Health Behaviors Among Women of Reproductive Age With and Without a History of Gestational Diabetes Mellitus

RESEARCH DESIGN AND METHODS

We used data from the 2001–2003 waves of the Behavioral Risk Factor Surveillance System (BRFSS) (12). This cross-sectional telephone survey is conducted by the Centers for Disease Control and Prevention in conjunction with state health departments. The BRFSS uses a multistage cluster design based on random-digit dialing methods of sampling to select a representative sample from each state’s noninstitutionalized civilian residents aged ≥18 years. Data collected from each state are pooled to produce nationally representative estimates. This study used responses to a core set of questions asked in all states. Median response rates varied from 77 to 80% over the study period (13). A detailed description of the survey methods has been previously published (14).

The sample included women aged 18–44 years who answered the 2001–2003 survey question, “Have you ever been told by a doctor that you have diabetes?” Responses included “yes,” “yes, but only during pregnancy,” “no,” and “don’t know or not sure.” Participants who first answered “yes” were further asked, “Was this only when you were pregnant?” Women who responded “yes” and “only during pregnancy” were classified as having hGDM and women who responded “yes” were classified as having current diabetes. Participants who responded “no” were classified as not having hGDM, and women who responded “don’t know or not sure” were classified as such. Thus, the categories of hGDM and diabetes were mutually exclusive. We excluded women who reported current diabetes from this analysis (n = 4,412) and women who replied “don’t know, not sure, or refused” (n = 132), for a sample size of 177,420. Five studies have reported high overall reliability of the BRFSS question about a diagnosis of diabetes (κ = 0.60–0.86) (14).

Primary outcome variables

Meeting U.S. guidelines for fruit and vegetable consumption and physical activity, sedentary physical activity level, and current smoking were the primary outcome variables. Daily consumption of fruits and vegetables was computed from six food frequency questions on fruit juice, fruit, green salad, potatoes, and carrots (number of times per day) and all other vegetables (number of servings per day). These numbers were truncated at 15 per day to eliminate unreasonable values. Meeting U.S. fruit and vegetable consumption was defined as five or more times or servings per day (15).

To assess physical activity level, participants were asked, “Now, thinking about the moderate physical activities you do in a usual week, when you are not working, do you do moderate activities for at least 10 min at a time, such as brisk walking, bicycling, vacuuming, gardening, or anything else that causes small increases in breathing or heart rate?” “How many days per week do you do these moderate activities for at least 10 min at a time?” and “On days when you do moderate activities for at least 10 min at a time, how much total time per day do you spend doing these activities?” Participants were then asked similar questions regarding vigorous physical activity, defined as activity causing large increases in breathing and heart rate. “Meeting U.S. physical activity guidelines” was defined as moderate activity at least 30 min a day and 5 days a week or vigorous activity at least 20 min a day and 3 days a week. “Some activity” was defined as less activity than would meet the guidelines, but not sedentary activity. “Sedentary” was defined as engaging in <10 min a day of moderate or vigorous physical activity (16). Retest reliability of these questions has been reported as moderate to excellent (17). Current smoking was defined as smoking weekly on some or all days among women who had lifetime smoking of at least 100 cigarettes previously. Studies have reported high overall reliability of BRFSS questions assessing current smoking (κ = 0.83–1.00) (14).

Analysis

Covariates examined included age (years), race or ethnicity (non-Hispanic white, non-Hispanic African American, Hispanic, Asian, American Indian, or Alaska Native), education level (less than high school, high school, and greater than high school), current employment, married or partnered status, presence of children aged <18 years in the household, BMI (kg/m²), current smoking, and self-rated health (excellent, very good, good, fair, and poor) (18). Reliability and validity of these measures is high with the exception of self-report of obesity, where validity is moderate (14).

Behavioral variables were included as covariates in models of the other behaviors. We evaluated interactions between age and hGDM, race and hGDM, and BMI and hGDM for both the physical activity and dietary models. These were not significant and therefore were not included in the final models.

We compared women with and without hGDM in unadjusted analyses using Rao-Scott χ² for categorical variables (19) and t tests with survey-based SEs for continuous variables. Because all primary outcomes were categorical, multivariate logistic regression analyses were conducted with the whole sample. We also conducted these analyses with only those women who reported having children in the household. All analyses were performed using SAS 9.1 (SAS Institute) to account for the weighting and complex survey design.

RESULTS

Of the 177,420 participants, 3% (n = 4,718) reported having hGDM. Unadjusted characteristics of women with and without hGDM are presented in Table 1. Compared with women without hGDM, women with hGDM were significantly older and heavier (mean BMI 27.4 vs. 25.4 kg/m², P < 0.0001). The prevalence of overweight (28.4%) and obesity (25.6%) was significantly higher among women with hGDM compared with women without hGDM (23.3 and 16.5%, respectively). Women with hGDM were less often high school graduates or employed, were more often married and living with children <18 years of age, and reported worse self-rated health. One-quarter of women with hGDM were Hispanic compared with only 15% of women without hGDM.

In unadjusted comparisons, the health behaviors of women with and without hGDM were similar. In each group, almost one-quarter of women reported current smoking, approximately half met physical activity guidelines, 13% were sedentary, and approximately one-quarter met fruit and vegetable consumption guidelines. Less than three servings of fruits and vegetables were eaten daily by 36.6 and 39.5% of women with and without hGDM, respectively.

In multivariable analyses, women with hGDM had reduced, but not statistically significant, odds of consuming at least five daily servings of fruits and vegetables (Table 2). The subgroup of women with hGDM who lived with children was significantly less likely to meet these guidelines. In the whole sample, meeting fruit and vegetable consumption guidelines was associated with older age, Asian race/ethnicity, having greater than a high school education, being married or partnered, and meeting physical activity guidelines. Current employment, obesity, and current smoking reduced the likelihood of meeting fruit and vegetable consumption guidelines.

Women with and without hGDM had similar odds of meeting physical activity guidelines, whether or not they lived with children. Correlates of meeting these guidelines included younger age, having greater than high school education, and meeting fruit and vegetable consumption guidelines. Hispanic, Asian, and African-American women; individuals who were employed; and individuals who were overweight or obese were less likely to meet the physical activity guidelines. Women with hGDM were no more likely to be sedentary than women without hGDM, regardless of whether they lived with children. Asian, African-American, Hispanic, and American Indian women were 2.7, 2.5, 2.1, and 1.7 times more likely to be sedentary, respectively, than non-Hispanic white women. Having less than a high school education, being obese, and reporting poor to fair health were additional risk factors associated with sedentary behavior. Being married or partnered, having more than a high school education, and meeting the fruit and vegetable consumption guidelines reduced the odds of being sedentary.

We also examined correlates of meeting both fruit and vegetable and physical activity guidelines. The results were similar to those for meeting physical activity guidelines for women with and without hDGM (data not shown, but available from the authors). Having more than a high school education and being married or partnered were associated with greater likelihood of meeting both guidelines. Hispanic, Asian, and African-American race or ethnicity; employment; poor to fair general health; smoking; obesity; and overweight were associated with decreased likelihood of meeting both guidelines. Results were similar regardless of whether the women lived with children.

Current smoking was not associated with hGDM in the total sample but was 20% more likely among women with hGDM who lived with children. In the whole sample, older age, having less than a high school education, employment, and poor to fair general health increased the odds of smoking. Decreased odds of smoking was associated with Hispanic, Asian, and African-American race or ethnicity; having more than a high school education; being married or partnered; being obese; and meeting fruit and vegetable consumption guidelines.

CONCLUSIONS

This first-time analysis of the current lifestyle habits of a nationally representative population-based sample of U.S. women of childbearing age found that, like their counterparts without hGDM, women with hGDM reported low levels of physical activity and fruit and vegetable consumption, and almost one-quarter were current smokers. Characteristics often associated with inadequate physical activity and fruit and vegetable consumption such as low educational level, obesity, and poor self-rated health (20,21) were significantly more common among women with than without hGDM. Nonetheless, although women with hGDM are at elevated risk for future diabetes, they were no more, or less, likely to have adopted healthy lifestyle behaviors, in unadjusted and adjusted models.

Alternative explanations for our results may be that women with hGDM overreport healthy behaviors, since they may be more aware and sensitive about these risk factors for diabetes, or that women with hGDM may actually try to compensate for their other risk factors for poor behaviors by exercising more often and eating more healthily. Women with hGDM may face many of the same barriers to healthy lifestyle as unaffected women of reproductive age. These barriers include lack of motivation and social support, confidence in one’s ability to improve lifestyle, and environmental barriers such as lack of convenient grocery stores or places to exercise (22,23). Competing work and family demands, including child care, pose additional barriers to women with children (10,11,24). Data from longitudinal surveys indicate that women with young children are less likely to have adequate physical activity than women without young children (20,24,25). It is notable that the 87% of women sampled in our study who live with children under the age of 18 years were at significantly greater risk of inadequate fruit and vegetable consumption and increased risk of smoking in the multivariate analyses. Because women with hGDM and their children already have increased risk of developing diabetes, finding additional risk factors for diabetes and its complications among women living with children is of great concern.

The strengths of this report include its large national population-based sample and its examination of health behaviors and status in a diverse population. Study limitations include the lack of more detailed information on individual, social, and environmental correlates of health and behavior and, in particular, more detailed measures of diet such as fat consumption. The BRFSS includes only adults with land-based telephone service. Therefore, these results may not apply to individuals without telephones, who are more likely to have low incomes (26). The BRFSS is conducted in annual nonlongitudinal waves, limiting the examination of change in practices over time. All BRFSS data are self-reported, which may lead to under- or overreporting of characteristics and behaviors and the diagnosis of GDM. The DPP also relied on self-report of GDM (R. Ratner, personal communication). Validation of population-based surveys with large numbers of participants with medical records is generally not feasible because of the excessive cost and complex logistics. Previous validation studies of the BRFSS question ascertaining the presence of diabetes noted a sensitivity of 70% and a specificity of 99% (27), suggesting that underreporting of chronic conditions may occur, which would bias results to the null.

Our report of hGDM prevalence is slightly lower than that reported in other studies using glucose levels (2,28). The prevalence of hGDM may be underestimated, since we excluded women with hGDM who had subsequently developed current diabetes. This exclusion may have resulted in higher than expected levels of physical activity and fruit and vegetable consumption among the remaining women with hGDM, since women with lower levels might be the most likely to develop diabetes.

High-risk pregnant women are routinely screened for GDM, thus identifying a population that is at high risk for glucose intolerance and future diabetes (5). Although current clinical practice guidelines recommend that all women with hGDM receive postpartum diagnostic testing for diabetes and healthy lifestyle education focused on nutrition, physical activity, and maintenance of normal body weight (29), routine screening for subsequent impaired glucose tolerance and impaired fasting glucose are not yet routinely performed (C.K., B. Tabaei, R. Burke, L. McEwen, R. Lash, S. Johnson, K. Schwartz, S. Bernstein, W. Herman, unpublished data). Thus, despite the success of the DPP in preventing or delaying the onset of diabetes in women with hGDM (R. Ratner, personal communication), many women may be unaware of their risk for future diabetes, and others do not take steps to reduce their risks (C.K., B. Tabaei, R. Burke, L. McEwen, R. Lash, S. Johnson, K. Schwartz, S. Bernstein, W. Herman, unpublished data). Surveillance of hGDM trends and associated demographic and behavioral correlates over time will also be useful in designing focused cost-effective interventions to reduce diabetes risk. Further studies of the determinants of successful behavioral change in women with hGDM are strongly recommended.