Impact of the diagnosis of gestational diabetes on maternal physical activity after pregnancy
Bibliographic record
Abstract
Abstract Aim The diagnosis of gestational diabetes (GDM) identifies women who are at future risk of developing type 2 diabetes. However, it is unclear if diagnosing GDM thus motivates women to increase physical activity after pregnancy or if this medicalization has the opposite effect of decreasing activity, possibly reflecting assumption of a sick role. We thus sought to evaluate the impact of diagnosing GDM on changes in maternal physical activity after pregnancy. Methods In this prospective cohort study, physical activity patterns were assessed by the Baecke questionnaire for the year before pregnancy and the first year postpartum in 405 white women comprising the following three gestational glucose tolerance groups: (a) those who did not have GDM (non‐GDM; n = 247), (b) women with undiagnosed GDM (n = 46) and (c) those diagnosed with GDM (n = 112). Results In the year before pregnancy, mean adjusted total physical activity progressively decreased from non‐GDM to undiagnosed GDM to diagnosed GDM ( p = .067). Conversely, at 1 year postpartum, total physical activity was highest in those who had been diagnosed with GDM ( p = .02). Compared with non‐GDM, diagnosed GDM predicted an increase in total physical activity from pre‐pregnancy to 1 year postpartum (t = 2.3, p = .02) whereas undiagnosed GDM predicted a concurrent decrease in leisure‐time activity (t = −2.74, p = .006). Accordingly, the mean adjusted increase in body mass index from pre‐pregnancy to 1 year postpartum was lowest in those with diagnosed GDM (0.26 ± 0.25 kg/m 2 ), highest in undiagnosed GDM (1.23 ± 0.38 kg/m 2 ) and intermediate in non‐GDM (0.89 ± 0.22 kg/m 2 ) (overall p = .04). Conclusion Diagnosis of GDM leads to increased physical activity after pregnancy that may partially attenuate postpartum weight retention.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".