Abstract
Few studies have assessed the association of sedentary time with leukocyte telomere length (LTL). In a cross-sectional study conducted in 2012–2013, we examined associations of accelerometer-measured and self-reported sedentary time with LTL in a sample of 1,481 older white and African-American women from the Women's Health Initiative and determined whether associations varied by level of moderate- to vigorous-intensity physical activity (MVPA). The association between sedentary time and LTL was evaluated using multiple linear regression models. Women were aged 79.2 (standard deviation, 6.7) years, on average. Self-reported sedentary time was not associated with LTL. In a model adjusting for demographic characteristics, lifestyle behaviors, and health-related factors, among women at or below the median level of accelerometer-measured MVPA, those in the highest quartile of accelerometer-measured sedentary time had significantly shorter LTL than those in the lowest quartile, with an average difference of 170 base pairs (95% confidence interval: 4, 340). Accelerometer-measured sedentary time was not associated with LTL in women above the median level of MVPA. Findings suggest that, on the basis of accelerometer measurements, higher sedentary time may be associated with shorter LTL among less physically active women.
Keywords: accelerometry, leukocyte telomere length, moderate- to vigorous-intensity physical activity, sedentary time, telomeres
Telomeres are repetitive DNA-protein structures located at the ends of chromosomes that protect and maintain chromosomal stability and integrity (1). Telomeres progressively shorten with age, leading to cellular senescence and apoptosis (2, 3). Shortened leukocyte telomere length (LTL) has been associated with cardiovascular disease, type 2 diabetes, and major cancers (3–6).
Emerging evidence has linked LTL to modifiable factors such as smoking, body mass index, and physical activity (7–12). Sedentary behavior has also been studied in relation to LTL, but with mixed findings. In the Nurses’ Health Study, there was no association of total sedentary time or specific sedentary behaviors with LTL (12), but in 2 recent studies, reduced sedentary time was associated with longer LTL (13, 14). However, these studies were limited by several factors, including failure to measure sedentary time objectively (i.e., by accelerometer). Accelerometer-measured sedentary time is not highly correlated with self-reported time, the latter of which often underestimates actual time spent in sedentary behaviors (15). These studies also did not measure LTL using the Southern blot method, which has low measurement error (16, 17). Additionally, they did not determine whether associations of sedentary time with LTL varied by level of physical activity. In previous studies, associations of sedentary time with adverse health outcomes were stronger among persons with low levels of physical activity (18–21).
In a cross-sectional study, we assessed associations of accelerometer-measured and self-reported sedentary time with LTL in older white and African-American women from the Objective Physical Activity and Cardiovascular Health (OPACH) Study, an ancillary study of the Women's Health Initiative (WHI). We also determined whether associations varied by hours of moderate- to vigorous-intensity physical activity (MVPA), race/ethnicity, or physical function. Understanding the relationship between sedentary time and LTL, a purported biomarker of cellular aging (22), is important among older adults, who spend 8.5–10.7 hours/day sedentary and are particularly vulnerable to the adverse health consequences (e.g., obesity, type 2 diabetes, and all-cause mortality) associated with prolonged sedentary time (19, 23–25).
METHODS
Study population and data collection
The WHI is a large US prospective study investigating the determinants of chronic diseases in postmenopausal women. Details on the study have been previously published (26, 27). Briefly, a racially and ethnically diverse cohort of 161,808 postmenopausal women aged 50–79 years was recruited from 40 clinical centers nationwide during 1993–1998. Women were randomized into one or more of 3 clinical trials, including one of 2 hormone therapy trials, or an observational study. In 2005, 77% of eligible women agreed to be followed through 2010 in the first WHI Extension Study. In 2010, 87% of women consented to an additional 5 years of follow-up in the second WHI Extension Study. Over 7,800 women from Extension Study 2 were enrolled in the WHI Long Life Study, which consisted of a one-time in-person visit conducted between March 2012 and May 2013. The population of the current study included women from OPACH, an ancillary study of the Long Life Study that enrolled 7,048 women.
At the 1993–1998 baseline examination, participants completed self-administered questionnaires assessing demographic characteristics, medical history, and lifestyle behaviors. The 2012–2013 visit involved collection of a blood sample and assessment of physical measurements (blood pressure, height, and weight) and physical functioning status. Study participants additionally wore an accelerometer for 1 week and completed a sleep log and physical activity questionnaire. A random sample of women from the Long Life Study was selected for participation in a case-cohort study on the relationship between LTL and coronary heart disease. The present study was exclusive to women with LTL measurements and complete information on either accelerometer-measured (n = 1,297) or self-reported (n = 1,383) sedentary time. There were 1,481 women in the final analytical sample.
All participants provided written informed consent, and institutional review board approval was received by all participating institutions.
Sedentary time and physical activity measures
Accelerometer-measured variables
Participants were asked to wear a triaxial accelerometer (ActiGraph GT3X+; ActiGraph LLC, Pensacola, Florida) on their right hip for 7 consecutive days during waking and sleeping hours, except during bathing or swimming. Movement was captured along 3 axes (vertical, anteroposterior, and mediolateral) in 15-second epochs, and activity counts were provided as composite vector magnitudes (VM) of these 3 axes. Accelerometer wear time was identified using sleep logs and a computer-automated algorithm developed specifically for this study (28). Nonwear time was defined as an interval of ≥90 minutes of consecutive zero VM counts/minute, with allowance of up to 2 minutes of nonzero VM counts if no counts were detected 30 minutes upstream or downstream from that interval; any other nonzero VM counts were considered wear time (29, 30). Only participants with 4–7 valid days of accelerometer data were included in the analysis, with a valid day being defined as having ≥10 hours of wear time (31).
A calibration study was performed in 200 study participants to determine relevant cutpoints along the distribution of VM counts to define sedentary behavior and physical activity intensity in older women (32). Based on this study, sedentary behavior was defined as 0–18 VM counts per 15 seconds and MVPA as ≥519 VM counts per 15 seconds. Data are presented as the average number of hours spent per day in these behaviors. For example, hours/day of sedentary time was calculated as the sum of total sedentary time during all valid days divided by the number of valid days.
Self-reported variables
In the physical activity questionnaire, participants were asked to estimate time spent sitting in response to the question: “During a usual day and night, about how many hours do you spend sitting? Be sure to include the time you spend sitting at work, sitting at the table eating, driving, or riding in a car or bus, and sitting up watching TV or talking.” Participants also estimated the time spent lying down: “During a usual day and night, about how many hours do you spend sleeping or lying down with your feet up? Be sure to include the time you spend sleeping or trying to sleep at night, resting or napping, and lying down watching TV.” A third question asked participants to estimate the number of hours they had typically spent sleeping per night during the past 4 weeks. Total daily sedentary time was calculated as the sum of sitting time and lying time minus sleeping time. This questionnaire previously showed moderate-to-high test-retest reliability (33).
Participants also completed the Community Healthy Activities Model Program for Seniors (CHAMPS) Physical Activity Questionnaire, which was developed for older adults and measures time spent in domestic and leisure-time activities in a typical week during the past 4 weeks (34). Data are presented as average number of hours per day spent in activities of moderate-to-vigorous intensity, calculated by summing the total number of hours spent in these activities during a typical week and dividing by 7.
Covariates
Variables assessed at the WHI baseline visit, at the 2012–2013 visit, and during WHI follow-up were used as covariates. Baseline covariates included race/ethnicity, education, marital status, smoking status, and alcohol consumption. At the 2012–2013 visit, trained clinic staff measured height and weight and systolic and diastolic blood pressures. Body mass index was calculated as weight in kilograms divided by squared height in meters and was categorized according to standard cutpoints (35). Current physical functioning status was measured objectively at the 2012–2013 visit using the Established Populations for Epidemiologic Studies of the Elderly (EPESE) Short Physical Performance Battery, which provides a summary score (range, 0–12) calculated as the sum of balance, chair-stand, and gait-speed scores, with a higher score indicating better physical performance (36, 37).
Variables assessed during WHI follow-up included self-rated health and history of hormone therapy, hypertension, and chronic diseases. Self-rated health was measured by means of a single item (38); the most recent value collected within 2 years of the 2012–2013 visit was used. History of hormone therapy was defined according to self-reported use of hormones or participation in one of the WHI hormone therapy trials. History of hypertension was defined as self-reported physician diagnosis of hypertension, use of antihypertensive medication, systolic blood pressure ≥140 mm Hg, or diastolic blood pressure ≥90 mm Hg (measured at baseline, during follow-up, or at the 2012–2013 visit). History of chronic diseases was defined as occurrence of 1 or more of the following diseases, each of which has been associated with both sedentary time and LTL in previous studies (4–6, 21, 39, 40): coronary heart disease, stroke, diabetes, and cancer (excluding nonmelanoma skin cancer). Disease status was self-reported at baseline. Incident diseases were identified through the date of the 2012–2013 visit via periodic clinic visits and via mailed questionnaires sent biannually to participants in the WHI Clinical Trial and annually to Observational Study and Extension Study participants. Diagnoses of incident diseases, except for diabetes, were adjudicated by physician medical record review (41). Diabetes was defined as self-reported physician's diagnosis of diabetes treated with either oral medication or insulin (42).
Measurement of LTL
DNA samples were extracted using the 5-prime method (5 PRIME, Inc., Gaithersburg, Maryland) and were sent in batches over a 1-year period to the Center of Human Development and Aging Laboratory at Rutgers University (New Brunswick, New Jersey) for LTL measurement. Each batch consisted of randomly selected samples. The laboratory performing the LTL measurements was blinded to all participant characteristics. Quality control procedures included assessment of DNA integrity prior to LTL measurement (16). DNA integrity was assessed visually after ethidium bromide-stained 1% agarose gel electrophoresis (200 V for 2 hours) and required that DNA appear as a single compact crown-shaped band migrating in parallel with the other samples on the gel. Telomere length (in kilobases) was determined by the mean length of the terminal restriction fragments using the Southern blot method, as previously described (16). Analysis of each sample was run in duplicate on different gels, and mean LTL was used in the analyses. The average interassay coefficient of variation for blinded pair sets was 2.0%.
Statistical analysis
Data on accelerometer-measured and self-reported sedentary time variables were divided into quartiles for the analysis. Categorical variables were compared across quartiles of sedentary time using χ2 tests. Analysis of variance and Kruskal-Wallis tests were used for comparisons of normally distributed and non-normally distributed continuous variables across quartiles of sedentary time, respectively. Because LTL was normally distributed, general linear models were used to determine age- and race/ethnicity-adjusted mean LTL values across quartiles of sedentary time. Correlations were measured using the Pearson correlation coefficient.
Associations of accelerometer-measured and self-reported sedentary time with LTL were evaluated using multiple linear regression models. The first model adjusted for age and race/ethnicity, and successive models adjusted for other potential confounders, including demographic characteristics (education and marital status), lifestyle behaviors (smoking, alcohol use, body mass index, and MVPA), and health-related variables (history of chronic diseases and hormone therapy). All models for accelerometer-measured sedentary time were also adjusted for wear time. Models for accelerometer-measured sedentary time adjusted for accelerometer-measured MPVA, and those for self-reported sedentary time adjusted for self-reported MVPA.
Multicollinearity between variables was evaluated using tolerance values, with a value less than 0.10 indicating multicollinearity. However, multicollinearity was not observed in any of the models. Tests for linear trend were performed by including sedentary time variables as continuous variables in the models. Interactions between sedentary time and race/ethnicity, physical performance score, and MVPA were tested by including interaction terms for the products of these variables with sedentary time in the models. Results were stratified according to the median MVPA level, based on an a priori assumption that associations of sedentary time with LTL may vary by MVPA (18–21). Cutpoints of 0.5 hours/day of MVPA, based on current recommendations of ≥30 minutes/day of MVPA for adults (43), and 0.36 hours/day (which equates to 2.5 hours/week based on current guidelines), were also used. P values were 2-tailed and were considered nominally statistically significant at P < 0.05. All analyses were performed using SAS, version 9.3 (SAS Institute, Inc., Cary, North Carolina).
RESULTS
In the overall sample, there were 863 (58.3%) white and 618 (41.7%) African-American women. Women were aged 79.2 (standard deviation (SD), 6.7) years, on average, ranging in age from 64 years to 95 years. Women wore the accelerometer for an average of 14.7 (SD, 1.3) hours/day over an average of 6.3 (SD, 0.8) days. The mean amounts of accelerometer-measured and self-reported sedentary time were 9.2 (SD, 1.5) hours/day and 8.6 (SD, 4.3) hours/day, respectively. The mean amounts of accelerometer-measured and self-reported MVPA were 0.8 (SD, 0.5) hours/day and 0.5 (SD, 0.6) hours/day, respectively. Accelerometer-measured and self-reported sedentary time were weakly correlated (r = 0.27; P < 0.001); accelerometer-measured and self-reported MVPA were similarly weakly correlated (r = 0.28; P < 0.001). Mean LTL was 6.6 (SD, 0.6) kilobases and ranged from 4.9 kilobases to 8.9 kilobases. LTL was inversely associated with age (r = −0.38; P < 0.001), and telomeres were longer in African-American women than in white women (age-adjusted mean lengths were 6.75 (standard error, 0.02) kilobases and 6.52 (standard error, 0.02) kilobases, respectively; P < 0.001).
Women with greater amounts of accelerometer-measured sedentary time were more likely to be older, white, and obese (Table 1). They were also more likely to have high blood pressure, a history of chronic diseases, a lower physical performance score, and fewer hours/day of MVPA and to have experienced a fall in the past 12 months. Women with higher self-reported sedentary time were more likely to be older, white, and obese and to have a history of chronic diseases (Table 2). They also had a lower physical performance score and lower levels of self-reported MVPA, and they were less likely to be in excellent or very good health.
Table 1.
Characteristics of Older Women in the Women's Health Initiative OPACH Study, by Accelerometer-Measured Sedentary Time (n = 1,297), 2012–2013a
| Characteristic | Quartile of Accelerometer-Measured Sedentary Time, hours/day | P Value | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <8.18 (n = 322) | 8.18–9.23 (n = 326) | 9.24–10.21 (n = 324) | ≥10.22 (n = 325) | ||||||||||||
| No. of Women | % | Mean (SD/SE) | No. of Women | % | Mean (SD/SE) | No. of Women | % | Mean (SD/SE) | No. of Women | % | Mean (SD/SE) | ANOVAb | χ2 Testc | GLMd | |
| Age, yearse | 77.8 (6.6) | 78.9 (6.8) | 79.6 (6.7) | 80.4 (6.5) | <0.01 | ||||||||||
| Age group, years | <0.01 | ||||||||||||||
| 64–69 | 36 | 11.2 | 32 | 9.8 | 26 | 8.0 | 19 | 5.9 | |||||||
| 70–74 | 71 | 22.1 | 66 | 20.3 | 62 | 19.1 | 46 | 14.2 | |||||||
| 75–79 | 83 | 25.8 | 67 | 20.6 | 56 | 17.3 | 61 | 18.8 | |||||||
| 80–84 | 84 | 26.1 | 78 | 23.9 | 93 | 28.7 | 110 | 33.9 | |||||||
| ≥85 | 48 | 14.9 | 83 | 25.5 | 87 | 26.9 | 89 | 27.4 | |||||||
| Race/ethnicityf | <0.01 | ||||||||||||||
| White | 155 | 48.1 | 181 | 55.5 | 196 | 60.5 | 224 | 68.9 | |||||||
| African-American | 167 | 51.9 | 145 | 44.5 | 128 | 39.5 | 101 | 31.1 | |||||||
| Educationf | 0.79 | ||||||||||||||
| Less than high school | 14 | 4.4 | 9 | 2.8 | 9 | 2.8 | 12 | 3.7 | |||||||
| High school diploma | 50 | 15.5 | 50 | 15.4 | 53 | 16.5 | 50 | 15.4 | |||||||
| Some college | 107 | 33.2 | 126 | 38.9 | 124 | 38.5 | 129 | 39.8 | |||||||
| College graduate | 151 | 46.9 | 139 | 42.9 | 136 | 42.2 | 133 | 41.1 | |||||||
| Baseline marital statusf | 0.48 | ||||||||||||||
| Married/living as married | 190 | 59.2 | 194 | 59.9 | 183 | 56.5 | 177 | 54.6 | |||||||
| Widowed | 46 | 14.3 | 55 | 17.0 | 56 | 17.3 | 68 | 21.0 | |||||||
| Divorced/separated | 73 | 22.7 | 60 | 18.5 | 75 | 23.2 | 65 | 20.1 | |||||||
| Never married | 12 | 3.7 | 15 | 4.6 | 10 | 3.1 | 14 | 4.3 | |||||||
| Baseline smoking historyf | 0.12 | ||||||||||||||
| Never smoked | 170 | 53.1 | 183 | 56.3 | 172 | 54.1 | 169 | 52.8 | |||||||
| Past smoker | 133 | 41.6 | 127 | 39.1 | 115 | 36.2 | 133 | 41.6 | |||||||
| Current smoker | 17 | 5.3 | 15 | 4.6 | 31 | 9.8 | 18 | 5.6 | |||||||
| Baseline alcohol consumptionf | 0.81 | ||||||||||||||
| Nondrinker | 40 | 12.6 | 37 | 11.4 | 35 | 10.8 | 40 | 12.3 | |||||||
| Past drinker | 61 | 19.2 | 68 | 20.9 | 71 | 22.0 | 56 | 17.2 | |||||||
| Current drinker | 217 | 68.2 | 220 | 67.7 | 217 | 67.2 | 229 | 70.5 | |||||||
| Body mass indexg | <0.01 | ||||||||||||||
| Underweight (<18.5) | 6 | 1.9 | 3 | 0.9 | 4 | 1.2 | 3 | 0.9 | |||||||
| Normal weight (18.5–24.9) | 125 | 38.9 | 106 | 33.0 | 96 | 29.8 | 65 | 20.4 | |||||||
| Overweight (≥25) | 113 | 35.2 | 114 | 35.5 | 105 | 32.6 | 122 | 38.2 | |||||||
| Obese (≥30) | 77 | 24.0 | 98 | 30.5 | 117 | 36.3 | 129 | 40.4 | |||||||
| Self-rated health | 0.07 | ||||||||||||||
| Excellent | 42 | 13.6 | 22 | 7.0 | 25 | 8.2 | 25 | 8.0 | |||||||
| Very good | 131 | 42.3 | 143 | 45.3 | 126 | 41.3 | 119 | 38.1 | |||||||
| Good | 112 | 36.1 | 120 | 38.0 | 116 | 38.0 | 134 | 43.0 | |||||||
| Fair/poor | 25 | 8.1 | 31 | 9.8 | 38 | 12.5 | 34 | 10.9 | |||||||
| Current high blood pressureh | 52 | 16.5 | 44 | 13.6 | 68 | 21.2 | 68 | 21.4 | 0.03 | ||||||
| History of hypertension | 252 | 78.3 | 263 | 80.7 | 268 | 82.7 | 261 | 80.3 | 0.56 | ||||||
| History of use of hormone therapy | 224 | 71.1 | 210 | 65.6 | 228 | 71.0 | 231 | 72.2 | 0.26 | ||||||
| History of chronic diseases | |||||||||||||||
| CHD | 14 | 4.4 | 16 | 4.9 | 19 | 5.9 | 30 | 9.2 | 0.04 | ||||||
| Stroke | 10 | 3.1 | 4 | 1.2 | 16 | 4.9 | 11 | 3.4 | 0.06 | ||||||
| Diabetes | 49 | 15.2 | 71 | 21.8 | 71 | 21.9 | 76 | 23.4 | 0.05 | ||||||
| Cancer | 43 | 13.4 | 67 | 20.6 | 68 | 21.0 | 63 | 19.4 | 0.05 | ||||||
| Any of the above | 104 | 32.3 | 135 | 41.4 | 141 | 43.5 | 140 | 43.1 | 0.01 | ||||||
| Experienced a fall in the past 12 months | 74 | 23.9 | 106 | 34.2 | 102 | 32.3 | 99 | 31.3 | 0.03 | ||||||
| Physical performance scoree | 8.5 (2.2) | 8.2 (2.4) | 7.9 (2.7) | 7.4 (2.6) | <0.01 | ||||||||||
| Accelerometer-measured MVPA, hours/dayi,j | 1.24 [0.02] | 0.86 [0.02] | 0.68 [0.02] | 0.39 [0.02] | <0.01 | ||||||||||
| Self-reported sedentary time, hours/daye | 7.2 (3.6) | 8.3 (4.1) | 9.2 (4.4) | 10.0 (4.2) | <0.01 | ||||||||||
| LTL, kilobasese | 6.70 (0.59) | 6.68 (0.60) | 6.56 (0.60) | 6.54 (0.60) | <0.01 | ||||||||||
| Age- and race-adjusted LTL, kilobasesj | 6.66 [0.03] | 6.68 [0.03] | 6.60 [0.03] | 6.62 [0.03] | 0.20 | ||||||||||
Abbreviations: ANOVA, analysis of variance; CHD, coronary heart disease; GLM, general linear model; MVPA, moderate- to vigorous-intensity physical activity; OPACH, Objective Physical Activity and Cardiovascular Health; LTL, leukocyte telomere length; SD, standard deviation; SE, standard error.
a All characteristics represent current status, unless otherwise noted. Sample sizes for variables in each column do not sum to totals because of missing data.
bP value was calculated using ANOVA.
cP value was calculated using a χ2 test.
dP value was calculated using a GLM.
e Values are expressed as mean (standard deviation).
f Determined at the 1993–1998 baseline visit.
g Weight (kg)/height (m)2.
h Systolic blood pressure ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg.
i Adjusted for hours of wear time.
j Values are expressed as mean [standard error].
Table 2.
Characteristics of Older Women in the Women's Health Initiative OPACH Study, by Self-Reported Sedentary Time (n = 1,383), 2012–2013a
| Characteristic | Quartile of Self-Reported Sedentary Time, hours/day | P Value | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <6 (n = 329) | 6–7.5 (n = 279) | 8–10.5 (n = 382) | ≥11 (n = 393) | ||||||||||||
| No. of Women | % | Mean (SD/SE) | No. of Women | % | Mean (SD/SE) | No. of Women | % | Mean (SD/SE) | No. of Women | % | Mean (SD/SE) | ANOVAb | χ2 Testc | GLMd | |
| Age, yearse | 77.7 (6.1) | 79.3 (6.6) | 79.7 (6.7) | 79.4 (7.1) | <0.01 | ||||||||||
| Age group, years | <0.01 | ||||||||||||||
| 64–69 | 39 | 11.9 | 19 | 6.8 | 31 | 8.1 | 37 | 9.4 | |||||||
| 70–74 | 69 | 21.0 | 51 | 18.3 | 66 | 17.3 | 78 | 19.9 | |||||||
| 75–79 | 78 | 23.7 | 71 | 25.5 | 72 | 18.9 | 58 | 14.8 | |||||||
| 80–84 | 101 | 30.7 | 74 | 26.5 | 110 | 28.8 | 115 | 29.3 | |||||||
| ≥85 | 42 | 12.8 | 64 | 22.9 | 103 | 27.0 | 105 | 26.7 | |||||||
| Race/ethnicityf | <0.01 | ||||||||||||||
| White | 144 | 43.8 | 160 | 57.4 | 243 | 63.6 | 257 | 65.4 | |||||||
| African-American | 185 | 56.2 | 119 | 42.7 | 139 | 36.4 | 136 | 34.6 | |||||||
| Educationf | 0.70 | ||||||||||||||
| Less than high school | 9 | 2.7 | 11 | 3.9 | 14 | 3.7 | 9 | 2.3 | |||||||
| High school diploma | 57 | 17.4 | 38 | 13.6 | 59 | 15.5 | 58 | 14.9 | |||||||
| Some college | 129 | 39.3 | 98 | 35.1 | 137 | 36.0 | 153 | 39.2 | |||||||
| College graduate | 133 | 40.6 | 132 | 47.3 | 171 | 44.9 | 170 | 43.6 | |||||||
| Baseline marital statusf | 0.67 | ||||||||||||||
| Married/living as married | 187 | 56.8 | 148 | 53.6 | 230 | 60.4 | 214 | 54.7 | |||||||
| Widowed | 60 | 18.2 | 50 | 18.1 | 66 | 17.3 | 67 | 17.1 | |||||||
| Divorced/separated | 71 | 21.6 | 67 | 24.3 | 71 | 18.6 | 89 | 22.8 | |||||||
| Never married | 11 | 3.3 | 11 | 4.0 | 14 | 3.7 | 21 | 5.4 | |||||||
| Baseline smoking historyf | 0.74 | ||||||||||||||
| Never smoked | 179 | 55.6 | 150 | 54.0 | 210 | 55.4 | 203 | 52.1 | |||||||
| Past smoker | 122 | 37.9 | 113 | 40.7 | 149 | 39.3 | 156 | 40.0 | |||||||
| Current smoker | 21 | 6.5 | 15 | 5.4 | 20 | 5.3 | 31 | 8.0 | |||||||
| Baseline alcohol consumptionf | 0.06 | ||||||||||||||
| Nondrinker | 47 | 14.4 | 35 | 12.7 | 41 | 10.7 | 41 | 10.5 | |||||||
| Past drinker | 78 | 23.9 | 44 | 15.9 | 66 | 17.3 | 79 | 20.2 | |||||||
| Current drinker | 202 | 61.8 | 197 | 71.4 | 275 | 72.0 | 272 | 69.4 | |||||||
| Body mass indexg | <0.01 | ||||||||||||||
| Underweight (<18.5) | 4 | 1.2 | 3 | 1.1 | 5 | 1.3 | 4 | 1.0 | |||||||
| Normal weight (18.5–24.9) | 106 | 32.6 | 98 | 35.3 | 120 | 31.8 | 95 | 24.6 | |||||||
| Overweight (≥25) | 111 | 34.2 | 106 | 38.1 | 141 | 37.3 | 118 | 30.5 | |||||||
| Obese (≥30) | 104 | 32.0 | 71 | 25.5 | 112 | 29.6 | 170 | 43.9 | |||||||
| Self-rated health | <0.01 | ||||||||||||||
| Excellent | 38 | 12.2 | 24 | 8.8 | 33 | 9.0 | 30 | 8.1 | |||||||
| Very good | 127 | 40.8 | 133 | 48.5 | 156 | 42.4 | 120 | 32.3 | |||||||
| Good | 121 | 38.9 | 100 | 36.5 | 145 | 39.4 | 163 | 43.8 | |||||||
| Fair/poor | 25 | 8.0 | 17 | 6.2 | 34 | 9.2 | 59 | 15.9 | |||||||
| Current high blood pressureh | 54 | 16.7 | 48 | 17.7 | 62 | 16.3 | 74 | 19.5 | 0.67 | ||||||
| History of hypertension | 262 | 79.6 | 211 | 75.6 | 309 | 80.9 | 329 | 83.7 | 0.07 | ||||||
| History of use of hormone therapy | 214 | 66.3 | 190 | 69.6 | 271 | 71.9 | 283 | 72.6 | 0.26 | ||||||
| History of chronic diseases | |||||||||||||||
| CHD | 15 | 4.6 | 19 | 6.8 | 22 | 5.8 | 27 | 6.9 | 0.55 | ||||||
| Stroke | 16 | 4.9 | 10 | 3.6 | 13 | 3.4 | 12 | 3.1 | 0.61 | ||||||
| Diabetes | 60 | 18.2 | 64 | 22.9 | 71 | 18.6 | 107 | 27.2 | <0.01 | ||||||
| Cancer | 46 | 14.0 | 56 | 20.1 | 78 | 20.4 | 71 | 18.1 | 0.12 | ||||||
| Any of the above | 111 | 33.7 | 122 | 43.7 | 152 | 40.0 | 180 | 45.8 | <0.01 | ||||||
| Experienced a fall in the past 12 months | 88 | 27.3 | 83 | 30.2 | 117 | 31.2 | 136 | 35.1 | 0.16 | ||||||
| Physical performance scoree | 8.3 (2.5) | 8.4 (2.3) | 8.0 (2.5) | 7.5 (2.7) | <0.01 | ||||||||||
| Accelerometer-measured sedentary time, hours/dayi,j | 8.63 [0.08] | 8.96 [0.09] | 9.32 [0.07] | 9.66 [0.07] | <0.01 | ||||||||||
| Self-reported MVPA, hours/daye | 0.7 (0.8) | 0.5 (0.5) | 0.5 (0.6) | 0.4 (0.5) | <0.01 | ||||||||||
| LTL, kilobasese | 6.71 (0.61) | 6.63 (0.56) | 6.58 (0.58) | 6.61 (0.63) | 0.04 | ||||||||||
| Age- and race-adjusted LTL, kilobasesj | 6.66 [0.03] | 6.66 [0.03] | 6.63 [0.03] | 6.65 [0.03] | 0.86 | ||||||||||
Abbreviations: ANOVA, analysis of variance; CHD, coronary heart disease; GLM, general linear model; MVPA, moderate- to vigorous-intensity physical activity; OPACH, Objective Physical Activity and Cardiovascular Health; LTL, leukocyte telomere length; SD, standard deviation; SE, standard error.
a All characteristics represent current status, unless otherwise noted. Sample sizes for variables in each column do not sum to totals because of missing data.
bP value was calculated using ANOVA.
cP value was calculated using a χ2 test.
dP value was calculated using a GLM.
e Values are expressed as mean (standard deviation).
f Determined at the 1993–1998 baseline visit.
g Weight (kg)/height (m)2.
h Systolic blood pressure ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg.
i Adjusted for hours of wear time.
j Values are expressed as mean [standard error].
In a model adjusting only for wear time, accelerometer-measured sedentary time was significantly associated with LTL (Table 3). After further adjustment for age and race/ethnicity, findings were no longer significant; in additional models, no significant findings were observed. After stratifying by the median accelerometer-measured MVPA value (0.69 hours/day), significant associations between accelerometer-measured sedentary time and LTL were not observed among women with MVPA levels above the median (Table 4). Among women at or below the median MVPA level, only women in the highest quartile of sedentary time had significantly shorter LTL in the full-adjustment model. LTL was on average 170 (95% confidence interval: 4, 340) base pairs shorter in the most sedentary women than in the least sedentary women. Although the stratum-specific signal for sedentary time and LTL was stronger among women at or below the median MVPA level, there was no significant interaction between sedentary time and MVPA (Pinteraction = 0.80).
Table 3.
Association of Accelerometer-Measured Sedentary Time With Leukocyte Telomere Length Among Older Women, Women's Health Initiative OPACH Study, 2012–2013
| Model | Quartile of Accelerometer-Measured Sedentary Time, hours/day | Ptrenda | ||||||
|---|---|---|---|---|---|---|---|---|
| <8.18 (Referent) | 8.18–9.23 | 9.24–10.21 | ≥10.22 | |||||
| βb | 95% CI | β | 95% CI | β | 95% CI | |||
| 1c | 0 | −0.04 | −0.13, 0.05 | −0.17 | −0.26, −0.07 | −0.21 | −0.31, −0.12 | <0.01 |
| 2d | 0 | 0.01 | −0.07, 0.10 | −0.08 | −0.16, 0.01 | −0.07 | −0.16, 0.01 | 0.05 |
| 3e | 0 | 0.01 | −0.07, 0.10 | −0.07 | 0.16, 0.02 | −0.06 | −0.16, 0.03 | 0.11 |
| 4f | 0 | 0.02 | −0.06, 0.11 | −0.06 | −0.15, 0.03 | −0.06 | −0.16, 0.04 | 0.15 |
| 5g | 0 | 0.02 | −0.07, 0.11 | −0.07 | −0.17, 0.03 | −0.07 | −0.18, 0.04 | 0.17 |
| 6h | 0 | 0.03 | −0.06, 0.12 | −0.06 | −0.16, 0.04 | −0.06 | −0.18, 0.05 | 0.22 |
Abbreviations: CI, confidence interval; OPACH, Objective Physical Activity and Cardiovascular Health.
aP values were calculated using multiple linear regression models.
b Beta coefficients represent the difference in leukocyte telomere length (in kilobases).
c Results were adjusted for hours of wear time (n = 1,297).
d Results were adjusted for model 1 variables plus age and race/ethnicity (n = 1,297).
e Results were adjusted for model 2 variables plus education and baseline marital status, smoking, and alcohol consumption (n = 1,270).
f Results were adjusted for model 3 variables plus body mass index (n = 1,256).
g Results were adjusted for model 4 variables plus hours/day of moderate- to vigorous-intensity physical activity (n = 1,256).
h Results were adjusted for model 5 variables plus history of chronic diseases and use of hormone therapy (n = 1,235).
Table 4.
Association of Accelerometer-Measured Sedentary Time With Leukocyte Telomere Length Among Older Women, by Hours/Day of Accelerometer-Measured Moderate- to Vigorous-Intensity Physical Activity, Women's Health Initiative OPACH Study, 2012–2013
| MVPA Time and Model | Quartile of Accelerometer-Measured Sedentary Time, hours/day | Ptrenda | ||||||
|---|---|---|---|---|---|---|---|---|
| <8.18 (Referent) | 8.18–9.23 | 9.24–10.21 | ≥10.22 | |||||
| βb | 95% CI | β | 95% CI | β | 95% CI | |||
| ≤0.69 hours/day | ||||||||
| 1c | 0 | −0.03 | −0.19, 0.13 | −0.16 | −0.32, −0.002 | −0.21 | −0.38, −0.05 | <0.01 |
| 2d | 0 | −0.03 | −0.18, 0.12 | −0.14 | −0.29, 0.01 | −0.16 | −0.31, −0.002 | 0.03 |
| 3e | 0 | −0.06 | −0.21, 0.09 | −0.17 | −0.32, −0.02 | −0.19 | −0.35, −0.03 | 0.02 |
| 4f | 0 | −0.05 | −0.21, 0.10 | −0.16 | −0.32, −0.01 | −0.19 | −0.35, −0.03 | 0.02 |
| 5g | 0 | −0.04 | −0.20, 0.11 | −0.15 | −0.31, 0.01 | −0.17 | −0.34, −0.004 | 0.25 |
| >0.69 hours/day | ||||||||
| 1 | 0 | −0.01 | −0.13, 0.11 | −0.11 | −0.24, 0.02 | −0.11 | −0.27, 0.05 | 0.07 |
| 2 | 0 | 0.04 | −0.07, 0.14 | −0.05 | −0.17, 0.07 | −0.02 | −0.17, 0.13 | 0.45 |
| 3 | 0 | 0.04 | −0.07, 0.15 | −0.03 | −0.15, 0.09 | −0.01 | −0.16, 0.14 | 0.68 |
| 4 | 0 | 0.05 | −0.06, 0.16 | −0.02 | −0.15, 0.10 | −0.01 | −0.16, 0.15 | 0.78 |
| 5 | 0 | 0.07 | −0.05, 0.18 | −0.02 | −0.14, 0.11 | −0.02 | −0.17, 0.14 | 0.91 |
Abbreviations: CI, confidence interval; MVPA, moderate- to vigorous-intensity physical activity; OPACH, Objective Physical Activity and Cardiovascular Health.
aP values were calculated using multiple linear regression models.
b Beta coefficients represent the difference in leukocyte telomere length (in kilobases).
c Results were adjusted for hours of wear time (n = 653 for ≤0.69 hours/day; n = 644 for >0.69 hours/day).
d Results were adjusted for model 1 variables plus age and race/ethnicity (n = 653 for ≤0.69 hours/day; n = 644 for >0.69 hours/day).
e Results were adjusted for model 2 variables plus education and baseline marital status, smoking, and alcohol consumption (n = 636 for ≤0.69 hours/day; n = 634 for >0.69 hours/day).
f Results were adjusted for model 3 variables plus body mass index (n = 629 for ≤0.69 hours/day; n = 627 for >0.69 hours/day).
g Results were adjusted for model 4 variables plus history of chronic diseases and use of hormone therapy (n = 620 for ≤0.69 hours/day; n = 615 for >0.69 hours/day).
After stratification on a cutpoint of 0.5 hours/day of MVPA, all P values for trend were significant among women with less than 0.5 hours/day of MVPA. At a cutpoint of 0.36 hours/day of MVPA, associations of accelerometer-measured sedentary time with LTL were stronger among women with less than 0.36 hours/day of MVPA; LTL was 369 (95% confidence interval: 60, 679) base pairs shorter among the most sedentary women than among the least sedentary women in the full-adjustment model. Sedentary time was not significantly associated with LTL among women with ≥0.36 or ≥0.5 hours/day of MVPA (data not shown).
In the model with no adjustment for covariates, self-reported sedentary time was significantly associated with LTL (Table 5). In subsequent models adjusting for age, race/ethnicity, and other factors, findings were no longer significant. Results did not vary by level of self-reported MVPA (data not shown).
Table 5.
Association of Self-Reported Sedentary Time With Leukocyte Telomere Length Among Older Women, Women's Health Initiative OPACH Study, 2012–2013
| Model | Quartile of Self-Reported Sedentary Time, hours/day | Ptrenda | ||||||
|---|---|---|---|---|---|---|---|---|
| <6 (Referent) | 6–7.5 | 8–10.5 | ≥11 | |||||
| βb | 95% CI | β | 95% CI | β | 95% CI | |||
| 1c | 0 | −0.07 | −0.17, 0.02 | −0.13 | −0.22, −0.04 | −0.10 | −0.19, −0.01 | <0.01 |
| 2d | 0 | 0.00 | −0.09, 0.09 | −0.03 | −0.11, 0.05 | −0.01 | −0.09, 0.08 | 0.43 |
| 3e | 0 | 0.01 | −0.08, 0.10 | −0.01 | −0.10, 0.07 | 0.01 | −0.07, 0.10 | 0.73 |
| 4f | 0 | 0.01 | −0.08, 0.10 | −0.01 | −0.09, 0.07 | 0.02 | −0.07, 0.10 | 0.86 |
| 5g | 0 | 0.02 | −0.07, 0.11 | 0.00 | −0.08, 0.08 | 0.03 | −0.06, 0.11 | 0.96 |
| 6h | 0 | 0.03 | −0.06, 0.12 | 0.00 | −0.08, 0.09 | 0.04 | −0.05, 0.12 | 0.81 |
Abbreviations: CI, confidence interval; OPACH, Objective Physical Activity and Cardiovascular Health.
aP values were calculated using multiple linear regression models.
b Beta coefficients represent the difference in leukocyte telomere length (in kilobases).
c Results were unadjusted (n = 1,383).
d Results were adjusted for model 1 variables plus age and race/ethnicity (n = 1,383).
e Results were adjusted for model 2 variables plus education and baseline marital status, smoking, and alcohol consumption (n = 1,354).
f Results were adjusted for model 3 variables plus body mass index (n = 1,339).
g Results were adjusted for model 4 variables plus hours/day of moderate- to vigorous-intensity physical activity (n = 1,339).
h Results were adjusted for model 5 variables plus history of chronic diseases and use of hormone therapy (n = 1,319).
Results did not vary by race/ethnicity, by physical performance score, or after exclusion of participants with a history of cancer (data not shown).
DISCUSSION
Among older women who were less physically active as measured by accelerometry, a greater amount of accelerometer-measured sedentary time was significantly associated with shorter LTL. Findings persisted after adjustment for demographic characteristics, lifestyle behaviors, and body mass index but were attenuated after adjustment for a history of chronic diseases and use of hormone therapy. In the full-adjustment model, LTL was on average 170 base pairs shorter in the most sedentary women compared with the least sedentary women. Since women may lose on average 21 base pairs/year (8), this suggests that the most sedentary women were biologically older by 8 years. Our findings have important implications for an aging population, in which greater time spent sedentary and less physical activity tends to be the norm (23).
We observed that self-reported sedentary time was not associated with LTL, similar to a previous study of 7,813 Nurses’ Health Study participants who were aged 59 years on average (12). Although results were not stratified by physical activity in the Nurses’ Health Study, joint classification of sedentary time and physical activity through a combined variable showed that women who were less active and more sedentary had shorter LTL than those who were more active and less sedentary. In a study of 2,401 primarily female white twins aged 49 years, on average, Cherkas et al. (8) observed that the LTL of inactive participants was 200 base pairs shorter than that of the most active participants; however, total sedentary time was not specifically evaluated. It is difficult to directly compare our results with those of other studies due to differences in sample size, methods used to assess sedentary time, the age ranges of the study populations, and low correlation between accelerometer-measured and self-reported sedentary time. Unlike previous studies, our study focused on older women and used accelerometer-measured sedentary time—an important consideration given that time spent sedentary may be underestimated in self-reported data (15). An absence of association between self-reported sedentary time and LTL may, to a large extent, reflect measurement imprecision in questionnaire assessments of sedentary time, particularly among older adults.
Although we did not observe a significant statistical interaction between sedentary time and MVPA, several studies examining joint associations of sedentary time and physical activity with adverse health outcomes have observed that disease and mortality incidence risks associated with higher sedentary time were either attenuated or eliminated among persons engaging in greater amounts of physical activity and were stronger in those with lower levels of physical activity (18–21). In our study, accelerometer-measured sedentary time was not associated with LTL among women who were more physically active. Additionally, sedentary time was not associated with LTL among women meeting current public health recommendations of ≥30 minutes/day of MVPA (43); in those not meeting this recommendation, higher sedentary time was associated with shorter LTL. Our findings suggest that prolonged sedentary time may be associated with shorter LTL when adequate levels of MVPA are not attained. However, our findings should be interpreted with caution given the lack of a statistical interaction, and further research is needed to determine whether there is a synergistic association of sedentary time and MVPA with LTL shortening.
We observed that women spent an average of 9.2 hours/day sedentary according to accelerometer data, in concordance with other studies carried out among older adults (44–46). In a study of 7,247 older Women's Health Study participants, Shiroma et al. (44) also observed an average of 9.2 hours/day spent in accelerometer-measured sedentary time. In our study, women reported spending an average of 8.6 hours/day sedentary. This is much higher than the total self-reported sedentary time observed in previous studies among older adults, which has ranged from 5.2 hours/day to 6.7 hours/day (23). We also observed that African-American women spent less time sedentary than white women. In a previous study carried out in a national sample of adults, Matthews et al. (47) observed that white and African-American women had similar patterns of sedentary behavior; however, older adults were not specifically evaluated.
Several mechanisms may explain the association of sedentary time with LTL. Oxidative stress and inflammation accelerate telomere attrition (2, 11, 48). It has been shown that regular engagement in physical activity increases antioxidant activity and may induce antiinflammatory responses (49, 50). Therefore, it is possible that women who spend long hours sedentary coupled with less time in MVPA may not be exposed to these antioxidant and antiinflammatory defenses. Increased time spent being sedentary and inactive may lead to insulin resistance (51), which has been previously associated with short LTL (52). The association of sedentary time with LTL may also be due to mediation by obesity. In previous studies, engaging in high amounts of sedentary behavior was associated with increased risk of obesity (25), and obesity has been associated with shorter LTL (7); however, findings persisted after adjustment for body mass index. Reverse causation due to chronic disease burden may also be possible; that is, women who have a history of chronic diseases may be more likely to have a sedentary lifestyle and shorter LTL.
Limitations of our study included its cross-sectional design, which precluded our ability to assess a temporal relationship between sedentary time and LTL. Our study was exclusive to older women, and our findings cannot be generalized to men or younger women. Our results apply to telomere length dynamics in leukocytes but not in other tissues. Women who enrolled in the WHI extension studies were more likely to be healthier at baseline; thus, those who experienced greater health-related LTL shortening may have been excluded. Strengths of our study include the diverse sample, adjustment for a large number of potential confounders, adjudication of data on chronic diseases, and accelerometer-measured sedentary time and MVPA.
In conclusion, higher accelerometer-measured sedentary time was associated with shorter LTL among less physically active women, suggesting that prolonged sedentary time and limited engagement in MVPA may act synergistically to shorten LTL among older women. Therefore, avoidance of a highly inactive lifestyle may provide health benefits at the cellular level. Longitudinal studies assessing sedentary time and MVPA in relation to changes in LTL are currently needed. Future studies should also determine whether cardiorespiratory fitness modifies the relationship between sedentary time and LTL (53).
ACKNOWLEDGMENTS
Author affiliations: San Diego State University/University of California, San Diego Joint Doctoral Program in Public Health (Epidemiology), San Diego, California (Aladdin H. Shadyab); Division of Epidemiology, Graduate School of Public Health, San Diego State University, San Diego, California (Aladdin H. Shadyab, Caroline A. Macera, Richard A. Shaffer); Division of Biostatistics and Bioinformatics, Department of Family Medicine and Public Health, School of Medicine, University of California, San Diego, La Jolla, California (Sonia Jain); Department of Psychology, College of Sciences, San Diego State University, San Diego, California (Linda C. Gallo); Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, State University of New York at Buffalo, Buffalo, New York (Michael J. LaMonte); Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington (Alexander P. Reiner); Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington (Charles Kooperberg, Chongzhi Di); Division of Biostatistics and Study Methodology, Center for Translational Science, George Washington University, Washington, DC (Cara L. Carty); Children's National Medical Center, Washington, DC (Cara L. Carty); Department of Aging and Geriatric Research, Institute on Aging, University of Florida, Gainesville, Florida (Todd M. Manini); Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois (Lifang Hou); and Division of Epidemiology, Department of Family Medicine and Public Health, School of Medicine, University of California, San Diego, La Jolla, California (Aladdin H. Shadyab, Andrea Z. LaCroix).
All authors contributed equally to this work.
This work was supported by the National Heart, Lung, and Blood Institute (contract HHSN268201300007C and grant R01 HL105065). The Women's Health Initiative is funded by the National Heart, Lung, and Blood Institute (contracts HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, and HHSN271201100004C). Additional support was provided to T.M.M. by the National Heart, Lung, and Blood Institute (grant R01 HL121023) and to A.H.S. by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (grant T32 AR064194).
A short list of WHI Investigators—Program Office: (National Heart, Lung, and Blood Institute, Bethesda, Maryland) Dr. Jacques Rossouw, Shari Ludlam, Dr. Dale Burwen, Dr. Joan McGowan, Dr. Leslie Ford, and Dr. Nancy Geller; Clinical Coordinating Center: (Fred Hutchinson Cancer Research Center, Seattle, WA) Dr. Garnet Anderson, Dr. Ross Prentice, Dr. Andrea LaCroix, and Dr. Charles Kooperberg; investigators and academic centers: (Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts) Dr. JoAnn E. Manson; (MedStar Health Research Institute/Howard University, Washington, DC) Dr. Barbara V. Howard; (Stanford Prevention Research Center, Stanford, California) Dr. Marcia L. Stefanick; (The Ohio State University, Columbus, Ohio) Dr. Rebecca Jackson; (University of Arizona, Tucson/Phoenix, Arizona) Dr. Cynthia A. Thomson; (University at Buffalo, Buffalo, New York) Dr. Jean Wactawski-Wende; (University of Florida, Gainesville/Jacksonville, Florida) Dr. Marian Limacher; (University of Iowa, Iowa City/Davenport, Iowa) Dr. Robert Wallace; (University of Pittsburgh, Pittsburgh, Pennsylvania) Dr. Lewis Kuller; (Wake Forest University School of Medicine, Winston-Salem, North Carolina) Dr. Sally Shumaker.
Conflict of interest: none declared.
REFERENCES
- 1. O'Sullivan RJ, Karlseder J. Telomeres: protecting chromosomes against genome instability. Nat Rev Mol Cell Biol. 2010;11(3):171–181. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Aviv A. Telomeres and human aging: facts and fibs. Sci Aging Knowledge Environ. 2004;2004(51):pe43. [DOI] [PubMed] [Google Scholar]
- 3. Müezzinler A, Zaineddin AK, Brenner H. A systematic review of leukocyte telomere length and age in adults. Ageing Res Rev. 2013;12(2):509–519. [DOI] [PubMed] [Google Scholar]
- 4. Wentzensen IM, Mirabello L, Pfeiffer RM, et al. . The association of telomere length and cancer: a meta-analysis. Cancer Epidemiol Biomarkers Prev. 2011;20(6):1238–1250. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Haycock PC, Heydon EE, Kaptoge S, et al. . Leukocyte telomere length and risk of cardiovascular disease: systematic review and meta-analysis. BMJ. 2014;349:g4227. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Zhao J, Miao K, Wang H, et al. . Association between telomere length and type 2 diabetes mellitus: a meta-analysis. PLoS One. 2013;8(11):e79993. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Valdes AM, Andrew T, Gardner JP, et al. . Obesity, cigarette smoking, and telomere length in women. Lancet. 2005;366(9486):662–664. [DOI] [PubMed] [Google Scholar]
- 8. Cherkas LF, Hunkin JL, Kato BS, et al. . The association between physical activity in leisure time and leukocyte telomere length. Arch Intern Med. 2008;168(2):154–158. [DOI] [PubMed] [Google Scholar]
- 9. Soares-Miranda L, Imamura F, Siscovick D, et al. . Physical activity, physical fitness, and leukocyte telomere length: the Cardiovascular Health Study. Med Sci Sports Exerc. 2015;47(12):2525–2534. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Kim JH, Ko JH, Lee DC, et al. . Habitual physical exercise has beneficial effects on telomere length in postmenopausal women. Menopause. 2012;19(10):1109–1115. [DOI] [PubMed] [Google Scholar]
- 11. Mundstock E, Zatti H, Louzada FM, et al. . Effects of physical activity in telomere length: systematic review and meta-analysis. Ageing Res Rev. 2015;22:72–80. [DOI] [PubMed] [Google Scholar]
- 12. Du M, Prescott J, Kraft P, et al. . Physical activity, sedentary behavior, and leukocyte telomere length in women. Am J Epidemiol. 2012;175(5):414–422. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Sjögren P, Fisher R, Kallings L, et al. . Stand up for health—avoiding sedentary behavior might lengthen your telomeres: secondary outcomes from a physical activity RCT in older people. Br J Sports Med. 2014;48(19):1407–1409. [DOI] [PubMed] [Google Scholar]
- 14. Loprinzi PD. Leisure-time screen-based sedentary behavior and leukocyte telomere length: implications for a new leisure-time screen-based sedentary behavior mechanism. Mayo Clin Proc. 2015;90(6):786–790. [DOI] [PubMed] [Google Scholar]
- 15. Owen N, Healy GN, Matthews CE, et al. . Too much sitting: the population health science of sedentary behavior. Exerc Sport Sci Rev. 2010;38(3):105–113. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Kimura M, Stone RC, Hunt SC, et al. . Measurement of telomere length by the Southern blot analysis of terminal restriction fragment lengths. Nat Protoc. 2010;5(9):1596–1607. [DOI] [PubMed] [Google Scholar]
- 17. Aviv A, Hunt SC, Lin J, et al. . Impartial comparative analysis of measurement of leukocyte telomere length/DNA content by Southern blots and qPCR. Nucleic Acids Res. 2011;39(20):e134. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Matthews CE, George SM, Moore SC, et al. . Amount of time spent in sedentary behaviors and cause-specific mortality in US adults. Am J Clin Nutr. 2012;95(2):437–445. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Seguin R, Buchner DM, Liu J, et al. . Sedentary behavior and mortality in older women: the Women's Health Initiative. Am J Prev Med. 2014;46(2):122–135. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Biswas A, Oh PI, Faulkner GE, et al. . Sedentary time and its association with risk for disease incidence, mortality, and hospitalization in adults: a systematic review and meta-analysis. Ann Intern Med. 2015;162(2):123–132. [DOI] [PubMed] [Google Scholar]
- 21. Chomistek AK, Manson JE, Stefanick ML, et al. . Relationship of sedentary behavior and physical activity to incident cardiovascular disease: results from the Women's Health Initiative. J Am Coll Cardiol. 2013;61(23):2346–2354. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Sanders JL, Newman AB. Telomere length in epidemiology: a biomarker of aging, age-related diseases, both, or neither. Epidemiol Rev. 2013;35:112–131. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Harvey JA, Chastin FM, Skelton DA. How sedentary are older people? A systematic review of the amount of sedentary behavior. J Aging Phys Act. 2015;23(3):471–487. [DOI] [PubMed] [Google Scholar]
- 24. Proper KI, Singh AS, van Mechelen W, et al. . Sedentary behaviors and health outcomes among adults: a systematic review of prospective studies. Am J Prev Med. 2011;40(2):174–182. [DOI] [PubMed] [Google Scholar]
- 25. 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):1785–1791. [DOI] [PubMed] [Google Scholar]
- 26. Anderson GL, Manson J, Wallace R, et al. . Implementation of the Women's Health Initiative study design. Ann Epidemiol. 2003;13(9 suppl):S5–S17. [DOI] [PubMed] [Google Scholar]
- 27. The Women's Health Initiative Study Group Design of the Women's Health Initiative clinical trial and observational study. Control Clin Trials. 1998;19(1):61–109. [DOI] [PubMed] [Google Scholar]
- 28. Rillamas-Sun E, Buchner DM, Di C, et al. . Development and application of an automated algorithm to identify a window of consecutive days of accelerometer wear for large-scale studies. BMC Res Notes. 2015;8:270. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Choi L, Liu Z, Matthews CE, et al. . Validation of accelerometer wear and nonwear time classification algorithm. Med Sci Sports Exerc. 2011;43(2):357–364. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Choi L, Ward SC, Schnelle JF, et al. . Assessment of wear/nonwear time classification algorithms for triaxial accelerometer. Med Sci Sports Exerc. 2012;44(10):2009–2016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Troiano RP, Berrigan D, Dodd KW, et al. . Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40(1):181–188. [DOI] [PubMed] [Google Scholar]
- 32. Evenson KR, Wen F, Herring AH, et al. . Calibrating physical activity intensity for hip-worn accelerometry in women age 60 to 91 years: the Women's Health Initiative OPACH Calibration Study. Prev Med Rep. 2015;2:750–756. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Meyer AM, Evenson KR, Morimoto L, et al. . Test-retest reliability of the Women's Health Initiative physical activity questionnaire. Med Sci Sports Exerc. 2009;41(3):530–538. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Stewart AL, Mills KM, King AC, et al. . CHAMPS physical activity questionnaire for older adults: outcomes for interventions. Med Sci Sports Exerc. 2001;33(7):1126–1141. [DOI] [PubMed] [Google Scholar]
- 35. National Institutes of Health Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults: the evidence report. Obes Res. 1998;6(suppl 2):51S–209S. [PubMed] [Google Scholar]
- 36. Guralnik JM, Ferrucci L, Pieper CF, et al. . Lower extremity function and subsequent disability: consistency across studies, predictive models, and value of gait speed alone compared with short physical performance battery. J Gerontol A Biol Sci Med Sci. 2000;55(4):M221–M231. [DOI] [PubMed] [Google Scholar]
- 37. Vasunilashorn S, Coppin AK, Patel KV, et al. . Use of the short physical performance battery score to predict loss of ability to walk 400 meters: analysis from the InCHIANTI study. J Gerontol A Biol Sci Med Sci. 2009;64(2):223–229. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Ware JE., Jr SF-36 health survey update. Spine (Phila Pa 1976). 2000;25(24):3130–3139. [DOI] [PubMed] [Google Scholar]
- 39. Grøntved A, Hu FB. Television viewing and risk of type 2 diabetes, cardiovascular disease, and all-cause mortality: a meta analysis. JAMA. 2011;305(23):2448–2455. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40. Lynch BM. Sedentary behavior and cancer: a systematic review of the literature and proposed biological mechanisms. Cancer Epidemiol Biomarkers Prev. 2010;19(11):2691–2709. [DOI] [PubMed] [Google Scholar]
- 41. Curb JD, McTiernan A, Heckbert SR, et al. . Outcomes ascertainment and adjudication methods in the Women's Health Initiative. Ann Epidemiol. 2003;13(9 suppl):S122–S128. [DOI] [PubMed] [Google Scholar]
- 42. Margolis KL, Lihong Qi, Brzyski R, et al. . Validity of diabetes self-reports in the Women's Health Initiative: comparison with medication inventories and fasting glucose measurements. Clin Trials. 2008;5(3):240–247. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43. Nelson ME, Rejeski WJ, Blair SN, et al. . Physical activity and public health in older adults: recommendation from the American College of Sports Medicine and the American Heart Association. Circulation. 2007;116(9):1094–1105. [DOI] [PubMed] [Google Scholar]
- 44. Shiroma EJ, Freedson PS, Trost SG, et al. . Patterns of accelerometer-assessed sedentary behavior in older women. JAMA. 2013;310(23):2562–2563. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45. Evenson KR, Buchner DM, Morland KB. Objective measurement of physical activity and sedentary behavior among US adults aged 60 years or older. Prev Chronic Dis. 2012;9:E26. [PMC free article] [PubMed] [Google Scholar]
- 46. Arnardottir NY, Koster A, van Domelen DR, et al. . Objective measurements of daily physical activity patterns and sedentary behavior in older adults: Age, Gene/Environment Susceptibility-Reykjavik Study. Age Ageing. 2013;42(2):222–229. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47. Matthews CE, Chen KY, Freedson PS, et al. . Amount of time spent in sedentary behaviors in the United States, 2003–2004. Am J Epidemiol. 2008;167(7):875–881. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48. Von Zglinicki T. Oxidative stress shortens telomeres. Trends Biochem Sci. 2002;27(7):339–344. [DOI] [PubMed] [Google Scholar]
- 49. Gomez-Cabrera MC, Domenech E, Viña J. Moderate exercise is an antioxidant: upregulation of antioxidant genes by training. Free Radic Biol Med. 2008;44(2):126–131. [DOI] [PubMed] [Google Scholar]
- 50. Kasapis C, Thompson PD. The effects of physical activity on serum C-reactive protein and inflammatory markers: a systematic review. J Am Coll Cardiol. 2005;45(10):1563–1569. [DOI] [PubMed] [Google Scholar]
- 51. Helmerhorst HJ, Wijndaele K, Brage S, et al. . Objectively measured sedentary time may predict insulin resistance independent of moderate- and vigorous-intensity physical activity. Diabetes. 2009;58(8):1776–1779. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52. Demissie S, Levy D, Benjamin EJ, et al. . Insulin resistance, oxidative stress, hypertension, and leukocyte telomere length in men from the Framingham Heart Study. Aging Cell. 2006;5(4):325–330. [DOI] [PubMed] [Google Scholar]
- 53. Loprinzi PD. Cardiorespiratory capacity and leukocyte telomere length among adults in the United States. Am J Epidemiol. 2015;182(3):198–201. [DOI] [PubMed] [Google Scholar]