Cycling reduces blood glucose excursions after an oral glucose tolerance test in pregnant women: a randomized crossover trial
Bibliographic record
Abstract
The aim of this study was to evaluate the effect of an acute bout of cycling immediately after oral glucose intake on glucose metabolism in pregnant women at risk for gestational diabetes mellitus (GDM). Fifteen pregnant women with BMI ≥ 27 kg/m2 were enrolled in a randomized crossover controlled study and underwent two oral glucose tolerance tests (OGTTs) ingesting 75 g of glucose followed by either 20 min of stationary cycling at moderate intensity (65%–75% maximal heart rate) or rest. Using continuous glucose monitors, glucose was measured up to 48 h after the OGTT. Glucose, insulin, and C-peptide were determined at baseline and after 1 and 2 h. One hour after glucose intake, mean blood glucose was significantly lower after cycling compared with rest (p = 0.002). Similarly, mean glucose peak level was significantly lower after cycling compared with after rest (p = 0.039). Lower levels of insulin and C-peptide were observed after 1 h (p < 0.01). Differences in glucose measurements after 2 h and up to 48 h were not statistically different. We found that 20 min of cycling at moderate intensity after glucose intake reduced blood glucose excursions in pregnant women at risk for GDM. ClinicalTrials.gov Identifier: NCT03644238. Novelty Bullets In pregnant women, we found that cycling after glucose intake resulted in significantly lower glucose levels compared with rest. The exercise intervention studied is feasible for pregnant women and could be readily used to reduce glucose excursions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".