The effect of a pre-meal snack and/or postprandial exercise on breakfast glycemic excursion in adults with cystic fibrosis: A pilot study
Bibliographic record
Abstract
BACKGROUND & AIMS: Cystic fibrosis (CF)-related diabetes (CFRD), a common comorbidity in CF, is often preceded and characterized with elevated postprandial glycemic (PPG) excursions. In the general population, the consumption of a pre-meal protein snack and/or physical activity (PA) hinder the elevation of PPG levels. Our objective is to evaluate the effect of a pre-meal snack and/or post-meal PA on PPG excursions in CF. METHODS: This is a double-blinded randomized controlled crossover interventional study in 14 adults with CF, with 4 interventions: placebo pre-meal snack + no PA (control: CTL), pre-meal soy snack + no PA (SK), placebo pre-meal snack + PA (PA), and pre-meal soy snack + PA (SK + PA). The pre-meal soy snack or placebo beverage (vanilla flavoured water) is served at 8 AM, followed by a standardized breakfast at 9 AM and, postprandially, 5 repeated bouts of 3-min walk every 30 min or sedentary activity. Blood glucose and insulin were measured every 15-30 min during the interventions. RESULTS: Plasma glucose (PG) was higher 30 min after snack consumption compared to placebo beverage. One-hour post-breakfast, PG levels were lower during both PA interventions than with sedentary behavior. However, the overall 3 h post-breakfast glucose area under the curve (AUC) was similar between interventions. Post-breakfast 3 h insulin AUC was significantly lower during the SK + PA intervention compared to the sedentary behavior interventions. CONCLUSION: Repeated short bouts of post-meal physical activity may positively impact PPG control in adults with CF, with or without the addition of a pre-meal soy snack. A pre-meal snack alone does not improve PPG.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".