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. Author manuscript; available in PMC: 2016 Oct 20.
Published in final edited form as: Circulation. 2015 Sep 28;132(16):1560–1569. doi: 10.1161/CIRCULATIONAHA.115.016938

Objectively-Measured Sedentary Time and Cardiometabolic Biomarkers in U.S. Hispanic/Latino Adults: The Hispanic Community Health Study/Study of Latinos (HCHS/SOL)

Qibin Qi 1,*, Garrett Strizich 1,*, Gina Merchant 2, Daniela Sotres-Alvarez 3, Christina Buelna 2, Sheila F Castañeda 2, Linda C Gallo 4, Jianwen Cai 3, Marc D Gellman 5, Carmen R Isasi 1, Ashley E Moncrieft 5, Lisa Sanchez-Johnsen 6, Neil Schneiderman 5, Robert C Kaplan 1
PMCID: PMC4618246  NIHMSID: NIHMS713470  PMID: 26416808

Abstract

Background

Sedentary behavior is recognized as a distinct construct from lack of moderate-vigorous physical activity and is associated with deleterious health outcomes. Previous studies have primarily relied on self-reported data, while data on the relationship between objectively-measured sedentary time and cardiometabolic biomarkers are sparse, especially among U.S. Hispanics/Latinos.

Methods and Results

We examined associations of objectively-measured sedentary time (via Actical accelerometers for 7 days) and multiple cardiometabolic biomarkers among 12,083 participants, aged 18–74 years, from the Hispanic Community Health Study/Study of Latinos. Hispanics/Latinos of diverse backgrounds (Central American, Cuban, Dominican, Mexican, Puerto Rican, and South American) were recruited from 4 U.S. cities between 2008 and 2011. Sedentary time (<100 counts/minute) was standardized to 16-hour/day of wear time. The mean sedentary time was 11.9 hours/day (74% of accelerometer wear time). After adjustment for moderate-vigorous physical activity and confounding variables, prolonged sedentary time was associated with decreased high-density lipoprotein (HDL)-cholesterol (P=0.04), and increased triglycerides, 2-hour glucose, fasting insulin and HOMA-IR (all P<0.0001). These associations were generally consistent across age, sex, Hispanic/Latino backgrounds, and physical activity levels. Even among individuals meeting physical activity guidelines, sedentary time was detrimentally associated with several cardiometabolic biomarkers (diastolic blood pressure, HDL-cholesterol, fasting and 2-hour glucose, fasting insulin and HOMA-IR; all P<0.05).

Conclusions

Our large population-based, objectively-derived data showed deleterious associations between sedentary time and cardiometabolic biomarkers, independent of physical activity, in U.S. Hispanics/Latinos. Our findings emphasize the importance of reducing sedentary behavior for the prevention of cardiometabolic diseases, even in those who meet physical activity recommendations.

Keywords: sedentary behavior, Hispanic, epidemiology, physical exercise, cardiovascular disease risk factors

Introduction

Sedentary behavior, characterized as sitting or reclining with the energy-expenditure between 1.0 to 1.5 metabolic equivalents, is recognized as a distinct construct that is qualitatively different from lack of physical activity.1 Recently, a systematic review and meta-analysis demonstrated that prolonged sedentary time is significantly associated with an increased risk of type 2 diabetes, cardiovascular disease (CVD), cancer and mortality, independent of physical activity.2 Several studies have investigated the relationships between sedentary behavior and cardiometabolic biomarkers.3–5 However, these previous studies are largely based on self-reported sedentary time and physical activity data.

The use of accelerometers reduces potential biases and measurement error inherent in self-reported data and allows for more accurate examination of sedentary time. One previous study suggested that using self-reported sedentary behavior rather than objective measurements may underestimate the magnitude of the relationships between sedentariness and cardiometabolic risk factors.6 Nevertheless, most existing studies of objectively-measured sedentary behavior and cardiometabolic risk are limited by small sample size and are largely restricted to non-Hispanic white populations.7–12 Data from the largest analysis to date of objectively-measured sedentary time and cardiometabolic biomarkers (U.S. National Health and Nutrition Examination Survey [NHANES]: full sample, n=4757; fasting sub-sample, n=2118), representing mostly non-Hispanic Whites (74%), indicated some significant racial/ethnic differences, and associations of sedentary time with some cardiometabolic biomarkers remains unclear in Mexican Americans and non-Hispanic Blacks.13 Thus, studies on sedentary behavior and cardiometabolic risk factors need to expand beyond predominantly White populations.

CVD is the major cause of death among U.S. Hispanics/Latinos who are also disproportionately affected by obesity and related cardiometabolic conditions.14 However, the relationship between objectively-measured sedentary time and cardiometabolic risk in this population has only been examined in Mexican Americans and results remain unclear, possibly due to the relatively small sample size.13 Moreover, whether associations between sedentary time and cardiometabolic biomarkers are different among Hispanic/Latino background groups has not been investigated. In the current study, therefore, we aimed to examine associations between objectively-measured sedentary time and multiple cardiometabolic biomarkers) in participants from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), a large population sample of Hispanics/Latinos adults of diverse backgrounds in the U.S.

Methods

Study Population

The HCHS/SOL is a prospective population-based study of 16,415 Hispanic/Latino adults age 18–74 years at recruitment living in 4 U.S. metropolitan areas (Bronx, NY; Chicago, IL; Miami, FL; and San Diego, CA). Participants were recruited using a 2-stage probability sample design, as described previously.15, 16 A comprehensive battery of interviews relating to personal and family characteristics, health status and behaviors, and a clinical assessment with blood draw, were conducted at in-person clinic baseline visit during 2008–2011. A total of up to 12,083 participants with complete data on cardiometabolic biomarkers were included in the current analysis after exclusion of 3,665 (22%) who were not adherent to the accelerometer protocol. Also excluded from all analyses were 119 (1%) with accelerometer wear time in excess of 23 hours per day; those without a fasting blood sample (n=188) or complete medication data (n=251); and those with body mass index (BMI) less than 18.5 (n=109). The study was approved by the Institutional Review Boards at all participating institutions, and all participants gave written informed consent.

Assessment of Physical Activity and Sedentary Behavior

At the HCHS/SOL baseline exam, participants were instructed to wear an Actical version B-1 (model 198-0200-03; Respironics Co. Inc., Bend, Oregon) accelerometer for 7 days, positioned above the iliac crest, with removal only for swimming, showering, and sleeping. Prior studies have shown the Actical to have acceptable technical reliability for counts and steps.17, 18 The Actical was programmed to capture accelerations in counts in one-minute epochs based on convention used in previous studies.19, 20 Non-wear time was determined using the Choi algorithm21, defined as at least 90 consecutive minutes of zero counts, with allowance of 1 or 2 minutes of nonzero counts if no counts were detected in a 30 minute window upstream and downstream of the 90 minute period. An adherent day was defined as at least 10 hours of wear time, and at least 3 adherent days were required for inclusion in this analysis. In the current analysis, 89% of participants had at least one adherent weekend day. Accelerometer counts were used to classify sedentary behavior (<100 counts/minute) and moderate to vigorous activity (MVPA) (≥1535 counts/minute).22, 23 Detailed information on accelerometer performance and adherence has been described elsewhere.24

Assessment of Cardiometabolic Biomarkers

Participants were asked to fast and refrain from smoking in the morning before the HCHS/SOL clinic visit. Following a 5-minute rest period, 3 seated blood pressure measurements were obtained with an automatic sphygmomanometer; the second and third readings were averaged. Measurements of total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, triglycerides, and fasting and 2-hour glucose have been described previously.25–27 Fasting insulin was measured using two commercial immunoassays (ELISA, Mercodia AB, Uppsala, Sweden; and sandwich immunoassay on a Roche Elecsys 2010 Analyzer, Roche Diagnostics, Indianapolis, IN); early measures conducted with the Mercodia assay were calibrated, and values were equivalent to the Roche method. Homeostatic model assessment of insulin resistance (HOMA-IR) was computed using the following equation: fasting glucose × fasting insulin/405.21.28 High sensitivity C-reactive protein (CRP) was measured using an immunoturbidimetric method (Roche Diagnostics).

Assessment of Covariates

Height, waist circumference, and hip circumference were measured to the nearest centimeter and weight to the nearest 0.1 kg. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Waist-to-hip ratio (WHR) was calculated as waist circumference divided by hip circumference. Interviewer-administered questionnaires were used to collect information on age, gender, annual household income, educational attainment, Hispanic/Latino background, employment status, health insurance status, cigarette use history, alcohol consumption, self-reported health, and number of doctor visits in the past 12 months. Participants were instructed to bring all prescription and nonprescription medications taken in the past four weeks; their preparations, concentrations, and units were coded for analysis. The alternative healthy eating index-2010 (AHEI-2010)29 was calculated based on two 24-hour dietary recalls using the National Cancer Institute (NCI) methodology.30

Statistical Analysis

All results were estimated using sampling weights to account for non-response and oversampling of specific population subgroups. Weights were trimmed and calibrated to 2010 U.S. Census characteristics by age, sex, and Hispanic/Latino background in each field center’s target population, in accordance with procedures commonly used in large population-based studies.15, 24 Despite favorable accelerometer compliance when compared with previous studies,24 we additionally adjusted for missing or incomplete accelerometer data (n=3,665, 22%) using inverse probability weighting (IPW).31 Briefly, an IPW weight was created from a logistic regression model predicting Actical compliance based on age, gender, income level, marital status, education, employment status, language preference, immigrant generation, self-reported physical activity from the World Health Organization Global Physical Activity Questionnaire, BMI, aggregate self-reported physical health score, field center by Hispanic/Latino background cross-classification, sampling stratum, and sampling weight. Because some participants (n=917, 5.6%) also had sporadic missing data for one or more covariates in the IPW model, the missing covariates were first imputed by multiple imputation. Finally, the weight used in the analyses of accelerometer-measured sedentary time in relation to cardiometabolic markers was the product of the IPW weight (to weight the results for the compliant subset back to the whole HCHS/SOL sample) and the HCHS/SOL sampling weight (to further weight the results back to the Hispanic/Latino population in the target areas). Unweighted analyses and data management were performed using SAS software version 9.3 (SAS Institute, Cary, NC). Weighted analyses were conducted with “survey regression procedures”,32 which properly accounted for the 2-stage stratified sampling and clustering of participants within sampling units, using SUDAAN release 11.0.1 (RTI International, Research Triangle Park, NC).

Because of a high correlation between sedentary time and wear time (r=0.83), we standardized sedentary time to 16 hours of wear time per day (the approximate average of both daily wear time and waking time in our study) using the residual from regressing sedentary time on wear time.13, 33 First, we regressed measured sedentary time against accelerometer wear time, field center, and the interaction between wear time and field center using weighted linear regression, and calculated residuals to represent the observed minus predicted sedentary time. We then summed each participant’s residual sedentary time value with the field center-specific mean predicted sedentary time given a mean wear time of 16 hours/day.

Age-adjusted descriptive characteristics across sedentary time quartiles were computed for continuous variables as predicted marginals of the mean from survey linear regression, and for categorical variables as predicted marginals of the prevalence from survey logistic regression.32 A series of survey linear regression models were constructed with cardiometabolic markers as the dependent variables and quartiles of sedentary time as the independent variables. Quartiles of sedentary time were modeled to better portray the nature of the relationship with outcomes and avoid assuming a linear association. Triglycerides, fasting insulin, HOMA-IR, and CRP variables were natural log-transformed before analysis. We adjusted for several sociodemographic, behavioral, and health-related potential confounders, including medications specific to each marker (antihypertensive medications for blood pressure; lipid-lowering drugs for blood lipids; and antidiabetic medications for glycemic traits). Subsequent models further adjusted for time spent in min/day of MVPA, BMI and WHR. MVPA was dichotomized for stratified analyses according to the 2008 Physical Activity Guidelines for Americans,34 which recommend at least 150 min/week moderate-intensity activity, 75 min/week vigorous-intensity activity, or ≥150 min/wk for a combination of the two (multiplying vigorous by 2 and summing). Additional stratified models were created across Hispanic/Latino background groups, age groups, sex, BMI categories, and field centers.

Results

Participant characteristics

After standardizing to a 16-hour waking day, the estimated mean time spent in sedentary behavior was 11.9 hours/day, accounting for 74% of accelerometer wear time. Participant characteristics according quartiles of sedentary time are shown in Table 1. Individuals in quartiles characterized by greater sedentary time were less likely to meet the U.S. physical activity guidelines than their lower sedentary time quartile counterparts, and were more likely to be residing in the Bronx, of Dominican or Puerto Rican background, and not currently employed. After adjusting for age, more sedentary time was also associated with poorer diet and self-reported health, and more healthcare utilization and medication use.

Table 1.

Age-adjusted characteristics of Hispanic/Latino individuals by quartiles of sedentary time, HCHS/SOL 2008–2011*

Quartile of Sedentary Time
Quartile 1 (n=3020) Quartile 2 (n=3021) Quartile 3 (n=3021) Quartile 4 (n=3021) P
Sedentary hours/day, median (range) † 9.9 (0.8, 10.9) 11.6 (10.9, 12.1) 12.6 (12.1, 13.1) 13.7 (13.1, 16.0) n/a
MVPA minutes/day, mean (95% CI) † 43 (40, 46) 24 (23, 26) 18 (17, 20) 13 (12, 14) <0.0001
2008 U.S. physical activity guidelines, n (%)† <0.0001
 Does not meet guidelines 1088 (33%) 1722 (54%) 2089 (65%) 2528 (79%)
 Meets guidelines 1932 (67%) 1299 (46%) 932 (35%) 493 (21%)
DEMOGRAPHIC CHARACTERISTICS
Age, mean (95% CI) 39 (38, 40) 40 (39, 41) 41 (40, 42) 45 (44, 46) <0.0001
Sex, % (95% CI) <0.0001
 Females 42 (39, 45) 54 (51, 57) 59 (57, 62) 54 (51, 57)
 Males 58 (55, 61) 46 (43, 49) 41 (38, 43) 46 (43, 49)
Annual household income, % (95% CI) <0.0001
 $20,000 or less 41 (38, 45) 40 (37, 43) 41 (38, 44) 45 (42, 49)
 $20,001 – $50,000 42 (39, 45) 37 (35, 40) 35 (32, 38) 34 (30, 37)
 More than $50,000 10 (8, 12) 14 (11, 16) 14 (12, 17) 10 (8, 13)
 Not reported 7 (5, 8) 9 (8, 11) 11 (9, 12) 11 (9, 13)
Education level, % (95% CI) <0.0001
 Less than 9th grade 21 (19, 24) 17 (15, 19) 15 (14, 17) 17 (15, 19)
 Some high school 14 (13, 17) 15 (13, 17) 14 (12, 17) 16 (14, 19)
 High school graduate/equivalent 30 (28, 32) 26 (23, 28) 27 (25, 30) 28 (25, 32)
 More than high school 34 (31, 37) 43 (40, 45) 43 (40, 46) 39 (36, 43)
Field center, % (95% CI) <0.0001
 Bronx 20 (17, 24) 22 (19, 25) 28 (24, 32) 46 (42, 51)
 Chicago 21 (18, 24) 16 (13, 18) 13 (11, 16) 13 (11, 15)
 Miami 29 (24, 34) 32 (28, 38) 33 (28, 38) 27 (22, 32)
 San Diego 30 (26, 35) 30 (27, 34) 26 (22, 31) 14 (12, 18)
Hispanic/Latino background, % (95% CI) <0.0001
 Dominican 6 (4, 8) 7 (5, 8) 10 (8, 12) 19 (15, 23)
 Central American 8 (6, 9) 7 (6, 9) 7 (6, 9) 8 (7, 10)
 Cuban 18 (15, 22) 22 (18, 26) 23 (19, 28) 19 (16, 23)
 Mexican 49 (45, 54) 41 (37, 45) 34 (30, 38) 24 (21, 27)
 Puerto Rican 12 (9, 14) 14 (12, 17) 17 (14, 20) 21 (18, 23)
 South American 4 (3, 5) 6 (4, 7) 6 (5, 7) 5 (4, 6)
 Other/more than one 3 (2, 5) 4 (3, 5) 3 (2, 5) 5 (3, 7)
Employment status, % (95% CI) <0.0001
 Retired and not currently employed 3 (2, 4) 5 (4, 6) 8 (7, 10) 16 (15, 19)
 Not retired and not currently employed 25 (23, 28) 40 (37, 43) 48 (45, 51) 51 (48, 54)
 Employed part-time (≤35 hours/week) 21 (19, 23) 20 (18, 22) 16 (14, 18) 11 (9, 13)
 Employed full-time (>35 hours/week) 51 (48, 54) 35 (33, 38) 28 (25, 30) 22 (19, 24)
Health insurance, % (95% CI) 44 (41, 47) 48 (45, 51) 53 (49, 56) 57 (54, 61) <0.0001
HEALTH STATUS
Waist-to-hip ratio (WHR), mean (95% CI) 0.92 (0.92, 0.93) 0.92 (0.91, 0.92) 0.91 (0.91, 0.91) 0.92 (0.92, 0.92) 0.1427
Body mass index (BMI) category, % (95% CI) 0.0009
 < 25 23 (21, 26) 22 (19, 24) 25 (22, 28) 20 (18, 23)
 25–30 40 (37, 43) 39 (37, 42) 37 (34, 40) 34 (31, 38)
 ≥ 30 37 (34, 39) 39 (37, 42) 38 (35, 41) 45 (42, 49)
Smoking status, % (95% CI) 0.0381
 Never 60 (57, 63) 62 (59, 65) 62 (60, 64) 65 (62, 68)
 Former 19 (17, 21) 19 (17, 21) 18 (16, 20) 15 (14, 17)
 Current 22 (19, 24) 19 (17, 21) 20 (18, 22) 20 (17, 23)
Alcohol consumption, % (95% CI) 0.1579
 No 45 (42, 48) 48 (45, 51) 49 (46, 52) 51 (47, 54)
 Low-risk drinker 49 (46, 52) 46 (42, 49) 46 (43, 49) 44 (41, 48)
 At-risk drinker (7+/14+ drinks/wk for F/M) 6 (5, 8) 6 (5, 8) 5 (4, 6) 5 (4, 7)
Doctor visits in past 12 months, % (95% CI) <0.0001
 Zero 38 (35, 41) 35 (32, 38) 28 (25, 31) 29 (25, 32)
 One 17 (15, 19) 16 (14, 18) 16 (14, 18) 13 (12, 15)
 Two to three 21 (18, 23) 24 (21, 26) 27 (24, 29) 26 (23, 29)
 Four or more 24 (22, 27) 26 (24, 28) 29 (27, 32) 32 (30, 35)
Antidiabetic medications, % (95% CI) 7 (6, 8) 7 (5, 8) 8 (7, 9) 12 (10, 13) <0.0001
Antihypertensive medications, % (95% CI) 11 (10, 12) 11 (10, 12) 12 (11, 14) 16 (14, 18) <0.0001
Lipid-lowering medications, % (95% CI) 9 (7, 10) 7 (6, 9) 9 (8, 10) 11 (10, 12) 0.0071
Alternative healthy eating index, mean (95% CI) 48.7 (48.2, 49.2) 47.7 (47.2, 48.1) 46.9 (46.3, 47.4) 46.6 (46.2, 47.0) <0.0001
Self-reported physical health score, mean (95% CI) 51.0 (50.4, 51.5) 51.0 (50.5, 51.4) 50.0 (49.5, 50.5) 48.3 (47.7, 48.9) <0.0001
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*

Figures are column percentages (95% confidence intervals) unless otherwise stated; all analyses account for the complex sampling scheme of HCHS/SOL and are adjusted for 10-year age groups

†

Sedentary and MVPA time not adjusted for age.

Sedentary time and cardiometabolic biomarkers

As shown in Table 2, more time spent sedentary was associated with decreased HDL cholesterol and increased diastolic blood pressure, triglycerides, 2-hour glucose, fasting insulin, HOMA-IR, and CRP after multivariable adjustment (all P for trend < 0.0001). After further adjustment for MVPA, the associations were attenuated, though still significant for HDL cholesterol (P=0.04), triglycerides (P<0.0001), 2-hour glucose (P<0.0001), fasting insulin (P<0.0001), and HOMA-IR (P<0.0001). After further adjustment for BMI and WHR, the relationships remained significant with respect to triglycerides, 2-hour glucose, fasting insulin, and HOMA-IR (all P for trend < 0.0001). After excluding participants with antihypertensive medications, lipid-lowering drugs or antidiabetic medications, and excluding those reporting a prior diagnosis of coronary heart disease and stroke,25 sensitivity analyses showed similar results with respect to all cardiometabolic biomarkers. In addition, sensitivity analyses among participants who contributed at least one adherent weekend day to the calculation of average daily sedentary time also yielded similar results (data not shown).

Table 2.

Adjusted means of cardiometabolic biomarkers by quartiles of sedentary time*

Systolic blood
pressure, mmHg
(n=11,607)		 Diastolic blood
pressure, mmHg
(n=11,601)		 LDL-
cholesterol,
mg/dl
(n=11,364)		 HDL-
cholesterol,
mg/dl
(n=11,586)		 Triglycerides,
mg/dl*
(n=11,588)		 Fasting glucose,
mg/dl
(n=11,566)		 2-h glucose,
mg/dl
(n=9,517)		 Fasting insulin,
mU/L†
(n=11,536)		 HOMA-IR†
(n=11,536)		 CRP, mg/L†
(n=11,584)
Model 1
Quartile 1 120 (119, 121) 71 (71, 72) 120 (117, 122) 50 (49, 50) 105 (102, 108) 101 (99, 103) 112 (110, 114) 9.2 (8.8, 9.6) 2.23 (2.13, 2.34) 1.83 (1.72, 1.94)
Quartile 2 120 (119, 121) 72 (72, 73) 120 (118, 121) 49 (48, 49) 111 (108, 115) 102 (100, 103) 118 (116, 119) 10.3 (9.9, 10.7) 2.52 (2.42, 2.62) 1.93 (1.82, 2.05)
Quartile 3 120 (119, 120) 72 (71, 73) 120 (118, 122) 48 (48, 49) 111 (108, 114) 101 (100, 103) 118 (116, 120) 10.3 (10.0, 10.7) 2.51 (2.42, 2.61) 1.82 (1.71, 1.93)
Quartile 4 120 (120, 121) 73 (72, 74) 122 (120, 123) 47 (47, 48) 122 (117, 127) 103 (101, 104) 122 (120, 124) 11.7 (11.2, 12.3) 2.87 (2.73, 3.03) 2.18 (2.04, 2.34)
P-trend 0.8500 <0.0001 0.1500 <0.0001 <0.0001 0.1300 <0.0001 <0.0001 <0.0001 <0.0001
Model 2 (MVPA adjusted)
Quartile 1 120 (120, 121) 72 (71, 72) 120 (118, 122) 49 (48, 50) 108 (105, 112) 101 (99, 103) 113 (111, 115) 9.6 (9.2, 10.1) 2.34 (2.22, 2.46) 1.91 (1.79, 2.04)
Quartile 2 120 (119, 121) 72 (72, 73) 120 (118, 121) 49 (48, 49) 112 (108, 115) 102 (100, 103) 118 (116, 119) 10.3 (10.0, 10.7) 2.52 (2.43, 2.62) 1.94 (1.83, 2.05)
Quartile 3 120 (119, 120) 72 (71, 72) 120 (118, 122) 49 (48, 49) 110 (107, 113) 101 (100, 103) 117 (115, 119) 10.2 (9.8, 10.6) 2.48 (2.39, 2.57) 1.79 (1.68, 1.91)
Quartile 4 120 (119, 121) 73 (72, 73) 121 (120, 123) 48 (47, 49) 119 (115, 124) 103 (101, 104) 121 (119, 124) 11.3 (10.8, 11.9) 2.77 (2.63, 2.92) 2.11 (1.96, 2.26)
P-trend 0.5900 0.0600 0.5000 0.0400 <0.0001 0.3400 <0.0001 <0.0001 <0.0001 0.2800
Model 3 (MVPA, BMI + WHR adjusted)
Quartile 1 120 (120, 121) 72 (71, 72) 120 (118, 122) 49 (48, 50) 108 (105, 112) 101 (99, 103) 113 (111, 115) 9.7 (9.3, 10.1) 2.36 (2.26, 2.45) 1.94 (1.83, 2.06)
Quartile 2 120 (119, 121) 72 (72, 73) 120 (118, 121) 49 (48, 49) 111 (109, 114) 102 (100, 103) 117 (116, 119) 10.3 (10.0, 10.6) 2.51 (2.44, 2.59) 1.94 (1.85, 2.04)
Quartile 3 120 (119, 120) 72 (71, 73) 120 (118, 122) 48 (48, 49) 111 (108, 114) 102 (100, 103) 118 (116, 120) 10.4 (10.1, 10.7) 2.54 (2.46, 2.62) 1.85 (1.76, 1.94)
Quartile 4 120 (119, 121) 73 (72, 73) 121 (119, 123) 48 (47, 49) 118 (114, 122) 103 (101, 104) 121 (118, 123) 11.0 (10.6, 11.4) 2.69 (2.59, 2.80) 2.01 (1.90, 2.13)
P-trend 0.4900 0.1400 0.5200 0.0700 <0.0001 0.3300 <0.0001 <0.0001 <0.0001 0.7800
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*

Values are means (95% confidence intervals), adjusted for: Model 1, adjusted for age, sex, household income, education, employment status, Hispanic/Latino background, field center, smoking, alcohol consumption, health insurance status, healthcare utilization, self-reported health, diet quality, and medications specific to each marker; Model 2, additionally adjusted for min/day in MVPA; Model 3, additionally adjusted for BMI and waist-hip ratio.

†

Geometric means are presented for triglycerides, fasting insulin, HOMA-IR, and CRP.

Stratification by physical activity

The deleterious associations between sedentary time and cardiometabolic biomarkers were similar among those meeting or not 2008 U.S. physical activity guidelines (Figure 1). Of note, even among those meeting the 2008 physical activity guidelines, sedentary time was significantly associated with increased fasting glucose (P for trend=0.02), 2-hour glucose (P for trend=0.003), HOMA-IR (P for trend <0.001) and fasting insulin (data not shown, very similar to HOMA-IR; P for trend<0.001), and decreased HDL cholesterol (P for trend =0.014). In analyses further adjusted for MVPA differences within strata, neither the magnitude nor the significance of the effect of sedentary behavior was meaningfully altered (data not shown).

Figure 1.

Figure 1

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Adjusted means of cardiometabolic biomarker by quartiles of sedentary time, by meeting or not the 2008 physical activity guidelines. Values are means (95% confidence intervals), adjusted for age, sex, household income, education, employment status and medications specific to each marker, Hispanic/Latino background, field center, smoking, alcohol consumption, health insurance status, healthcare utilization, self-reported health, diet quality, BMI, and waist-hip ratio. Geometric means are presented for triglycerides, HOMA-IR, and hs-CRP.

Stratifications by Hispanic/Latino background and other variables

Sedentary time varied by Hispanic/Latino background groups, ranging from 11.5 hours/day among individuals of Mexican background, to 12.5 hours/day among those of Dominican background. Associations between sedentary time and cardiometabolic biomarkers were, in general, consistent across Hispanic/Latino background groups (Figure 2). For markers with little evidence for an association with sedentary time from overall models (systolic/diastolic blood pressure, LDL-cholesterol, fasting glucose, and CRP), there was no significant interaction between sedentary time and Hispanic/Latino background (all P for interaction ≥0.09; data not shown). For markers with significant findings in the aggregate population (HDL-cholesterol, triglycerides, 2-hour glucose, HOMA-IR and fasting insulin), we also observed generally consistent associations across most Hispanic/Latino background groups. However, there was possible heterogeneity in the association between sedentary time and 2-hour glucose (P for interaction=0.001), driven mainly by a lack of association in those of Dominican background. Stratification analyses where sedentary time was treated as continuous variables indicate generally consistent associations with cardiometabolic biomarkers across subgroups defined by sex, age, BMI, and field center (Supplement Table 1). Significant interactions were only observed for diastolic blood pressure (P for interaction with age, 0.013), 2-hour glucose (P for interaction with field center, 0.016), and CRP (P for interaction with BMI, 0.003). Results were qualitatively similar in stratification analyses using quartiles of sedentary time (data not shown).

Figure 2.

Figure 2

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Cardiometabolic markers and quartiles of sedentary time, by Hispanic/Latino background. Values are means (95% confidence intervals), adjusted for moderate-vigorous physical activity, age, sex, household income, education, employment status and medications specific to each marker, Hispanic/Latino background, field center, smoking, alcohol consumption, health insurance status, healthcare utilization, self-reported health, diet quality, BMI, and waist-hip ratio. Geometric means are presented for triglycerides and HOMA-IR.

Discussion

Our findings are, in general, consistent with previous studies with objectively-measured data showing that sedentary time was strongly associated with triglycerides9–11, 13 and indices of insulin resistance,7, 8, 13 but not related to blood pressure or cholesterol levels. We also confirmed a strong association between sedentary behavior and 2-hour plasma glucose,11, 12 which was not observed in the NHANES probably due to a relatively small subsample of participants with OGTT data.13 Interestingly, the associations with insulin resistance and 2-hour plasma glucose are consistent with a recent meta-analysis showing the largest and most persistent effect of sedentary behavior on health outcomes is risk of type 2 diabetes.2 This is in line with potential mechanisms to explain the association. Specifically, decreased skeletal muscle contractions from prolonged sedentary time may reduce uptake of plasma triglycerides and free fatty acid into skeletal muscle through suppression of lipoprotein lipase activity, and also reduce plasma glucose uptake through blunted translocation of GLUT-4 glucose transporters.35–37 Finally, we did not observe an association between sedentary behavior and CRP levels; prior studies on this association have been largely inconsistent.10, 13, 38

Two unique contributions of our study as compared to most previous work are the use of accelerometer-measured sedentary time and the diverse Hispanic/Latino backgrounds of our sample. Self-reported sedentary time has previously been reported to be associated with obesity, glucose tolerance, diabetes and hypertension in Hispanics/Latinos.39–41 However, the relationships between objectively-measured sedentary time and cardiometabolic risk factors in U.S. Hispanics have been only examined among Mexican Americans from the NHANES, where a relatively small sample size may have obscured associations.13 The associations have not previously been investigated in other Hispanic/Latino groups. Sedentary time varied by Hispanic/Latino background groups and accounted for a larger percentage of accelerometer wear time (74%) in the present study compared to prior work in the NHANES that described the sedentariness of White, Black and Mexican American adults.42 In general, even though sedentary time and levels of some cardiometabolic biomarkers (e.g., triglycerides) differed among Hispanic/Latino background groups, our data show consistent detrimental associations between sedentary time and cardiometabolic biomarkers across these groups. We also showed little evidence of effect modification by other characteristics such as age or BMI, with a few nominally statistically significant tests for interaction likely reflecting chance findings.

Another important contribution of this study comes from analyses stratified by physical activity. Stratification rather than adjustment is helpful to minimize residual confounding because physical activity is strongly related to sedentary behavior and cardiometabolic risk factors. Our results indicate that the associations between sedentary behavior and several cardiometabolic risk factors persist even among those with high levels of MVPA. Due to limited sample size in prior studies with objectively-measured sedentary time, analyses stratified by physical activity level have not previously been performed. Our findings are consistent with at least one previous study that showed self-reported television viewing time positively associated with metabolic risk factors in healthy Australian adults who met physical activity guidelines.5 Several studies have also suggested that sedentary behavior is associated with health outcomes at both low and high levels of physical activity,43–45 though more research is needed since the number of available studies remains limited.2 Taken together, our findings emphasize the public health priority of reducing sedentary behavior for the prevention of cardiometabolic diseases, even among those who meet physical activity guidelines.

To the best of our knowledge, this is the largest study to date with sedentary behavior and physical activity measured using accelerometers. This is also the first study investigating relationships between sedentary behavior and cardiometabolic risk factors in a large representative population sample of U.S. Hispanics/Latinos of diverse national backgrounds across a wide age range. Additional strengths of the study are adjustment for non-compliance with device wear protocols, thorough reporting of accelerometer-data handling/methodological decisions,46 multiple measures of cardiometabolic risk factors including 2-hour glucose levels from a large OGTT dataset, and careful adjustment for potential confounding factors.

Objective measurement is more accurate and may reduce biases of self-report, but some limitations of accelerometer-derived measures need to be acknowledged. Our measures of sedentary time and MVPA were inferred from measures of spatial displacement (or lack thereof) in multiple dimensions derived from accelerometers. Therefore, some standing still time may be measured as sitting time because different postures cannot be differentiated by the accelerometer, and variability in accelerometer placement or body habitus may also affect measurements.47 Because accelerometer-derived sedentary time is highly correlated with wear time, we used a previously-reported residual approach13, 33 to standardize sedentary time to a wear time of 16 hours/day. However, the results might be biased if sedentary time while wearing the device were substantively different from that while the device was not worn. In addition, our accelerometer protocol specified a 1-minute epoch length19, 20 and it is unknown whether use of a shorter epoch length may affect our results. Despite these potential biases, because vagaries in accelerometer measurement were most likely non-selective with respect to cardiometabolic markers, they would generally be expected to attenuate rather than exaggerate observed associations.

Other limitations of the study include the self-reported nature of several potential behavioral confounders including diet, alcohol drinking and smoking. The study is also limited in its cross-sectional nature and hence, a lack of data on incidence of CVD or its risk factors. However, our analysis focuses on subclinical markers of cardiometabolic risk, which should reduce the impact of reverse causation. In addition, we adjusted for a comprehensive measure of self-reported physical health, and performed sensitivity analyses excluding those with prevalent related chronic diseases (CHD, stroke, and diabetes), in order to account for the fact that those with prevalent disease may be more sedentary due to their health conditions. Despite the consistency in our observed associations and the rigorous control for alternative explanations, further prospective and intervention studies are needed to clarify the causal nature of deleterious associations between sedentary behavior and health outcomes.

In summary, our findings based on objectively-measured data provide strong evidence for the link between sedentary behavior and cardiometabolic risk, regardless of physical activity, in US Hispanic/Latino adults. These associations, in general, were consistent across Hispanic/Latino background groups. Furthermore, the significant deleterious associations between sedentary time and cardiometabolic risk are observed even in individuals meeting physical activity guidelines. Our data support the recommendation for both increasing exercise levels and reducing sedentary behavior for the prevention of cardiometabolic diseases.

Supplementary Material

Clinical Perspective
Supplemental Material

Acknowledgments

The authors thank the staff and participants of HCHS/SOL for their important contributions. A complete list of staff and investigators has been provided by Sorlie P., et al. in Ann Epidemiol. 2010; 20:642–649 and is also available on the study website http://www.cscc.unc.edu/hchs/.

Funding Sources: The baseline examination of the Hispanic Community Health Study/Study of Latinos was carried out as a collaborative study supported by contracts from the National Heart, Lung, and Blood Institute (NHLBI) to the University of North Carolina (N01-HC65233), University of Miami (N01-HC65234), Albert Einstein College of Medicine (N01-HC65235), Northwestern University (N01-HC65236), and San Diego State University (N01-HC65237). The following Institutes/Centers/Offices contributed to the HCHS/SOL first funding period through a transfer of funds to the NHLBI: National Institute on Minority Health and Health Disparities, the National Institute of Deafness and Other Communications Disorders, the National Institute of Dental and Craniofacial Research, the National Institute of Diabetes and Digestive and Kidney Diseases, the National Institute of Neurological Disorders and Stroke, and the NIH Office of Dietary Supplements.

Footnotes

Disclosures: None.

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