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Background: Moderate-vigorous physical activity (MVPA) reduces colon cancer risk; however, it is unclear how the timing of MVPA throughout the adult life course impacts colon cancer risk. We evaluated whether maintenance and changes in MVPA levels over time are associated with colon cancer risk. Methods: We assessed 293,198 adults ages 50 to 71 years in the NIH-AARP Diet and Health Study. Participants completed baseline health and physical activity questionnaires between 1995 and 1997 and were followed through 2011, (average follow-up of 13.1 years). There were 5,072 colon cancer cases over the study period. Using latent class trajectory models, we identified seven distinct MVPA trajectories across the adult life course (15–18, 19–29, 30–35, and past 10-years) and ran Cox proportional hazards regression models. Results: Compared with those who maintained low MVPA levels, those who maintained high and moderate levels of MVPA had a lower risk of colon cancer [HR, 0.85; confidence interval (CI), 0.78–0.93; HR = 0.87; CI, 0.76–1.00)], and those who increased MVPA levels early and later during adulthood had a lower colon cancer risk (HR, 0.90; CI, 0.80–1.01) and (HR, 0.92; CI, 0.80–1.06), respectively. Those who decreased MVPA early in adulthood had an increased risk of colon cancer (HR, 1.12; CI, 1.02–1.23). These associations were stronger in adults ages Conclusions: Consistent participation in MVPA throughout life may reduce colon cancer risk. Impact: These findings emphasize that engaging in MVPA throughout adulthood lowers risk of colon cancer.

Objective The purpose of this study was to prospectively examine the effect of state stay‐at‐home mandates on weight of US adults by BMI over 3 months during COVID‐19. Methods US adults completed an online questionnaire containing demographics, weight, physical activity, sedentary time, fruit/vegetable intake, depressive symptoms, stress, and sleep at baseline (May 2020) and after 3 months (August 2020). Results Participants gained 0.6 kg (76.7‐77.3 kg, p = 0.002). A total of 26% of those with obesity gained > 2 kg compared with 14.8% of those with normal weight (p < 0.001). A total of 53.3% of individuals with obesity maintained weight within 2 kg compared with 72.5% of those with normal weight (p < 0.001). Greater weight gain was related to longer stay‐at‐home mandates (β = 0.078, p = 0.010), lower baseline minutes of physical activity per day (β = −0.107, p = 0.004), greater declines in minutes of physical activity per day (β = −0.076, p = 0.026), depressive symptoms (β = 0.098, p = 0.034), and greater increases in time preparing food (β = 0.075, p = 0.031). Conclusions US adults gained weight, and stay‐at‐home mandates were associated with atypical weight gain and greater reported weight gain in individuals with obesity over 3 months.

Background It is important for sport scientists and health professionals to have estimative methods for energy demand during diferent physical activities. The metabolic equivalent of task (MET) provides a feasible approach for classifying activity intensity as a multiple of the resting metabolic rate (RMR). RMR is generally assumed to be 3.5 mL of oxygen per kilogram of body mass per minute (mL O2 kg−1 min−1), a value that has been criticized and considered to be overestimated in the older adult population. However, there has been no comprehensive efort to review available RMR estimations, equivalent to 1 MET, obtained in the older adult population. Objective The aim of this review was to examine the existing evidence reporting measured RMR values in the older adult population and to provide descriptive estimates of 1 MET. Methods A systematic review was conducted by searching PubMed, Web of Science, Scopus, CINAHL, SPORTDiscus, and Cochrane Library, from database inception to July 2021. To this end, original research studies assessing RMR in adults≥60 years old using indirect calorimetry and reporting results in mL O2 kg−1 min−1 were sought. Results Twenty-three eligible studies were identifed, including a total of 1091 participants (426 men). All but two studies reported RMR values lower than the conventional 3.5 mL O2 kg−1 min−1. The overall weighted average 1 MET value obtained from all included studies was 2.7±0.6 mL O2 kg−1 min−1; however, when considering best practice studies, this value was 11% lower (2.4±0.3 mL O2 kg−1 min−1). Conclusion Based on the results of this systematic review, we would advise against the application of the standard value of 1 MET (3.5 mL O2 kg−1 min−1) in people ≥ 60 years of age and encourage the direct assessment of RMR using indirect calorimetry while adhering to evidence-based best practice recommendations. When this is not possible, assuming an overall value of 2.7 mL O2 kg−1 min−1 might be reasonable. Systematic review registration: International Prospective Register of Systematic Reviews on 30 September 2020, with registration number CRD42020206440.

Over the past 40 years, physical activity (PA) and public health has been established as a field of study. A robust evidence base has emerged demonstrating that participation in recommended amounts of PA results in a wide array of physical and mental health benefits. This led to the establishment of federal and global PA guidelines and surveillance programs. Strong evidence supports the efficacy of individual-level (e.g., goal setting) and environmental (e.g., policies) interventions to promote PA. There has also been progress in establishing a skilled and diverse workforce to execute the work of PA and public health. Looking forward, major challenges include stemming the obesity and chronic disease epidemics, addressing health inequities, and diversifying the workforce. Given the known benefits of PA and the availability of evidence-based interventions, efforts now must focus on implementing this knowledge to improve population health and reduce inequities through PA.

PURPOSE Higher levels of sedentary behavior are associated with early mortality, but the distribution of sedentary time by classes of behavior and demographic groups is poorly described in U.S. adults. To quantify the amount and sources of sedentary time in U.S. adults, we conducted a nationwide survey using a novel validated self-administered previous-day recall method and compared these values with a commonly used sitting time question. METHODS Participants from the AmeriSpeak panel 20 to 75 yr of age (N = 2640) completed up to two activities completed over time in 24 h (ACT24) previous-day recalls. Recalls were conducted on randomly selected days in October and November 2019. Survey sample designs were applied to reflect the U.S. population. RESULTS Mean age was 45.3 yr, 51% were female, 67% non-Hispanic White, and 37% had a body mass index of ≥30 kg·m-2. U.S. adults reported a mean 9.5 h·d-1 of sedentary time (95% confidence interval = 9.4, 9.7 h·d-1), which was 34% more than reported using a common surveillance measure (P < 0.01). Most daily sedentary time was accumulated in the leisure and work life domains, with leisure accounting for 47% (4.3 h·d-1, 95% confidence interval = 4.2, 4.5 h·d-1) of the total sedentary time. Eighty-two percent of leisure time was spent sedentary, mainly watching television/videos or engaged in Internet/computer use. CONCLUSIONS U.S. adults appear to spend more time in sedentary behavior than previously thought, and the majority of this time is accumulated at work and in leisure time. Reducing sedentary screen time during leisure in favor of physically active could be an important intervention target in the effort to increase physical activity in U.S. adults.

PURPOSE: To confirm that a multi-component physical activity index (PAI), with physical activity (PA)-related behaviors and performance measures, enhances PA assessment for adults with cancer history (“survivors”) v. the general population for key health variables. METHODS: Data from 2011-2014 NHANES were analyzed (n= 9,620 adults, including 866 survivors). We generated PAI scores by calculating sub-scales for activity-related behaviors (i.e., moderate-to-vigorous PA and TV viewing hours/sedentary time) (n/8) and performance (i.e., cardiorespiratory fitness and muscle strength) (n/8), then combined for an overall PAI score (n/16 points). Dependent variables (self-reported health, multimorbidity and functional limitations) were dichotomized and tested with logistic regression to estimate associations with PAI variables. RESULTS: Survivors obtaining 22.5+ MET-hours/week of PA were >5x more likely to report excellent health than non-exercisers (OR=5.5, 95% CI 3.27-9.28). We observed a general decrease in likelihood of multimorbidity and functional limitations with increasing PA. Models with the combined score showed that higher PAI scores had larger positive associations with all three for all adults. Survivors with higher PAI scores were >30% more likely to report excellent self-rated health (OR=1.3, 95% CI 1.2-1.4) and 20% less likely to report multimorbidity and functional limitations compared to survivors with lower PAI scores, considering covariates. CONCLUSIONS: Greater PA-related behavior and performance was beneficial for adults irrespective of cancer history. However, using a combined score elucidates unique needs and benefits for survivors v. general population. IMPLICATIONS FOR SURVIVORS: Using a multi-component approach to PA assessment could help in developing validated tools to plan exercise programs and interventions for survivors.

This prospective, observational study investigated changes in sleep and the effect on energy intake, gestational weight gain, and cardiometabolic health across pregnancy in 52 healthy pregnant women with obesity. Habitual sleep was assessed by wrist-worn actigraphy (time spent in bed; TIB, total sleep time; TST, and sleep efficiency) in early (13(0)-15(6) weeks) and late (35(0)-36(6)) pregnancy. A change to habitual sleep was defined as change of one-half of the standard deviation of TIB and TST across six consecutive nights from early pregnancy. Energy intake and changes in weight, fasting glucose, insulin, and lipids across pregnancy were compared between women who changed sleep. During early pregnancy, TIB was 9:24±0:08h and varied by 1:37±0:07h across the six nights. TST and sleep efficiency significantly declined from early to late pregnancy (7:03±0:08h to 6:28±0:09h, p

PURPOSE To examine the shape of the relationship between physical activity (PA) and total energy expenditure (TEE) and to explore the role of energy balance status (negative, stable, positive) in influencing this association. METHODS Cross-sectional. Participants were 584 older adults (50-74 yrs.) participating in the Interactive Diet and Activity Tracking in AARP study. TEE was assessed by doubly labeled water and PA by accelerometer. The relationship between PA and TEE was assessed visually and using non-linear methods (restricted cubic splines). Percent weight change (>3%) over a six-month period was used as a proxy measurement of energy balance status. RESULTS TEE generally increased with increasing deciles of PA averaging 2354 (SD = 351) kcal/d in the bottom decile to 2693 (SD = 480) kcal/d in the top decile. Cubic spline models showed an approximate linear association between PA and TEE (linear relation p < 0.0001, curvature p = 0.920). Results were similar in subgroup analyses for individuals classified as stable or positive energy balance. For those in negative energy balance, TEE was generally flat with increasing deciles of PA averaging 2428 (SD = 285) kcal/d in the bottom decile to 2372 (SD = 560) kcal/d in the top decile. CONCLUSION Energy balance status appears to play an important role in the relationship between PA and TEE. When in a positive energy balance, the relationship between TEE and PA was consistent with an additive model, however, when energy balance was negative, TEE appears to be consistent with a constrained model. These findings support PA for weight gain prevention by increasing TEE; however, the effect of PA on TEE during periods of weight loss may be limited. An adequately powered, prospective study is warranted to confirm these exploratory findings.

To compare estimates of moderate-vigorous physical activity (MVPA) duration derived from accelerometers calibrated only to walking and running activities to estimates from calibrations based on a broader range of lifestyle and ambulatory activities.In a study of 932 older (50-74 yr) adults we compared MVPA estimates from accelerometer counts based on three ambulatory calibration methods (Freedson 1952 counts per minute; Sasaki 2690 counts per minute; activPAL 3+ METs) to estimates based on calibrations from lifestyle and ambulatory activities combined (Matthews 760 counts per minute; Crouter 3+ METs; Sojourn3x 3+ METs). We also examined data from up to 6 previous-day recalls describing the MVPA in this population.The MVPA duration values derived from ambulatory calibration methods were significantly lower than methods designed to capture a broader range of both lifestyle and ambulatory activities (P0.05). The MVPA (h·d) estimates in all participants were: Freedson (median, 0.35; interquartile range, 0.17-0.58); Sasaki (median, 0.91; interquartile range, 0.59-1.32); and activPAL (median, 0.97; interquartile range, 0.71-1.26) compared with Matthews (median, 1.82; interquartile range, 1.37-2.34); Crouter (2.28 [1.72-2.82]); and Sojourn3x (median, 1.85; interquartile range, 1.42-2.34). Recall-based estimates in all participants were comparable (median, 1.61; interquartile range, 0.89-2.57) and indicated participation in a broad range of lifestyle and ambulatory MVPA.Accelerometer calibration studies that employ only ambulatory activities may produce MVPA duration estimates that are substantially lower than methods calibrated to a broader range of activities. These findings highlight the potential to reduce potentially large differences among device-based measures of MVPA due to variation in calibration study design by including a variety of lifestyle and ambulatory activities.

Higher insulin following sedentary behavior may be due to increased insulin secretion (IS), decreased hepatic insulin extraction (HIE), or a combination of both. Ten healthy adults completed glucose tolerance tests following 7 days of normal activity and 7 days of increased sitting. There were no differences in IS; however, HIE at 120 min after ingestion (85.4% ± 7.2% vs. 74.6% ± 6.6%, p < 0.05) and the area under the curve (73.6% ± 9.4% vs. 67.5% ± 11.3%, p < 0.05) were reduced following 7 days of increased sedentary time.

Studies for the associations between physical activity and disease risk have been supported by newly developed wearable accelerometer-based devices. These devices record raw activity/movement information in real time on a second-by-second basis and the data can be converted to a variety of summary metrics, such as energy expenditure, sedentary time and moderate-vigorous intensity physical activity. Here we review some of the methods used to analyze the accelerometer data and the R packages that can generate activity related variables from raw data. We also discuss longitudinal data and functional data approaches to perform analyses for various research purposes.

Objective This study aimed to investigate sitting time, the home sedentary environment, and physical activity among weight-loss maintainers in WW (formerly Weight Watchers). Methods Participants were 4,305 weight-loss maintainers who had maintained ≥9.1 kg of weight loss (24.7 kg on average) for 3.3 years and had an average current BMI of 27.6 kg/m2 . A control group of weight-stable individuals with obesity (n = 619) had an average BMI of 38.9 kg/m2 . The Multicontext Sitting Time Questionnaire and Paffenbarger physical activity questionnaire were administered. Results Weight-loss maintainers versus controls spent 3 hours less per day sitting during the week (10.9 vs. 13.9; η p 2 = 0.039; P = 0.0001) and weekends (9.7 vs. 12.6; η p 2 = 0.038). Weight-loss maintainers versus controls spent 1 hour less per day in non-work-related sitting using a computer or video games during the week (1.4 vs. 2.3; η p 2 = 0.03; P = 0.0001) and weekends (1.5 vs. 2.5; η p 2 = 0.03; P = 0.0001). Weight-loss maintainers versus controls had similar numbers of sedentary-promoting devices (15.8 vs. 14.8) and expended significantly more calories per week in physical activity (1,835 vs. 785; η p 2 = 0.036; P = 0.0001). Conclusions Weight-loss maintainers reported less time sitting than weight-stable individuals with obesity. Future research should test the efficacy of targeting sitting time to help promote long-term weight-loss maintenance.

To determine the feasibility, acceptability and preliminary efficacy of an eHealth intervention with charity-based incentives to increase physical activity (PA) among young adult cancer survivors. Participants were randomized into two groups: PA (N = 25; Fitbit, step goal, electronic weekly newsletter) or PA + Charity (N = 26; same as PA plus charity donation if step goal achieved). At baseline and 12 weeks, steps/day were assessed using an activPAL. Motivation (e.g., BREQ-3) and patient reported outcomes (e.g., sleep quality, fatigue) were self-reported. The mean age was 36.8 years, 56.9% were Non-Hispanic White. We retained 82% (42/51) of participants. The PA + Charity vs. PA group had significantly higher satisfaction with intervention experience (100% vs 85%), greater increases in steps/day (1689 vs 516) and increases in overall self-determination score (13.5 vs 2.2). Both groups significantly improved sleep quality and reduced fatigue. A low-intensity eHealth intervention with charity-based incentives was feasible, acceptable, increased PA and self-determination. Trial registration Clinicaltrials.gov NCT03322059.

Background: This study reexamines the energy cost of lower intensity activities compared to the 2011 Adult Compendium of Physical Activities. Methods: Participants (n = 32, age = 35 [13.8] y, 16 females) wore a portable metabolic system (COSMED), during 5 different conditions: sitting quietly, watching TV, sitting while working, driving, and walking at 2.0 mph. The metabolic equivalent (MET) values (VO2 mL·kg−1·min−1/3.5 mL·kg−1·min−1) were calculated. Results: The mean (SD) MET value for driving (1.46 [0.24]) was significantly lower than the Adult Compendium value of 2.5 (P P P > .05) and yielded significantly higher MET values than quiet sitting (1.08 [0.23] METs, P P Conclusion: Existing Adult Compendium METs are significantly higher than measured METs for driving, which more closely correspond to sedentary behaviors than slow walking. The TV and quiet sitting also differed from their Adult Compendium values, which should be updated to reflect these findings, given that researchers and practitioners rely on Adult Compendium MET values to estimate energy cost.

PurposeTo compare estimates of moderate-vigorous physical activity (MVPA) duration derived from accelerometers calibrated only to walking and running activities to estimates from calibrations based on a broader range of lifestyle and ambulatory activities.MethodsIn a study of 932 older (50–7

Introduction The goal of this study was to describe differences in time use and energy expenditure associated with exercise, prolonged TV viewing, and work days in a longitudinal study of older adults. Methods Participants were 1,020 adults who completed previous-day recalls that provided a profile of the use of time in sedentary and physical activity. Time use and physical activity energy expenditure were predicted for each type of day (exercise, prolonged TV, work) using linear mixed models, adjusting for age, sex, season of the year, and day of the week. Data were collected in 2012–2013; analysis was completed in 2017. Results Exercise days had less sedentary time (–0.37 hours/day) and light activity (–0.29 hours/day), and less household, work, and shopping activities, such that the increase in total physical activity energy expenditure on exercise days (2.83 MET-hours/day) was only about half that expended during exercise (5.98 MET-hours/day). Prolonged TV viewing days had more total sedentary time (0.86 hours/days) and less light (–0.45 hours/day) and moderate–vigorous intensity activity (–0.41 hours/day), and thus lower total physical activity energy expenditure (–2.43 MET-hours/day). Work days had less sleep (–0.91 hours/day) and more total sedentary time (1.32 hours/day). Conclusions Exercise days had more physical activity energy expenditure, but because of reductions in other activities, only about half of the energy expended during exercise was added to total daily physical activity energy expenditure. Prolonged TV viewing days had less physical activity energy expenditure and less moderate–vigorous activity. These findings provide new insights into possible compensation associated with exercise, and suggest a strong link between TV viewing and physical inactivity.

We take a functional data approach to longitudinal studies with complex bivariate outcomes. This work is motivated by data from a physical activity study that measured two responses over time in five-minute intervals. One response is the proportion of time active in each interval, a continuous proportions with excess zeros and ones. The other response, energy expenditure rate in the interval, is a continuous variable with excess zeros and skewness. This outcome is complex because there are three possible activity patterns in each interval (inactive, partially active and completely active), and those patterns, which are observed, induce both non-random and random associations between the responses. More specifically, the inactive pattern requires a zero value in both the proportion for active behavior and the energy expenditure rate; a partially active pattern means that the proportion of activity is strictly between zero and one and that the energy expenditure rate is greater than zero and likely to be moderate, and the completely active pattern means that the proportion active is exactly one, and the energy expenditure rate is greater than zero and likely to be higher. To address these challenges, we propose a three-part functional data joint modeling approach. The first part is a continuation-ratio model to reorder the ordinal valued three activity patterns. The second part models the proportions when they are in interval (0, 1). The last component specifies the skewed continuous energy expenditure rate with Box-Cox transformations when they are greater than zero. In this three-part model, the regression structures are specified as smooth curves measured at various time-points with random effects that have a correlation structure. The smoothed random curves for each variable are summarized using a few important principal components, and the association of the three longitudinal components is modeled through the association of the principal component scores. The difficulties in handling the ordinal and proportional variables are addressed using a quasilikelihood type approximation. We develop an efficient algorithm to fit the model which also involves the selection of the number of principal components. The method is applied to physical activity data and is evaluated empirically by a simulation study.

Introduction Both excessive weight gain and weight loss are important risk factors in the older population. Neighborhood environment may play an important role in weight change, but neighborhood effects on weight gain and weight loss have not been studied separately. This study examined the associations between neighborhood socioeconomic deprivation and excessive weight gain and weight loss. Methods This analysis included 153,690 men and 105,179 women (aged 51–70 years). Baseline addresses were geocoded into geographic coordinates and linked to the 2000 U.S. Census at the Census tract level. Census variables were used to generate a socioeconomic deprivation index by principle component analysis. Excessive weight gain and loss were defined as gaining or losing >10% of baseline (1995–1996) body weight at follow-up (2004–2006). The analysis was performed in 2015. Results More severe neighborhood socioeconomic deprivation was associated with higher risks of both excessive weight gain and weight loss after adjusting for individual indicators of SES, disease conditions, and lifestyle factors (Quintile 5 vs Quintile 1: weight gain, OR=1.36, 95% CI=1.28, 1.45 for men and OR=1.20, 95% CI=1.13, 1.27 for women; weight loss, OR=1.09, 95%% CI=1.02, 1.17 for men and OR=1.23, 95% CI=1.14, 1.32 for women). The findings were fairly consistent across subpopulations with different demographics and lifestyle factors. Conclusions Neighborhood socioeconomic deprivation predicts higher risk of excessive weight gain and weight loss.

The purpose of this study was to explore breast cancer survivors’ interest in and preferences for technology-supported exercise interventions. Post-treatment survivors [n = 279; M age = 60.7 (SD = 9.7)] completed a battery of online questionnaires in August 2015. Descriptive statistics were calculated for all data. Logistic regression analyses were conducted to examine relationships between survivors’ interest in a technology-supported exercise interventions and demographic, disease, and behavioral factors. These same factors were examined in relation to perceived effectiveness of such interventions using multiple regression analyses. About half (53.4%) of survivors self-reported meeting public health recommendations for physical activity. Fewer than half reported using an exercise or diet mobile app (41.2%) or owning an activity tracker (40.5%). The majority were interested in receiving remotely delivered exercise counseling (84.6%), participating in a remotely delivered exercise intervention (79.5%), and using an exercise app or website (68%). Survivors reported that the most helpful technology-supported intervention components would be an activity tracker (89.5%), personalized feedback (81.2%), and feedback on how exercise is influencing mood, fatigue, etc. (73.6%). Components rated as least helpful were social networking integration (31.2%), group competitions (33.9%), and ability to see others’ progress (35.1%). Preferences for technology-supported exercise interventions varied among breast cancer survivors. Nonetheless, data indicate that technology-supported interventions may be feasible and acceptable. Engaging stakeholders may be important in developing and testing potential intervention components.

The activPAL (AP) monitor is well established for distinguishing sitting, standing, and stepping time. However, its validity in predicting time in physical activity intensity categories in a free-living environment has not been determined.This study aimed to determine the validity of the AP in estimating time spent in sedentary, light, and moderate-to-vigorous physical activity (MVPA) in a free-living environment.Thirteen participants (mean ± SD age = 24.8 ± 5.2 yr, BMI = 23.8 ± 1.9 kg·m) were directly observed for three 10-h periods wearing an AP. A custom R program was developed and used to summarize detailed active and sedentary behavior variables from the AP. AP estimates were compared with direct observation.The AP accurately and precisely estimated time in activity intensity categories (bias [95% confidence interval]; sedentary = 0.8 min [-2.9 to 4.5], light = 1.7 min [2.2-5.7], and -2.6 min [-5.8 to 0.7]). The overall accuracy rate for time in intensity categories was 96.2%. The AP also accurately estimated guideline minutes, guideline bouts, prolonged sitting minutes, and prolonged sitting bouts.The AP can be used to accurately capture individualized estimates of active and sedentary behavior variables in free-living settings.

Body-worn devices that estimate physical behavior have tremendous potential to address key research gaps. However, there is no consensus on how devices and processing methods should be developed and evaluated, resulting in large differences in summary estimates and confusion for end users. We propose a phase-based framework for developing and evaluating devices that emphasizes robust validation studies in naturalistic conditions.

© Lippincott Williams & Wilkins. Purpose Interest in a variety of physical behaviors (e.g., exercise, sitting time, sleep) in relation to health outcomes creates a need for new statistical approaches to analyze the joint effects of these distinct but inter-related physical behaviors. We developed and tested an integrated physical behavior score (PBS). Methods National Institutes of Health-American Association of Retired Persons Diet and Health Study participants (N = 163,016) completed a questionnaire (2004-2006) asking about time spent in five exercise and nonexercise physical activities, two sedentary behaviors (television and nontelevision), and sleep. In half of the sample, we used shape-constrained additive regression to model the relationship between each behavior and survival. Maximum logit scores from each of the eight behavior-survival functions were summed to produce a PBS that was proportionally rescaled to range from 0 to 100. We examined predictive validity of the PBS in the other half-sample using Cox Proportional Hazards models after adjustment for covariates for all-cause and cause-specific mortality. Results In the testing sample, over an average of 6.6 yr of follow-up, 8732 deaths occurred. We found a strong graded decline in risk of all-cause mortality across quintiles of PBS (Q5 vs Q1 hazard ratio [95% CI] = 0.53 [0.49, 0.57]). Risk estimates for the PBS were higher than any of the components in isolation. Results were similar but stronger for cardiovascular disease (Q5 vs Q1 = 0.42 [0.39, 0.48]) and other mortality (Q5 vs Q1 = 0.42 [0.36, 0.48]). The relationship between PBS and mortality was observed in stratified analyses by median age, sex, body mass index, and health status. Conclusions We developed a novel statistical method generated a composite physical behavior that is predictive of mortality outcomes. Future research is needed to test this approach in an independent sample.

Key Points Question Does an association exist between patterns in leisure-time physical activity occurring during adolescence (15-18 years of age) or early (19-29 years of age), middle (35-39 years of age), and later (40-61 years of age) adulthood and all-cause or cause-specific mortality? Findings This cohort study of 315 059 participants found that maintaining physical activity from adolescence into later adulthood was associated with 29% to 36% lower risk for all-cause mortality and that being inactive but increasing physical activity during midlife was associated with 32% to 35% lower risk for mortality. Meaning Although long-term participation in physical activity may be important to lower mortality risk, the present study provides evidence that becoming physically active later in adulthood (40-61 years of age) may provide comparable health benefits., Importance Although the benefits of leisure-time physical activity (LTPA) in middle age are established, the health effects of long-term participation and changes in LTPA between adolescence and middle age have not been documented. Objective To determine whether an association exists between LTPA life course patterns and mortality. Design, Setting, and Participants This prospective cohort study used data from the National Institutes of Health–AARP (formerly American Association of Retired Persons) Diet and Health Study established in 1995 to 1996. Data analysis was conducted from March 2017 through February 2018. Data were analyzed for 315 059 adult AARP members living in 6 states, namely, California, Florida, Louisiana, New Jersey, North Carolina, or Pennsylvania, or 2 metropolitan areas, Atlanta, Georgia, or Detroit, Michigan. Exposures Self-reported LTPA (hours per week) at the baseline interview for ages grouped as 15 to 18, 19 to 29, 35 to 39, and 40 to 61 years. Main Outcomes and Measures All-cause, cardiovascular disease (CVD)–related, and cancer-related mortality records available through December 31, 2011. Results Of 315 059 participants, 183 451 (58.2%) were men, and the participants were 50 to 71 years of age at enrollment. Ten LTPA trajectories (categorized as maintaining, increasing, and decreasing LTPA across time) were identified, and 71 377 deaths due to all causes, 22 219 deaths due to CVD, and 16 388 deaths due to cancer occurred. Compared with participants who were consistently inactive throughout adulthood, participants who maintained the highest amount of LTPA in each age period were at lower risks for all-cause, CVD-related, and cancer-related mortality. For example, compared with participants who were consistently inactive, maintaining higher amounts of LTPA was associated with lower all-cause (hazard ratio [HR], 0.64; 95% CI, 0.60-0.68), CVD-related (HR, 0.58; 95% CI, 0.53-0.64), and cancer-related (HR, 0.86; 95% CI, 0.77-0.97) mortality. Adults who were less active throughout most of the adult life course but increased LTPA in later adulthood (40-61 years of age) also had lower risk for all-cause (HR, 0.65; 95% CI, 0.62-0.68), CVD-related (HR, 0.57; 95% CI, 0.53-0.61), and cancer-related (HR, 0.84; 95% CI, 0.77-0.92) mortality. Conclusions and Relevance Maintaining higher LTPA levels and increasing LTPA in later adulthood were associated with comparable low risk of mortality, suggesting that midlife is not too late to start physical activity. Inactive adults may be encouraged to be more active, whereas young adults who are already active may strive to maintain their activity level as they get older., This cohort study analyzes questionnaire data from the National Institutes of Health–AARP (formerly American Association of Retired Persons) Diet and Health Study to assess whether an association exists between risk of all-cause, cardiovascular disease–related, or cancer-related mortality and self-reported leisure-time physical activity patterns across the life course.

PURPOSE This study examined the 1-yr test-retest reliability and criterion validity of sedentary time survey items in a subset of participants from a large, nationwide prospective cohort. METHODS Participants included 423 women and 290 men age 31 to 72 yr in the Cancer Prevention Study-3. Reliability was assessed by computing Spearman correlation coefficients between responses from prestudy and poststudy surveys. Validity was assessed by comparing survey-estimated sedentary time with a latent variable representing true sedentary time estimated from the 7-d diaries, accelerometry, and surveys through the method of triads. Sensitivity analyses were restricted to 566 participants with an average of 14+ h of diary and accelerometer data per day for 7 d per quarter. RESULTS Reliability estimates for total sitting time were moderate or strong across all demographic strata (Spearman ρ ≥ 0.6), with significant differences by race (P = 0.01). Reliability estimates were strongest for the TV-related sedentary time item (Spearman ρ, 0.74; 95% confidence interval, 0.70-0.77). The overall validity coefficient (VC) for survey-assessed total sedentary time was 0.62 (95% confidence interval, 0.55-0.69), although VC varied by age group and activity level (P < 0.05). However, VC were similar across groups (P < 0.05) when restricting to highly compliant participants in a sensitivity analysis. CONCLUSIONS The Cancer Prevention Study-3 sedentary behavior questionnaire has acceptable reliability and validity for ranking or categorizing participants according to sedentary time. Acceptable reliability and validity estimates persist across various demographic subgroups.

This paper examined how many older adults (65+ yrs) are meeting physical activity (PA) Guidelines (PAG; 150 min/wk of moderate-to-vigorous PA) using data from three leading national surveys (NHANES, BRFSS and NHIS). The proportion of individuals meeting aerobic PAG was determined for the most recent cycle available for each survey (NHANES 2011–12, NHIS and BRFSS 2013). We also assessed whether PAG adherence has changed over time. Predicted margins from multinomial logistic regression were computed after adjusting for age, race/ethnicity and gender and sample weights. The proportion of older adults meeting PAG was 27.3% for NHANES, 35.8% for NHIS and 44.3% for BRFSS. Across all surveys, men reported higher levels of activity than women, Non-Hispanic Whites reported higher levels than Non-Hispanic Blacks and Hispanics, activity declined with age and was lower in those with functional limitations, all P

Background Physical activity is associated with a variety of health benefits, but the biological mechanisms that explain these associations remain unclear. Metabolomics is a powerful tool to comprehensively evaluate global metabolic signature associated with physical activity and helps to pinpoint the pathways that mediate the health effects of physical activity. There has been limited research on metabolomics and habitual physical activity, and no metabolomics study has examined sedentary behaviour and physical activity of different intensities. Methods In a group of Chinese adults (N = 277), we used an untargeted approach to examine 328 plasma metabolites in relation to accelerometer-measured physical activity, including overall volume of physical activity (physical activity energy expenditure (PAEE) and duration of physically active time) and sedentary time, and measures related to different intensities of physical activity (moderate-to-vigorous activity (MVPA), light activity, average physical activity intensity). Results We identified 11 metabolites that were associated with total activity, with a false discovery rate of 0.2 or lower. Notably, we observed generally lower levels of amino acids in the valine, leucine and isoleucine metabolism pathway and of carbohydrates in sugar metabolism among participants with higher activity levels. Moreover, we found that PAEE, time spent in light activity and duration of physically active time were associated with a similar metabolic pattern, whereas the metabolic signature associated with sedentary time mirrored this pattern. In contrast, average activity intensity and time spent in MVPA appeared to be associated with somewhat different metabolic patterns. Conclusions Overall, the metabolomics patterns support a beneficial role of higher volume of physical activity in cardiometabolic health. Our findings identified candidate pathways and provide insight into the mechanisms underlying the health effects of physical activity.

Purpose: Less time spent in sedentary behaviors is associated with improved health and disease outcomes in breast cancer survivors. However, little is known about survivors’ interest in sedentary behavior reduction interventions and how to effectively reduce this risk behavior. The purpose of this study was to explore breast cancer survivors’ interest in and preferences for technology-supported sedentary behavior reduction interventions. Methods: Breast cancer survivors (n = 279; Mage = 60.7 (SD = 9.7)) completed a battery of online questionnaires. Descriptive statistics were calculated for all data. To examine potential relationships between demographic, disease and behavioral factors, and survivors’ interest in a technology-supported sedentary behavior reduction intervention, we conducted logistic regression analyses. These same factors were examined in relation to the perceptions of the effectiveness of such intervention using multiple regression analyses. Results: On average, survivors spent 10.1 (SD = 4.3) hours/day in sedentary activity. They believed prolonged periods of sedentary behavior were harmful to their health (87.0%) and that reducing sedentary behavior could improve their health (88.4%). Survivors believed they should move around after 30–60 (56.7%) or ≥ 60 (29.9%) minutes of sedentary behavior and indicated they were most likely to replace sedentary behaviors with walking around (97.1%) or walking in place (73.4%). The majority of survivors (79.9%) was interested in participating in a technology-supported sedentary behavior reduction intervention and indicated they would use a smartphone application (61.3%) 2–3 times/day (48.0%), 6 to 7 days/week (52.0%). Most survivors (73.5%) believed reminders would help them decrease sedentary behavior and preferred they be delivered after sitting for 60 minutes (60.5%) via vibrations on a wrist worn activity tracker (77.3%) or text messages (54.4%). Conclusions: Technology-supported sedentary behavior reduction interventions may be feasible and acceptable to breast cancer survivors. Data regarding user preferences for content, features, delivery mode and design will aid researchers in developing sedentary interventions that are potentially more relevant and effective from the outset.

Motivated by physical activity data obtained from the BodyMedia FIT device (www.bodymedia.com), we take a functional data approach for longitudinal studies with continuous proportional outcomes. The functional structure depends on three factors. In our three-factor model, the regression structures are specified as curves measured at various factor-points with random effects that have a correlation structure. The random curve for the continuous factor is summarized using a few important principal components. The difficulties in handling the continuous proportion variables are solved by using a quasilikelihood type approximation. We develop an efficient algorithm to fit the model, which involves the selection of the number of principal components. The method is evaluated empirically by a simulation study. This approach is applied to the BodyMedia data with 935 males and 84 consecutive days of observation, for a total of 78, 540 observations. We show that sleep efficiency increases with increasing physical activity, while its variance decreases at the same time.

OBJECTIVE This randomized trial tested whether a behavioral intervention with meal replacements in pregnancy could increase the proportion of women who returned to prepregnancy weight and reduce postpartum weight retention by 12 months after delivery. METHODS Women (N = 264; 13.7 weeks' gestation) with overweight or obesity were randomly assigned to usual care or intervention. The intervention reduced excess gestational weight gain and was discontinued at delivery. At follow-up, 83.7% completed the 12-month assessment. RESULTS Compared with usual care, prenatal intervention had no significant effect on odds of achieving prepregnancy weight (38/128 [29.7%] vs. 41/129 [31.8%]; P = 0.98) or in reducing the magnitude of weight retained (3.3 vs. 3.1 kg; P = 0.82) at 12 months. After delivery, significant (P

PURPOSE: Sedentary behavior is associated with poor health outcomes including obesity, lower quality of life, and mortality in breast cancer survivors. This study sought to identify motivational, demographic, and disease characteristics of breast cancer survivors who engage in greater amounts of sedentary behavior. METHODS: Multivariate linear regression models estimated associations between demographic, disease, and health characteristics with reported sitting in breast cancer survivors (n = 279; M(age) = 60.7 (± 9.7) years). Regression models estimated associations between motivational factors and reported sitting adjusted for demographic and disease and health covariates. RESULTS: Working at least part-time and marital status were associated various sitting domains including weekday and non-leisure sitting. Higher BMI was associated with more average daily, weekend, and weekday sitting. High income was additionally associated with less non-leisure sitting. The belief that sedentary behavior is bad for health, physical function, and self-evaluative OE, and lifestyle self-efficacy were associated with multiple sitting domains in both univariate and covariate-adjusted models. CONCLUSIONS: Future work should examine the relationships between motivational, demographic, and disease predictors and objectively measured sedentary behavior over time and across different sedentary behavior domains. Understanding activity changes during and after treatment is needed to identify intervention targets and develop effective interventions.

Objective:The purpose of this study was to examine the accuracy of the Fitbit wireless activity tracker in assessing energy expenditure (EE) for different activities.Methods:Twenty participants (10 males, 10 females) wore the Fitbit Classic wireless activity tracker on the hip and the Oxycon Mobile portable metabolic system (criterion). Participants performed walking and running trials on a treadmill and a simulated free-living activity routine. Paired t tests were used to test for differences between estimated (Fitbit) and criterion (Oxycon) kcals for each of the activities.Results:Mean bias for estimated energy expenditure for all activities was −4.5 ± 1.0 kcals/6 min (95% limits of agreement: −25.2 to 15.8 kcals/6 min). The Fitbit significantly underestimated EE for cycling, laundry, raking, treadmill (TM) 3 mph at 5% grade, ascent/descent stairs, and TM 4 mph at 5% grade, and significantly overestimated EE for carrying groceries. Energy expenditure estimated by the Fitbit was not significantly different than EE calculated from the Oxycon Mobile for 9 activities.Conclusion:The Fitbit worn on the hip significantly underestimates EE of activities. The variability in underestimation of EE for the different activities may be problematic for weight loss management applications since accurate EE estimates are important for tracking/monitoring energy deficit.

Copyright © 2017 by the American College of Sports Medicine Introduction/Purpose: To assess the utility of measurement methods that may be more accurate and precise than traditional questionnaire-based estimates of habitual physical activity and sedentary behavior we compared the measurement properties of a past year questionnaire (AARP) and more comprehensive measures: an internet-based 24-h recall (ACT24), and a variety of estimates from an accelerometer (ActiGraph). Methods: Participants were 932 adults (50-74 yr) in a 12-month study that included reference measures of energy expenditure from doubly labeled water (DLW) and active and sedentary time via activPAL. Results: Accuracy at the group level (mean differences) was generally better for both ACT24 and ActiGraph than the AARP questionnaire. The AARP accuracy for energy expenditure ranged from j4% to j13% lower than DLW, but its accuracy was poorer for physical activity duration (j48%) and sedentary time (j18%) versus activPAL. In contrast, ACT24 accuracy was within 3% to 10% of DLW expenditure measures and within 1% to 3% of active and sedentary time from activPAL. For ActiGraph, accuracy for energy expenditure was best for the Crouter 2-regression method (j2% to j7%), and for active and sedentary time the 100 counts per minute cutpoint was most accurate (j1% to 2%) at the group level. One administration of the AARP questionnaire was significantly correlated with long-term average from the reference measures (Q TX = 0.16-0.34) overall, but four ACT24 recalls had higher correlations (Q TX = 0.48-0.60), as did 4 d of ActiGraph assessment (Q TX = 0.54-0.87). Conclusions: New exposure assessments suitable for use in large epidemiologic studies (ACT24, ActiGraph) were more accurate and had higher correlations than a traditional questionnaire. Use of better more comprehensive measures in future epidemiologic studies could yield new etiologic discoveries and possibly new opportunities for prevention.

Neighborhood conditions may have an important impact on physical activity and sedentary behaviors in the older population. Most previous studies in this area are cross-sectional and report mixed findings regarding the effects of neighborhood environment on different types of physical activity. Moreover, little is known about the prospective relationship between neighborhood environment and sedentary behaviors. Our analysis included 136,526 participants from the NIH-AARP Diet and Health Study (age 51–70). Neighborhood socioeconomic deprivation was measured with an index based on census variables and developed using principal component analysis. Physical activity and sedentary behaviors were measured both at baseline (1995–1996) and follow-up (2004–2006). Multiple regression analyses were conducted to examine the prospective relationship between neighborhood deprivation and exercise, non-exercise physical activity, and sedentary behaviors, adjusting for baseline physical activity and sedentary behaviors as well as potential confounders. We found that more severe neighborhood socioeconomic deprivation was prospectively associated with reduced time for exercise (β (Q5) vs (Q1) (95% confidence interval), hour, −0.85 (−0.95, −0.75)) but increased time spent in non-exercise physical activities (1.16 (0.97, 1.34)), such as household activities, outdoor chores, and walking for transportation. Moreover, people from more deprived neighborhoods were also more likely to engage in prolonged (≥5 h/day) TV viewing (Odds ratio (Q5) vs (Q1) (95% confidence interval), 1.21 (1.15, 1.27)). In conclusion, neighborhood socioeconomic deprivation is associated with physical activity and sedentary behavior in the older population. These associations may differ for different types of physical activities.

New evidence suggests that reductions in sedentary behavior may increase physical activity and improve health. These findings point to new behavioral targets for intervention and new ways to think about intervening to increase overall physical activity in the population. This report provides a knowledge update reflecting the rapid accumulation of new evidence related to sedentary behavior and health among adults. Recent observational studies suggest that leveraging the time-inverse relationship between sedentary and active behaviors by replacing sitting with standing, light- or moderate-intensity activity can have important health benefits, particularly among less active adults. Clinical studies are providing evidence of the probable physiologic mechanisms underlying these associations, as well as insights into the cardiometabolic impact of breaking up and reducing sedentary behavior. In contrast to the well-established behavioral theories that guide the development and dissemination of evidence-based interventions to increase moderate- to vigorous-intensity physical activity, much less is known about how to reduce sedentary time to increase daily activities. It has become clear that the environmental, social, and individual level determinants for sedentary time are distinct from those linked to the adoption and maintenance of moderate- to vigorous-intensity physical activity. As a result, novel intervention strategies that focus on sitting and lower-intensity activities by leveraging the surrounding environment (e.g., workplace, school, and home) as well as individual-level cues and habits of sedentary behavior are being tested to increase the potency of interventions designed to increase overall physical activity. Herein we summarize the solutions-oriented research across the behavioral research framework, with a focus on highlighting areas of synergy across disciplines and identifying gaps for future research.

BACKGROUND: Behavioral lifestyle interventions during pregnancy can prevent excessive gestational weight gain (GWG) in women with normal weight; however, effective interventions to reduce GWG in ethnically diverse women with obesity are lacking. OBJECTIVE: A randomized controlled trial was conducted to test whether a behavioral lifestyle intervention with partial meal replacement reduces GWG rate in Hispanic and non-Hispanic women with overweight or obesity relative to enhanced usual care. DESIGN: Participants (n = 257) were recruited in San Luis Obispo, California, and Providence, Rhode Island, between November 2012 and May 2016. Participants were pregnant (mean ± SD: 13.6 ± 1.8 wk of gestation) with overweight or obesity and had a mean age of 30.3 y; 41.6% of participants were Hispanic. Women were randomly assigned within site and by ethnicity to enhanced usual care (n = 128) or to a behavioral lifestyle intervention with partial meal replacement (n = 129). The primary outcome was GWG per week of observation. Secondary outcomes were proportions exceeding Institute of Medicine (IOM) guidelines for total GWG, changes in weight-control behaviors and cardiovascular disease risk factors, and incidence of pregnancy complications. Study retention was 99.6% (256 of 257). RESULTS: The intervention compared with usual care resulted in less mean ± SD weekly GWG (0.33 ± 0.25 compared with 0.39 ± 0.23 kg/wk; P = 0.02) and total GWG (9.4 ± 6.9 compared with 11.2 ± 7.0 kg; P = 0.03) and reduced the proportion of women who exceeded IOM guidelines for total GWG (41.1% compared with 53.9%; P = 0.03). No significant group × time × demographic subgroup (ethnicity, BMI, age, parity, and income) interactions were observed. Among intervention participants, greater meal replacement intake was related to reduced GWG rate (β = −0.07; 95% CI:−0.12, −0.03; P = 0.002). The intervention compared with usual care increased weight-control strategies (P

Copyright © 2017 John Wiley & Sons, Ltd. A mixed effect model is proposed to jointly analyze multivariate longitudinal data with continuous, proportion, count, and binary responses. The association of the variables is modeled through the correlation of random effects. We use a quasi-likelihood type approximation for nonlinear variables and transform the proposed model into a multivariate linear mixed model framework for estimation and inference. Via an extension to the EM approach, an efficient algorithm is developed to fit the model. The method is applied to physical activity data, which uses a wearable accelerometer device to measure daily movement and energy expenditure information. Our approach is also evaluated by a simulation study.

Purpose: Television (TV) viewing time is associated with increased risk of all-cause, cardiovascular and cancer mortality. Although TV time is detrimentally associated with key inflammatory markers, the associations of TV time with other inflammatory-related mortality (with a predominant inflammatory, oxidative or infectious component, but not attributable to cancer or cardiovascular causes), are unknown. Methods: Among 8933 Australian adults (4593 never-smokers) from the baseline (1999–2000) Australian Diabetes, Obesity and Lifestyle Study (median follow-up, 13.6 yr), we examined TV time in relation to noninflammatory and inflammatory-related mortality (not attributable to cancer or cardiovascular causes, hereafter ‘‘inflammatory-related’’ mortality). Because smoking has a significant inflammatory component, we also examined this relationship in never-smokers. Results: Of 896 deaths, 248 were attributable to cardiovascular disease, 346 to cancer, 130 to other inflammatory-related causes (71 for never-smokers), and 172 to noninflammatory-related causes (87 for never-smokers). After multivariate adjustment for age, sex, education, household income, smoking status, alcohol intake, energy intake, diet, and cardiometabolic risk biomarkers (model 3), every additional hours per day of TV time was associated with increased risk of inflammatory-related mortality in the overall population (hazard ratio, 1.12; 95% confidence interval, 1.00–1.25) and in never-smokers (1.18; 1.00, 1.40). These results were attenuated after additional adjustment for leisure-time physical activity. After multivariate adjustment (model 3), no association was observed for noninflammatory mortality in the overall population (0.95; 0.85, 1.07), but risk tended to decrease for never-smokers (0.85; 0.75, 1.02). Conclusions: In summary, before adjustment for leisure-time physical activity, TV time was associated with increased risk of inflammatory-related mortality. This is consistent with the hypothesis that high TV viewing may be associated with a chronic inflammatory state.

Background:This paper tested whether a physical activity index (PAI) that integrates PA-related behaviors (ie, moderate-to-vigorous physical activity [MVPA] and TV viewing) and performance measures (ie, cardiorespiratory fitness and muscle strength) improves prediction of health status.Methods:Participants were a nationally representative sample of US adults from 2011 to 2012 NHANES. Dependent variables (self-reported health status, multimorbidity, functional limitations, and metabolic syndrome) were dichotomized. Wald-F tests tested whether the model with all PAI components had statistically significantly higher area under the curve (AUC) values than the models with behavior or performance scores alone, adjusting for covariates and complex survey design.Results:The AUC (95% CI) for PAI in relation to health status was 0.72 (0.68, 0.76), and PAI-AUC for multimorbidity was 0.72 (0.69, 0.75), which were significantly higher than the behavior or performance scores alone. For functional limitations, the PAI AUC was 0.71 (0.67, 0.74), significantly higher than performance, but not behavior scores, while the PAI AUC for metabolic syndrome was 0.69 (0.66, 0.73), higher than behavior but not performance scores.Conclusions:These results provide empirical support that an integrated PAI may improve prediction of health and disease. Future research should examine the clinical utility of a PAI and verify these findings in prospective studies.

Introduction Accelerometers are used increasingly in large epidemiologic studies, but, given logistic and cost constraints, most studies are restricted to a single, 7-day accelerometer monitoring period. It is unknown how well a 7-day accelerometer monitoring period estimates longer-term patterns of behavior, which is critical for interpreting, and potentially improving, disease risk estimates in etiologic studies. Methods A subset of participants from the Women’s Health Study (N=209; mean age, 70.6 [SD=5.7] years) completed at least two 7-day accelerometer administrations (ActiGraph GT3X+) within a period of 2–3 years. Monitor output was translated into total counts, steps, and time spent in sedentary, light-intensity, and moderate to vigorous–intensity activity (MVPA) and bouted-MVPA (i.e., 10-minute bouts). For each metric, intraclass correlations (ICCs) and 95% CIs were calculated using linear-mixed models and adjusted for wear time, age, BMI, and season. The data were collected in 2011–2015 and analyzed in 2015–2016. Results The ICCs ranged from 0.67 (95% CI=0.60, 0.73) for bouted-MVPA to 0.82 (95% CI=0.77, 0.85) for total daily counts and were similar across age, BMI, and for less and more active women. For all metrics, classification accuracy within 1 quartile was >90%. Conclusions These data provide reassurance that a 7-day accelerometer-assessment protocol provides a reproducible (and practical) measure of physical activity and sedentary time. However, ICCs varied by metric; therefore, future prospective studies of chronic diseases might benefit from existing methods to adjust risk estimates for within-person variability in activity to get a better estimate of the true strength of association.

Both short and long durations of sleep are associated with higher mortality, but little is known about the interrelationship between sleep and other modifiable factors in relation to mortality. In the National Institutes of Health-AARP Diet and Health Study (1995-1996), we examined associations between sleep duration and total, cardiovascular disease (CVD), and cancer mortality among 239,896 US men and women aged 51-72 years who were free of cancer, CVD, and respiratory disease. We evaluated the influence of moderate-to-vigorous physical activity, television viewing, and body mass index (BMI; weight (kg)/height (m)(2)) on the sleep-mortality association and assessed their combined association with mortality. During an average of 14 years of follow-up, we identified 44,100 deaths. Compared with 7-8 hours of sleep per day, both shorter and longer sleep durations were associated with higher total and CVD mortality. We found a greater elevation in CVD mortality associated with shorter sleep among overweight and obese people, suggesting a synergistic interaction between sleep and BMI. People in the unhealthy categories of all 4 risk factors (sleep