Importance It is unclear whether the number of steps per day and the intensity of stepping are associated with lower mortality. Objective Describe the dose-response relationship between step count and intensity and mortality. Design, Setting, and Participants Representative sample of US adults aged at least 40 years in the National Health and Nutrition Examination Survey who wore an accelerometer for up to 7 days ( from 2003-2006). Mortality was ascertained through December 2015. Exposures Accelerometer-measured number of steps per day and 3 step intensity measures (extended bout cadence, peak 30-minute cadence, and peak 1-minute cadence [steps/min]). Accelerometer data were based on measurements obtained during a 7-day period at baseline. Main Outcomes and Measures The primary outcome was all-cause mortality. Secondary outcomes were cardiovascular disease (CVD) and cancer mortality. Hazard ratios (HRs), mortality rates, and 95% CIs were estimated using cubic splines and quartile classifications adjusting for age; sex; race/ethnicity; education; diet; smoking status; body mass index; self-reported health; mobility limitations; and diagnoses of diabetes, stroke, heart disease, heart failure, cancer, chronic bronchitis, and emphysema. Results A total of 4840 participants (mean age, 56.8 years; 2435 [54%] women; 1732 [36%] individuals with obesity) wore accelerometers for a mean of 5.7 days for a mean of 14.4 hours per day. The mean number of steps per day was 9124. There were 1165 deaths over a mean 10.1 years of follow-up, including 406 CVD and 283 cancer deaths. The unadjusted incidence density for all-cause mortality was 76.7 per 1000 person-years (419 deaths) for the 655 individuals who took less than 4000 steps per day; 21.4 per 1000 person-years (488 deaths) for the 1727 individuals who took 4000 to 7999 steps per day; 6.9 per 1000 person-years (176 deaths) for the 1539 individuals who took 8000 to 11 999 steps per day; and 4.8 per 1000 person-years (82 deaths) for the 919 individuals who took at least 12 000 steps per day. Compared with taking 4000 steps per day, taking 8000 steps per day was associated with significantly lower all-cause mortality (HR, 0.49 [95% CI, 0.44-0.55]), as was taking 12 000 steps per day (HR, 0.35 [95% CI, 0.28-0.45]). Unadjusted incidence density for all-cause mortality by peak 30 cadence was 32.9 per 1000 person-years (406 deaths) for the 1080 individuals who took 18.5 to 56.0 steps per minute; 12.6 per 1000 person-years (207 deaths) for the 1153 individuals who took 56.1 to 69.2 steps per minute; 6.8 per 1000 person-years (124 deaths) for the 1074 individuals who took 69.3 to 82.8 steps per minute; and 5.3 per 1000 person-years (108 deaths) for the 1037 individuals who took 82.9 to 149.5 steps per minute. Greater step intensity was not significantly associated with lower mortality after adjustment for total steps per day (eg, highest vs lowest quartile of peak 30 cadence: HR, 0.90 [95% CI, 0.65-1.27];Pvalue for trend = .34). Conclusions and Relevance Based on a representative sample of US adults, a greater number of daily steps was significantly associated with lower all-cause mortality. There was no significant association between step intensity and mortality after adjusting for total steps per day.

Methods The three examples illustrate different uses of equivalence tests. Example 1 uses PA data to evaluate an activity monitor’s equivalence to a known criterion. Example 2 illustrates the equivalence of two field-based measures of physical fitness with no known reference method. Example 3 uses regression to evaluate an activity monitor’s equivalence across a suite of 23 activities. Results The examples illustrate the appropriate reporting and interpretation of results from equivalence tests. In the first example, the mean criterion measure is significantly within ±15% of the mean PA monitor. The mean difference is 0.18 METs and the 90% confidence interval of −0.15 to 0.52 is inside the equivalence region of −0.65 to 0.65. In the second example, we chose to define equivalence for these two measures as a ratio of mean values between 0.98 and 1.02. The estimated ratio of mean V˙O2 values is 0.99, which is significantly (P = 0.007) inside the equivalence region. In the third example, the PA monitor is not equivalent to the criterion across the suite of activities. The estimated regression intercept and slope are −1.23 and 1.06. Neither confidence interval is within the suggested regression equivalence regions. Conclusions When the study goal is to show similarity between methods, equivalence testing is more appropriate than traditional statistical tests of differences (e.g., ANOVA and t-tests)....

National guidelines for aerobic physical activity recommend adults should participate in at least 150 minutes per week of moderate intensity aerobic activity or 75 minutes per week of vigorous intensity activity or an equivalent combination of both.^1-3 Levels of physical activity equivalent to meeting this guideline have been associated with substantial health benefits, including reductions in all-cause,^4,5 cardiovascular disease (CVD)–related,^4,6 and cancer-related^4,7,8 mortality. However, most of the evidence on the mortality benefits of physical activity comes from studies that measure leisure-time physical activity (LTPA) at only 1 point, usually during midlife (eg, 40-60 years of age). To date, no study to our knowledge has taken a life course approach to examine how participation in physical activity throughout the different stages of adulthood may be associated with mortality risk.

The purpose of this study was to calibrate and cross-validate the Youth Activity Profile (YAP), a self-report tool designed to capture physical activity (PA) and sedentary behaviors (SB) in youth. Eight schools in the Midwest part of the U.S. were involved and a total of 291 participants from grades 4–12 agreed to wear an accelerometer (SWA Armband) and complete the YAP in two separate weeks (5–7 days apart). Individual YAP items capture PA behavior during specific segments of the week and these items were combined with temporally matched estimates of moderate-to-vigorous PA (MVPA) and sedentary time from the SWA to enable calibration. Quantile regression procedures yielded YAP prediction algorithms that estimated MVPA at School, MVPA at Out-of-School, MVPA on Weekend, as well as time spent in SB. The YAP estimates of time spent in MVPA and SB were cross-validated using Pearson product correlations and limits of agreement, as indicative of individual error and, equivalence testing techniques as indicative of group-level error. Following calibration, the correlations between YAP and SWA estimates of MVPA were low to moderate (r_range = .19 to .58) and individual-level YAP estimates of MVPA ranged from -134.9% to +110.0% of SWA MVPA values. Differences between aggregated YAP and SWA MVPA ranged from -3.4 to 21.7 minutes of MVPA at the group-level and predicted YAP MVPA estimates were within 15%, 20%, and 30%, of values from the SWA for the School, Out-of-School, and Weekend time periods, respectively. Estimates of time spent in SB were highly correlated with each other (r = .75). The individual estimates of SB ranged from -54.0% to +44.0% of SWA sedentary time, and the aggregated group-level estimates differed by 49.7 minutes (within 10% of the SWA aggregated estimates). This study provides preliminary evidence that the calibration procedures enabled the YAP to provide estimates of MVPA and SB that approximated values from an objective monitor. The YAP provides a simple, low-cost and educationally sound method to accurately estimate children’s MVPA and SB at the group level.

Background The 2008 Physical Activity Guidelines for Americans recommends that adults accumulate moderate‐to‐vigorous physical activity (MVPA) in bouts of ≥10 minutes for substantial health benefits. To what extent the same amount of MVPA accumulated in bouts versus sporadically reduces mortality risk remains unclear. Methods and Results We analyzed data from the National Health and Nutrition Examination Survey 2003–2006 and death records available through 2011 (follow‐up period of ≈6.6 years; 700 deaths) to examine the associations between objectively measured physical activity accumulated with and without a bout criteria and all‐cause mortality in a representative sample of US adults 40 years and older (n=4840). Physical activity data were processed to generate minutes per day of total and bouted MVPA. Bouted MVPA was defined as MVPA accumulated in bouts of a minimum duration of either 5 or 10 minutes allowing for 1‐ to 2‐minute interruptions. Hazard ratios for all‐cause mortality associated with total and bouted MVPA were similar and ranged from 0.24 for the third quartile of total to 0.44 for the second quartile of 10‐minute bouts. The examination of jointly classified quartiles of total MVPA and tertiles of proportion of bouted activity revealed that greater amounts of bouted MVPA did not result in additional risk reductions for mortality. Conclusions These results provide evidence that mortality risk reductions associated with MVPA are independent of how activity is accumulated and can impact the development of physical activity guidelines and inform clinical practice.

Introduction: A lack of standardization with accelerometry-based monitors has made it hard to advance applications for both research and practice. Resolving these challenges is essential for developing methods for consistent, agnostic reporting of physical activity outcomes from wearable monitors in clinical applications.Methods: This article reviewed the literature on the methods used to evaluate the validity of contemporary consumer activity monitors. A rationale for focusing on energy expenditure as a key outcome measure in validation studies was provided followed by a summary of the strengths and limitations of different analytical methods. The primary review included 23 recent validation studies that collectively reported energy expenditure estimates from 58 monitors relative to values from appropriate criterion measures.Results: The majority of studies reported weak indicators such as correlation coefficients (87%), but only half (52%) reported the recommended summary statistic of mean absolute percent error needed to evaluate actual individual error. Fewer used appropriate tests of agreement such as equivalence testing (22%).Conclusions: The use of inappropriate analytic methods and incomplete reporting of outcomes is a major limitation for systematically advancing research with both research grade and consumer-grade activity monitors. Guidelines are provided to standardize analytic methods and reporting in these types of studies to enhance the utility of the devices for clinical mHealth applications.