A Randomized Controlled Pilot Study of the Food Order Behavioral Intervention in Prediabetes
Bibliographic record
Abstract
(1) Background: Prior research in individuals with overweight/obesity and prediabetes or type 2 diabetes has shown that the ingestion of protein-rich food and non-starchy vegetables before concentrated carbohydrates (a carbohydrate-last food order) led to lower postprandial glucose excursions over 180 min, compared to eating the same foods in the reverse order. To expand upon this research, we sought to examine the feasibility and impact of carbohydrate-last food order behavioral intervention on glucose tolerance (GT), HbA1c, weight, and nutrient intake in adults with prediabetes in the real world over a 16-week span. (2) Methods: A total of 45 adults with overweight/obesity and prediabetes were randomized to receive 4-monthly standard nutritional counseling (C) or standard nutritional counseling plus carbohydrate-last food order counseling (FO) sessions (NCT# NCT03896360). (3) Results: The FO group decreased in body weight (−3.6 ± 5.7 lbs, p = 0.017), and trended toward lower HbA1c (−0.1 ± 0.2, p = 0.054). The C group weight trended lower (−2.6 ± 6.8 lbs, p = 0.102) without altering HbA1c (−0.03 ± 0.3, p = 0.605). GT was unchanged in both groups after 16 weeks. Changes in weight, HbA1c, and GT were similar between groups. Sensitivity analysis of pre-COVID participants showed significant weight loss in the FO group (−5.9 ± 5.3 lbs, p = 0.003) but not in C group (−1.0 ± 6.8 lbs, p = 0.608). After 16 weeks, the C group significantly reduced its daily intake of calories, fat, protein, and grains whereas the FO group increased its daily intake of vegetables and protein. There were 17 (94%) FO participants that reported high intervention adherence and 13 (72%) reported it was easy to eat protein/vegetables before carbohydrates. (4) Conclusions: A carbohydrate-last food order is a feasible behavioral strategy in individuals with prediabetes that improves diet quality, notably increasing protein and vegetable intake.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".