Remote Monitoring Technologies for the Prevention of Metabolic Syndrome: The Diabetes and Technology for Increased Activity (DaTA) Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: Remote monitoring technologies are ideally suited for rural communities with limited access to health care. In an 8-week pilot study, we examined the feasibility of implementing and conducting a technology-intensive intervention in an underserviced rural setting. Our goal was to test the utility of self-monitoring technologies, physical activity, and education as tools to manage health indicators for the development of the cardiovascular complications (CVCs) of type 2 diabetes. RESEARCH DESIGN AND METHODS: The Diabetes and Technology for Increased Activity study was an open single-center study conducted in a community-based research setting. All 24 participants were provided with a Blackberry™ Smartphone, blood pressure monitor, glucometer, and pedometer. Smartphones transmitted measurements and survey results to the database, interfaced participants with the clinical team, and allowed for self-monitoring. RESULTS: Outcomes were improved body composition, improved markers of CVC risk factors, increased daily exercise, and interest in or awareness of lifestyle changes that impact health outcomes. Participants had excellent compliance for measurements, as self-monitoring provided a sense of security that improved from week 4 to week 8. CONCLUSIONS: Our team gained substantial insight into the operational requirements of technology-facilitated health care, including redefined hours of service; data reporting, management, and access protocols; and the utility of real-time clinical measures by remote monitoring. We developed an understanding of knowledge translation strategies as well as successful motivational and educational tools. Importantly, remote monitoring technology was found to be feasible and accepted in a rural setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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