Association Between a Technology-Enabled Postpartum Program and Cardiovascular Disease Risk Reduction in Women With Prior Hypertensive Disorders of Pregnancy
Bibliographic record
Abstract
BACKGROUND: To test the association between a behavior-change, risk-reduction intervention and cardiovascular disease (CVD) risk scores in women with previous hypertensive disorders of pregnancy (HDP). METHODS: We completed a prospective, single-arm comparative pre–post study of women with recent HDP. The intervention was the CardioPrevent Postpartum Program, which involves health behavior-change coaches who counseled participants in 17 sessions over 12 months regarding behavioral changes (smoking, nutrition, physical activity), and worked with primary-care practitioners to optimize medical management of underlying risk factors (hypertension, dyslipidemia, diabetes mellitus). We measured changes in global risk scores (lifetime, Framingham Risk score) and clinical, psychosocial, and health behavior outcomes at 12 months compared with baseline. RESULTS: Overall, 190 women were enrolled. Absolute reductions in Framingham and Lifetime Risk Scores at 12 months postintervention were 53.7% and 22.6%, respectively (both P≤.001). The intervention was associated with decreases in body weight (81.7±19.4 vs 74.1±19.8 kg, P<.001), waist circumference (96.9±15.5 vs 89.5±14.9 cm, P<.001), hypertension (50% vs 4.3%), and metabolic syndrome (53.6% vs 43.5%) between baseline and 12 months, but fasting plasma glucose and hemoglobin A1c were unchanged. Triglycerides, low-density lipoprotein cholesterol, and total cholesterol decreased; however, high-density lipoprotein cholesterol also decreased. We observed a 40.6% absolute reduction in postpartum depression, a 28.2% absolute reduction in anxiety, and a 67.9% absolute reduction in perceived stress at 12 months compared with baseline. Compared with baseline, the intervention was associated with a higher proportion of participants who met guideline recommendations for physical activity levels (48.4% vs 75.2%, P<.001), fruit and vegetable intake (21.5% vs 44.4%, P<.001), and medication adherence (29.4% vs 48.7%, P<.001) at 12 months. CONCLUSION: This behavior-change, risk-reduction intervention was associated with improvements in CVD risk factors at 12 months after initiation. Multidisciplinary health behavior-change programs may be effective CVD risk-reduction strategies for women with previous HDP.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".