Diabetes and Technology for Increased Activity (DaTA) Study
Bibliographic record
Abstract
PURPOSE: To determine the impact of an 8-week exercise program delivered to patients with metabolic syndrome using wireless patient self monitoring technologies METHODS: Exercise prescription was delivered by a trained kinesiologist at baseline to 25 patients with metabolic syndrome in rural Ontario Canada living > 1 hour from a health provider. Clinical outcomes included fasting glucose, lipids, CRP, waist circumference, blood pressure and daily steps. Subjects received daily advice and sent daily clinical measures wirelessly using a Blackberry and Bluetooth enabled devices. RESULTS: Eighteen women and 6 men with mean age of 56.6 (±8.9) completed the eight-week intervention. Six people had T2D. Technology burden was minimal and compliance was 95% (±2.2) for all Blackberry measures. From baseline, improvements were found in diastolic BP (p=0.046), BMI (p=0.030), waist circumference (p=0.002), CHOL (p=0.009), V02max, (p=0.000) and training heart rate (p=0.000). Daily pedometer steps increased (r2=0.3099). BG, CRP, and LDL levels decreased from baseline, but changes were not statistically significant. CONCLUSIONS: Self-managed monitoring technology is feasible, usable, and may assist in the prevention of developing CVCs of T2D, especially where access to health care is inconsistent. Willingness to change, patient satisfaction and interest in improving health indicators were positive outcomes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".