Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2018 Oct 1.
Published in final edited form as: J Phys Act Health. 2017 Sep 13;14(10):766–772. doi: 10.1123/jpah.2016-0438

Childhood Socioeconomic Disadvantage, Occupational, Leisure-time, and Household Physical Activity, and Diabetes in Adulthood

Vera K Tsenkova 1, Chioun Lee 1, Jennifer Morozink Boylan 2
PMCID: PMC5607102  NIHMSID: NIHMS884467  PMID: 28513316

Abstract

Background

Regular physical activity is a key way to prevent disease. However, we have a limited understanding of the socioeconomic precursors and glucoregulatory sequelae of engaging in physical activity in different domains.

Methods

We examined the associations among lifecourse socioeconomic disadvantage, meeting the physical activity guidelines with leisure-time physical activity (LTPA), occupational physical activity (OPA), or household physical activity (HPA), and prediabetes and diabetes in the MIDUS (Midlife in the U.S.) national study (n=986).

Results

Childhood disadvantage was associated with lower odds of meeting the guidelines with LTPA (OR=0.75; 95% CI: 0.65; 0.86). Adulthood disadvantage was associated with higher odds of meeting the guidelines with OPA (OR=1.94; 95% CI: 1.49; 2.53). Importantly, while meeting the guidelines with LTPA was associated with lower odds of prediabetes and diabetes, we found no evidence for associations among OPA, HPA, and glucoregulation.

Conclusion

Current U.S. physical activity guidelines do not differentiate between physical activity for leisure or work, assuming that physical activity in any domain confers comparable health benefits. We documented important differences in the associations among lifetime socioeconomic disadvantage, physical activity domain, and diabetes, suggesting that physical activity domain potentially belongs in the guidelines, similarly to other characteristics of activity (e.g., type, intensity).

Keywords: exercise, chronic disease, health disparities, epidemiology, physical activity assessment, public health

Diabetes is a significant problem in the U.S. and accounts for substantial morbidity and mortality. Currently, 9.3% have diabetes and 37% have milder forms of hyperglycemia such as prediabetes that typically transition to overt diabetes 1. The economic costs of diabetes are staggering: the total cost of diagnosed diabetes in 2012 was $245 billion and care for individuals with diabetes accounted for more than 1 in 5 healthcare dollars 2. The social determinants of type 2 diabetes and its risk factors are increasingly recognized: low socioeconomic status (SES), indexed by a variety of indicators across the life course, consistently predicts higher risk for prediabetes and diabetes 3–9. Therefore, understanding the pathways to diabetes in disadvantaged groups assumes increasing importance.

Physical activity is a critical cornerstone of diabetes prevention and management. Current U.S. Physical Activity guidelines provide specific recommendations about intensity, frequency, duration, and types of physical activity that confer health benefits 10. Physical activity occurs in different domains (e.g., at work, around the house, for leisure) and the role of physical activity domain is currently understudied. Differentiating among leisure-time physical activity (LTPA), occupational physical activity (OPA), and household physical activity (HPA) has offered evidence that domain matters. For example, the association between LTPA and glucoregulation is widely studied and consistently shows a lower risk for type 2 diabetes and related glycemic parameters 11–17. The association between OPA and diabetes has received significantly less attention and mixed results are documented: some have found that OPA is associated with lower diabetes risk and mortality18,19, while others have found no evidence for significant associations 16,20. While LTPA appears to be universally beneficial for cardiometabolic outcomes, engaging in OPA is often associated with worse health and higher mortality, which has been described as a “health paradox”21–24. It is unknown whether OPA is associated with prediabetes and diabetes in a U.S. sample. Answering this question has significant implications: current physical activity guidelines suggest that physical activity goals could be met with LTPA, OPA, or HPA, effectively assuming that any kind of physical activity confers equal health benefits 10. Further, rates of physical activity are patterned by SES: both childhood disadvantage and contemporaneous SES predict less frequent physical activity in adulthood 25–27. Importantly, opportunities to engage in physical activity for leisure or work differ as a function of SES and higher physical activity at work often comes at the expense of leisure-time exercise 28–31 Therefore, it important to examine both the socioeconomic precursors and health correlates of engaging in physical activity in different domains.

Our study uses the MIDUS (Midlife in the U.S.) national study to first examine whether early life socioeconomic inequality affects physical activity in adulthood using a lifecourse approach and then investigate the links among physical activity domains and glucoregulation 32. Our predictions about the influence of the “long arm” of childhood disadvantage on physical activity were informed by the critical period model and the pathway model. The critical period model suggests that exposure to stressful environments during childhood, such as low SES, may have lifelong effects on physiological systems 32. The lifecourse pathway model further suggests that early-life circumstances influence adult morbidity through health behaviors such as physical activity 33,34. To summarize, we had two overarching goals: 1) examine the associations among lifecourse SES and engaging in physical activity in different domains in adulthood and 2) determine whether meeting the physical activity guidelines with LTPA, OPA, or HPA was associated with similar glucoregulatory benefits, such as lower odds of prediabetes and diabetes.

Methods

Sample

The National Survey of Midlife Development in the U.S. (MIDUS 1) began in 1995–96 as a national random digit dial sample of non-institutionalized, English-speaking adults representing every state in the United States. A final sample of 7108 participants ages 25–74 completed telephone and mail surveys in MIDUS 1. Approximately 9–10 years later, 4963 (75% response rate adjusted for mortality) were successfully contacted to participate in another phone interview and self-administered questionnaire (MIDUS 2 Survey). Participants who completed both MIDUS 1 and MIDUS 2 Survey were invited to be part of the MIDUS 2 Biomarker project. Biological data were collected from a subset of respondents (N = 1054) who agreed to an overnight visit at one of three General Clinical Research Centers (GCRCs) at University of Wisconsin-Madison, Georgetown, and University of California-Los Angeles from 2004–2009. Participants in MIDUS 2 Biomarker were compensated $200 for their participation. The response rate was 43% among those eligible (adjusted for those who could not be reached), a rate somewhat lower than other epidemiological studies involving a clinic visit (e.g., 57% in the Cardiovascular Health Study) 35. However, the MIDUS protocol is demanding in requiring extensive travel for many participants and two full days of assessments. The biological sample was comparable with overall MIDUS 2 sample on most sociodemographic and health characteristics, although the participants were significantly better educated and less likely to smoke than nonparticipants 36. The study was approved by the institutional review board at each GCRC and informed written consent was obtained from all participants.

Further details of the study design, recruitment, and retention are available at midus.wisc.edu. The current study used data from MIDUS Survey (MIDUS 1 and 2) and MIDUS Biomarker (MIDUS 2). Of the 1054 participants who participated in the MIDUS 1 Survey, MIDUS 2 Survey, and MIDUS 2 Biomarker, 68 cases were excluded from the present analyses in three occasions: (a) having diabetes at MIDUS 1 (N=23) in order to avoid confounding by the influence of long-standing diabetes on physical activity, (b) missing information on diabetes in MIDUS 1 (N=32), or (c) missing data on any variable in the analysis (N=13). Thus, our final analytic sample included 986 participants.

Measures

Prediabetes/Diabetes

Fasting glucose and glycosylated hemoglobin A1c (HbA1c) samples were obtained during the overnight stay in a GCRC during MIDUS 2 Biomarker. Criteria from the American Diabetes Association were used to define presence of prediabetes (HbA1c between 5.7–6.5% or fasting glucose between 100–126 mg/dl, and NOT taking diabetes medications) and diabetes (HbA1c above 6.5%, fasting glucose above 126 mg/dl, or taking medications that lower glucose levels such as Metformin) 37. Fasting glucose was measured via an enzymatic assay photometrically on an automated analyzer (Roche Modular Analytics P). The HbA1c assay was a colorimetric total-hemoglobin determination combined with an immunoturbidometric HbA1c assay, carried out using a Cobas Integra Systems instrument (Roche Diagnostics) 38. The dependent variable was an ordered categorical variable with three levels: no diabetes, prediabetes, and diabetes.

Socioeconomic Status

SES disadvantage scores were created for childhood and adulthood. Information on childhood SES was collected retrospectively at the MIDUS 1 exam. This study relied on retrospective recall of childhood SES, which might produce some recall bias. However, the validity of recall of childhood SES is supported in twin studies 39, and if retrospective reports have caused any bias, it is likely to be an underestimation 40. Childhood socioeconomic disadvantage was computed by summing values on 3 indicators: financial level growing up (2 - worse off than others, 1 - about the same as others, 0 - better off than others), highest level of parental education (2 - less than high school, 1 - high school/GED, 0 - some college or higher), and childhood welfare status (2 - ever on welfare, 0 - never on welfare). Adulthood socioeconomic disadvantage was computed by summing values on 5 indicators obtained in MIDUS 1: education level (2 - high school/GED or less, 1 - some college/associate arts degree, 0 - bachelor’s degree or higher), family-size adjusted income to poverty ratio (2 - less than 300%, 1–300 -599%, 0 – 600% or more), current financial situation (2 - worst possible, 1 - average, 0 - best possible), availability of money to meet basic needs (2 - not enough, 1 - just enough, 0 - more than enough), and difficulty level of paying bills (2 - very or somewhat difficult, 1 - not very difficult, 0 - not at all difficult). Within-person mean substitution was used in cases of missing data on childhood disadvantage where data was available on 2 out of 3 variables used in calculating the score and in cases of missing data on adulthood disadvantage where data was available on 4 out of 5 variables used in calculating the score. The socioeconomic disadvantage scores have been previously linked to diabetes, allostatic load, and neck bone strength 41–43.

Physical Activity

Physical activity data were collected as part of medical history data collection during the MIDUS 2 Biomarker GCRC visit. Respondents were first asked if they engaged in any type of regular physical activity for 20 minutes or more at least three times a week. Those who indicated ‘no’ were classified as non-exercisers. If a respondent answered ‘yes,’ they then provided specific type(s) of physical activity, and duration, frequency, and intensity (light, moderate, or vigorous) of each type of physical activity. Uniform definitions of what constituted light, moderate, and vigorous activity were provided to respondents. As per instructions in the physical activity guidelines, only moderate and vigorous physical activity was included in calculating the physical activity levels10. Data were converted to metabolic equivalents (METs) minutes per week (MMW) following established criteria: minutes per week of activity was multiplied by an intensity factor (moderate = 3; vigorous activities = 6) 44. The domain of activity (LTPA, OPA, or HPA) was determined by referencing the major activity categories within the Compendium of Physical Activity 44. OPA was determined by cross-referencing respondents’ occupation indicated earlier in the MIDUS 2 survey. Specific activities that fit in the major categories of home activities and home repair were classified as HPA. All other activities were considered LTPA. Two of the study authors (J.M.B. and V.K.T) evaluated the domain of each activity independently and any discrepancies (~4% of activities coded) were resolved via discussion. Total physical activity was computed as the sum of LTPA, OPA, and HPA. Federal guidelines recommend that adults receive at least 150 minutes of moderate or vigorous physical activity each week, which is equivalent to 500 METs MMW 45. Therefore, we created binary variables reflecting whether federal recommendations were met (≥ 500 MMW) by total physical activity and by three types of physical activity (LTPA, OPA, and HPA).

Control Variables

Demographic covariates included age (in years), sex (male or female), and race/ethnicity (White or Other). Additional covariates included body mass index (BMI), alcohol intake (ranging from 0=every day to 5=never), currently smoking (yes or no), and depressive symptoms (0–49) 46.

Statistical Analyses

First, descriptive statistics were generated. Means, standard deviations, and ranges for all continuous variables and proportions for categorical variables were examined. Binary logistic regression models were employed to examine prospective predictors of meeting physical activity guidelines. Initial models included MIDUS 1 sociodemographic factors (childhood socioeconomic disadvantage, age, race and sex) as predictors of meeting physical activity guidelines in MIDUS 2 (≥ 500 MMW). Adulthood socioeconomic disadvantage was included as an additional predictor in the follow-up models.

Ordinal logistic regression models were used to predict glucoregulatory status in MIDUS 2. The ordered logit model estimates one equation over all levels of the outcome variable (normal glycemia/prediabetes/diabetes) and the validity of the one-equation model is established via a test of the proportional odds assumption. We ascertained that the assumption was met (p>.05) in all ordinal models. Models 1–4 tested the associations between physical activity (LTPA, OPA, HPA) and glucoregulation (normal glycemia/prediabetes/diabetes), adjusting for age, race, sex, childhood socioeconomic disadvantage, and adulthood socioeconomic disadvantage. Models 1a, 2a, 3a, and 4a included additional adjustments for BMI, depressive symptoms, alcohol intake, and smoking.

Results

Descriptive statistics for all measures are presented in Table 1. The majority of participants in the current study showed evidence for hyperglycemia: overt diabetes was present in 123 participants (13%) and 479 met the criteria for pre-diabetes (49%). On average, participants were 55 years old, 93% were white, and 55% were female. Among individuals without prediabetes or diabetes, 46% met the physical activity guidelines with LTPA, compared to 27% of those with diabetes.

Table 1.

Descriptive Statistics

Full Sample (N=986) No Diabetesa (N=384) Prediabetesb (N=479) Diabetesc (N=123) Significant Differences
Mean (SD) or % (N) Mean (SD) or % (N) Mean (SD) or % (N) Range
SES disadvantage
 Childhood 1.87 (1.4) 1.67 (1.4) 2 (1.4) 1.96 (1.2) 0–6 ab
 Adulthood (M1) 4.56 (2.6) 4.57 (2.6) 4.55 (2.6) 4.60 (2.5) 0–10 --
Physical Activity 0–1
 ≥500 MMW Any Domain 52% (509) 58% (221) 50% (239) 40% (49) ab, ac, bc
 ≥500 MMW Leisure 39% (385) 46% (178) 36% (174) 27% (33) ab, ac, bc
 ≥500 MMW Occupational 9% (85) 9% (33) 9% (45) 6% (7) --
 ≥500 MMW Household 10% (98) 9% (34) 10% (50) 11% (14) --
 Race 0–1 ac
  White 93% (920) 95% (366) 93% (444) 89% (110)
  Other 7% (66) 5% (18) 7% (35) 11% (13)
 Age 55.24 (11.8) 50.7 (10.7) 57.37 (11.5) 61.07 (11.5) 34–84 ab, ac, bc
 Sex 0–1 ac
  Female 55% (542) 60% (229) 54% (257) 45% (56)
  Male 45% (444) 40% (155) 46% (222) 55% (67)
Depressive Symptoms 7.87 (7.7) 8.03 (7.8) 7.63 (7.7) 8.34 (7.3) 0–49 --
Alcohol Intake 3.44 (1.6) 3.38 (1.60) 3.42(1.6) 3.75 (1.6) 0–5 --
Body Mass Index (BMI) 29.07 (5.9) 27.79 (5.4) 29.43 (5.9) 31.63 (6.3) 15–57 ab, ac, bc
Currently Smoking 0–1 --
 Yes 10% (101) 11% (41) 9% (44) 13% (16)
 No 90% (885) 89% (343) 91% (435) 87% (107)
Open in a new tab

Is childhood socioeconomic disadvantage associated with physical activity in adulthood? Do the associations differ across physical activity domains?

Table 2 displays the associations observed between socioeconomic disadvantage and meeting physical activity guidelines. We found that childhood socioeconomic disadvantage was prospectively associated with lower odds of meeting physical activity guidelines using total activity (OR=0.79; 95% CI: 0.69; 0.90). The association was slightly attenuated but remained significant after controlling for adulthood socioeconomic disadvantage (Model 1a; OR = .83; 95% CI: .72; .94).

Table 2.

Childhood Socioeconomic Disadvantage and Meeting Physical Activity Guidelines (≥ 500 MMW) in Adulthood (N=986)

Total Physical Activity Leisure Physical Activity Occupational Physical Activity Household Physical Activity
Model 1 Model 1a Model 2 Model 2a Model 3 Model 3a Model 4 Model 4a
OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI)
Childhood SES disadvantage .79 (.69; .90) .83 (.72; .94) .69 (.60; .79) .75 (.65; .86) 1.11 (.87; 1.40) .95 (.74; 1.22) 1.23 (1.01; 1.52) 1.26 (1.02; 1.56)
Adulthood SES disadvantage .83 (.72; .95) .71 (.62; .82) 1.94 (1.49; 2.53) .92 (.73; 1.15)
Open in a new tab

Note. All models adjust for age, sex, and race. Models 1a, 2a, 3a, and 4a also include adulthood socioeconomic disadvantage. The reported associations for childhood socioeconomic disadvantage and adulthood socioeconomic disadvantage are per 1 SD increment.

We then tested the associations among childhood socioeconomic disadvantage and physical activity in different domains. We found that both childhood socioeconomic disadvantage (OR=0.75; 95% CI: 0.65; 0.86) and adulthood socioeconomic disadvantage (OR=0.71; 95% CI: 0.62; 0.82) were associated with lower odds of meeting physical activity guidelines with LTPA (Model 2a). We found no evidence that childhood socioeconomic disadvantage was associated with meeting physical activity goals with OPA. However, adulthood socioeconomic disadvantage was associated with higher odds of meeting guidelines with OPA (OR=1.94; 95% CI: 1.49; 2.53). Model 4 focused on meeting physical activity guidelines using HPA. We found that childhood socioeconomic disadvantage was associated with higher odds of meeting guidelines with HPA (OR=1.23; 95% CI: 1.01; 1.52). The association remained significant even after controlling for adulthood socioeconomic disadvantage (OR=1.26; 95% CI: 1.02; 1.56). We found no evidence that the association between disadvantage (childhood or adulthood) and LTPA, OPA, or HPA depended on age or sex (interaction terms ps>.05).

Do the associations between physical activity and glucoregulation differ by domain (LTPA, OPA, and HPA)?

Table 3 shows the associations between physical activity and glucoregulation. We found that engaging in the recommended amount of physical activity with LTPA was associated with lower odds for prediabetes and diabetes (Model 1 OR=0.64; 95% CI: 0.49; 0.83), net of the influence of age, race, sex, childhood socioeconomic disadvantage, and adulthood socioeconomic disadvantage. The association remained significant after further adjusting for BMI, depressive symptoms, alcohol intake, and smoking (OR=0.74; 95% CI: 0.56; 0.96), and after including OPA and HPA (OR=0.73; 95% CI: 0.56; 0.96). There was no evidence that engaging in OPA or HPA was associated with glucoregulation (see Models 2 and 3). Further, there was no evidence that the association between physical activity (LTPA, OPA, or HPA) and glucoregulation depended on age or sex (interaction terms ps>.05).

Table 3.

Physical Activity Domains and Odds of Prediabetes/Diabetes (N=986)

Model 11 Model 1a2
+Health
Behaviors		 Model 21 Model 2a2
+Health
Behaviors		 Model 31 Model 3a2
+Health
Behaviors		 Model 41 Model 4a2
+Health
Behaviors
OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI)
Leisure-time Physical Activity .64 (.49; .83) .74 (.56; .96) .63 (.49; .82) .73 (.56; .96)
Occupational Physical Activity .92 (.59; 1.44) .98 (.62; 1.55) .86 (.55; 1.35) .94 (.59; 1.48)
Household Physical Activity 1.01 (.67; 1.53) 1.03 (.68; 1.56) 1.05 (.70; 1.59) 1.06 (.70; 1.61)
Open in a new tab

Note:

1

Models include age, race, sex, childhood socioeconomic disadvantage, and adulthood socioeconomic disadvantage.

2

Models include age, race, sex, childhood socioeconomic disadvantage, adulthood socioeconomic disadvantage, BMI, depressive symptoms, currently smoking, and alcohol intake.

Physical activity domains are binary variables reflecting whether federal recommendations were met (≥ 500 MMW) by leisure, occupational, or household physical activity.

Follow-up analyses limited the sample to individuals who reported being currently employed in MIDUS 2 (N=661). While the coefficients and significance levels varied slightly, all associations were consistent with what we documented in Tables 2 and 3.

Discussion

Physical activity is a modifiable health behavior whose benefits for prevention of chronic disease cannot be overstated. We found evidence that combining physical activity across domains masks important variation in the socioeconomic precursors and glucoregulatory sequelae of physical activity. Consistent with the findings of recent systematic reviews, we found that socioeconomic disadvantage predicted lower physical activity in adulthood 25–27. However, once we examined physical activity by domain, different patterns of associations were observed between socioeconomic adversity and LTPA, OPA, and HPA. Childhood and adulthood socioeconomic disadvantage independently predicted lower LTPA in adulthood, suggesting a pervasive, lifecourse link between disadvantage and LTPA. Further, adulthood socioeconomic disadvantage was positively associated with meeting physical activity guidelines with OPA. Of note, while we confirmed that LTPA was associated with lower odds of prediabetes and diabetes, we found no evidence for associations between OPA, HPA, and glucoregulation.

This is the first study to determine the associations among socioeconomic disadvantage over the lifecourse, physical activity domain, and diabetes. Our findings regarding LTPA are consistent with the large literature on the health benefits of physical activity. However, our findings that OPA and HPA were not associated with glucoregulation are novel and support the emerging literature that questions the health benefits of OPA. While current physical activity guidelines suggest that OPA is an option for meeting physical activity recommendations 10, others have suggested that not differentiating among physical activity domains may be “a major error in public health recommendations” (p.771) 47. Some studies have found no significant associations between OPA and cardiovascular risk factors17,48,49 and others have documented positive associations with obesity and insulin resistance 22, blood pressure50, and mortality23,24,51. Further adding to the complexity, other studies have documented that different activities involved in OPA have contrasting associations with health outcomes50,51. Physical activity such as aerobic and strength training reduces diabetes risk through improved glucose uptake into active muscles and better insulin sensitivity 52. In contrast to these health promoting movements usually associated with LTPA, OPA typically includes lifting heavy objects, standing for extended period of time, highly repetitive work, working with the hands lifted to shoulder height or higher, and working with the back twisted or bent forward 53, with limited control and opportunities to rest.

The key limitation of our study is the cross-sectional assessment of physical activity domain and glucoregulation which prevents us from examining the longitudinal patterns that would best inform our understanding of the temporality and directionality of associations between SES, physical activity, and glucoregulation. While the retrospective and self-reported assessment of childhood SES is another potential limitation, others have established the validity of recall 39. Further, our sample was comprised primarily of white participants, and it is important for future research to look at diverse populations, as minority groups, especially blacks, may derive fewer health benefits from higher SES compared to white adults 54. Nevertheless, our study provides important initial evidence that the domain in which physical activity is performed is closely linked to SES and might not always confer the assumed health benefits. Our final set of limitations pertains to our measures of diabetes: while we excluded participants who self-reported diabetes at MIDUS 1, we do not have biological data that could help ascertain glycemic status at MIDUS 1. Further, our analyses were modeled to capture known risk influences for type 2 diabetes, but we did not have information on whether participants with diabetes had type 1 or type 2 diabetes. Given that approximately 90–95% of individuals with diabetes have type 2 diabetes 37, our results are not significantly affected by this imprecision. Despite these caveats, our key findings that socioeconomic disadvantage shows different associations with physical activity across domains and importantly, that only LTPA is associated with better glucoregulation, are novel and help advance understanding of the social inequalities in type 2 diabetes. Future studies will benefit from more precise measures of physical activity, both in terms of specific domain (e.g., transportation, occupation) and objective characteristics of underlying physiological sequelae of physical activity. A notable strength of our study is that glycemic status was ascertained using biomarkers and allowed for investigating not only the odds of diabetes, but also prediabetes, a clinically relevant outcome that is an important step on the progression from normoglycemia to type 2 diabetes.

Better understanding the associations between physical activity domain and diabetes risk is critical for developing successful preventive efforts. Patients and providers will benefit from recognizing that LTPA provides health benefits that might not be achieved by engaging in OPA and HPA. Individuals who engage in high OPA might assume that their work responsibilities provide health benefits, and encouraging them to add LTPA could be an important intervention target. Further, our central finding that early-life SES disadvantage propels individuals on unhealthy trajectories such as lower LTPA in adulthood suggests that policies addressing socioeconomic inequality among children may be an important route to alleviating socioeconomic health disparities in later life. These findings have important implications. Current U.S. physical activity guidelines have specific recommendations about intensity, frequency, duration, and type of physical activity that are associated with health benefits but do not currently differentiate physical activity domains 10, and our findings suggest that physical activity domain also belongs in the guidelines. Ultimately, successful prevention of type 2 diabetes will depend on better understanding its preclinical progression, both in terms of identifying pre-disease pathways to morbidity and how they are contoured by antecedent factors following from one’s socioeconomic standing.

Acknowledgments

Funding Source

This work was supported by a grant from the National Institute on Aging (P01-AG020166; Carol D. Ryff, Principal Investigator) to conduct a longitudinal follow-up of the MIDUS (Midlife in the US) investigation. The original study was supported by the John D. and Catherine T. MacArthur Foundation Research Network on Successful Midlife Development. Data collection was supported by the following grants: M01- RR023942 (Georgetown), M01-RR00865 (UCLA) from the General Clinical Research Centers Program, and 1UL1RR025011 (UW) from the Clinical and Translational Science Award (CTSA) program of the National Center for Research Resources, National Institutes of Health. The first author (VKT) of this study was supported by the National Institute on Aging (K01AG041179). No conflict of interest. The second author (CL) was supported by a career development grant from by the National Institute on Aging (K99AG052458). The third author (JMB) was supported by training grants from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (T32HD049302) and National Heart, Lung, and Blood Institute (T32HL07560).

References

  • 1.Centers for Disease Control and Prevention. National Diabetes Statistics Report: Estimates of Diabetes and Its Burden in the United States, 2014. Atlanta, GA: 2014. [Google Scholar]
  • 2.American Diabetes Association. Economic costs of diabetes in the U.S. in 2012. Diabetes Care. 2013;36(4):1033–1046. doi: 10.2337/dc12-2625. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Demakakos P, Marmot M, Steptoe A. Socioeconomic position and the incidence of type 2 diabetes: the ELSA study. European journal of epidemiology. 2012;27(5):367–378. doi: 10.1007/s10654-012-9688-4. [DOI] [PubMed] [Google Scholar]
  • 4.Kumari M, Head J, Marmot M. Prospective study of social and other risk factors for incidence of type 2 diabetes in the Whitehall II study. Arch Intern Med. 2004;164(17):1873–1880. doi: 10.1001/archinte.164.17.1873. [DOI] [PubMed] [Google Scholar]
  • 5.Bardenheier BH, Bullard KM, Caspersen CJ, Cheng YJ, Gregg EW, Geiss LS. A Novel Use of Structural Equation Models to Examine Factors Associated With Prediabetes Among Adults Aged 50 Years and Older: National Health and Nutrition Examination Survey 2001–2006. Diabetes Care. 2013 doi: 10.2337/dc12-2608. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Lawlor DA, Ebrahim S, Davey Smith G British women’s h, health s. Socioeconomic position in childhood and adulthood and insulin resistance: cross sectional survey using data from British women’s heart and health study. BMJ. 2002;325(7368):805. doi: 10.1136/bmj.325.7368.805. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Johnson RC, Schoeni RF. Early-life origins of adult disease: national longitudinal population-based study of the United States. Am J Public Health. 2011;101(12):2317–2324. doi: 10.2105/AJPH.2011.300252. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Kavanagh A, Bentley RJ, Turrell G, Shaw J, Dunstan D, Subramanian SV. Socioeconomic position, gender, health behaviours and biomarkers of cardiovascular disease and diabetes. Soc Sci Med. 2010;71(6):1150–1160. doi: 10.1016/j.socscimed.2010.05.038. [DOI] [PubMed] [Google Scholar]
  • 9.Tamayo T, Christian H, Rathmann W. Impact of early psychosocial factors (childhood socioeconomic factors and adversities) on future risk of type 2 diabetes, metabolic disturbances and obesity: a systematic review. BMC Public Health. 2010;10:525. doi: 10.1186/1471-2458-10-525. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Physical Activity Guidelines Advisory Committee report, 2008. To the Secretary of Health and Human Services. Part A: executive summary. NutrRev. 2009;67(2):114–120. doi: 10.1111/j.1753-4887.2008.00136.x. [DOI] [PubMed] [Google Scholar]
  • 11.Waller K, Kaprio J, Lehtovirta M, Silventoinen K, Koskenvuo M, Kujala UM. Leisure-time physical activity and type 2 diabetes during a 28 year follow-up in twins. Diabetologia. 2010;53(12):2531–2537. doi: 10.1007/s00125-010-1875-9. [DOI] [PubMed] [Google Scholar]
  • 12.Pai LW, Li TC, Hwu YJ, Chang SC, Chen LL, Chang PY. The effectiveness of regular leisure-time physical activities on long-term glycemic control in people with type 2 diabetes: A systematic review and meta-analysis. Diabetes Res Clin Pract. 2016;113:77–85. doi: 10.1016/j.diabres.2016.01.011. [DOI] [PubMed] [Google Scholar]
  • 13.Okada K, Hayashi T, Tsumura K, Suematsu C, Endo G, Fujii S. Leisure-time physical activity at weekends and the risk of Type 2 diabetes mellitus in Japanese men: the Osaka Health Survey. Diabetic medicine: a journal of the British Diabetic Association. 2000;17(1):53–58. doi: 10.1046/j.1464-5491.2000.00229.x. [DOI] [PubMed] [Google Scholar]
  • 14.Meisinger C, Lowel H, Thorand B, Doring A. Leisure time physical activity and the risk of type 2 diabetes in men and women from the general population. The MONICA/KORA Augsburg Cohort Study. Diabetologia. 2005;48(1):27–34. doi: 10.1007/s00125-004-1604-3. [DOI] [PubMed] [Google Scholar]
  • 15.Huai P, Han H, Reilly KH, Guo X, Zhang J, Xu A. Leisure-time physical activity and risk of type 2 diabetes: a meta-analysis of prospective cohort studies. Endocrine. 2016;52(2):226–230. doi: 10.1007/s12020-015-0769-5. [DOI] [PubMed] [Google Scholar]
  • 16.Honda T, Kuwahara K, Nakagawa T, Yamamoto S, Hayashi T, Mizoue T. Leisure-time, occupational, and commuting physical activity and risk of type 2 diabetes in Japanese workers: a cohort study. BMC Public Health. 2015;15:1004. doi: 10.1186/s12889-015-2362-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Tsenkova VK. Leisure-time, occupational, household physical activity and insulin resistance (HOMAIR) in the Midlife in the United States (MIDUS) national study of adults. Preventive medicine reports. 2017;5:224–227. doi: 10.1016/j.pmedr.2016.12.025. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Hu G, Qiao Q, Silventoinen K, et al. Occupational, commuting, and leisure-time physical activity in relation to risk for Type 2 diabetes in middle-aged Finnish men and women. Diabetologia. 2003;46(3):322–329. doi: 10.1007/s00125-003-1031-x. [DOI] [PubMed] [Google Scholar]
  • 19.Hu G, Eriksson J, Barengo NC, et al. Occupational, commuting, and leisure-time physical activity in relation to total and cardiovascular mortality among Finnish subjects with type 2 diabetes. Circulation. 2004;110(6):666–673. doi: 10.1161/01.CIR.0000138102.23783.94. [DOI] [PubMed] [Google Scholar]
  • 20.Villegas R, Shu XO, Li H, et al. Physical activity and the incidence of type 2 diabetes in the Shanghai women’s health study. Int J Epidemiol. 2006;35(6):1553–1562. doi: 10.1093/ije/dyl209. [DOI] [PubMed] [Google Scholar]
  • 21.Holtermann A, Hansen JV, Burr H, Sogaard K, Sjogaard G. The health paradox of occupational and leisure-time physical activity. British journal of sports medicine. 2012;46(4):291–295. doi: 10.1136/bjsm.2010.079582. [DOI] [PubMed] [Google Scholar]
  • 22.Larsson CA, Kroll L, Bennet L, Gullberg B, Rastam L, Lindblad U. Leisure time and occupational physical activity in relation to obesity and insulin resistance: a population-based study from the Skaraborg Project in Sweden. Metabolism. 2012;61(4):590–598. doi: 10.1016/j.metabol.2011.09.010. [DOI] [PubMed] [Google Scholar]
  • 23.Richard A, Martin B, Wanner M, Eichholzer M, Rohrmann S. Effects of leisure-time and occupational physical activity on total mortality risk in NHANES III according to sex, ethnicity, central obesity, and age. Journal of physical activity & health. 2015;12(2):184–192. doi: 10.1123/jpah.2013-0198. [DOI] [PubMed] [Google Scholar]
  • 24.Holtermann A, Marott JL, Gyntelberg F, et al. Occupational and leisure time physical activity: risk of all-cause mortality and myocardial infarction in the Copenhagen City Heart Study. A prospective cohort study. BMJ open. 2012;2(1):e000556. doi: 10.1136/bmjopen-2011-000556. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Elhakeem A, Cooper R, Bann D, Hardy R. Childhood socioeconomic position and adult leisure-time physical activity: a systematic review. The international journal of behavioral nutrition and physical activity. 2015;12:92. doi: 10.1186/s12966-015-0250-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Juneau CE, Benmarhnia T, Poulin AA, Cote S, Potvin L. Socioeconomic position during childhood and physical activity during adulthood: a systematic review. International journal of public health. 2015;60(7):799–813. doi: 10.1007/s00038-015-0710-y. [DOI] [PubMed] [Google Scholar]
  • 27.Beenackers MA, Kamphuis CB, Giskes K, et al. Socioeconomic inequalities in occupational, leisure-time, and transport related physical activity among European adults: a systematic review. The international journal of behavioral nutrition and physical activity. 2012;9:116. doi: 10.1186/1479-5868-9-116. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Pudrovska T, Anishkin A. Early-life socioeconomic status and physical activity in later life: evidence from structural equation models. J Aging Health. 2013;25(3):383–404. doi: 10.1177/0898264312468601. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Kuh DJ, Cooper C. Physical activity at 36 years: patterns and childhood predictors in a longitudinal study. J Epidemiol Community Health. 1992;46(2):114–119. doi: 10.1136/jech.46.2.114. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Stringhini S, Dugravot A, Shipley M, et al. Health behaviours, socioeconomic status, and mortality: further analyses of the British Whitehall II and the French GAZEL prospective cohorts. PLoS medicine. 2011;8(2):e1000419. doi: 10.1371/journal.pmed.1000419. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Brunner E, Shipley MJ, Blane D, Smith GD, Marmot MG. When does cardiovascular risk start? Past and present socioeconomic circumstances and risk factors in adulthood. J Epidemiol Community Health. 1999;53(12):757–764. doi: 10.1136/jech.53.12.757. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Ben-Shlomo Y, Kuh D. A life course approach to chronic disease epidemiology: conceptual models, empirical challenges and interdisciplinary perspectives. Int J Epidemiol. 2002;31(2):285–293. [PubMed] [Google Scholar]
  • 33.Dong M, Giles WH, Felitti VJ, et al. Insights into causal pathways for ischemic heart disease: adverse childhood experiences study. Circulation. 2004;110(13):1761–1766. doi: 10.1161/01.CIR.0000143074.54995.7F. [DOI] [PubMed] [Google Scholar]
  • 34.Power C, Hertzman C. Social and biological pathways linking early life and adult disease. Br Med Bull. 1997;53(1):210–221. doi: 10.1093/oxfordjournals.bmb.a011601. [DOI] [PubMed] [Google Scholar]
  • 35.Fried LP, Kronmal RA, Newman AB, et al. Risk factors for 5-year mortality in older adults: the Cardiovascular Health Study. JAMA. 1998;279(8):585–592. doi: 10.1001/jama.279.8.585. [DOI] [PubMed] [Google Scholar]
  • 36.Dienberg Love G, Seeman TE, Weinstein M, Ryff CD. Bioindicators in the MIDUS national study: protocol, measures, sample, and comparative context. J Aging Health. 2010;22(8):1059–1080. doi: 10.1177/0898264310374355. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.American Diabetes Association. 2. Classification and Diagnosis of Diabetes. Diabetes Care. 2017;40(Suppl 1):S11–S24. doi: 10.2337/dc17-S005. [DOI] [PubMed] [Google Scholar]
  • 38.Wolf HU, Lang W, Zander R. Alkaline haematin D-575, a new tool for the determination of haemoglobin as an alternative to the cyanhaemiglobin method. II. Standardisation of the method using pure chlorohaemin. Clin Chim Acta. 1984;136(1):95–104. doi: 10.1016/0009-8981(84)90251-1. [DOI] [PubMed] [Google Scholar]
  • 39.Krieger N, Okamoto A, Selby JV. Adult female twins’ recall of childhood social class and father’s education: a validation study for public health research. Am J Epidemiol. 1998;147(7):704–708. doi: 10.1093/oxfordjournals.aje.a009512. [DOI] [PubMed] [Google Scholar]
  • 40.Senese LC, Almeida ND, Fath AK, Smith BT, Loucks EB. Associations between childhood socioeconomic position and adulthood obesity. Epidemiol Rev. 2009;31:21–51. doi: 10.1093/epirev/mxp006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Karlamangla AS, Mori T, Merkin SS, et al. Childhood socioeconomic status and adult femoral neck bone strength: findings from the Midlife in the United States Study. Bone. 2013;56(2):320–326. doi: 10.1016/j.bone.2013.06.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Tsenkova V, Pudrovska T, Karlamangla A. Childhood socioeconomic disadvantage and prediabetes and diabetes in later life: a study of biopsychosocial pathways. Psychosomatic medicine. 2014;76(8):622–628. doi: 10.1097/PSY.0000000000000106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Gruenewald TL, Karlamangla AS, Hu P, et al. History of socioeconomic disadvantage and allostatic load in later life. Soc Sci Med. 2012;74(1):75–83. doi: 10.1016/j.socscimed.2011.09.037. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Ainsworth BE, Haskell WL, Herrmann SD, et al. 2011 Compendium of Physical Activities: a second update of codes and MET values. Medicine and science in sports and exercise. 2011;43(8):1575–1581. doi: 10.1249/MSS.0b013e31821ece12. [DOI] [PubMed] [Google Scholar]
  • 45.Office of Disease Prevention and Health Promotion. Appendix 1. Translating scientific evidence about total amount and intensity of physical activity into guidelines. 2017 https://health.gov/paguidelines/guidelines/appendix1.aspx.
  • 46.Radloff L. The CES-D Scale: A Self-Report Depression Scale for Research in the General Population. Applied Psychological Measurement. 1977;1(3):385–401. [Google Scholar]
  • 47.de Souto Barreto P. Time to challenge public health guidelines on physical activity. Sports Med. 2015;45(6):769–773. doi: 10.1007/s40279-015-0326-7. [DOI] [PubMed] [Google Scholar]
  • 48.Sofi F, Capalbo A, Marcucci R, et al. Leisure time but not occupational physical activity significantly affects cardiovascular risk factors in an adult population. European journal of clinical investigation. 2007;37(12):947–953. doi: 10.1111/j.1365-2362.2007.01884.x. [DOI] [PubMed] [Google Scholar]
  • 49.Sisson SB, Camhi SM, Church TS, et al. Leisure time sedentary behavior, occupational/domestic physical activity, and metabolic syndrome in U.S. men and women. Metabolic syndrome and related disorders. 2009;7(6):529–536. doi: 10.1089/met.2009.0023. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Clays E, De Bacquer D, Van Herck K, De Backer G, Kittel F, Holtermann A. Occupational and leisure time physical activity in contrasting relation to ambulatory blood pressure. BMC Public Health. 2012;12:1002. doi: 10.1186/1471-2458-12-1002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Petersen CB, Eriksen L, Tolstrup JS, Sogaard K, Gronbaek M, Holtermann A. Occupational heavy lifting and risk of ischemic heart disease and all-cause mortality. BMC Public Health. 2012;12:1070. doi: 10.1186/1471-2458-12-1070. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Colberg SR, Sigal RJ, Fernhall B, et al. Exercise and type 2 diabetes: the American College of Sports Medicine and the American Diabetes Association: joint position statement. Diabetes Care. 2010;33(12):e147–167. doi: 10.2337/dc10-9990. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Lund T, Labriola M, Christensen KB, Bultmann U, Villadsen E. Physical work environment risk factors for long term sickness absence: prospective findings among a cohort of 5357 employees in Denmark. BMJ. 2006;332(7539):449–452. doi: 10.1136/bmj.38731.622975.3A. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Cummings JL, Jackson PB. Race, gender, and SES disparities in self-assessed health, 1974–2004. Research on Aging. 2008;30:137–167. [Google Scholar]

RESOURCES