Effectiveness of Walking Prescription Using Mobile Health Technology on the Changes in Daily Steps in Older Adults With Cognitive Impairment: Randomized Controlled Study
Bibliographic record
Abstract
Background: Walking is frequently recommended as a beneficial physical activity for older adults, as it can enhance both their physical and mental well-being and help prevent cognitive decline and dementia. While it is known that mobile health (mHealth) technology can help improve physical activity among older adults, there is limited research on its effectiveness for older individuals with cognitive impairment. Objective: This study aimed to determine the effectiveness and feasibility of walking prescriptions using mHealth technology for older adults with cognitive impairment. Methods: In total, 60 older adults (mean=76.1, SD 5.4) years; female, n=34) with mild cognitive impairment or mild dementia (n=28 and n=32, respectively; Mini-Mental State Examination [MMSE], mean=20.7, SD 4.0) who visited the memory clinic were enrolled. They were randomly assigned into three groups: (1) group A (n=20) was prescribed with a goal of daily steps based on their telemonitored activity using a smart band; (2) group B (n=19) only wore a smart band without a prescription; and (3) group C (n=21) took a monthly education to encourage their walking. All participants took monthly face-to-face sessions with a coach to check their performance and modify the goal of daily steps. Changes in daily steps (primary outcome), cognitive function, physical status, and depressive symptoms from baseline to post-intervention (12 weeks) and follow-up (24 weeks) were assessed by unblinded researchers. Linear mixed effect models with factors of group (reference: control), time (reference: baseline), and their interaction were used for data analysis. Post hoc analyses using paired t tests were also conducted. Results: For group A, there was a significant group × time interaction effect on daily steps both at 12 and 24 weeks (β (SE)=2205.88 (672.34), P=.001; β (SE)=2194.63 (884.33), P=.015). Group B showed increased numbers of steps only at 12 weeks but not at 24 weeks. Group C showed a continuous decrease in daily steps during the study period. Regarding secondary outcomes, group C showed a significant decline in cognitive function measured by MMSE both at 12 and 24 weeks. However, groups A and B showed stationary MMSE scores during 24 weeks. The number of withdrawn participants did not differ among the 3 groups. Conclusions: Our findings suggest that walking prescriptions using mHealth technology can effectively increase daily steps in older adults with cognitive impairment.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| 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".