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 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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".