Validation of ACT24 Version 2.0 for Estimating Behavioral Domains, Active and Sedentary Time.

PURPOSE: The purpose of this study was to determine the criterion validity of Activities Completed over Time (ACT24), an automated previous-day recall tool designed for mobile devices for 1) estimating sedentary versus active time compared with an activPAL, and 2) estimating time spent in activity domains (e.g., work, household, leisure) compared with direct observation (DO). METHODS: Over a 7-d study period, 53 participants were sent invitations to complete three automated ACT24 recalls and to wear an activPAL device. A subset ( N = 24) consented to two, 3-h video-recorded DO sessions. activPAL and ACT24 data were matched by date, and agreement for sedentary versus active time was compared between methods using paired t -tests for mean differences and spearman correlations. We compared DO and ACT24 results by domain for overall time use and separately for sedentary and active time using κ statistics and tested mean differences with linear mixed models. RESULTS: Compared with the activPAL, the mean difference in ACT24 sedentary time was 1.9% (mean (95% confidence interval), -0.17 (-0.75 to 0.40) h·d -1 ), and the mean difference for ACT24 active time was 2.2% (0.14 (-0.32 to 0.60) h·d -1 ). Correlations were R = 0.61 (95% confidence interval, 0.39 to 0.76) and R = 0.65 (0.44 to0.78) for sedentary and active time, respectively. Domain-specific agreement was substantial for leisure-time, work, and shopping/errands ( κ range, 0.63-0.79), moderate for transportation ( κ = 0.49), and fair for personal care and household activities ( κ = 0.24 and 0.33). ACT24 estimates of average time within each domain were not significantly different from DO. CONCLUSIONS: The present study confirms that ACT24 is accurate for group-level estimation of active and sedentary time. Domain-specific agreement tended to be higher for more commonly reported activities and those that were of longer duration.

Impact of Moderate-Vigorous Physical Activity Trajectories on Colon Cancer Risk over the Adult Life Course.

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 <65 years at baseline and in men (P < 0.001). 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.

Resting Oxygen Uptake Value of 1 Metabolic Equivalent of Task in Older Adults: A Systematic Review and Descriptive Analysis.

BACKGROUND: It is important for sport scientists and health professionals to have estimative methods for energy demand during different 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 effort 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 identified, 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.

Physical Activity and Total Daily Energy Expenditure in Older US Adults: Constrained versus Additive Models.

PURPOSE: This study aimed 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 yr) 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 nonlinear methods (restricted cubic splines). Percent weight change (>3%) over a 6-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-1 in the bottom decile to 2693 (SD, 480) kcal·d-1 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-1 in the bottom decile to 2372 (SD, 560) kcal·d-1 in the top decile. CONCLUSIONS: Energy balance status seems 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 seems 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.

Charity-based incentives motivate young adult cancer survivors to increase physical activity: a pilot randomized clinical trial.

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.

Reexamining the Energy Cost of Sedentary Behaviors From the 2011 Adult Compendium.

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 < .001). Driving and slow walking have similar Adult Compendium values, but driving METs were significantly lower than slow walking (P < .001). Driving was similar to sitting while working (1.32 [0.25] METs, P > .05) and yielded significantly higher MET values than quiet sitting (1.08 [0.23] METs, P < .001) and watching TV (1.12 [0.22] METs, P < .001), both of which were lower than their respective Adult Compendium MET values. 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.

Reproducibility of Accelerometer and Posture-derived Measures of Physical Activity.

PURPOSE: The present study estimated the long-term reproducibility of accelerometer-based measures over 6 months in adults and the implications for statistical power, and attenuation in regression coefficients for future activity-disease studies. METHODS: We used data from 914 adults in the Interactive Diet and Activity Tracking in AARP study. Participants wore an activPAL 3 (AP) and an ActiGraph GT3X (AG) twice, 6 months apart. AP measures included time spent sitting or lying, standing, and stepping, whereas AG measures included time spent in sedentary, light, and moderate-to-vigorous physical activity (PA). Reproducibility of each metric and implications for epidemiological studies were determined based on intraclass correlation coefficients (ICC; 95% confidence interval). RESULTS: The ICC values for AP estimates were 0.58 (95% confidence interval, 0.53-0.63) for sitting, 0.62 (0.57-0.67) for standing, and 0.57 (0.51-0.62) for stepping. The ICC values for AG were 0.56 (0.50-0.61) for sedentary, 0.54 (0.49-0.60) for light PA, and 0.58 (0.52-0.63) for moderate-to-vigorous PA. Modeling showed that increasing the number of replicate administrations to two or three resulted in the most noticeable increases in ICC values, statistical power, and reductions in attenuation coefficients. For example, administering the AP twice reduced within-subject variability by half and resulted in an increase in the ICC associated with sitting time from 0.58 to 0.74. Similar comparisons for AG and measure of sedentary time resulted in an increase in ICC values from 0.56 to 0.72. Increasing the number of replicate administrations from one to two reduced the attenuation in activity-outcome associations from 40% to 25%. CONCLUSION: Accelerometer-based classifications of activity are moderately stable over time, but there is considerable within-subject variability that needs to be considered when estimating usual activity in future studies.

A Framework to Evaluate Devices That Assess Physical Behavior.

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.

Development and Testing of an Integrated Score for Physical Behaviors.

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.

A Review of Statistical Analyses on Physical Activity Data Collected from Accelerometers.

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.

Use of Time and Energy on Exercise, Prolonged TV Viewing, and Work Days.

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.

Influence of Accelerometer Calibration Approach on Moderate-Vigorous Physical Activity Estimates for Adults.

PURPOSE: 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. METHODS: 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. RESULTS: 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 (P < 0.05). 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. CONCLUSIONS: 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.

Three-part joint modeling methods for complex functional data mixed with zero-and-one-inflated proportions and zero-inflated continuous outcomes with skewness.

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 2 responses over time in 5-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 3 possible activity patterns in each interval (inactive, partially active, and completely active), and those patterns, which are observed, induce both nonrandom 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 of activity is exactly one, and the energy expenditure rate is greater than zero and likely to be higher. To address these challenges, we propose a 3-part functional data joint modeling approach. The first part is a continuation-ratio model to reorder the ordinal valued 3 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 3-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 3 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 quasi-likelihood type approximation. We develop an efficient algorithm to fit the model that 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.

A joint modeling and estimation method for multivariate longitudinal data with mixed types of responses to analyze physical activity data generated by accelerometers.

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.

Breast cancer survivors' preferences for technology-supported exercise interventions.

PURPOSE: The purpose of this study was to explore breast cancer survivors' interest in and preferences for technology-supported exercise interventions. METHODS: 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. RESULTS: 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%). CONCLUSIONS: 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 activPALTM Accurately Classifies Activity Intensity Categories in Healthy Adults.

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. PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: The AP can be used to accurately capture individualized estimates of active and sedentary behavior variables in free-living settings.

Combining Activity-Related Behaviors and Attributes Improves Prediction of Health Status in NHANES.

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.

Reproducibility of Accelerometer-Assessed Physical Activity and Sedentary Time.

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.