Despite the small sample size and homogeneity of participants, this pilot study suggests multiple valuable directions for future research using DC-SSEP.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".