Journal Logo


Prepregnancy Fitness and Risk of Gestational Diabetes: A Longitudinal Analysis


Author Information
Medicine & Science in Sports & Exercise: August 2018 - Volume 50 - Issue 8 - p 1613-1619
doi: 10.1249/MSS.0000000000001600


Gestational diabetes mellitus (GDM) is a common pregnancy complication, affecting up to 14% of pregnant women, depending on the population and diagnostic criteria (1). Risk factors for the development of GDM include family history of diabetes, older maternal age, and other prepregnancy attributes, including overweight or obesity, low HDL cholesterol (HDL-C), impaired fasting glucose, and higher fasting insulin (2,3). Women with GDM are at increased risk for perinatal morbidity and future risk of type 2 diabetes and metabolic syndrome (4,5). Furthermore, offspring born to women with GDM are at greater risk for obesity and type 2 diabetes, thus perpetuating a cycle of adverse health outcomes (6).

It is well known that lifestyle behaviors, such as physical activity, can delay or prevent the development of type 2 diabetes (7). Physical activity increases the rate of glucose uptake in the skeletal muscle, thereby improving glucose tolerance and insulin sensitivity. Moderate- to vigorous-intensity physical activity (MVPA) and higher levels of cardiorespiratory fitness (henceforth known as fitness) are both associated with a reduced risk of type 2 diabetes (8,9). Emerging evidence also suggests that time spent in sedentary behaviors, such as television viewing, is related to a greater risk of diabetes, independent of physical activity level and body mass index (BMI) (9,10). Therefore, MVPA, fitness, and sedentary behaviors may play important roles in the development of GDM.

Findings from epidemiologic studies on the associations of MVPA before or during pregnancy with subsequent risk of GDM are inconsistent. Several studies have shown an inverse relationship (11–19), whereas others report no association (12,13,20–22). Randomized controlled studies suggest that MVPA in pregnancy provides a small beneficial effect on GDM risk, although again the literature is mixed, in part because pregnant women struggle with adhering to exercise routines (23). There are several limitations of the existing literature. First, prior studies examining the association of MVPA with risk of GDM have not evaluated prepregnancy metabolic status, including fasting blood glucose, insulin, or insulin resistance, which are known risk factors of GDM (2). This is particularly important because approximately half of women have recurrent GDM, indicating that traditional metabolic risk factors do not fully explain the association with GDM (24). A second limitation is the reliance on self-reported physical activity. Fitness may be a better measure of habitual MVPA (25). Third, although sedentary behaviors have been linked with type 2 diabetes, few studies have evaluated sedentary behaviors and subsequent risk of GDM (13,15,21).

To address these gaps in knowledge, we examined the associations between objectively measured fitness, self-reported MVPA, and self-reported time spent watching television (a surrogate for sedentary behaviors) before pregnancy with subsequent development of GDM, independent of potential confounders and other risk factors for GDM, including prepregnancy obesity and cardiometabolic status. A better understanding of the relationships between prepregnancy fitness, physical activity, and sedentary behaviors with GDM can inform the development of targeted behavioral interventions to reduce the risk of developing GDM.


Coronary Artery Risk Development in Young Adults (CARDIA) is an ongoing prospective cohort study of 5115 Black and White adults who were initially recruited in 1985–1986 from four field centers (Birmingham, AL; Chicago, IL; Minneapolis, MN; and Oakland, CA). Participants completed follow-up visits in 1987–1988 (year 2), 1990–1991 (year 5), 1992–1993 (year 7), 1995–1996 (year 10), 2000–2001 (year 15), 2005–2006 (year 20), and 2010–2011 (year 25). Attendance rates at each examination were 91%, 86%, 81%, 79%, 74%, 72%, and 72% of the surviving cohort, respectively. Data collection and follow-up protocols were approved by institutional review boards at each field center. All participants provided written informed consent.

A total of 2787 women were enrolled in baseline, and 1362 women had at least one birth during the 25-yr of follow-up. Women were excluded from analyses if they reported type 1 or type 2 diabetes at baseline or before births occurring after baseline (n = 12), self-reported GDM in a pregnancy before baseline (n = 3), or were missing data on fitness (n = 13) or MVPA (n = 1), resulting in a final sample size of 1333 for analyses of fitness and MVPA. Data on television viewing were first collected at year 5; therefore, for these analyses, participants were eligible if they had one or more births between years 5 and 25. Of the 1333 women eligible at baseline for the fitness and MVPA analyses, 906 attended the year 5 visit and had at least one birth subsequently during follow-up. Women who self-reported GDM at year 2 or 5 (n = 20) or were missing data on television viewing at year 5 (n = 130) were excluded from these analyses, resulting in a sample size of 756 for the analyses of television viewing and GDM.

Fitness was assessed in all medically eligible participants with a graded symptom-limited maximal exercise treadmill test using a modified Balke protocol at baseline (26). The test progressively increased in difficulty for up to nine 2-min stages. Stage 1 consisted of a 2% grade at 3 mph; stages 2–6 used a 6%–22% grade at 3.4 mph, increasing grade by 4% each stage; stages 7 and 8 were at a 22% and 25% grade at 4.2 mph; and stage 9 used a 25% grade at 5.6 mph. Participants were encouraged to continue the test for as long as possible until reaching maximal exertion. The rate of energy expenditure at the end of each stage was estimated and reported in METs. One MET is the energy expenditure per minute of sitting at rest, expressed in oxygen consumption (3.5 mL·kg−1·min−1). The estimated rate of energy expenditure in METs for completing each stage ranged from 4.1 for stage 1 to 19.0 for stage 9. We examined maximal METs as a continuous variable and also classified into quintiles and then categorized into three levels as previously reported in CARDIA and the Aerobics Center Longitudinal Study (27,28). Women in the lowest 20% were classified as low fitness (≤8.3 METs), women in the 20th to 60th percentile were classified as intermediate fitness (8.4–10.6 METs), and women above the 60th percentile were classified as high fitness (≥10.7 METs).

Self-reported physical activity was assessed with the validated CARDIA Physical Activity History at each examination (29,30). Participants were asked about the frequency of participation in 13 moderate or vigorous activities related to recreational sports, exercise, leisure, and occupational activities. Total MVPA was calculated by multiplying the intensity of the activity by the number of months the activity was performed, weighting activities performed more frequently, and then summing across all activities. Details on frequency or duration of physical activity were not assessed directly; therefore, physical activity is conveyed in exercise units. Separate scores are reported for moderate, vigorous, and total physical activity. Physical activity was expressed as a continuous variable and also categorized into tertiles based on the sampling distribution: <200, 200–400, and >400 exercise units. The equivalent of meeting physical activity recommendations is approximately 300 exercise units, or 30 min of moderate physical activity 5 d·wk−1 (30).

To assess television viewing, participants were asked how often they watched television during their leisure time at the year 5 examination. Response options were never, seldom, sometimes, often, or very often. A never response was assigned a value of 0. If participants responded seldom, sometimes, often, or very often, they were then asked, “On average, about how many hours per day do you watch television?” We examined hours per day of television viewing as a continuous variable, and because of the right skewed distribution, we also categorized into three groups: 0–1, 2, or ≥3 h·d−1.

Pregnancy history and GDM diagnosis were ascertained through self-report at each CARDIA study visit. Women were asked how many times they had been pregnant at baseline and the number of pregnancies since the previous CARDIA visit, including abortions, miscarriages, stillbirths, or live births, as well as delivery dates and lengths of gestation. Pregnancies that ended in abortion, miscarriage, or those <20 wk gestation were classified as pregnancy losses. We defined live births as those delivered ≥20 wk gestation that occurred between baseline and year 25. Women were asked at each examination, and for each birth, whether the pregnancy was complicated by diabetes. In the absence of type 1 or type 2 diabetes before pregnancy, the pregnancy with diabetes was classified as GDM. Prenatal chart reviews from a subsample of 165 parous women, reflecting 200 births, were used to validate self-reported GDM. Sensitivity and specificity for self-reported GDM was 100% and 92%, respectively (31).

Participants self-reported their race, education, first-degree family history of diabetes, cigarette smoking status, and alcohol consumption. Percent of total calories from saturated fat was derived from an interviewer-administered diet history questionnaire developed for the CARDIA study (32). Body weight was measured in light clothing with no shoes to the nearest 0.2 kg using a calibrated balance beam scale. Height was measured using a vertical rule to the nearest 0.5 cm. BMI was calculated as weight in kilograms divided by height in meters squared. Waist circumference was measured horizontally midway between the iliac crest and the lowest portion of the ribcage and anteriorly midway between the xiphoid process of the sternum and the umbilicus. Fasting serum collection for glucose, insulin, and HDL-C were conducted based on standardized CARDIA protocols and processed at central laboratories. Glucose was measured using hexokinase coupled to glucose-6-phosphate dehydrogenase. Insulin levels were assessed using radioimmunoassay. We used the homeostasis model assessment of insulin resistance (HOMA-IR) to evaluate insulin resistance (fasting glucose (mg·dL−1) × fasting insulin (μU·mL−1))/405.0, because of its strong correlation with physiologic measures of insulin sensitivity across a range of glucose levels (33). HDL-C levels were measured using enzymatic methods after dextran–sulfate–magnesium precipitation of other lipoproteins and categorized into two groups: <40 and ≥ 40 mg·dL−1.

Descriptive statistics, stratified by GDM status, were calculated for all baseline variables, using chi-square and t-test for categorical and continuous variables, respectively. For nonnormally distributed continuous variables, differences across groups were examined using the Wilcoxon–Mann–Whitney test with data presented as medians with interquartile ranges. Spearman correlations were examined between fitness, physical activity, television viewing, and potential confounders.

Multivariable logistic regression analysis was used to estimate the odds ratio (OR) and 95% confidence intervals (CI) for the associations of baseline fitness and MVPA and year 5 television viewing with risk of GDM. Each exposure was included in separate models as both continuous and categorical variables. For continuous models, results are expressed per 1-SD increment in fitness (2.3 METs), MVPA (256 exercise units), or television viewing (2.1 h·d−1) to allow for comparison between the exposures of interest. The crude association is depicted in model 1. Covariates were then added to the model in a stepwise fashion. Model 2 was adjusted for study center, age, race, education, first-degree family history of diabetes, parity, cigarette smoking status, alcohol consumption, percent of total calories from saturated fat, and time from baseline to delivery. For women with multiple GDM diagnoses or pregnancies, we used the time from baseline to the first GDM diagnosis or first pregnancy, respectively. On the basis of the known relationships between fitness, physical activity, and sedentary behaviors with obesity and cardiometabolic risk factors, we consider these potential mediators of the relationship between fitness, physical activity, and television viewing with GDM. Our theoretical models can be summarized as follows: fitness/physical activity/television viewing → obesity → adverse cardiometabolic risk factors → GDM. Therefore, model 3 was additionally adjusted for baseline BMI or waist circumference, but not both, because of the high correlation between these variables (r = 0.87). Results did not differ between the BMI and waist circumference models; therefore, we present the models with waist circumference only. Model 4 additionally adjusted for baseline cardiometabolic status (i.e., HOMA-IR and HDL-C category). Television viewing models (first assessed at year 5) were adjusted separately, using either baseline or year 5 covariates. Results did not differ; therefore, due to higher levels of missing data at year 5, we present models with baseline covariate adjustment.

In exploratory analyses, we controlled for MVPA in the fitness and television viewing models. Results were not changed; therefore, MVPA was removed from final analyses. The associations of moderate physical activity and vigorous physical activity with GDM were also examined separately. Analyses were also examined controlling for an overall diet quality score assessed at baseline (34). Findings were unchanged; therefore, due to missing dietary data (n = 24), we present data controlling for percent of calories from saturated fat only. We further examined the interactions of race, BMI, and waist circumference with each exposure. All statistical analyses were conducted using SAS, version 9.4 (SAS Institute, Inc., Cary, NC). All tests were two-sided, with statistical significance set at P < 0.05.


A total of 164 women reported GDM during the 25-yr follow-up (12% of eligible sample). Baseline participant characteristics, stratified by GDM status, are presented in Table 1. Women who developed GDM were more likely to have a family history of diabetes, higher prepregnancy BMI and waist circumference, and lower levels of fitness compared with those without GDM. Women with GDM also had worse cardiometabolic profiles, including elevated fasting glucose, insulin, and HOMA-IR levels and lower HDL levels (all P < 0.05).

Sociodemographic and prepregnancy characteristics at baseline by subsequent gestational diabetes status, the CARDIA study (1985–2011).

Baseline fitness was positively associated with total-, moderate-, and vigorous-intensity physical activity and inversely associated with year 5 television time in bivariate analyses (See Table, Supplemental Digital Content 1, Spearman correlations, Significant inverse associations were also observed between fitness and BMI, waist circumference, fasting insulin, HOMA-IR, and HDL-C at baseline. Similar but weaker associations were observed between total physical activity and measures of obesity or cardiometabolic status. Year 5 television time was positively associated with baseline BMI, waist circumference, fasting insulin, HOMA-IR, and inversely associated with HDL-C.

As shown in Table 2, for each additional 1 SD increment in prepregnancy fitness (2.3 METs), there was a 28% lower odds of GDM after controlling for demographic and lifestyle factors (model 2: OR = 0.72, 95% CI = 0.61–0.86). The association between GDM and fitness was slightly attenuated, but remained significant, after additional adjustment for prepregnancy waist circumference (model 3: OR = 0.79, 95% CI = 0.65–0.95) and cardiometabolic status (model 4: OR = 0.79, 95% CI = 0.65–0.96). When fitness was modeled categorically, women with high fitness (>10.7 METs) had a 52% lower odds of GDM compared with those with low fitness (<8.3 METs) after adjustment for demographics and lifestyle factors (model 2: OR = 0.48, 95% CI = 0.29–0.80). This association was attenuated with the addition of waist circumference and cardiometabolic status but remained in the hypothesized direction (model 4: OR = 0.74, 95% CI = 0.41–1.35).

Associations of baseline fitness with risk of gestational diabetes, the CARDIA study (1985–2011).

Self-reported MVPA was not associated with odds of GDM when modeled continuously in all of the tested models (Table 3). When modeled categorically, the odds of GDM did not differ between those reporting intermediate (200–400 exercise units) or high (>400 exercise units) levels of MVPA, as compared with those reporting the lowest levels of MVPA (<200 exercise units). Findings did not differ when examining moderate physical activity and vigorous physical activity separately (data not shown).

Associations of baseline physical activity with risk of gestational diabetes, the CARDIA study (1985–2011).

For the television viewing analyses, a total of 86 women reported GDM between years 5 and 25 (52.4% of all GDM cases). Self-reported television time, whether modeled continuously or categorically, was not associated with GDM (Table 4).

Associations of year 5 television viewing time with risk of gestational diabetes, the CARDIA study (1990–2011).

There were no differences in the associations of fitness, MVPA, and television viewing with odds of GDM by race, BMI, or waist circumference (P for interaction > 0.1 for all; data not shown). Participants included in the final models for fitness, MVPA, and television viewing were similar to the eligible study population in characteristics and health behaviors at study entry (see Table, Supplemental Digital Content 2, baseline characteristics of study population and subpopulations,


In this population-based longitudinal study, a higher level of objectively measured prepregnancy fitness was associated with lower odds of GDM, independent of baseline demographics, lifestyle behaviors, obesity, and cardiometabolic measures. We did not observe an association between self-reported prepregnancy MVPA and television viewing with GDM. Our findings contribute to the care of reproductive-age women in several important ways. First, our results show the importance of fitness versus MVPA as it pertains to GDM risk. To our knowledge, this is the first study to examine associations of objectively measured prepregnancy fitness with subsequent development of GDM. Second, this information can be used to aid clinicians in preconception counseling on lifestyle behaviors to reduce GDM in pregnancy.

Many studies have shown that MVPA before pregnancy is associated with a reduced risk of GDM (11–16), although others have not (20,21). Similar to our study, van der Ploeg and colleagues (21) reported that self-reported prepregnancy physical activity was not associated with GDM in the Australian Longitudinal Study on Women’s Health. The lack of association between MVPA before pregnancy and GDM observed in our study and others may be due in part to the reliance on self-reported measures of physical activity, which generally have low-to-moderate correlations with objectively measured physical activity (35). Even if prepregnancy physical activity was accurately reported, this may not reflect activity levels during early pregnancy, which others have shown is a predictor of GDM risk (11,12,16–19). Women who engage in physical activity both before and during pregnancy seem to be at the lowest risk for GDM (12,16). In the current study, we did not assess MVPA during pregnancy, which may in part explain the lack of association observed in this study.

Although MVPA was not significantly associated with GDM in this study, we did observe an inverse association with higher levels of fitness. One possible explanation for the discrepancy in findings is that fitness may be a more reliable and objective measure of higher-intensity habitual physical activity than self-reported MVPA. Although MVPA was significantly associated with fitness, the effect size was weak (r = 0.31, P < 0.001), which is consistent with other studies examining the associations of objectively measured fitness and self-reported physical activity (36,37). There may be a threshold of exercise intensity, frequency, and duration that must be achieved to modify GDM risk, which was not captured using self-reported physical activity. Vigorous-intensity activities in particular have a larger effect on fitness than activities performed at lower intensities (38); therefore, our findings point to the importance of encouraging higher intensity activity early in life, before a pregnancy occurs, to decrease GDM risk.

Given that fitness is inversely associated with overall adiposity, fasting insulin, homeostatic model assessment-insulin resistance, and glycosylated hemoglobin A1C (39), it is logical that women with higher prepregnancy fitness have a reduced risk of GDM because they enter pregnancy with higher insulin sensitivity and lower central and overall adiposity than women with lower levels of fitness. However, in our study, the association between fitness and GDM persisted even after including measures of prepregnancy obesity and cardiometabolic status, indicating that the relationship between fitness and GDM may not be fully explained by body size or biomarkers of cardiometabolic risk. Further research is needed to elucidate the mechanisms responsible for the fitness–GDM relationship.

For our analyses of sedentary behavior, we failed to observe significant associations between television viewing and risk of GDM. Although less is known about television viewing or overall sedentary behaviors and GDM, our findings are largely consistent with the existing literature (13,15,21). Zhang and colleagues (15) found that greater time spent watching television was associated with higher GDM risk among women in the Nurses Health Study II; however, this association was attenuated after additional adjustment for BMI. Similarly, Oken et al. (13) found no associations between television viewing before or during pregnancy with risk of GDM. Therefore, whereas evidence suggests that sedentary time is detrimentally associated with cardiometabolic risk in nonpregnant populations, current findings do not support a direct association between sedentary time, assessed by self-report, and risk of GDM. Additional studies with objectively measured sedentary time are needed.

A major strength of this study is the inclusion of three exposure measures, namely, fitness, MVPA, and television viewing, as this provides a more comprehensive analysis of activity patterns. Further, we are one of the only studies to evaluate the effect of prepregnancy biochemical measures of cardiometabolic risk (HOMA-IR and HDL-C) in addition to measures of adiposity. The longitudinal population-based cohort design allows us to prospectively examine the associations of activity-related variables with future development of GDM. Furthermore, the CARDIA study population also included an approximately equal number of White and Black women, whereas the majority of studies in the literature have focused exclusively on non-Hispanic White populations.

Limitations of this study include the use of treadmill test duration to estimate fitness, rather than a direct measure of maximum oxygen consumption. However, previous studies have reported a high correlation between test duration on a symptom-limited test and maximal oxygen consumption (40). MVPA and television viewing were self-reported, which may be subject to social desirability bias and nondifferential misclassification, biasing results toward the null. Television viewing was first assessed at the year 5 visit, and therefore the sample size of participants was smaller for these analyses. Given that we adjusted for baseline covariates in the television viewing analyses, this assumes that television viewing is correlated across time within a person, which may not be the case. However, study findings were not changed when we adjusted for year 5 covariates. In addition, the amount of time between assessment of the exposure and pregnancy varied across individuals, and we did not have data on the specific date of GDM diagnosis. However, we did control for time from baseline to delivery in our analyses. Finally, the CARDIA cohort includes Black and White adults only; therefore, these findings may not be generalizable to other racial/ethnic groups.

In conclusion, objectively measured prepregnancy fitness, but not self-reported MVPA or television viewing, was associated with subsequent development of GDM. Our findings indicate that habitual engagement in physical activities that achieve the frequency, intensity, and duration required to improve fitness in the preconception period is important for reducing GDM risk. These findings may be useful for guiding patient–provider counseling on physical activity before and during pregnancy. Although there are many health benefits associated with increasing moderate intensity physical activities and reducing sedentary time, it may be important for clinicians to also emphasize the importance of improving fitness in the preconception period through high-intensity physical activities, particularly among women at higher risk for GDM. Given that this is the first study, to our knowledge, to report associations between prepregnancy fitness and GDM, additional studies are needed to replicate these findings.

The CARDIA study is supported by contracts HHSN268201300025C, HHSN268201300026C, HHSN268201300027C, HHSN268201300028C, HHSN268201300029C, and HHSN268200900041C from the National Heart, Lung, and Blood Institute (NHLBI), the Intramural Research Program of the National Institute on Aging (NIA), and an intra-agency agreement between NIA and NHLBI (AG0005). The analyses were supported by grants from R01 DK090047 (Gunderson, PI) and K01 DK059944 (Gunderson, PI) from the National Institute of Diabetes, Digestive and Kidney Diseases. KMW was supported by research training grant T32 HL007779. The authors thank the investigators, the staff, and the participants of the CARDIA study for their valuable contributions.

CEL, PJS, and EPG report grant funding from the National Institutes of Health (NIH) during the conduct of this study. EPG also reports funding from Jansen Pharmaceutical Inc. that was unrelated to this study. No other conflicts of interest were reported. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the NHLBI, the NIH, the U.S. Department of Health and Human Services, or the American College of Sports Medicine. The results of this study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation.


1. DeSisto CL, Kim SY, Sharma AJ. Prevalence estimates of gestational diabetes mellitus in the United States, Pregnancy Risk Assessment Monitoring System (PRAMS), 2007–2010. Prev Chronic Dis. 2014;11:E104.
2. Gunderson EP, Quesenberry CP Jr, Jacobs DR Jr, et al. Longitudinal study of prepregnancy cardiometabolic risk factors and subsequent risk of gestational diabetes mellitus: the CARDIA study. Am J Epidemiol. 2010;172(10):1131–43.
3. Di Cianni G, Volpe L, Lencioni C, et al. Prevalence and risk factors for gestational diabetes assessed by universal screening. Diabetes Res Clin Pract. 2003;62(2):131–7.
4. Gunderson EP, Jacobs DR Jr, Chiang V, et al. Childbearing is associated with higher incidence of the metabolic syndrome among women of reproductive age controlling for measurements before pregnancy: the CARDIA study. Am J Obstet Gynecol. 2009;201(2):177.e1–9.
5. Bellamy L, Casas JP, Hingorani AD, et al. Type 2 diabetes mellitus after gestational diabetes: a systematic review and meta-analysis. Lancet. 2009;373(9677):1773–9.
6. Ma RC, Chan JC, Tam WH, et al. Gestational diabetes, maternal obesity, and the NCD burden. Clin Obstet Gynecol. 2013;56(3):633–41.
7. Sigal RJ, Kenny GP, Wasserman DH, et al. Physical activity/exercise and type 2 diabetes: a consensus statement from the American Diabetes Association. Diabetes Care. 2006;29(6):1433–8.
8. Hu FB, Manson JE, Stampfer MJ, et al. Diet, lifestyle, and the risk of type 2 diabetes mellitus in women. N Engl J Med. 2001;345(11):790–7.
9. Knowler WC, Barrett-Connor E, Fowler SE, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346(6):393–403.
10. Hu FB, Li TY, Colditz GA, et al. Television watching and other sedentary behaviors in relation to risk of obesity and type 2 diabetes mellitus in women. JAMA. 2003;289(14):1785–91.
11. Dempsey JC, Butler CL, Sorensen TK, et al. A case–control study of maternal recreational physical activity and risk of gestational diabetes mellitus. Diabetes Res Clin Pract. 2004;66(2):203–15.
12. Dempsey JC, Sorensen TK, Williams MA, et al. Prospective study of gestational diabetes mellitus risk in relation to maternal recreational physical activity before and during pregnancy. Am J Epidemiol. 2004;159(7):663–70.
13. Oken E, Ning Y, Rifas-Shiman SL, et al. Associations of physical activity and inactivity before and during pregnancy with glucose tolerance. Obstet Gynecol. 2006;108(5):1200–7.
14. Rudra CB, Williams MA, Lee IM, et al. Perceived exertion in physical activity and risk of gestational diabetes mellitus. Epidemiology. 2006;17(1):31–7.
15. Zhang C, Solomon CG, Manson JE, et al. A prospective study of pregravid physical activity and sedentary behaviors in relation to the risk for gestational diabetes mellitus. Arch Intern Med. 2006;166(5):543–8.
16. Badon SE, Wartko PD, Qiu C, et al. Leisure time physical activity and gestational diabetes mellitus in the omega study. Med Sci Sports Exerc. 2016;48(6):1044–52.
17. Dye TD, Knox KL, Artal R, et al. Physical activity, obesity, and diabetes in pregnancy. Am J Epidemiol. 1997;146(11):961–5.
18. Liu J, Laditka JN, Mayer-Davis EJ, et al. Does physical activity during pregnancy reduce the risk of gestational diabetes among previously inactive women? Birth. 2008;35(3):188–95.
19. van Poppel MN, Oostdam N, Eekhoff ME, et al. Longitudinal relationship of physical activity with insulin sensitivity in overweight and obese pregnant women. J Clin Endocrinol Metab. 2013;98(7):2929–35.
20. Solomon CG, Willett WC, Carey VJ, et al. A prospective study of pregravid determinants of gestational diabetes mellitus. JAMA. 1997;278(13):1078–83.
21. van der Ploeg HP, van Poppel MN, Chey T, et al. The role of pre-pregnancy physical activity and sedentary behaviour in the development of gestational diabetes mellitus. J Sci Med Sport. 2011;14(2):149–52.
22. Yin YN, Li XL, Tao TJ, et al. Physical activity during pregnancy and the risk of gestational diabetes mellitus: a systematic review and meta-analysis of randomised controlled trials. Br J Sports Med. 2014;48(4):290–5.
23. Russo LM, Nobles C, Ertel KA, et al. Physical activity interventions in pregnancy and risk of gestational diabetes mellitus: a systematic review and meta-analysis. Obstet Gynecol. 2015;125(3):576–82.
24. Kim C, Berger DK, Chamany S. Recurrence of gestational diabetes mellitus: a systematic review. Diabetes Care. 2007;30(5):1314–9.
25. Wang CY, Haskell WL, Farrell SW, et al. Cardiorespiratory fitness levels among US adults 20–49 years of age: findings from the 1999–2004 National Health and Nutrition Examination Survey. Am J Epidemiol. 2010;171(4):426–35.
26. Sidney S, Haskell WL, Crow R, et al. Symptom-limited graded treadmill exercise testing in young adults in the CARDIA study. Med Sci Sports Exerc. 1992;24(2):177–83.
27. Carnethon MR, Evans NS, Church TS, et al. Joint associations of physical activity and aerobic fitness on the development of incident hypertension: coronary artery risk development in young adults. Hypertension. 2010;56(1):49–55.
28. Church TS, LaMonte MJ, Barlow CE, et al. Cardiorespiratory fitness and body mass index as predictors of cardiovascular disease mortality among men with diabetes. Arch Intern Med. 2005;165(18):2114–20.
29. Jacobs DR Jr, Hahn L, Haskell WL, et al. Validity and reliability of short physical activity history: CARDIA and the Minnesota Heart Health Program. J Cardiopulm Rehabil Prev. 1989;9(11):448–59.
30. Gabriel KP, Sidney S, Jacobs DR Jr, et al. Convergent validity of a brief self-reported physical activity questionnaire. Med Sci Sports Exerc. 2014;46(8):1570–7.
31. Gunderson EP, Lewis CE, Tsai AL, et al. A 20-year prospective study of childbearing and incidence of diabetes in young women, controlling for glycemia before conception: the Coronary Artery Risk Development in Young Adults (CARDIA) Study. Diabetes. 2007;56(12):2990–6.
32. McDonald A, Van Horn L, Slattery M, et al. The CARDIA dietary history: development, implementation, and evaluation. J Am Diet Assoc. 1991;91(9):1104–12.
33. Haffner SM, Miettinen H, Stern MP. The homeostasis model in the San Antonio Heart Study. Diabetes Care. 1997;20(7):1087–92.
34. Sijtsma FP, Meyer KA, Steffen LM, et al. Longitudinal trends in diet and effects of sex, race, and education on dietary quality score change: the Coronary Artery Risk Development in Young Adults study. Am J Clin Nutr. 2012;95(3):580–6.
35. Prince SA, Adamo KB, Hamel ME, et al. A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review. Int J Behav Nutr Phys Act. 2008;5:56.
36. Chase NL, Sui X, Lee DC, et al. The association of cardiorespiratory fitness and physical activity with incidence of hypertension in men. Am J Hypertens. 2009;22(4):417–24.
37. Laaksonen DE, Lakka HM, Salonen JT, et al. Low levels of leisure-time physical activity and cardiorespiratory fitness predict development of the metabolic syndrome. Diabetes Care. 2002;25(9):1612–8.
38. Wenger HA, Bell GJ. The interactions of intensity, frequency and duration of exercise training in altering cardiorespiratory fitness. Sports Med. 1986;3(5):346–56.
39. Lin X, Zhang X, Guo J, et al. Effects of exercise training on cardiorespiratory fitness and biomarkers of cardiometabolic health: a systematic review and meta-analysis of randomized controlled trials. J Am Heart Assoc. 2015;4(7).
40. Pollock ML, Bohannon RL, Cooper KH, et al. A comparative analysis of four protocols for maximal treadmill stress testing. Am Heart J. 1976;92(1):39–46.


Supplemental Digital Content

Copyright © 2018 by the American College of Sports Medicine