Health Promotion Through Primary Care: Enhancing Self-Management With Activity Prescription and mHealth
Bibliographic record
Abstract
BACKGROUND: It is well established in the literature that regular participation in physical activity is effective for chronic disease management and prevention. Remote monitoring technologies (ie, mHealth) hold promise for engaging patients in self-management of many chronic diseases. The purpose of this study was to test the effectiveness of an mHealth study with tailored physical activity prescription targeting changes in various intensities of physical activity (eg, exercise, sedentary behavior, or both) for improving physiological and behavioral markers of lifestyle-related disease risk. METHODS: Forty-five older adults (aged 55-75 years; mean age 63 ± 5 years) were randomly assigned to receive a personal activity program targeting changes to either daily exercise, sedentary behavior, or both. All participants received an mHealth technology kit including smartphone, blood pressure monitor, glucometer, and pedometer. Participants engaged in physical activity programming at home during the 12-week intervention period and submitted physical activity (steps/day), blood pressure (mm Hg), body weight (kg), and blood glucose (mmol/L) measures remotely using study-provided devices. RESULTS: There were no differences between groups at baseline (P > 0.05). The intervention had a significant effect (F(10 488) = 2.947, P = 0.001, ηP² = 0.057), with similar changes across all groups for physical activity, body weight, and blood pressure (P > 0.05). Changes in blood glucose were significantly different between groups, with groups prescribed high-intensity activity (ie, exercise) demonstrating greater reductions in blood glucose than the group prescribed changes to sedentary behavior alone (P < 0.05). CONCLUSIONS: Findings demonstrate the utility of pairing mHealth technologies with activity prescription for prevention of lifestyle-related chronic diseases among an at-risk group of older men and women. RESULTS support the novel approach of prescribing changes to sedentary behaviors (alone, and in conjunction with exercise) to reduce risk of developing lifestyle-related chronic conditions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".