Feasibility of the Diabetes and Technology for Increased Activity (DaTA) Study: A Pilot Intervention in High-Risk Rural Adults
Bibliographic record
Abstract
BACKGROUND: Rural Canadians are at increased risk of metabolic syndrome. Physical inactivity is a primary target for preventing and reversing metabolic syndrome. Adherence to lifestyle interventions may be enhanced using cell phones and self-monitoring technologies. This study investigated the feasibility of a physical activity and self-monitoring intervention targeting high-risk adults in rural Ontario. METHODS: Rural adults (n = 25, mean = 57.0 ± 8.7 years) with ≥ 2 criteria for metabolic syndrome participated in an 8-week stage-matched physical activity and self-monitoring intervention. Participants monitored blood glucose, blood pressure, weight, and physical activity using self-monitoring devices and Blackberry Smart phones. VO2max, stage of change, waist circumference, weight, blood lipids, and HbA1c were measured at weeks 1, 4, and 8. RESULTS: Adherence to self-monitoring was > 94%. Participants' experiences and perceptions of the technology were positive. Mean stage of change increased 1 stage, physical activity increased 26%, and predicted VO2max increased 17% (P < .05). Significant changes in weight, waist circumference, diastolic blood pressure, LDL cholesterol, and total cholesterol were found. CONCLUSIONS: This stage-matched technology intervention for increased physical activity was feasible and effective.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".