Effects of meal preparation training on body weight, glycemia, and blood pressure: results of a phase 2 trial in type 2 diabetes
Bibliographic record
Abstract
BACKGROUND: Modest reductions in weight and small increases in step- related activity (e.g., walking) can improve glycemic and blood pressure control in type 2 diabetes mellitus (DM2). We examined changes in these parameters following training in time- efficient preparation of balanced, low- energy meals combined with pedometer- based step count monitoring. METHODS: Seventy- two adults with DM2 were enrolled in a 24- week program (i.e., 15 three- hour group sessions). They prepared meals under a chef's supervision, and discussed eating behaviours/nutrition with a registered dietitian. They maintained a record of pedometer- assessed step counts. We evaluated changes from baseline to 24 weeks in terms of weight, step counts, hemoglobin A1c (HbA1c, glycemic control), blood pressure, and eating control ability (Weight Efficacy Lifestyle WEL Questionnaire). 53 participants (73.6%) completed assessments. RESULTS: There were improvements in eating control (11.2 point WEL score change, 95% CI 4.7 to 17.8), step counts (mean change 869 steps/day, 95% CI 198 to 1,540), weight (mean change -2.2%; 95% CI -3.6 to -0.8), and HbA1c (mean change -0.3% HbA1c, 95% CI -0.6 to -0.1), as well as suggestion of systolic blood pressure reduction (mean change -3.5 mm Hg, 95% CI -7.8 to 0.9). Findings were not attributable to medication changes. In linear regression models (adjusted for age, sex, ethnicity, insulin use, season), a -2.5% weight change was associated with a -0.3% HbA1c change (95% CI -0.4 to -0.2) and a -3.5% systolic blood pressure change (95% CI -5.5 to -1.4). CONCLUSIONS: In this 'proof of concept' study, persistence with the program led to improvements in eating and physical activity habits, glycemia reductions, and suggestion of blood pressure lowering effects. The strategy thus merits further study and development to expand the range of options for vascular risk reduction in DM2.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".