Evaluating the Feasibility and Impact of an Internet-Based Lifestyle Management Program in a Diabetes Care Setting
Bibliographic record
Abstract
BACKGROUND: This study determined the impact of an online lifestyle management program on obese individuals with type 2 diabetes in a large academic diabetes clinic. SUBJECTS AND METHODS: Using a single cohort design, individuals with type 2 diabetes and a body mass index (BMI) of ≥ 30 kg/m(2) were recruited to have access to the Virtual Lifestyle Management (VLM) program for 1 year, in addition to their routine care. Participants were assessed at baseline and at 6 and 12 months for the following outcomes: (a) glycated hemoglobin (A1C), (b) lipids (total cholesterol [TC] and TC/high-density lipid [HDL] ratio), (c) weight, (d) BMI, and (e) body fat percentage (BFP). RESULTS: Seventy-eight individuals consented to the study, 66 (84%) logged onto the system two or more times, and 49 (62%) contributed data at the 1-year program. At baseline, the mean age of participants was 57.9 years of age, and 52% were female. At 12 months, mean differences from baseline were as follows: A1C, -0.3% (95% confidence interval [CI] -0.1, -0.5; P<0.05); TC/HDL, -0.2 (95% CI -0.01, -0.04; P<0.05); weight, -8.6 pounds (95% CI -3.7, -13.6; P<0.05); BMI, -1.5 kg/m(2) (95% CI -0.7, -2.3; P<0.05); and BFP, -1.8% (95% CI -0.9, -2.7; P<0.05). After adjusting for program use, age, and sex, changes in BMI, BFP, and self-reported daily steps taken were statistically significant at 12 months. CONCLUSIONS: This before-after pilot study demonstrates that adding an Internet-based lifestyle modification program to usual care may improve clinically important outcomes.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".