Diabetes and Technology for Increased Activity (DaTA) Study: Results of a Remote Monitoring Intervention for Prevention of Metabolic Syndrome
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: An increasingly aged, overweight, and sedentary population has resulted in elevated risk of cardiovascular disease (CVD). The escalating incidence of diabetes and other chronic illnesses, deficits in health care budgets, and physician shortages, especially in rural communities, have prompted investigations of feasible solutions. The Diabetes and Technology for Increased Activity (DaTA) study was designed to test the effectiveness of a lifestyle intervention driven by self-monitoring of blood glucose (BG), blood pressure (BP), physical activity (PA), and weight to positively impact CVD risk factors in a medically underserviced rural population with a high incidence of metabolic syndrome (MS). RESEARCH DESIGN AND METHODS: Conducted in a community-based research setting, this single-center open feasibility study used smart phones to transmit BP, BG, pedometer, weight, heart rate, and activity measurements to a database. Technology allowed participants to interface with the clinical team and self-monitor their personal health indicators. RESULTS: Twenty-four participants aged 30 to 71 years completed the 8-week intervention. Participants had significant improvement in clinic (p = .046) and self-monitored diastolic BP (p = .001), body mass index (p = .002), and total cholesterol (p = .009), and steps per day. Daily PA increased as well as participants' interest in and willingness to make lifestyle changes that impact health outcomes. CONCLUSIONS: The DaTA study demonstrated that self-monitoring of the risk factors for MS and increased PA improved the participant's CVD risk profile. Considering the 8-week time period of this intervention, results are encouraging. This lifestyle intervention, which uses education and technology as tools, confirms the utility of remote health monitoring.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it