Sarah Kozey Keadle

Association of Leisure-Time Physical Activity With Risk of 26 Types of Cancer in 1.44 Million Adults

IMPORTANCE Leisure-time physical activity has been associated with lower risk of heart-disease and all-cause mortality, but its association with risk of cancer is not well understood. OBJECTIVE To determine the association of leisure-time physical activity with incidence of common types of cancer and whether associations vary by body size and/or smoking. DESIGN, SETTING, AND PARTICIPANTS We pooled data from 12 prospective US and European cohorts with self-reported physical activity (baseline, 1987-2004). We used multivariable Cox regression to estimate hazard ratios (HRs) and 95% confidence intervals for associations of leisure-time physical activity with incidence of 26 types of cancer. Leisure-time physical activity levels were modeled as cohort-specific percentiles on a continuous basis and cohort-specific results were synthesized by random-effects meta-analysis. Hazard ratios for high vs low levels of activity are based on a comparison of risk at the 90th vs 10th percentiles of activity. The data analysis was performed from January 1, 2014, to June 1, 2015. EXPOSURES Leisure-time physical activity of a moderate to vigorous intensity. MAIN OUTCOMES AND MEASURES Incident cancer during follow-up. RESULTS A total of 1.44 million participants (median [range] age, 59 [19-98] years; 57% female) and 186 932 cancers were included. High vs low levels of leisure-time physical activity were associated with lower risks of 13 cancers: esophageal adenocarcinoma (HR, 0.58; 95% CI, 0.37-0.89), liver (HR, 0.73; 95% CI, 0.55-0.98), lung (HR, 0.74; 95% CI, 0.71-0.77), kidney (HR, 0.77; 95% CI, 0.70-0.85), gastric cardia (HR, 0.78; 95% CI, 0.64-0.95), endometrial (HR, 0.79; 95% CI, 0.68-0.92), myeloid leukemia (HR, 0.80; 95% CI, 0.70-0.92), myeloma (HR, 0.83; 95% CI, 0.72-0.95), colon (HR, 0.84; 95% CI, 0.77-0.91), head and neck (HR, 0.85; 95% CI, 0.78-0.93), rectal (HR, 0.87; 95% CI, 0.80-0.95), bladder (HR, 0.87; 95% CI, 0.82-0.92), and breast (HR, 0.90; 95% CI, 0.87-0.93). Body mass index adjustment modestly attenuated associations for several cancers, but 10 of 13 inverse associations remained statistically significant after this adjustment. Leisure-time physical activity was associated with higher risks of malignant melanoma (HR, 1.27; 95% CI, 1.16-1.40) and prostate cancer (HR, 1.05; 95% CI, 1.03-1.08). Associations were generally similar between overweight/obese and normal-weight individuals. Smoking status modified the association for lung cancer but not other smoking-related cancers. CONCLUSIONS AND RELEVANCE Leisure-time physical activity was associated with lower risks of many cancer types. Health care professionals counseling inactive adults should emphasize that most of these associations were evident regardless of body size or smoking history, supporting broad generalizability of findings.

Validity of two wearable monitors to estimate breaks from sedentary time.

UNLABELLED Investigations using wearable monitors have begun to examine how sedentary time behaviors influence health. PURPOSE The objective of this study is to demonstrate the use of a measure of sedentary behavior and to validate the activPAL (PAL Technologies Ltd., Glasgow, Scotland) and ActiGraph GT3X (Actigraph, Pensacola, FL) for estimating measures of sedentary behavior: absolute number of breaks and break rate. METHODS Thirteen participants completed two 10-h conditions. During the baseline condition, participants performed normal daily activity, and during the treatment condition, participants were asked to reduce and break up their sedentary time. In each condition, participants wore two ActiGraph GT3X monitors and one activPAL. The ActiGraph was tested using the low-frequency extension filter (AG-LFE) and the normal filter (AG-Norm). For both ActiGraph monitors, two count cut points to estimate sedentary time were examined: 100 and 150 counts per minute. Direct observation served as the criterion measure of total sedentary time, absolute number of breaks from sedentary time, and break rate (number of breaks per sedentary hour (brk·sed-h)). RESULTS Break rate was the only metric sensitive to changes in behavior between baseline (5.1 [3.3-6.8] brk·sed-h) and treatment conditions (7.3 [4.7-9.8] brk·sed-h) (mean (95% confidence interval)). The activPAL produced valid estimates of all sedentary behavior measures and was sensitive to changes in break rate between conditions (baseline, 5.1 [2.8-7.1] brk·sed-h; treatment, 8.0 [5.8-10.2] brk·sed-h). In general, the AG-LFE and AG-Norm were not accurate in estimating break rate or the absolute number of breaks and were not sensitive to changes between conditions. CONCLUSION This study demonstrates the use of expressing breaks from sedentary time as a rate per sedentary hour, a metric specifically relevant to free-living behavior, and provides further evidence that the activPAL is a valid tool to measure components of sedentary behavior in free-living environments.

Mortality Benefits for Replacing Sitting Time with Different Physical Activities

PURPOSE Prolonged sitting has emerged as a risk factor for early mortality, but the extent of benefit realized by replacing sitting time with exercise or activities of everyday living (i.e., nonexercise activities) is not known. METHODS We prospectively followed 154,614 older adults (59-82 yr) in the National Institutes of Health-AARP Diet and Health Study who reported no major chronic diseases at baseline and reported detailed information about sitting time, exercise, and nonexercise activities. Proportional hazard models were used to estimate adjusted hazard ratios and 95% confidence intervals (HR (95% confidence interval)) for mortality. An isotemporal modeling approach was used to estimate associations for replacing sitting time with specific types of physical activity, with separate models fit for less active and more active participants to account for nonlinear associations. RESULTS During 6.8 yr (SD, 1.0) of follow-up, 12,201 deaths occurred. Greater sitting time (≥12 vs < 5 h·d(-1)) was associated with increased risk for all-cause and cardiovascular mortality. In less active adults (<2 h·d(-1) total activity), replacing 1 h·d(-1) of sitting with an equal amount of activity was associated with lower all-cause mortality for both exercise (HR, 0.58 (0.54-0.63)) and nonexercise activities (HR, 0.70 (0.66-0.74)), including household chores, lawn and garden work, and daily walking. Among more active participants (2+ h·d(-1) total activity), replacement of sitting time with purposeful exercise was associated with lower mortality (HR, 0.91 (0.88-0.94)) but not with nonexercise activity (HR, 1.00 (0.98-1.02)). Similar results were noted for cardiovascular mortality. CONCLUSIONS Physical activity intervention strategies for older adults often focus on aerobic exercise, but our findings suggest that reducing sitting time and engaging in a variety of activities is also important, particularly for inactive adults.

Sleep Duration and Total and Cause-Specific Mortality in a Large US Cohort: Interrelationships With Physical Activity, Sedentary Behavior, and Body Mass Index

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 <7 hours/day, moderate-to-vigorous physical activity ≤1 hour/week, television viewing ≥3 hours/day, and BMI ≥25) had significantly higher all-cause (relative risk (RR) = 1.42, 95% confidence interval (CI): 1.34, 1.52), CVD (RR = 1.90, 95% CI: 1.67, 2.17), and cancer (RR = 1.21, 95% CI: 1.09, 1.34) mortality. Short sleep duration may predict higher mortality, particularly CVD mortality, among overweight and obese people.

Validation of a Previous-Day Recall Measure of Active and Sedentary Behaviors

PURPOSE A previous-day recall (PDR) may be a less error-prone alternative to traditional questionnaire-based estimates of physical activity and sedentary behavior (e.g., past year), but the validity of the method is not established. We evaluated the validity of an interviewer administered PDR in adolescents (12-17 yr) and adults (18-71 yr). METHODS In a 7-d study, participants completed three PDR, wore two activity monitors, and completed measures of social desirability and body mass index. PDR measures of active and sedentary time was contrasted against an accelerometer (ActiGraph) by comparing both to a valid reference measure (activPAL) using measurement error modeling and traditional validation approaches. RESULTS Age- and sex-specific mixed models comparing PDR to activPAL indicated the following: 1) there was a strong linear relationship between measures for sedentary (regression slope, β1 = 0.80-1.13) and active time (β1 = 0.64-1.09), 2) person-specific bias was lower than random error, and 3) correlations were high (sedentary: r = 0.60-0.81; active: r = 0.52-0.80). Reporting errors were not associated with body mass index or social desirability. Models comparing ActiGraph to activPAL indicated the following: 1) there was a weaker linear relationship between measures for sedentary (β1 = 0.63-0.73) and active time (β1 = 0.61-0.72), (2) person-specific bias was slightly larger than random error, and (3) correlations were high (sedentary: r = 0.68-0.77; active: r = 0.57-0.79). CONCLUSIONS Correlations between the PDR and the activPAL were high, systematic reporting errors were low, and the validity of the PDR was comparable with the ActiGraph. PDR may have value in studies of physical activity and health, particularly those interested in measuring the specific type, location, and purpose of activity-related behaviors.

Resistance to exercise-induced weight loss : compensatory behavioral adaptations

In many interventions that are based on an exercise program intended to induce weight loss, the mean weight loss observed is modest and sometimes far less than what the individual expected. The individual responses are also widely variable, with some individuals losing a substantial amount of weight, others maintaining weight, and a few actually gaining weight. The media have focused on the subpopulation that loses little weight, contributing to a public perception that exercise has limited utility to cause weight loss. The purpose of the symposium was to present recent, novel data that help explain how compensatory behaviors contribute to a wide discrepancy in exercise-induced weight loss. The presentations provide evidence that some individuals adopt compensatory behaviors, that is, increased energy intake and/or reduced activity, that offset the exercise energy expenditure and limit weight loss. The challenge for both scientists and clinicians is to develop effective tools to identify which individuals are susceptible to such behaviors and to develop strategies to minimize their effect.

Validation of the Fitbit wireless activity tracker for prediction of energy expenditure.

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.

A Method to Estimate Free-Living Active and Sedentary Behavior from an Accelerometer

INTRODUCTION Methods to estimate physical activity (PA) and sedentary behavior (SB) from wearable monitors need to be validated in free-living settings. PURPOSE The purpose of this study was to develop and validate two novel machine-learning methods (Sojourn-1 Axis [soj-1x] and Sojourn-3 Axis [soj-3x]) in a free-living setting. METHODS Participants were directly observed in their natural environment for 10 consecutive hours on three separate occasions. Physical activity and SB estimated from soj-1x, soj-3x, and a neural network previously calibrated in the laboratory (lab-nnet) were compared with direct observation. RESULTS Compared with lab-nnet, soj-1x and soj-3x improved estimates of MET-hours (lab-nnet: % bias [95% confidence interval] = 33.1 [25.9 to 40.4], root-mean-square error [RMSE] = 5.4 [4.6-6.2]; soj-1x: % bias = 1.9 [-2.0 to 5.9], RMSE = 1.0 [0.6 to 1.3]; soj-3x: % bias = 3.4 [0.0 to 6.7], RMSE = 1.0 [0.6 to 1.5]) and minutes in different intensity categories {lab-nnet: % bias = -8.2 (sedentary), -8.2 (light), and 72.8 (moderate-to-vigorous PA [MVPA]); soj-1x: % bias = 8.8 (sedentary), -18.5 (light), and -1.0 (MVPA); soj-3x: % bias = 0.5 (sedentary), -0.8 (light), and -1.0 (MVPA)}. Soj-1x and soj-3x also produced accurate estimates of guideline minutes and breaks from sedentary time. CONCLUSIONS Compared with the lab-nnet algorithm, soj-1x and soj-3x improved the accuracy and precision in estimating free-living MET-hours, sedentary time, and time spent in light-intensity activity and MVPA. In addition, soj-3x is superior to soj-1x in differentiating SB from light-intensity activity.

Prevalence and trends in physical activity among older adults in the United States: A comparison across three national surveys

This paper examined how many older adults (65+years) are meeting physical activity (PA) Guidelines (PAG; 150min/week 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<0.05. The proportion of older adults meeting PAG in the NHIS survey, the only survey where PA questions remained the same over time, increased from 25.7% in 1998 to 35.8% in 2013 (P<0.01). Point-estimates for activity levels are different between surveys but they consistently identify sub-groups who are less active. Although older adults are reporting more activity over time, adherence to aerobic and strength training PAG remains low in this population and there is a need for effective interventions to prevent age-related declines in PA and address health disparities among older adults.

Discrete Features of Sedentary Behavior Impact Cardiometabolic Risk Factors

PURPOSE Sedentary behavior is linked to numerous poor health outcomes. This study aims to determine the effects of 7 d of increased sitting on markers of cardiometabolic risk among free-living individuals. METHODS Ten recreationally active participants (>150 min of moderate-intensity physical activity per week; mean ± SD age, 25.2 ± 5.7 yr; mean ± SD body mass index, 24.9 ± 4.3 kg·m(-2)) completed a 7-d baseline period and a 7-d sedentary condition in their free-living environment. At baseline, participants maintained normal activity. After baseline, participants completed a 7-d sedentary condition. Participants were instructed to sit as much as possible, to limit standing and walking, and to refrain from structured exercise and leisure time physical activity. ActivPAL monitor was used to assess sedentary behavior and physical activity. Fasting lipids, glucose, and insulin were measured, and oral glucose tolerance test was performed after baseline and sedentary condition. RESULTS In comparison to baseline, total sedentary time (mean Δ, 14.9%; 95% CI, 10.2-19.6) and time in prolonged/uninterrupted sedentary bouts significantly increased, whereas the rate of breaks from sedentary time was significantly reduced (mean Δ, 21.4%; 95% CI, 6.9-35.9). For oral glucose tolerance test, 2-h plasma insulin (mean Δ, 38.8 μU·mL(-1); 95% CI, 10.9-66.8) and area under the insulin curve (mean Δ, 3074.1 μU·mL(-1) per 120 min; 95% CI, 526.0-5622.3) were significantly elevated after the sedentary condition. Lipid concentrations did not change. Change in 2-h insulin was negatively associated with change in light-intensity activity (r = -0.62) and positively associated with change in time in sitting bouts longer than 30 min (r = 0.82) and 60 min (r = 0.83). CONCLUSION Increased free-living sitting negatively impacts markers of cardiometabolic health, and specific features of sedentary behavior (e.g., time in prolonged sitting bouts) may be particularly important.