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.

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.

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.

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.

Invited commentary: meta-physical activity and the search for the truth.

Measurement error in self-reported data from questionnaires is a well-recognized challenge in studies of physical activity and health. In this issue of the Journal, Lim et al. (Am J Epidemiol. 2015;181(9):648-655) used data from accelerometers in a small measurement study to correct self-reported physical activity data from a larger study of adults from New York City and to develop an error correction model. They showed that correction of measurement error in self-reported physical activity levels strengthened the associations of physical activity with both obesity and diabetes by 30%-50% compared with using the self-reported questionnaire data alone. Thus, Lim et al. demonstrated a method to improve potentially biased estimates of the association between self-reported physical activity and disease. However, as this field develops, we feel it is important to call attention to a sometimes overlooked problem that occurs when comparing these instruments: Questionnaires and accelerometers are often calibrated (i.e., designed) to measure different types of physical activity, and accelerometers are still subject to measurement error. Thus, physical activity estimates corrected with an imperfect accelerometer measurement might over- or undercorrect the strength of the associations. We take this opportunity to further comment on physical activity measurement in epidemiologic studies and the implications for research.

Impact of changes in television viewing time and physical activity on longevity: a prospective cohort study.

BACKGROUND: Television viewing is a highly prevalent sedentary behavior among older adults, yet the mortality risks associated with hours of daily viewing over many years and whether increasing or decreasing viewing time affects mortality is unclear. This study examined: 1) the long-term association between mortality and daily viewing time; 2) the influence of reducing and increasing in television viewing time on longevity and 3) combined effects of television viewing and moderate-to-vigorous physical activity (MVPA) on longevity. METHODS: Participants included 165,087 adults in the NIH-AARP Diet and Health (aged 50-71 yrs) who completed questionnaires at two-time-points (Time 1: 1994-1996, and Time 2: 2004-2006) and were followed until death or December 31, 2011. Multivariable-adjusted Cox proportional hazards regression was used to estimate Hazard Ratios and 95% confidence intervals (CI) with self-reported television viewing and MVPA and all-cause mortality. RESULTS: Over 6.6 years of follow-up, there were 20,104 deaths. Compared to adults who watched < 3 h/day of television at both time points, mortality risk was 28% greater (CI:1.21,1.34) those who watched 5+ h/day at both time-points. Decreasing television viewing from 5 + h/day to 3-4 h/d was associated with a 15% reduction in mortality risk (CI:0.80, 0.91) and decreasing to <3 h/day resulted in an 12% lower risk (CI:0.79, 0.97). Conversely, adults who increased their viewing time to 3-4 h/day had an 17% greater mortality risk (CI:1.10, 1.24) and those who increased to 5+ h/day had a 45% greater risk (CI:1.32, 1.58), compared to those who consistently watched <3 h/day. The lowest mortality risk was observed in those who were consistently active and watched < 3 h/day of television. CONCLUSIONS: We confirm that prolonged television viewing time was associated with greater mortality in older adults and demonstrate for the first time that individuals who reduced the amount of time they spent watching television had lower mortality. Our findings provide new evidence to support behavioral interventions that seek to reduce sedentary television viewing in favor of more physically active pursuits, preferably MVPA. Given the high prevalence of physical inactivity and prolonged television viewing in older adults, favorable changes in these two modifiable behaviors could have substantial public health impact. TRIAL REGISTRATION: ClinicalTrials.gov number, NCT00340015.

Impact of accelerometer data processing decisions on the sample size, wear time and physical activity level of a large cohort study.

BACKGROUND: Accelerometers objectively assess physical activity (PA) and are currently used in several large-scale epidemiological studies, but there is no consensus for processing the data. This study compared the impact of wear-time assessment methods and using either vertical (V)-axis or vector magnitude (VM) cut-points on accelerometer output. METHODS: Participants (7,650 women, mean age 71.4 y) were mailed an accelerometer (ActiGraph GT3X+), instructed to wear it for 7 days, record dates and times the monitor was worn on a log, and return the monitor and log via mail. Data were processed using three wear-time methods (logs, Troiano or Choi algorithms) and V-axis or VM cut-points. RESULTS: Using algorithms alone resulted in "mail-days" incorrectly identified as "wear-days" (27-79% of subjects had >7-days of valid data). Using only dates from the log and the Choi algorithm yielded: 1) larger samples with valid data than using log dates and times, 2) similar wear-times as using log dates and times, 3) more wear-time (V, 48.1 min more; VM, 29.5 min more) than only log dates and Troiano algorithm. Wear-time algorithm impacted sedentary time (~30-60 min lower for Troiano vs. Choi) but not moderate-to-vigorous (MV) PA time. Using V-axis cut-points yielded ~60 min more sedentary time and ~10 min less MVPA time than using VM cut-points. CONCLUSIONS: Combining log-dates and the Choi algorithm was optimal, minimizing missing data and researcher burden. Estimates of time in physical activity and sedentary behavior are not directly comparable between V-axis and VM cut-points. These findings will inform consensus development for accelerometer data processing in ongoing epidemiologic studies.

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.

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.

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.

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 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.

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.

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.

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.