Mobile health, exercise and metabolic risk: a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: It was hypothesized that a mobile health (mHealth) intervention would elicit greater improvements in systolic blood pressure and other cardiometabolic risk factors at 12 weeks, which would be better maintained over 52 weeks, compared to the active control intervention. METHODS: Eligible participants (≥2 metabolic syndrome risk factors) were randomized to the mHealth intervention (n = 75) or the active control group (n = 74). Blood pressure and other cardiometabolic risk factors were measured at baseline and at 12, 24 and 52 weeks. Both groups received an individualized exercise prescription and the intervention group additionally received a technology kit for home monitoring of biometrics and physical activity. RESULTS: Analyses were conducted on 67 participants in the intervention group (aged 56.7 ± 9.7 years; 71.6% female) and 60 participants in the active control group (aged 59.1 ± 8.4 years; 76.7% female). At 12 weeks, baseline adjusted mean change in systolic blood pressure (primary outcome) was greater in the active control group compared to the intervention group (-5.68 mmHg; 95% CI -10.86 to -0.50 mmHg; p = 0.03), but there were no differences between groups in mean change for secondary outcomes. Over 52-weeks, the difference in mean change for systolic blood pressure was no longer apparent between groups, but remained significant across the entire population (time: p < 0.001). CONCLUSIONS: In participants with increased cardiometabolic risk, exercise prescription alone had greater short-term improvements in systolic blood pressure compared to the mHealth intervention, though over 52 weeks, improvements were equal between interventions. TRIAL REGISTRATION: ClinicalTrials.gov http://NCT01944124.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".