Leisure Physical Activity, Sedentary Behavior, and Cardiovascular Health 2-7 yr after Pregnancy in the nuMoM2b Heart Health Study Cohort.

Leisure Physical Activity, Sedentary Behavior, and Cardiovascular Health 2–7 Years after Pregnancy in the nuMoM2b HHS Cohort - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Med Sci Sports Exerc . Author manuscript; available in PMC: 2026 Jun 1. Published in final edited form as: Med Sci Sports Exerc. 2025 Feb 3;57(6):1229–1238. doi: 10.1249/MSS.0000000000003660 Leisure Physical Activity, Sedentary Behavior, and Cardiovascular Health 2–7 Years after Pregnancy in the nuMoM2b HHS Cohort Bethany Barone Gibbs 1 , Sarah Modlin 1 , Noel Bairey Merz 2 , David M Haas 3 , Sadiya S Khan 4 , Abbi Lane 5 , Rebecca B McNeil 6 , Jasmina Varagic 7 , Kimberly Huber 8 , Ligia Vasquez-Huot 3 , Janet M Catov 8 ; for the National Heart, Lung, and Blood Institute nuMoM2b Heart Health Study Network Author information Article notes Copyright and License information 1 West Virginia University School of Public Health, Morganton, WV; 2 Barbra Streisand Women’s Heart Center, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA; 3 Indiana University School of Medicine, Indianapolis, IN; 4 Northwestern Feinberg School of Medicine, Chicago, IL; 5 University of Michigan School of Kinesiology, Ann Arbor, MI; 6 RTI International, Raliegh, NC; 7 National Heart, Lung, and Blood Institute, Bethesda, MD; 8 Magee-Womens Research Institute and University of Pittsburgh, School of Medicine, Department of Obstetrics and Gynecology, Pittsburgh, PA Authorship Statement: B Barone Gibbs conceptualization, methodology, formal analysis, writing – original draft, funding acquisition; S Modlin formal analysis, writing – review & editing. Noel Bairey Merz conceptualization, writing – review & editing, funding acquisition. DM Haas conceptualization, writing – review & editing, funding acquisition. SS Khan conceptualization, writing – review & editing. A Lane conceptualization, writing – review & editing. RB McNeil conceptualization, methodology, writing – review & editing, funding acquisition; Jasmina Varagic conceptualization, methodology, writing – review & editing; Kimberly Huber conceptualization, writing – review & editing; L Vasquez-Huot conceptualization, writing – review & editing; JM Catov conceptualization, methodology, formal analysis, writing – review & editing, funding acquisition. ✉ Address for correspondence : Bethany Barone Gibbs, PhD, Professor and Chair, Department of Epidemiology and Biostatistics, West Virginia University School of Public Health, 64 Medical Center Drive, PO Box 9190, Morgantown, WV 26506; bethany.gibbs@hsc.wvu.edu Issue date 2025 Jun 1. PMC Copyright notice PMCID: PMC12081195  NIHMSID: NIHMS2051914  PMID: 39898583 The publisher's version of this article is available at Med Sci Sports Exerc Abstract Purpose: To evaluate whether higher moderate-to-vigorous intensity physical activity (MVPA) and lower sedentary behavior (SB) are related to better cardiovascular health (CVH) following pregnancy. Methods: In the nuMoM2b Heart Health Study (HHS) cohort, Life’s Essential 8 health factors (BMI, blood pressure, glucose, and lipid) were assessed in the first trimester of pregnancy and 2–7 years later (n=3,985). The four health factors were scored with Life’s Essential 8 criteria (0=poorest; 100=ideal) and averaged to calculate a CVH health factor score (CVH hf ). Self-reported leisure MVPA and SB were assessed at follow-up. Linear regression evaluated associations of MVPA levels and SB quartiles with follow-up CVH hf after adjustment for first trimester CVH hf and covariates. Analyses were repeated after stratification by history of hypertensive disorders of pregnancy (HDP) or gestational diabetes (GDM). Results: Over follow-up (mean 3.2 years after delivery), CVH hf declined from 85.6 to 81.6 points (−4.0 points, p<0.001). The decline was greatest in lipid (−8.4 points, p<0.001) and BMI scores (−5.6 points, p<0.001). Healthier activity profiles (higher MVPA level and lower SB quartile) were each associated with higher CVH hf at follow-up: 0.7 points higher per level of MVPA, p<0.001; 0.5 points lower per quartile of SB, p=0.0042. Higher MVPA level associated with healthier BMI, glucose, and lipid scores; lower SB quartile associated with healthier BMI and blood pressure scores (p<0.05). Although HDP/GDM history was associated with lower CVH hf , patterns of CVH hf across healthier leisure MVPA levels and SB quartiles were similar in those with and without HDP or GDM. Conclusions: Higher MVPA and lower SB during leisure could help to maintain CVH health factors in the years following pregnancy, including among those who experienced HDP or GDM. Keywords: YOUNG WOMEN, ADVERSE PREGNANCY OUTCOMES, BODY MASS INDEX, GLUCOSE, LIPIDS, BLOOD PRESSURE INTRODUCTION Optimal cardiovascular health (CVH), defined according to ideal levels of health factors and health behaviors, was introduced in 2010 ( 1 ) and ‘updated and enhanced’ in 2022 ( 2 ) by the American Heart Association (AHA) as a new primordial prevention target to reduce population-level cardiovascular disease. Young adulthood is a critical life period during which CVH typically worsens, specifically for the four CVH health factors which include body mass index (BMI), blood pressure (BP), blood lipids, and blood glucose.( 3 ) Further, individuals who maintain versus have a declining trajectory of the CVH health factors during young adulthood are three times less likely to develop subclinical cardiovascular disease in middle age.( 4 ) Therefore, maintaining the CVH health factors through young adulthood is a specific target that could help to reduce the population burden of cardiovascular disease. However, clarification of strategies to maintain CVH health factors through young adulthood is needed. Women, in particular, may have accelerated deterioration of the CVH health factors during young adulthood compared to their male counterparts.( 5 , 6 ) While the reasons for these sex differences are incompletely understood, pregnancy itself and related complications, such as hypertensive disorders of pregnancy (HDP) and gestational diabetes (GDM), are sex-specific exposures that have recently been recognized as potential risk-enhancing factors for future cardiovascular disease.( 7 ) Guidelines and expert reviews agree that nonpharmacological, lifestyle approaches to maintain and improve the CVH health factors are the optimal first-line approaches.( 8 – 11 ) Yet, limited studies have quantified how these approaches assist in the maintenance of CVH health factors in young women, especially among those with a history of adverse pregnancy outcomes.( 10 , 12 , 13 ) All adults are recommended to accumulate at least 150 minutes per week of moderate-to-vigorous intensity physical activity (MVPA) to maintain health levels of the CVH health factors.( 11 ) Yet, national surveys estimate that less than 1 in 3 women meet current recommendations, a rate lower than men.( 14 ) Further, declines in MVPA following the birth of a child are commonly observed among women.( 15 ) At the same time, it is increasingly recognized that MVPA is only part of the 24-hour activity cycle ( 16 ) and accounts for about 3% of the waking day. The rest of the day is spent in a combination of sedentary behaviors (SB), defined as low-intensity, seated, reclined, or lying behavior,( 17 ) and light physical activities. SB has been recognized as highly prevalent and a novel risk factor associated with cardiovascular disease, independent from MVPA.( 18 ) Accordingly, the most recent physical activity guidelines recommend decreasing SB and replacing it with light-intensity physical activities, such as standing and activities of daily living, in addition to MVPA.( 11 , 19 ) Data also suggest reducing SB may be more achievable than increasing MVPA for young women,( 15 ) though scarce research investigates associations between SB and the CVH health factors in young women outside of pregnancy. To understand whether MVPA and SB could promote CVH health factors after pregnancy, our primary objectives were to examine changes in CVH health factors in young women from pregnancy to 2–7 years following delivery and to evaluate whether concurrent self-reported leisure MVPA and SB were associated with better CVH health factors. We hypothesized that CVH health factors would worsen following pregnancy, but that higher MVPA and lower SB would be associated with better maintenance of health factor CVH following pregnancy. A secondary objective was to evaluate changes in health factor CVH among women with and without a history of HDP or GDM as well as to understand whether associations with leisure MVPA and SB were similar across groups. We anticipated that favorable associations between higher MVPA and lower SB would be present regardless of these adverse pregnancy outcomes. METHODS Study Design We used data from the Nulliparous Pregnancy Outcomes Study: monitoring mothers-to-be (nuMoM2b)( 20 ) and the subsequent nuMoM2b-Heart Health Study (nuMoM2b-HHS).( 21 ) The nuMoM2b cohort study recruited nulliparous women (n=10,038) in their first trimester of pregnancy from eight clinical centers in the U.S. between 2010 and 2013 and then followed participants through delivery. nuMoM2b aimed to identify maternal characteristics that could predict adverse pregnancy outcomes. The nuMoM2b-HHS follow-up study began in 2013 and conducted an in-person follow-up visit among a subset of nuMoM2b participants 2–7 years after delivery (n=4,508). The wide range of follow-up intervals for the in-person visit reflected the cohort study design which allowed for rolling recruitment (nuMoM2b) and had a flexible follow-up window (nuMoM2b-HHS). nuMoM2b-HHS aimed to study relationships between adverse pregnancy outcomes and future risk factors for cardiovascular disease. For each study, local governing institutional review boards provided approval and participants provided informed consent prior to engaging in research activities. For the current analysis, we evaluated changes in the health factors of CVH from the first trimester of pregnancy (baseline) to 2–7 years after pregnancy (follow-up) and whether self-reported leisure MVPA and SB measured at the nuMoM2b-HHS follow-up visit were associated with CVH related to these health factors. We first conducted analyses in the full sample, then repeated analyses after stratification of participants into groups that did and did not experience HDP or GDM during their nuMoM2b pregnancy. Study Sample nuMoM2b participants had to meet key eligibility criteria: (1) nulliparous with viable, singleton pregnancy; (2) receiving prenatal care and planned to deliver at a study center; (3) gestational age between 6 0 and 13 6 at enrollment; and (4) ≥13 years old. For the nuMoM2b-HHS in-person assessment, addition eligibility criteria included: (1) obstetrical delivery information available for the index pregnancy; (2) agreed to be contacted for future studies; (3) ≥18 years old; (4) ≥2 years after index pregnancy; and (5) not currently pregnant and at least 6 months postpartum. For the current analysis, we included participants who completed the nuMoM2b-HHS in-person visit (n=4,508). Participants missing any CVH health factor measurements at baseline (n=436) or follow-up (n=82) or missing the length of the follow-up interval (n=5) were excluded, resulting in a final analytic sample of n=3,985). Measures Leisure Physical Activity and Sedentary Behavior. Usual leisure MVPA and SB were assessed at the nuMoM2b-HHS follow-up visit. We chose to use an exposure assessment that aligned temporally with the follow-up CVH health factor assessment rather than at baseline (during pregnancy) for two reasons. First, we reasoned that MVPA and SB measured during the follow-up visit would better represent usual levels during the exposure period of scientific interest (i.e., after delivery) as compared to pregnancy measures. Second, compared to the nuMoM2b (baseline) protocol, nuMoM2b-HHS (follow-up) used a different MVPA questionnaire with superior psychometric properties (convergent validity and reliability) ( 22 ) and uniquely assessed SB. Leisure time MVPA was measured by an adapted version of the Modifiable Activity Questionnaires (MAQ). The MAQ has good reliability and validity for a self-report physical activity instrument (ICCs between 0.4–0.6).( 23 ) The adapted questionnaire queried usual frequency and duration of 36 common physical activities in the past month (e.g., aerobics, gardening, strength training) and up to three write-in activities. To calculate participation in leisure MVPA per week, we calculated the monthly volume (frequency × duration) of each activity of at least moderate intensity (≥ 3.0 metabolic equivalents [METs]( 24 )). Then, we converted to minutes per week by dividing by 30 days per month and multiplying by 7 days per week. Finally, we calculated weekly minutes of moderate-equivalent physical activity as the sum of moderate-intensity (3.0–5.9 METs) physical activity minutes per week + 2 times vigorous-intensity (≥6.0 METs) physical activities per week. Moderate-equivalent minutes per week were used to categorize participants into MVPA levels with respect to current federal guidelines( 11 ): none = 0 minutes per week; low = 1–149 minutes per week, sufficient = 150–299 minutes per week; or high = 300 minutes or greater per week. Usual leisure SB was assessed by a single item: “In the past month (4 weeks) excluding time at work, how many HOURS per DAY do you usually spend sitting doing sedentary activities, such as watching television, sitting at a computer, using a smart phone or tablet, or reading a book?” Single-item instruments are a preferred method for assessing SB by self-report in large-scale observational studies with acceptable validity and good reliability.( 25 ) As the U.S. does not have quantitative SB guidelines,( 11 ) we separated women into quartiles of self-reported leisure SB. CVH Health Factor Scores. CVH health factor score (CVH hf ) was calculated as the average from the four health factor components (BMI, BP, glucose, and lipids) as specified by the American Heart Association’s Life’s Essential 8 framework.( 2 ) This framework generates a standardized score from 0 (least optimal) to 100 (most optimal) for each health factor and provides an overall score (CVH hf ) as the mean score averaged across the four factors.( 2 ) BMI was calculated from measured height and weight at baseline and follow-up and converted to a BMI score. BMI score is 100 for BMI <25 kg/m 2 (optimal), 70 for 25.0–29.9 kg/m 2 , 30 for 30.0–34.9 kg/m 2 , 15 for 35.0–39.9 kg/m 2 , and 0 for BMI ≥40 kg/m 2 . BP was measured in duplicate at baseline by trained staff using an aneroid sphygmomanometer, an appropriately sized cuff, and a standardized protocol; ( 26 ) at follow-up, BP was measured in triplicate using a calibrated automatic oscillometric device (OMRON HEM-907XL) and the average of the second and third measures was used for analysis.( 21 ) BP score is 100 if systolic/diastolic BP is <120/80 mmHg (optimal), 75 if systolic/diastolic BP is 120–129/<80 mmHg (elevated), 50 if systolic BP is 130–139 mmHg or diastolic BP is 80–89 mmHg (stage 1), 25 if systolic BP is 140–159 mmHg or diastolic BP is 90–99 mmHg, and 0 if systolic BP is >160 mmHg or diastolic BP is >100 mmHg. BP score is also reduced by 20 points if treated to level with antihypertensive medications. Blood samples were obtained at baseline (non-fasted protocol) and follow-up (fasted protocol). From these, non-fasting or fasting glucose/non-HDL cholesterol along with self-reported use of glucose-lowering/lipid-lowering medication were used to calculate the glucose and lipid scores, respectively. Scoring for blood glucose begins at 100 with no diabetes and fasting glucose <100 mg/dL (optimal), is 60 for no diabetes and fasting glucose 100–125 mg/dL, and scores range from 40 to 0 for those with diabetes based on glucose control by HbA1c. Since HbA1c was not available in all participants at both assessments, we employed a modified algorithm where individuals with blood glucose >125 mg/dL or self-reporting use of glucose-lowering medications were assigned a score of 20. Note that glucose scores were calculated at baseline (early pregnancy) even though participants were not required to be fasting. Lastly, CVH scoring for lipids is 100 with non-HDL cholesterol <130 mg/dL (optimal), 60 if non-HDL cholesterol is between 130–159 mg/dL, 40 if non-HDL cholesterol is between 150–189 mg/dL, 20 if non-HDL cholesterol is between 190–219 mg/dL, and 0 with non-HDL cholesterol ≥220 mg/dL, with 20 points deducted if drug-treated. HDP and GDM. Our secondary objective evaluated our research questions separately among women with and without a history of HDP and GDM. We repeated analyses after this stratification to better understand whether MVPA and SB were related to CVH health factors 2–7 years after delivery in the higher-risk group of women whom had experienced one of these adverse pregnancy outcomes.( 27 , 28 ) nuMoM2b captured HDP and GDM by medical record abstraction and adjudication conducted by certified research personnel after delivery.( 29 ) HDP was defined using standard criteria and included new onset gestational hypertension or intrapartum/postpartum hypertension (n=556) as well as preeclampsia with and without severe features and super-imposed preeclampsia (n=367).( 30 ) GDM was defined using standard definitions from glucose tolerance tests or documented diagnosis of GDM during prenatal care (n=165).( 30 ) Altogether, n=1,037 had one or both adverse pregnancy outcomes (n=21 excluded for missing HDP or GDM data). Covariates. Baseline age, race/ethnicity, and insurance type were reported using standardized questionnaires. Current smoking was self-reported at baseline and follow-up. Time between visits was included to account for variable lengths of follow-up. Diet quality was assessed by a the Modified Block Food Frequency Questionnaire( 31 ) and converted into a Healthy Eating Index (HEI) score; ( 32 ) due to substantial missing data (26%), covariate adjustment for HEI was conducted in a sensitivity analyses rather than the primary analysis. Statistical Analysis Analyses were conducted using SAS version 9.4. An α-level=0.05 was considered statistically significant. The sample size for nuMoM2b-HHS was determined to detect a 2.1 to 2.7-fold increase in the risk of hypertension at follow-up comparing women experiencing HDP or other adverse pregnancy outcomes compared to women who did not. As the current secondary analysis was set within this existing cohort of fixed sample size, no a priori sample size calculations were conducted. Participant characteristics were summarized descriptively and compared across guideline leisure MVPA levels and SB quartiles using ANOVA or chi-square. CVH hf score and individual health factor scores (BMI, BP, glucose, and lipids) were summarized at baseline and follow-up. Longitudinal changes in scores were calculated as follow-up (nuMoM2b-HHS) minus baseline (from the first trimester in nuMoM2b) and baseline vs. follow-up values were compared with a paired t test. Next, linear regression models estimated how CVH hf and health factor scores at follow-up differed across guideline levels of leisure MVPA and quartiles of leisure SB at follow-up, with adjustment for baseline CVH hf or health factor score. As such, differences across groups are equivalent and can be interpreted as the differences in the changes in CVH hf score over time with adjustment for baseline CVH hf .( 33 ) Primary models also adjusted for age, race/ethnicity, insurance, length of follow-up, and smoking at baseline and follow-up. MVPA level and SB quartile were modelled as factor variables to ascertain non-linear associations, with ‘none’ as the reference category for MVPA and ‘highest quartile’ as the reference category for SB. We also evaluated dose-response associations with a linear test-for-trend across increasing levels of MVPA and quartiles of SB. A first sensitivity analysis evaluated interactions between MVPA level and SB quartile, as high SB has been found to be more detrimental in the context of low MVPA.( 18 ) Though we chose to present our primary results without adjustment for HEI due to substantial missing HEI data, a second sensitivity analysis evaluated the impact of adjustment for HEI at baseline and follow-up in the subset of participants with complete dietary data. We also repeated analyses using cross-sectional models that did not include the baseline CVH hf scores due to temporality concerns regarding the exposure assessment and differences in measurement methodology between visits. To evaluate our secondary research question, analyses were repeated considering history of HDP or GDM. Longitudinal changes in CVH hf and health factor scores were evaluated separately by group. Then, regression models associated MVPA levels and SB quartiles with CVH hf and health factors scores separately within each group. Along with reporting of regression coefficients, adjusted follow-up CVH hf and health factor scores were plotted to demonstrate average CVH hf with increasing MVPA level and SB quartile separately among those with and without a history of HDP or GDM. Lastly, linear regression models using the full sample added two interaction terms to evaluate the presence of effect modification by history of HDP or GDM (MVPA level × history of HDP or GDM; SB quartile × history of HDP or GDM). RESULTS Participant characteristics across leisure MVPA levels at follow-up are presented in Table 1 . About half (52%) of participants met MVPA guidelines ( 11 ) (i.e., sufficient or high levels) by self-report at follow-up. Median leisure SB in each quartile were: lowest=1.0 hour per day; low-middle=2.0 hours per day; high-middle=3.0 hours per day; and highest=8.0 hours per day. Sociodemographic characteristics differed by MVPA level. Women reporting lower leisure MVPA had younger average age, higher SB, and lower HEI as well as were more likely to self-report Black, Hispanic, or Asian race/ethnicity, have government insurance, report current smoking, and have higher SB. Similar comparisons across quartiles of SB found that higher SB was associated with younger age, self-reported Black race, government insurance, smoking, lower diet quality, and lower MVPA ( Supplemental Table 1 , Supplemental Digital Content ). Table 1. Participant Characteristics by MVPA Level Leisure MVPA Level at Follow-up Overall N= 3985 None (0 minutes per week) n = 764 Low (0–149 minutes per week) n = 1163 Sufficient (150–299 minutes per week) n = 819 High (≥300 minutes per week) n = 1239 p-value * Time between Baseline and Follow-up, years 3.2 (0.9) 3.2 (0.9) 3.2 (0.9) 3.2 (0.9) 3.2 (0.9) 0.7244 Age at baseline, years 27.1 (5.6) 25.2 (5.8) 27.4 (5.6) 27.8 (5.2) 27.4 (5.3) <0.0001 Race/Ethnicity <0.0001 White 2555 (64%) 344 (45%) 734 (63%) 589 (72%) 888 (72%) Black 505 (13%) 171 (22%) 161 (14%) 73 (9%) 100 (8%) Hispanic 623 (16%) 184 (24%) 174 (15%) 96 (12%) 169 (14%) Asian 118 (3%) 27 (4%) 41 (3%) 25 (3%) 25 (2%) Other 184 (5%) 38 (5%) 53 (5%) 36 (4%) 57 (5%) Insurance <0.0001 Commercial/Military 2811 (70%) 397 (52%) 814 (70%) 642 (78%) 958 (77%) Government 1023 (26%) 336 (44%) 298 (26%) 153 (19%) 236 (19%) Other + Missing 151 (4%) 31 (4%) 51 (4%) 24 (3) 45 (46%) Smoking N (%) Yes at Baseline 221 (6%) 82 (11%) 70 (6%) 28 (3%) 41 (3%) <0.0001 N (%) Yes at Follow-up 428 (11%) 138 (18%) 131 (11%) 64 (8%) 95 (8%) <0.0001 HEI a , points Baseline 63.3 (12.5) 57.6 (12.3) 62.0 (12.6) 64.9 (11.8) 66.7 (11.5) <0.0001 Follow-up 62.8 (11.6) 56.6 (11.5) 61.1 (11.1) 64.8 (10.8) 66.5 (10.7) <0.0001 Leisure MVPA, minutes per week b 260.7 (332) 00 (00) 75.6 (42.0) 219 (42.8) 623 (381) <0.0001 Leisure SB, hours per day 3.8 (3.2) 4.4 (3.6) 4.0 (3.4) 3.6 (2.9) 3.4 (2.9) <0.0001 Leisure SB Quartiles (n, %) <0.0001 Lowest 713 (18%) 115 (15%) 196 (17%) 133 (16%) 269 (22%) Low-middle 1087 (27%) 172 (23%) 301 (26%) 248 (30%) 366 (30%) High-middle 1123 (28%) 222 (29%) 326 (28%) 250 (31%) 325 (26%) Highest 1062 (27%) 255 (33%) 340 (29%) 188 (23%) 279 (22%) Open in a new tab Data presented as n (%) or mean (SD); Abbreviations: HEI, healthy eating index; MVPA, moderate-to-vigorous intensity physical activity; SB, sedentary behavior * chi-square for categorical variables; ANOVA for continuous variables a N=2965 completed dietary assessment at baseline and follow-up b Guideline minutes calculated as moderate intensity minutes + 2 × vigorous intensity minutes Health factor CVH hf scores at baseline and follow-up are presented in Table 2 . Average CVH hf declined by 4.0 points over follow-up (p<0.001). Individual health factor scores also changed significantly over follow-up, with notable declines in the lipid (−8.4 points) and BMI (−5.6 points) scores. Glucose score improved slightly over follow-up, which is likely an artifact of the non-fasted baseline and fasted follow-up protocols. Table 2. Changes in CVH hf and Health Factor Scores from Baseline to Follow-up (n=3,985) Baseline Follow-up Change p-value CVH hf score 85.6 (85.1, 86.0) 81.6 (81.1, 82.1) −4.0 (−4.4, −3.6) <0.0001 BMI score 73.6 (72.6, 74,6) 68.0 (66.9, 69.1) −5.6 (−6.2, −5.0) <0.0001 BP score 90.4 (89.8, 90.9) 87.1 (86.4, 87.8) −3.3 (−4.0, −2.5) <0.0001 Glucose score * 91.7 (91.1, 92.3) 92.8 (92.2, 93.4) 1.2 (0.44, 1.9) 0.0015 Lipid score * 86.7 (86.0, 87.4) 78.3 (77.4, 79.1) −8.4 (−9.2, −7.6) <0.0001 Open in a new tab Data are presented as mean (95% CI) * Blood samples were non-fasting at baseline and fasting at follow-up Abbreviations: BMI, body mass index; BP, blood pressure; CVH, cardiovascular health Table 3 presents associations of MVPA levels and SB quartiles with CVH hf and health factor scores. Compared to the reference category of none, higher levels of MVPA were associated with higher CVH hf change score at follow-up (range: 1.3 – 2.3 points, all p<0.05). In contrast, compared to the reference category of highest SB quartile, only the lowest SB quartile was associated with a higher CVH hf change score (1.5 points, p<0.05). Across levels, a significant linear trend was observed with the CVH hf change score being 0.7 points higher per MVPA level (p-for-trend<0.001) and −0.5 lower per SB quartile (p-for-trend=0.0042). Table 3. Differences in CVH hf and Health Factor Scores at Follow-up by Leisure MVPA Levels and SB Quartiles (n=3,985) CVH hf score MVPA level None Low Sufficient High β per 1-level increase p-for-trend Reference 1.3 (0.2, 2.4) * 2.0 (0.8, 3.3) ** 2.3 (1.2, 3.4) *** 0.7 (0.4, 1.1) <0.001 SB Quartile Lowest Low-middle High-middle Highest 1.5 (0.3, 2.6) * 0.8 (−0.2, 1.9) −0.3 (−1.4, 0.7) Reference −0.5 (−0.9, −0.2) 0.0042 BMI score MVPA level None Low Sufficient High β per 1-level increase p-for-trend Reference 1.4 (−0.3, 3.2) 2.8 (0.9, 4.7) ** 4.2 (2.5, 6.0) *** 1.4 (0.9, 1.9) <0.0001 SB Quartile Lowest Low-middle High-middle Highest 3.3 (1.5, 5.1) *** 2.9 (1.3, 4.5) *** 0.9 (−0.7, 2.4) Reference −1.2 (−1.8, −0.7) <0.0001 BP score MVPA level None Low Sufficient High β per 1-level increase p-for-trend Reference 1.0 (−0.9, 3.0) 1.2 (−0.9, 3.4) 1.0 (−0.9, 3.0) 0.3 (−0.3, 0.9) 0.399 SB Quartile Lowest Low-middle High-middle Highest 2.0 (0.0, 4.1) * 0.5 (−1.4, 2.3) −0.6 (−2.4, 1.2) Reference −0.7 (−1.3, 0.0) 0.0445 Glucose score MVPA level None Low Sufficient High β per 1-level increase p-for-trend Reference 0.6 (−1.1, 2.2) 1.3 (−0.5, 3.1) 1.5 (−0.1, 3.2) 0.5 (0.0, 1.0) 0.0473 SB Quartile Lowest Low-middle High-middle Highest 0.6 (−1.1, 2.3) 0.5 (−1.0, 2.0) 0.6 (−0.9, 2.1) Reference −0.2 (−0.7, 0.4) 0.5089 Lipid score MVPA level None Low Sufficient High β per 1-level increase p-for-trend Reference 1.2 (−1.0, 3.5) 2.9 (0.5, 5.4) * 3.1 (0.9, 5.4) ** 1.1 (0.3, 1.8) 0.0034 SB Quartile Lowest Low-middle High-middle Highest 0.3 (−2.1, 2.7) −0.3 (−2.4, 1.8) −1.8 (−3.9, 0.2) Reference −0.2 (−0.9, 0.5) 0.6129 Open in a new tab Changes are calculated as the difference in health factor score from baseline (first trimester of pregnancy) to follow-up (2-y years after delivery). Results are adjusted for PA level, SB quartile, baseline health factor score, age, follow-up interval, race/ethnicity, insurance type, and smoking * p<0.05; ** p<0.01; *** p<0.001 for individual categories of MVPA or SB vs. reference group Abbreviations: BMI, body mass index; BP, blood pressure; CVH hf , overall cardiovascular health factor score, MVPA, moderate-vigorous intensity physical activity; SB, sedentary behavior Similar models evaluating associations with changes in BMI, BP, glucose, and lipid scores revealed distinct patterns. Both higher MVPA levels and lower SB quartiles were associated with healthier changes in BMI score with approximately twice the magnitude of effect observed for the CVH hf change score. For BP score, only SB quartile was associated such that the lowest vs. highest quartile of SB was associated with a 2.0-point (p<0.05) higher change in BP score and a linear trend of −0.7 points per quartile (p-for-trend=0.0445). For glucose and lipid scores, only MVPA level was associated with more favorable changes in scores such that each higher level of MVPA was associated with a 0.5-point higher change in glucose score (p-for-trend=0.0473) and a 1.1-point higher change in lipid score (p-for-trend = 0.0034). We conducted three sensitivity analyses to assess the robustness of findings. First, we evaluated whether there were any interactions between MVPA level and SB quartile on CVH hf and health factor scores, and none were detected (all p-for-interaction > 0.3, Supplemental Table 2 , Supplemental Digital Content ). Next, we evaluated the effect of adjustment for HEI in the subsample (74%) with valid dietary data ( Supplemental Table 3 , Supplemental Digital Content ). Models with and without adjustment for HEI yielded similar, though in some cases slightly attenuated, associations. Though attenuation of statistical significance is likely in part due to the smaller sample size, comparison of the magnitude of associations suggests that diet quality may partially confound some associations between MVPA level, SB quartile, and CVH hf scores. Lastly, sensitivity analyses removing covariate adjustment for baseline CVH hf and health factor scores did not change findings (data not shown). Our secondary objective repeated analyses among women who did and did not experience HDP or GDM during nuMoM2b. Supplemental Table 4 ( Supplemental Digital Content ) compares baseline characteristics between groups. Women who had HDP or GDM during their nuMoM2b pregnancy were older and more likely to report Black or Other race, smoke at follow-up, and had lower HEI at baseline and follow-up (all p<0.05). Notably, leisure MVPA and SB were similar across groups (all p>0.36). CVH hf and health factor scores at baseline and follow-up stratified by group are presented in Supplemental Table 5 ( Supplemental Digital Content ). Women with a history of HDP or GDM tended to have lower CVH hf and health factor scores at baseline than women who did not experience these pregnancy outcomes; however, CVH hf and most health factor scores (BMI, BP, and lipid scores) worsened over follow-up for both groups (all p<0.001). An exception was that the glucose score did not change among women with HDP or GDM during pregnancy but improved over follow-up among women without these adverse pregnancy outcomes. Figure 1 displays the adjusted follow-up CVH hf scores across leisure MVPA levels and SB quartiles stratified by history of HDP and GDM (regression model results in Supplemental Table 6 , Supplemental Digital Content ). Though women with a history of HDP or GDM had consistently lower CVH hf scores, patterns of these scores across leisure MVPA levels and SB quartiles were similar between groups. For MVPA, CVH hf score was higher by 1.0 points (p=0.0109) per level in women who had HDP and GDM and 0.6 points (p=0.0011) higher per level among women who did not. For SB, CVH hf score was −0.5 points (p=0.2273) lower per quartile for women who had HDP or GDM and −0.6 points (p=0.0039) lower per quartile for women who did not. Evaluation of effect modification from models adding interaction terms in the full sample suggested associations between MVPA level and SB Quartile with CVH hf were similar in women with and without a history of GDM or HDP (all p-for-interaction>0.26). Figure 1. CVH hf Score by Leisure MVPA Level and SB Quartile comparing Women with (●) and without HDP or GDM (●) during pregnancy (n=3,964). Open in a new tab Statistically significant p-for-trend with increasing level of MVPA or quartile of SB within group is designated *p<0.05, **p<0.01, ***p<0.001. p-for-interaction evaluates whether the linear trend with increasing MVPA level or SB quartile differs between groups. Figure 2 displays the adjusted follow-up BMI, BP, glucose, and lipid scores across leisure MVPA levels and SB quartiles stratified by history of HDP and GDM (regression model results in Supplemental Table 7 , Supplemental Digital Content ). Again, women with a history of HDP or GDM had consistently lower health factor scores, but patterns of association between leisure MVPA and SB with health factor scores were similar from stratified analyses. Models testing interactions terms confirmed the absence of effect modification (all p-for-interaction>0.21). Figure 2. Health Factor Scores at Follow-up by Leisure MVPA Level and SB Quartile comparing Women with (●) and without HDP or GDM (●) during pregnancy (n=3,964). Open in a new tab Statistically significant p-for-trend with increasing level of MVPA or quartile of SB within group is designated *p<0.05, **p<0.01, ***p<0.001. p-for-interaction evaluates whether the linear trend with increasing MVPA level or SB quartile differs between groups. DISCUSSION In a large, diverse cohort of young women, healthier current levels of leisure MVPA and SB were associated with maintenance of CVH hf and health factor scores during the critical period following a first pregnancy. In mutually adjusted models, MVPA and SB were independently associated with CVH hf and BMI scores at follow-up. While only higher MVPA level was associated with better glucose and lipid scores, only lower SB quartile was associated with better BP scores. These findings support the premise that achieving healthy levels of both behaviors would best maintain overall health factor CVH among young women during the years following pregnancy. In addition, women with HDP and GDM during their first pregnancy, as compared to women without, had lower absolute CVH hf scores but similar, favorable associations between higher MVPA and lower SB with higher CVH hf . This observation is among the first empirical evidence that supports the lifestyle strategy of increasing MVPA and lowering SB to improve CVH health factors specifically in a population who experienced a pregnancy complicated by HDP and GDM. Comparison to other studies Our sample’s follow-up CVH hf and health factor scores were similar to U.S. population-based estimates from the National Health and Nutrition Examination Survey (NHANES) in women aged 18–44 years.( 34 ) Average BP, glucose, and lipids scores were within one point of the NHANES averages, though BMI score was approximately five points higher (healthier) in our sample. Further, our findings that about half of women reported meeting MVPA guidelines were comparable to estimates from the National Health Interview Survey that found 50% of U.S. women report meeting aerobic MVPA guidelines.( 35 ) Thus, our findings are likely generalizable to women in the U.S. We uniquely report longitudinal changes in CVHhf and health factor scores as our participants progressed from early in their first pregnancy through about three years after that pregnancy. Worsening CVHhf was mostly driven by worsening BMI and lipid scores. These findings harmonize with published research finding that parity is associated with worsening adiposity and lipids, with less consistent evidence that parity impacts BP or glycemia.( 36 – 41 ) Collectively, these findings highlight that the peripartum period, encompassing pregnancy and the years following, is an important window for interventions to improve the CVH health factors. Despite established cardiovascular benefits of MVPA and emerging evidence of cardioprotection with lower SB in general populations,( 11 , 18 ) a paucity of research has examined these associations among young women and especially during the life transitions of pregnancy into early parenthood. MVPA benefits pregnancy health, but less research demonstrates health behaviors that could enhance cardiovascular recovery and CVH health factors in the years following pregnancy.( 10 ) Our findings that healthy levels of both leisure MVPA and SB in mutually adjusted models each contributed to more favorable BMI scores at follow-up are important as the BMI score declined markedly across follow-up. Beneficial associations between higher MVPA with glucose and lipid scores and between lower SB and healthier BP score suggest different physiological mechanisms of benefit across these distinct behaviors.( 42 , 43 ) These data specifically support recommendations to increase MVPA during the postpartum period and beyond which have thus far been largely based on the cardiovascular benefits of MVPA in all adults.( 9 , 44 ) Importantly, our results promote an additional strategy of reducing leisure SB to improve CVH health factors for young women in the years after their first pregnancy. Women with a history of HDP or GDM had lower CVH hf at follow-up. These findings are in line with the growing recognition that adverse pregnancy outcomes like HDP and GDM are associated with elevated risk for future cardiovascular disease among women.( 7 , 8 ) For example, a previous analyses in nuMoM2b-HHS demonstrated that women with HDP had nearly triple the risk of incident hypertension over the average 3-year follow-up.( 27 ) Meta-analyses indicate that GDM is associated with a 5-fold increase in the risk of incident type 2 diabetes during the first five years after pregnancy.( 45 ) Our findings that each health factor score was lower for women who experienced HDP and GDM is important and suggests prevention efforts in this high-risk subgroup should ideally address multiple cardiovascular risk factors.( 11 , 43 ) We also provide new, encouraging data that women who experienced HDP or GDM had comparable benefits to CVH health factors associated with leisure MVPA in the years following delivery as compared to women who did not. This agrees with a small experimental study that found women with a history of preeclampsia had consistently worse cardiovascular risk measures than women without preeclampsia history, though both improved similarly with exercise training.( 13 ) To our knowledge, we are the first to demonstrate that lower leisure SB also provides similar cardiovascular benefits in the higher risk group of women who previously experienced HDP or GDM. Thus, activity interventions that encourage both increasing MVPA and decreasing SB during leisure time are promising and should be experimentally tested for optimizing CVH health factors after HDP and GDM. Strengths and Limitations Strengths of our study include the longitudinal design evaluating changes in CVH hf and health factor scores, large sample, racial/ethnic and geographic diversity, and prospectively assessed pregnancy outcomes and CVH health factors.( 20 , 21 ) Another strength is use of the recently updated Life’s Essential 8 framework for operationalizing CVH hf , which provides a range of scoring to translate the dose-dependent nature of suboptimal levels.( 2 ) This approach may be especially powerful in young women where CVH health factors are often within healthy ranges.( 46 ) However, it should be acknowledged that the clinical meaningfulness of the observed changes and differences in these newly defined CVH hf and health factor scores is not yet fully understood and our report may be less comparable to other research using standard clinical values (e.g., mmHg of BP).( 47 ) Lastly, we evaluated MVPA and SB simultaneously, recognizing that the two behaviors are distinct and may each be targeted to maximize CVH hf .( 17 ) However, this study was observational, precluding causal inferences on the effects of MVPA and SB on CVH health factors. Our study was also limited by the measurement of MVPA and SB which were self-reported (susceptible to recall and social desirability biases), only assessed in the leisure domain, and measured at the follow-up assessment ( 23 , 25 ) To address these limitations, we used guidelines levels of MVPA and SB quartiles rather than using the absolute reported amounts to reduce the impact of potential measurement error. To address the temporality issue, we also conducted sensitivity analyses removing adjustment for baseline CVH hf and health factors scores; the results of these were encouragingly similar. This sensitivity analysis also addressed this study’s limitation of differences in measurement protocols for blood sampling and BP between visits. Like other pregnancy cohorts,( 48 , 49 ) nuMoM2b (baseline) did not require pregnant participants to fast when providing blood samples; this may have resulted in higher baseline glucose and lipids and lower baseline CVH hf , lipid, and glucose scores. The expected worse-than-fasting scores at baseline coupled with the fasting scores at follow-up would likely have resulted in an underestimation of CVH hf , lipid, and glucose worsening over follow-up. The different protocols for BP would likely have resulted in nondifferential error and weakened associations. Overall, we expect that our findings, where CVH hf worsened over follow-up and higher leisure MVPA and lower SB after pregnancy were associated with better CVH hf in the years following delivery of a first child among women with and without HDP or GDM, are robust. Still, future research with rigorous longitudinal, device-measured MVPA and SB and that relates these exposures to CVH hf changes across the pre-pregnancy, pregnancy, and post-pregnancy life transitions is needed to better clarify how activity patterns can promote CVH health factors in women. Implications and Future Directions Our findings provide directly support encouraging leisure MVPA and discouraging leisure SB in the years following pregnancy to promote CVH health factors. However, MVPA declines with pregnancy and parenting, indicating that robust clinical and public health efforts may be necessary to facilitate adequate MVPA. 12 However, some data suggest that becoming a parent also leads to a reduction in SB; this phenomenon could be encouraged and enhanced for additional CVH health factor benefits.( 15 , 50 ) Experimental studies that quantify the CVH health factor benefits of increasing MVPA and decreasing SB in women following their first pregnancy are needed to inform specific guidelines.( 8 ) Of equal importance is research to identify durable and scalable interventions for promoting MVPA and reducing SB that directly address the unique circumstances of women in the years following their first delivery and caring for young children.( 15 , 50 ) CONCLUSIONS Promoting MVPA and discouraging SB is a promising strategy to combat declines in CVH among young women during the critical years after delivery of their first child, including among those who experienced HDP or GDM. Supplementary Material Supplemental Data File (.doc, .tif, pdf, etc.) NIHMS2051914-supplement-Supplemental_Data_File___doc___tif__pdf__etc__.docx (59.2KB, docx) Acknowledgements Grant funding from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD): U10 HD063036; U10 HD063072; U10 HD063047; U10 HD063037; U10 HD063041; U10 HD063020; U10 HD063046; U10 HD063048; and U10 HD063053. In addition, support was provided by Clinical and Translational Science Institutes: UL1TR001108 and UL1TR000153 with supplemental support to NHLBI U10-HL119991 from the Office of Research on Women’s Health and the Office of Disease Prevention. Cooperative agreement funding from the National Heart, Lung, and Blood Institute and the Eunice Kennedy Shriver National Institute of Child Health and Human Development: U10-HL119991; U10-HL119989; U10-HL120034; U10-HL119990; U10-HL120006; U10-HL119992; U10-HL120019; U10-HL119993; U10-HL120018, and U01HL145358; and the National Center for Advancing Translational Sciences through UL-1-TR000124, UL-1-TR000153, UL-1-TR000439, and UL-1-TR001108; and the Barbra Streisand Women’s Cardiovascular Research and Education Program, and the Erika J. Glazer Women’s Heart Research Initiative, Cedars-Sinai Medical Center, Los Angeles with supplemental support to U10-HL119991 from the Office of Research on Women’s Health and the Office of Disease Prevention. This research was additionally supported by R01 HL158652. Disclaimer: The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services. The results of the study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation. The results of the present study do not constitute endorsement by American College of Sports Medicine. Footnotes Conflicts of Interest: Dr. Noel Bairey Merz has board service and stock in iRhythm and consulting for SHL Telemedicine. All other authors have no financial disclosures. 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[ DOI ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials Supplemental Data File (.doc, .tif, pdf, etc.) NIHMS2051914-supplement-Supplemental_Data_File___doc___tif__pdf__etc__.docx (59.2KB, docx) Data Availability Statement Requests to access the data used in this report from qualified researchers trained in human subject confidentiality protocols will be considered by the nuMoM2b-HHS data coordinating center (Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-To-be Heart Health Study). Requests to access the data may be submitted to the nuMoM2b-HHS Study at https://numom2b.org . 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