UTILIZING TECHNOLOGICAL DEVICES TO ENHANCE PREVENTION OF TYPE II DIABETES MELLITUS
Bibliographic record
Abstract
A. Underhill, L. Woodard Washington State University, Spokane, WA PURPOSE: To evaluate the inclusion of technology, a wearable device and smartphone, into the popular lifestyle modification program: the Diabetes Prevention Program - Group Lifestyle Balance Program (DPP - GLB). The DPP - GLB Program has shown great success in reduction of progression toward T2DM. However, it was unknown how integrating technology would affect overall program outcomes, which included the attainment of 150 minutes of physical activity (PA)/week and weight loss trending toward 7%. METHODS: Men and women over the age of 40, and at risk for prediabetes, were recruited. The study included an initial four weeks of baseline PA testing, followed by 12 weeks of lifestyle intervention. Individualized weight loss and PA goals were set. Participants self-randomized to the control (N = 11) or the technology group (N = 13). Session participation was high. Technology participants missed 8.3% of sessions, while control participants missed 18.2% of sessions. RESULTS: Participants were aged 66.24 (SD = 7.38) years. At baseline, 21% of participants were overweight, and 79% were obese. Using a generalized estimating equation to estimate average weight lost, participants lost 0.46 lbs/week. One hundred percent of technology participants lost weight, while 73% of control participants lost weight. Only 7.7% of technology participants reached the 7% weight loss goal, while 27.2% of control participants reached the 7% weight loss goal. Both groups lost similar amounts of weight in averages, with technology participants losing an average of 7.35 lbs and control participants losing an average of 7.79 lbs. At study conclusion, 37% of participants were overweight, and 63% were obese. Although statistical significance was not found, we believe clinical significance was found. The majority of participants (63%) self-reported inactivity during the initial meeting. Technology participants averaged PA tracking for 72.5 days while the control group averaged 47.7 days of PA tracking of 77 days available. CONCLUSION: Monitoring PA with technology can reinforce positive lifestyle changes to encourage users and increase activity due to instant feedback from the device. Participants can be successful with weight loss by going through the GLB Program, with or without technology, reinforcing the importance of lifestyle modification. Supported by the Dean Fletcher Graduate Fellowship in Clinical Nutrition and the Alberta Hill Academic Excellence Award.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".