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. Author manuscript; available in PMC: 2026 Oct 6.
Published in final edited form as: Am J Health Promot. 2026 Jan 2;40(7):832–842. doi: 10.1177/08901171251413224

Factors Associated with Leisure Time Physical Activity and Sedentary Behavior in Young Women 2–7 Years after Pregnancy in the nuMoM2b Heart Health Study

Bethany Barone Gibbs 1, Sarah Modlin 1, Subashan Perera 2, Alexis N Thrower 1, Kimberly A Huber 3, Andrea C Kozai 3, Jeremy Chiu 4, David M Haas 5, Philip Greenland 6, William A Grobman 7, Lauren Theilan 8, Natalie A Cameron 6, C Noel Bairey Merz 9, Kara M Whitaker 10
PMCID: PMC13638264  NIHMSID: NIHMS2209445  PMID: 41481421

Abstract

Purpose:

Identify demographic, behavioral lifestyle, psychological, and clinical factors associated with suboptimal activity patterns several years after a first pregnancy and delivery.

Design:

Cross-sectional, secondary analysis

Setting & Sample:

Women (n=2,843) from eight U.S. centers assessed 2–7 years after delivery in the nuMom2b Heart Health Study.

Measures:

Self-reported leisure time moderate-to-vigorous intensity physical activity (MVPA) and sedentary behavior (SB) were used to define patterns based on meeting recommendations for MVPA (active at ≥150 minutes/week) and leisure time SB (low SB at ≤3 hours/day). Factors (demographic, behavioral lifestyle, psychological, and clinical) assessed at study visits and hypothesized to be related to MVPA-SB patterns were included.

Analysis:

Multinomial logistic regression with forward selection identified factors associated with MVPA-SB patterns.

Results:

Participants most frequently reported the optimal active + low SB pattern (37.8%). Modifiable factors significantly associated with suboptimal patterns included lower diet quality (odds ratios [OR] 0.18–0.62; p<0.001), higher body mass index (BMI) (ORs 1.15–2.49; p=0.001), less sleep (ORs 1.27–1.46; p=0.0013), and higher perceived stress (ORs 1.20–1.52, p=0.0015). Lower income (ORs 0.39–0.60, p=0.0002), lower education (ORs 0.38–0.40; p=0.0323) and working/studying full-time (ORs 1.42–1.97, p-0.0021) were also associated with suboptimal patterns.

Conclusions:

Future research designing “sit less, move more” interventions following pregnancy and delivery could consider simultaneous intervention for modifiable factors and tailored strategies for low income/education and full-time working/student women who may face additional barriers.

Keywords: postpartum, determinants, correlates, activity patterns

PURPOSE

Strategies to reduce sex-specific accelerated cardiovascular risk development in women as they transition from young to middle age are needed.1–5 In the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-be Heart Health Study (nuMoM2b-HHS), we recently reported that achieving recommended levels of moderate-to-vigorous intensity physical activity (MVPA) and engaging in less sedentary behavior (SB) are distinct behavioral strategies to maintain cardiovascular health.6 Specifically, though the American Heart Association’s Life’s Essential 8 health factor score declined by an average 4.0 points between early pregnancy and follow-up (mean 3.2 years after delivery), follow-up scores were healthier in those with high MVPA (+2.3 points, p<0.001) and low SB (+1.5 points, p<0.05). These data support a “sit less, move more” approach to lower cardiovascular risk in the years following delivery of a first child.7 However, only 1 in 3 U.S. women 18–34 years old report meeting current physical activity guidelines.8 Though U.S. guidelines lack a quantitative upper limit for SB, National Health and Nutrition Examination Survey accelerometer data indicate that women accumulate more SB than men in childhood, adolescence and early adulthood.9 Further, pregnancy and postpartum are critical life course stages associated with a decline in MVPA and variable SB (higher during pregnancy but lower after pregnancy).10 Thus, tailored interventions and public health programming that address sex- and life course-specific barriers are likely necessary to achieve healthy MVPA and SB levels during this period of women’s lives.

Identification of correlates of healthy MVPA and SB in a contemporary cohort of women during the years following a first delivery could help to inform the design of future interventions incorporating modifiable behavioral targets, clusters of co-occurring lifestyle behaviors, clinical conditions that could be addressed simultaneously, or subpopulations most in need of intervention. Several demographic, behavioral lifestyle, psychological, and clinical factors are associated with inactivity (i.e., insufficient MVPA) in general and pregnant populations,11–13 but research focused specifically on women after delivery is lacking. Further, both shared and distinct factors appear to be associated with excessive SB as compared to low MVPA, though there is limited research in perinatal populations.13,14 As achieving an activity pattern of both sufficient MVPA and lower SB appears to optimally maintain cardiovascular health after delivery,6 research that identifies which factors are associated with ideal levels of each addresses an important knowledge gap in an understudied population. Thus, leveraging existing data, we sought to identify demographic, behavioral lifestyle, psychological, and clinical factors associated with less optimal MVPA and SB patterns in women 2–7 years after their first birth.

MATERIALS AND METHODS

Design and Sample

We performed a cross-sectional secondary analysis of data from the first follow-up visit of the nuMoM2b-HHS cohort study. nuMoM2b15 recruited 10,037 nulliparous women in their first trimester of pregnancy across eight U.S. clinical centers from 2010–2013. Women were followed prospectively through delivery to identify factors associated with adverse pregnancy outcomes. The nuMoM2b-HHS cohort follows a subset of nuMoM2b participants to investigate how adverse pregnancy outcomes relate to the development of modifiable cardiovascular risk factors.16 nuMoM2b-HHS follow-up is ongoing, but data collected at the first follow-up visit (2014–2020) that occurred 2–7 years (mean 3.2 years) after delivery were used herein. Participants in the nuMoM2b-HHS follow-up visit could not be currently pregnant or <6 months postpartum from any subsequent pregnancy. Human subjects research approval was provided by Institutional Review Boards at each site. All participants provided informed consent prior to participating. To be included in the current analysis, participants were required to have complete data on the considered factors and outcomes.

Measurements

MVPA was self-reported in nuMoM2b-HHS using an adapted Modifiable Activity Questionnaire (MAQ). MAQ has good reliability and validity among self-report instruments with intraclass correlation coefficients ranging from 0.4–0.6 compared to device-based measures.17 The adapted questionnaire asked participants to report their typical frequency and duration of participation in 36 common leisure time physical activities (e.g., aerobics, strength training, gardening) and allowed for up to three additional write-in activities over the past month. We classified activity intensities using the Compendium of Physical Activities,18 then converted durations and frequencies of those with ≥3.0 metabolic equivalents of task (i.e., MVPA) into minutes per week. Achieving aerobic MVPA guidelines was defined as ≥150 minutes of moderate intensity physical activity, ≥75 minutes of vigorous intensity physical activity, or an equivalent combination per week.7 Those who met guidelines were classified as “active;” those not meeting guidelines were classified as “inactive”.

SB was also self-reported in nuMoM2b-HHS with a single item querying typical duration (hours per day) of non-work time spent in sedentary activities such as watching television, reading, or using a computer, smartphone or tablet over the past month. When using self-report to assess SB durations, single-item instruments are a preferred method for large observational studies, with acceptable validity, good reliability, low participant burden, and less overestimation compared to multi-item instruments.19 In the absence of quantitative U.S. guidelines for SB, we used the Canadian 24-hour Movement Behavior Guidelines20 which recommend ≤3 hours of recreational screen time per day. Though the nuMoM2b-HHS study queried all leisure SB, we used the ≥3 hours cut point to classify high SB.

Finally, using the dichotomous MVPA and SB categories, we defined four joint MVPA-SB patterns as the dependent variable in our analysis as follows: (i) active + low SB (optimal); (ii) active + high SB; (iii) inactive + low SB; and (iv) inactive + high SB (least optimal).

Factors evaluated were based on previous literature of correlates and determinants of MVPA and/or SB in similar populations (e.g., adults, pregnant women) and data available in nuMoM2b-HHS.11–14 Demographic factors considered were assessed by standardized questionnaires and included ordinal groups of age, education, income, and household size as well as categories of living with a partner (yes/no), occupational status (none/student/full-time/part-time), and insurance type (commercial/government/other or missing). Of note, race/ethnicity was not included since it is a social construct and we have shown in nuMoM2b that racial/ethnic differences in cardiovascular health are explained by other factors already included in this analysis.21 Behavioral lifestyle factors assessed included alcohol use (yes/no), smoking (current/former/never), and cannabis use (yes/no). Diet quality was quantified as quartiles of the Healthy Eating Index (HEI) score22 calculated from a Modified Block Food Frequency Questionnaire.23 Self-reported sleep duration was categorized by National Sleep Foundation Guidelines (7–9 hours/day optimal; fewer or more hours/day suboptimal).24 Psychological factors included depressive symptoms from the Edinburgh Postnatal Depression Scale25 categorized as high risk of depression if ≥11 points.26 Stress was measured by the Perceived Stress Scale and categorized as no/low stress (0–13 points), moderate (14–26 points), or high (27–40 points).27 Clinical factors included body mass index (BMI) categorized by standard clinical cut points. Hypertension was defined as measured blood pressure >130/80 mmHg at the study visit or reported use of antihypertensive medications.28 Hyperlipidemia was defined as low density lipoprotein (LDL) ≥160 mg/dL or use of lipid-lowering medications.29 Diabetes was defined as fasting glucose ≥126 mg/dL, HbA1c ≥6.5, or use of glucose-lowering medications.30 Asthma was self-reported (yes/no) on a medical history questionnaire.

Statistical Methods

Participant characteristics, including MVPA and SB, were described using frequencies and percentages and compared across those included versus excluded in the analytic sample using chi-square tests. Next, we fit a series of multinomial logistic regression models with a generalized logit link function with MVPA-SB pattern as the dependent variable, and demographic, behavioral, psychological, and clinical factors as independent variables. Independent variables were considered individually in bivariate analyses and collectively using a forward selection approach in multivariable modeling. Statistical significance was assessed both at the individual parameter level (corresponding to one level of a categorical variable compared to a reference level) and at the overall level (corresponding to testing significance of multiple levels simultaneously), with the latter used in model selection. The multivariable modeling sought to identify a parsimonious set of factors most strongly and independently associated with MVPA-SB patterns.

RESULTS

Of 4,508 participants completing the nuMoM2b-HHS follow-up visit, we excluded 51 with missing MVPA or SB. Of the factors considered, participants were most often excluded due to missing or invalid dietary data (n=869). An additional 745 were excluded for missing one or more other factor, resulting in a final analytic sample of 2,843. A majority of women in the included sample were aged 25–34 years old (59.6%), had a higher education degree (74.8%) and household income ≥$50,000 per year(72.9%), lived with a partner (93.7%), had 3–4 people in their household (84.9%), had commercial or military insurance (79.4%), and worked full time (53.4%) (Supplemental Table 1). In our analytic sample, the optimal active + low SB pattern was most frequent (37.8%), followed by inactive + low SB (26.3%), inactive + high SB (least optimal, 18.9%), and active + high SB (17.0%).

Compared to those included, those excluded from the sample were younger, less educated, had lower income, and were more likely to have a household size of 1–2 or 5+, government insurance, and to be in the least optimal MVPA-SB group (27.1% vs. 18.9%). Excluded women also had less favorable behavioral lifestyle, psychological, and clinical characteristics for most factors investigated (p<0.0002; Supplemental Table 1).

Table 1 describes associations between demographic factors and MVPA-SB patterns. Women in older age categories were less likely to have suboptimal activity patterns (p<0.0001) and especially the least optimal inactive + high SB pattern. The odds of having the least optimal pattern (vs. the optimal pattern) in women age 35+ were about a quarter of those among women younger than 25 (odds ratio [OR], 0.27). Similar associations were observed for education and income, where women with higher education and income were less likely to have suboptimal MVPA-SB patterns and especially the inactive + high SB pattern (OR 0.14–0.44). Living with a partner and larger household size were also related to lower odds of both high SB patterns, but not the inactive + low SB pattern. Compared to commercial or military insurance, government insurance was related to greater odds of suboptimal activity patterns and especially the inactive + high SB pattern (OR 3.54). Compared to unemployed women, students and full-time workers were more likely to be in the inactive + low SB pattern but not other patterns.

Table 1.

Associations between Demographic Factors and MVPA-SB Patterns in nuMoM2b-HHS (n=2843)

Active + low SB (n=1074, 37.7%) Active + high SB (n=484, 17.0%) Inactive + low SB (n=748, 26.3%) Inactive + high SB (n=537, 18.9%) p-value
graphic file with name nihms-2209445-t0001.webp
Age, years
 <25 (n=284) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) <0.0001
 25–29 (n=763) 1.0 (ref.) 0.59 (0.39, 0.89) 0.70 (0.47, 1.03) 0.46 (0.32, 0.66)
 30–34 (n=933) 1.0 (ref.) 0.43 (0.29, 0.65) 0.59 (0.41, 0.86) 0.25 (0.17, 0.35)
 35+ (n=863) 1.0 (ref.) 0.48 (0.32, 0.72) 0.73 (0.50, 1.06) 0.27 (0.19, 0.39)
Education, highest degree
 Less than high school (n=69) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) <0.0001
 Highschool or G.E.D. (n=645) 1.0 (ref.) 0.51 (0.21, 1.21) 0.64 (0.28, 1.50) 0.37 (0.17, 0.80)
 Associate/Bachelor’s (n=1292) 1.0 (ref.) 0.26 (0.11, 0.61) 0.35 (0.15, 0.81) 0.12 (0.06, 0.26)
 Graduate (n=837) 1.0 (ref.) 0.23 (0.10, 0.54) 0.33 (0.14, 0.76) 0.08 (0.04, 0.18)
Income
 $0–$24,999 (n=347) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) <0.0001
 $25,000–$49,999 (n=426) 1.0 (ref.) 0.47 (0.30, 0.74) 0.70 (0.47, 1.05) 0.44 (0.30, 0.64)
 $50,000–$99,999 (n=954) 1.0 (ref.) 0.42 (0.29, 0.62) 0.49 (0.34, 0.70) 0.21 (0.15, 0.30)
 $100,000+ (n=1116) 1.0 (ref.) 0.40 (0.27, 0.58) 0.52 (0.37, 0.74) 0.14 (0.10, 0.20)
Living with partner
 No (n=178) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) <0.0001
 Yes (n=2665) 1.0 (ref.) 0.45 (0.28, 0.72) 0.63 (0.40, 1.00) 0.24 (0.16, 0.36)
Household size
 1–2 people (n=84) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) 0.0001
 3–4 people (n=2413) 1.0 (ref.) 0.48 (0.25, 0.92) 1.08 (0.53, 2.19) 0.29 (0.16, 0.51)
 5+ people (n=346) 1.0 (ref.) 0.56 (0.28, 1.14) 1.03 (0.49, 2.18) 0.29 (0.16, 0.55)
Insurance type
 Commercial/military (n=2258) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) <0.0001
 Government (n=503) 1.0 (ref.) 1.75 (1.29, 2.36) 1.87 (1.43, 2.44) 3.54 (2.71, 4.63)
 Other/missing (n=82) 1.0 (ref.) 1.40 (0.73, 2.67) 1.40 (0.79, 2.48) 1.80 (0.97, 3.32)
Occupational status
 None (n=665) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) 0.0001
 Student (n=63) 1.0 (ref.) 1.52 (0.71, 3.33) 2.10 (1.06, 4.16) 1.78 (0.87, 3.63)
 Part-Time (n=596) 1.0 (ref.) 0.94 (0.68, 1.28) 1.18 (0.88, 1.58) 0.94 (0.69, 1.28)
 Full-Time (n=1519) 1.0 (ref.) 1.07 (0.83, 1.39) 1.81 (1.42, 2.31) 1.13 (0.88, 1.46)
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Data presented are odds ratios (95% confidence intervals) from multinomial logistic regression models.

Example interpretation: Odds of having the least optimal pattern (vs the optimal pattern) in women age 35+ was about a quarter of those age <25 (OR, 0.27; 95% CI, 0.19–0.39). Results unadjusted.

Table 2 describes associations between behavioral lifestyle factors and MVPA-SB patterns. Alcohol and cannabis use were marginally significantly associated with MVPA-SB patterns in variable directions, while current smoking was consistently associated with greater odds of less optimal patterns and especially the inactive + high SB pattern (OR 3.65). Healthy Eating Index was strongly associated with lower odds of less favorable activity patterns in a graded manner, especially for the inactive + high SB pattern (OR 0.39, 0.19, and 0.10 with increasing quartiles). Short sleep duration was consistently associated with ~50% greater odds of the inactive activity patterns (OR 1.50 and 1.59), while long sleep duration was associated with over twice greater odds of the least optimal inactive + high SB pattern (OR 2.65).

Table 2.

Associations between Behavioral Lifestyle Factors and MVPA-SB Patterns in nuMoM2b-HHS (n=2843)

Active + low SB (n=1074, 37.7%) Active + high SB (n=484, 17.0%) Inactive + low SB (n=748, 26.3%) Inactive + high SB (n=537, 18.9%) p-value
graphic file with name nihms-2209445-t0002.webp
Alcohol
 No (n=1136) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) 0.0200
 Yes (n=1707) 1.0 (ref.) 1.02 (0.82, 1.26) 1.17 (0.96,1.42) 0.81 (0.66, 1.00)
Smoking
 Never (n=2163) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) <0.0001
 Former (n=471) 1.0 (ref.) 1.21 (0.91, 1.62) 1.13 (0.88, 1.46) 1.19 (0.90, 1.58)
 Current (n=209) 1.0 (ref.) 1.88 (1.18, 2.99) 2.25 (1.51, 3.37) 3.65 (2.45, 5.44)
Cannabis
 No (n=2679) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.)  1.0 (ref.) 0.0548
 Yes (n=164) 1.0 (ref.) 1.74 (1.14, 2.66) 1.03 (0.67, 1.58) 1.26 (0.81, 1.97)
Healthy Eating Index quartile
 Low (n=602) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) <0.0001
 Low-middle (n=692) 1.0 (ref.) 0.53 (0.37, 0.76) 0.43 (0.32, 0.58) 0.39 (0.29, 0.53)
 High-middle (n=758) 1.0 (ref.) 0.44 (0.31, 0.62) 0.33 (0.24, 0.44) 0.19 (0.14, 0.26)
 High (n=791) 1.0 (ref.) 0.41 (0.30, 0.58) 0.20 (0.15, 0.27) 0.10 (0.07, 0.14)
Sleep duration
 < 7 Hours (n=1737) 1.0 (ref.) 0.97 (0.78, 1.22) 1.59 (1.31,1.92) 1.50 (1.21, 1.85) <0.0001
 7–9 Hours (n=1084) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.)
 10+ Hours (n=22) 1.0 (ref.) - 1.08 (0.35, 3.31) 2.65 (1.01, 6.94)
Open in a new tab

Data presented are odds ratios (95% confidence intervals) from multinomial logistic regression models.

- indicates cell size <5 with insufficient sample sizes for computation.

Table 3 describes associations between psychological and clinical factors and MVPA-SB patterns. Depression and higher perceived stress were associated with 2–3 times greater odds of the high SB patterns. Higher BMI category was associated with greater odds of less optimal patterns in a graded manner, especially the least optimal pattern (OR 1.71, 2.75, and 4.88 with increasing category). Diabetes was associated with nearly three times the odds of the least optimal pattern (OR 2.84). Hypertension was associated with 41–59% greater odds of the two less optimal high SB patterns (OR 1.41–1.59), while asthma was associated with 44–50% greater odds of the two less optimal inactive patterns (OR 1.44–1.50).

Table 3.

Associations between Psychological and Clinical Factors and MVPA-SB Patterns in nuMoM2b-HHS (n=2843)

Active + low SB (n=1074, 37.7%) Active + high SB (n=484, 17.0%) Inactive + low SB (n=748, 26.3%) Inactive + high SB (n=537, 18.9%) p-value
graphic file with name nihms-2209445-t0003.webp
Depression
 No (n=2508) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) <0.0001
 Yes (n=335) 1.0 (ref.) 1.87 (1.34, 2.59) 1.17 (0.85, 1.62) 2.35 (1.73, 3.20)
Perceived Stress
 No or low stress (n=1311) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) <0.0001
 Moderate stress (n=1385) 1.0 (ref.) 1.16 (0.93, 1.44) 1.41 (1.17, 1.71) 2.09 (1.68, 2.60)
 High stress (n=147) 1.0 (ref.) 1.93 (1.19, 3.11) 1.22 (0.75, 1.98) 3.11 (1.97, 4.92)
BMI, kg/m2
 <18.5 (n=74) 1.0 (ref.) 1.02 (0.49, 2.14) 1.61 (0.91, 2.86) 1.72 (0.88, 3.35) <0.0001
 18.5–24.9 (n=1296) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.)
 25.0–29.9 (n=716) 1.0 (ref.) 1.22 (0.93, 1.59) 1.39 (1.10, 1.75) 1.71 (1.31, 2.23)
 30.0–34.9 (n=369) 1.0 (ref.) 1.76 (1.26, 2.46) 1.62 (1.19, 2.20) 2.75 (1.99, 3.80)
 35.0+ (n=388) 1.0 (ref.) 2.44 (1.70, 3.48) 2.95 (2.15, 4.04) 4.88 (3.50 6.79)
Diabetes
 No (n=2744) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) <0.0001
 Yes (n=99) 1.0 (ref.) 1.44 (0.76, 2.71) 1.39 (079, 2.46) 2.84 (1.67, 4.81)
Hypertension
 No (n=2294) 1.0 (ref.) 1.0 (ref.)  1.0 (ref.) 1.0 (ref.) 0.0080
 Yes (n=549) 1.0 (ref.) 1.59 (1.22, 2.07) 1.20 (0.94, 1.53) 1.41 (1.08, 1.83)
Hyperlipidemia
 No (n=2635) 1.0 (ref.) 1.0 (ref.)  1.0 (ref.) 1.0 (ref.) 0.5713
 Yes (n=208) 1.0 (ref.) 1.09 (0.72, 1.64) 0.97 (0.67, 1.40) 1.33 (0.91, 1.94)
Asthma
 No (n=2472) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) 0.0012
 Yes (n=371) 1.0 (ref.) 1.14 (0.81, 1.59) 1.44 (1.09, 1.90) 1.50 (1.11, 2.02)
Open in a new tab

Data presented are odds ratios (95% confidence intervals) from multinomial logistic regression models.

Table 4 describes the final parsimonious multivariable model of independent associations. Diet quality remained most strongly associated in a graded manner with lower odds of all less optimal MVPA-SB patterns with higher Healthy Eating Index (OR 0.18–0.62). Similar graded associations remained where higher BMI category was associated with increased odds of less optimal MVPA-SB patterns (OR 1.15–2.69). Higher income was most consistently associated with 40–61% lower odds of the inactive + high SB category (OR 0.39–0.60). Factors associated with greater odds of being in the two less optimal inactive groups included short sleep duration (OR 1.27–1.46), moderate stress (OR 1.20–1.52), and full-time occupational status (OR 1.42–1.97). Finally, having a higher educational attainment was associated with approximately 60% lower odds of being in the least optimal inactive + high SB group (OR 0.38–0.40). While cannabis use was marginally associated with MVPA-SB patterns overall in variable directions (p=0.0335), no ORs were statistically significant.

Table 4.

Multivariable Associations between Demographic, Behavioral Lifestyle, Psychological, and Clinical Factors with MVPA-SB Patterns in nuMoM2b-HHS

Active + low SB (n=1074, 37.7%) Active + high SB (n=484, 17.0%) Inactive + low SB (n=748, 26.3%) Inactive + high SB (n=537, 18.9%) Type III p-value
graphic file with name nihms-2209445-t0004.webp
Healthy Eating Index quartile
 Low (n=602) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) <0.0001
 Low-middle (n=692) 1.0 (ref.) 0.62 (0.43, 0.90) 0.47 (0.35, 0.65) 0.53 (0.38, 0.73)
 High-middle (n=758) 1.0 (ref.) 0.56 (0.39, 0.80) 0.37 (0.27, 0.50) 0.28 (0.20, 0.40)
 High (n=791) 1.0 (ref.) 0.57 (0.40, 0.82) 0.25 (0.18, 0.34) 0.18 (0.12, 0.25)
BMI, kg/m2
 <18.5 (n=74) 1.0 (ref.) 0.91 (0.43, 1.93) 1.65 (0.91, 3.00) 1.31 (0.64, 2.68) 0.0001
 18.5–24.9 (n=1296) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.)
 25.0–29.9 (n=716) 1.0 (ref.) 1.15 (0.88, 1.52) 1.29 (1.01, 1.63) 1.42 (1.07, 1.88)
 30.0–34.9 (n=369) 1.0 (ref.) 1.48 (1.05, 2.10) 1.25 (0.91, 1.72) 1.75 (1.24, 2.47)
 35.0+ (n=388) 1.0 (ref.) 1.91 (1.31, 2.78) 2.10 (1.51, 2.93) 2.49 (1.74, 3.56)
Income
 $0–$24,999 (n=347) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) 0.0002
 $25,000–$49,999 (n=426) 1.0 (ref.) 0.57 (0.35, 0.91) 0.89 (0.57, 1.37) 0.60 (0.39, 0.92)
 $50,000–$99,999 (n=954) 1.0 (ref.) 0.65 (0.42, 1.00) 0.76 (0.51, 1.15) 0.41 (0.27, 0.61)
 $100,000+ (n=1116) 1.0 (ref.) 0.73 (0.46, 1.16) 0.94 (0.61, 1.45) 0.39 (0.25, 0.60)
Sleep duration
 < 7 Hours (n=1737) 1.0 (ref.) 0.89 (0.7, 1.11) 1.46 (1.19, 1.78) 1.27 (1.00, 1.60) 0.0013
 7–9 Hours (n=1084) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.)
 10+ Hours (n=22) 1.0 (ref.) - 0.88 (0.26, 2.94) 1.36 (0.45, 4.08)
Perceived Stress
 No or low stress (n=1311) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) 0.0015
 Moderate stress (n=1385) 1.0 (ref.) 1.03 (0.82, 1.30) 1.20 (0.98, 1.46) 1.52 (1.20, 1.93)
 High stress (n=147) 1.0 (ref.) 1.49 (0.90, 2.47) 0.78 (0.46, 1.30) 1.53 (0.93, 2.54)
Occupational status
 None (n=665) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) 0.0021
 Student (n=63) 1.0 (ref.) 1.37 (0.63, 3.00) 1.97 (0.96, 4.00) 1.46 (0.67, 3.17)
 Part-Time (n=596) 1.0 (ref.) 0.99 (0.72, 1.37) 1.22 (0.90, 1.66) 1.09(0.78, 1.52)
 Full-Time (n=1519) 1.0 (ref.) 1.14 (0.86, 1.51) 1.83 (1.41, 2.38) 1.42 (1.07, 1.89)
Education, highest degree
 Less than high school (n=69) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) 0.0323
 Highschool or G.E.D. (n=645) 1.0 (ref.) 0.76 (0.31, 1.89) 0.85 (0.35, 2.07) 0.65 (0.28, 1.49)
 Associate/Bachelor’s (n=1292) 1.0 (ref.) 0.48 (0.19, 1.20) 0.62 (0.26, 1.52) 0.40 (0.18, 0.93)
 Graduate (n=837) 1.0 (ref.) 0.42 (0.17, 1.08) 0.58 (0.23, 1.44) 0.38 (0.16, 0.91)
Cannabis
 No (n=2679) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) 1.0 (ref.) 0.0335
 Yes (n=164) 1.0 (ref.) 1.33 (0.85, 2.07) 0.76 (0.48, 1.19) 0.67 (0.40, 1.10)
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Data presented are odds ratios (95% confidence intervals) from multinomial logistic regression using a forward selection process.

DISCUSSION

To our knowledge, we are the first to evaluate factors associated with MVPA-SB patterns in women during the unique period several years after delivery of a first child. This a critical window for primordial prevention, since early parenthood is associated with declines in certain health behaviors (e.g., MVPA) and worsening cardiovascular health factors (e.g., BMI, blood pressure, lipids).3,6,10,31,32 Engaging in sufficient MVPA and limiting SB are behavioral targets to curtail these declines.6,33,34 A variety of factors were associated with not meeting MVPA guidelines, having high SB, or both, among women several years after delivery of their first child. Our final multivariable model suggested that specific modifiable factors, including lower quality diet, higher BMI, insufficient sleep, and higher perceived stress, constituted a parsimonious set of factors most strongly associated with less favorable MVPA-SB patterns. In addition, more factors were associated in bivariate analyses representing potential additional targets for intervention. Cannabis use was marginally associated overall and in variable directions, limiting its utility as a target for designing future interventions. Consistent, independent associations of less favorable MVPA-SB patterns among women with lower income, lower education, and full-time work or student status identified groups potentially needing extra support to achieve healthy MVPA-SB patterns.

Comparison to Previous Studies

Most comparable prior research has focused on insufficient MVPA (i.e., inactivity). Our findings add to this literature which has generally focused on general populations or adult women.11–13,35,36 A systematic review of factors associated with being active in working-aged women found mostly similar conclusions to our multivariable model.35 They determined that at least some evidence supported associations between lower education and full-time employment with inactivity, associations with depression were mixed, and being married (living with a partner in our study) was not associated with inactivity. However, lower income and higher stress were associated with inactivity in our study while they were mostly not associated in the systematic review. Results from one study included in the review, the Australian Longitudinal Study on Women’s Health (n=11,695 women; 20–27 years old), are particularly relevant.37 Across four surveys over nine years of follow-up, multivariate modeling similarly identified higher BMI, lower education, and higher stress to be associated with lower odds of being active. However, differences from our findings included that smoking, less alcohol, not working or working less than full time, and being married (or in a marriage-like relationship) were associated with lower odds of being active in their study. These differences could reflect that we included a homogenous population of women who had delivered a child 2–7 years prior whereas the Australian study included young women with and without children. Indeed, having children was consistently associated with lower MVPA in their study and the systematic review in working-aged women.35,37 This distinction underscores the relevance of the life period post pregnancy for MVPA promotion and indicates that factors associated with being active among women with young children may simply differ. We also included unique factors such as diet, sleep, and income in our analysis, which persisted as important predictors in our multivariable model and may serve as proxies for other factors.

High SB and low MVPA are distinct, both behaviorally and with respect to health risks.38 Yet, fewer studies investigate factors associated with higher SB or combined MVPA-SB patterns – especially in women after delivery of a first child. Unlike MVPA, which tends to have unfavorable decreases after having children, limited data suggest lower SB in parents of young children.10,13 Despite this, high SB is now recognized as a behavioral risk factor for poor health outcomes,34 and we have previously shown that both lower SB and higher MVPA are associated with distinct, additive benefits for maintaining cardiovascular health in this cohort of women 2–7 years after delivery.6 Thus, maintaining low SB appears to have benefits, even following a first delivery when SB may be lower. A separate longitudinal analysis of the aforementioned Australian Longitudinal Study on Women’s Health evaluated factors associated with total self-reported sitting time in young women.39 They identified similar factors associated with higher weekend sitting (most comparable to our leisure time SB variable) including higher BMI, higher stress, lower education and working full-time. However, in contrast, they found that smoking and no/rare or higher/risky alcohol drinking were also associated with more weekend sitting. Women who were inactive also had more weekend sitting (considered as joint patterns in our analysis), while being married and having a more children were associated with less weekend sitting. Once again, differences in findings likely reflect our sample who had all recently had their first child and that we considered different factors that remained important in our analysis (e.g., healthy diet, sleep, and income).

Strengths, Limitations, and Future Research Recommendations

A major strength is the sample from a large, well-defined cohort of U.S. women during the critical window following a first pregnancy and delivery. We considered joint leisure MVPA-SB patterns that have demonstrated associations with cardiovascular health in this cohort and evaluated a variety of factors measured by the parent study. Limitations include the cross-sectional observational design, which is vulnerable to reverse causality and confounding and limits our ability to speculate on causal pathways. Also, the secondary analysis design resulted in our ability to only consider factors already measured. Other factors may influence MVPA-SB patterns in the present population and should be considered in future research and interventions, e.g., attitudes, motivation, perceived barriers, other health conditions, multi-level socioecological influences, and pre-pregnancy activity levels.11,14 Thus, while we do not offer a comprehensive resource for informing the design of new interventions inclusive of all possible determinants, we do offer some guidance on potential approaches for this understudied, life-stage specific population. Generalizability of our sample is limited as participants volunteered to participate in nuMoM2b-HHS, and participants with incomplete data were excluded. Further, important factors may vary in countries outside of the U.S. due to cultural and socioeconomic differences. However, these limitations are not unique to our study. Finally, self-reported MVPA and especially SB are susceptible to measurement errors including social desirability and recall bias.40,41 Future studies using device-based measures are needed to confirm our findings.

Implications

Our findings uniquely inform approaches for designing activity promotion interventions for women in the years after delivery of a first child by identifying factors associated with more favorable MVPA-SB patterns during this life stage. Evaluation of MVPA and SB during leisure time is especially relevant, as less optimal levels during leisure are more often associated with health risks compared to overall (across all domains) or occupational MVPA and SB.42–44 Also, leisure time activities are more likely to be modifiable. Factors that persisted in the multivariable model could be used as an operational set of criteria to identify those at greater risk for poor activity patterns and to design effective health promotion interventions for testing in future research. Such interventions could be coupled with cardiovascular risk assessment (e.g., subclinical vascular changes) in women during this life stage to further personalize prevention strategies.

Poorer diet quality was most strongly associated with less optimal MVPA-SB patterns. As these behaviors appear to cluster and may share common antecedents, intervention designs that combine the promotion of a “sit less, move more” approach with healthy eating may be especially helpful.45 As MVPA and SB appear to be bidirectionally associated with weight in adults,46 promoting a healthy BMI should also be considered. Since short sleep and higher stress also persisted in the multivariable model, interventions incorporating such modifiable factors that could be clustered or bidirectionally related to MVPA-SB patterns may also be worth testing.46 Lastly, our population of women at a similar life stage allowed us to identify subgroups, such as those working full-time, enrolled as students, or with low income or low education, as less likely to achieve the optimal MVPA-SB pattern. Research on interventions that identify and address specific socioeconomic barriers is especially needed to maximize the effectiveness of promoting healthy MVPA-SB patterns.

CONCLUSIONS

We found several modifiable lifestyle factors were associated with less healthy MVPA-SB patterns, including low diet quality, high BMI, insufficient sleep, and high perceived stress, among women several years after delivering their first child. Women with lower income and education, as well as those who were working full time or attending school, were also less likely to have the optimal MVPA-SB pattern. Future studies could evaluate ‘sit-less, move more’ interventions that are augmented by co-interventions for related modifiable factors and strategies that address unique barriers in subpopulations with less optimal MVPA-PA patterns, with the overall goal of promoting cardiovascular health in women through adulthood.

Supplementary Material

1

Acknowledgements

Special thanks to the participants of the nuMoM2b-HHS study and the many investigators and clinical staff involved in data collection for the cohort.

Funding Statement

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. 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. The Sedentary Behavior and Cardiovascular Health in Young Women ancillary study is supported by NHLBI R01HL158652. ACK is supported under NHLBI T32HL083825.

Footnotes

Declaration of competing interests

None reported

Ethical approval and informed consent

Human subjects research approval was provided by Institutional Review Boards at each site and all participants provided informed consent prior to participating in research activities.

Data availability

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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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS2209445-supplement-1.pdf (290.3KB, pdf)

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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