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

Breaking up sitting with light physical activity (PA) is effective in reducing hyperglycemia in the laboratory. Whether the same effects are observed in the free-living environment remains unknown. We evaluated how daily and postprandial glycemia is impacted by 20, 40, or 60 min of activity performed as either breaks from sitting after each meal (BR) or as one continuous walk after breakfast (WALK). Thirty individuals with type 2 diabetes completed three experimental conditions [BR, WALK, and control (CON)] in a randomized crossover design. Conditions were performed in a free-living environment with strict dietary control over 7 days. Participants increased PA in BR and WALK by 20, 40, or 60 min ( n = 10 in each group) and maintained habitual levels of PA during CON. A continuous glucose monitor (iPro2) and activPAL activity monitor were worn to quantify glycemic control and PA. Using linear mixed models with repeated measures, we 1) compared postprandial glucose (PPG) across conditions and 2) assessed the relationship between activity volume and glucose responses. Whereas WALK tended to shorten the daily duration of hyperglycemia compared with CON ( P = 0.0875), BR was not different from CON. BR and WALK significantly attenuated the breakfast PPG versus CON ( P ≤ 0.05), but lunch and dinner PPG were unaffected by BR and WALK. In conclusion, continuous walking was more effective than breaks from sitting in lowering daily hyperglycemia for the group, but both conditions lowered breakfast PPG. In contrast to tightly controlled laboratory studies, breaks from sitting did not lower hyperglycemia in the free-living environment. NEW & NOTEWORTHY Our “ecolabical” approach is new and noteworthy. This approach combines the external validity of the free-living environment (ecological) with the control of key confounding variables in the laboratory and allows for highly translatable findings by minimizing confounding variables. We found that both postmeal continuous walking and short breaks from sitting similarly attenuated the postprandial glucose (PPG) response to breakfast. Unlike previous laboratory studies, neither condition (walk after breakfast or postmeal breaks) significantly impacted PPG at lunch or dinner.

BACKGROUND/OBJECTIVES: Individuals successful at weight loss maintenance engage in high amounts of physical activity (PA). Understanding how and when weight loss maintainers accumulate PA within a day and across the week may inform PA promotion strategies and recommendations for weight management. METHODS: We compared patterns of PA in a cohort of weight loss maintainers (WLM, n=28, maintaining ≥13.6 kg weight loss for ≥1 year, BMI 23.6 ± 2.3 kg/m(2)), controls without obesity (NC, n=30, BMI similar to current BMI of WLM, BMI 22.8 ± 1.9 kg/m(2)), and controls with overweight/obesity (OC, n=26, BMI similar to pre-weight loss BMI of WLM, 33.6 ± 5.1 kg/m(2)). PA was assessed during 7 consecutive days using the activPAL(™) activity monitor. The following variables were quantified; sleep duration, sedentary time (SED), light-intensity PA (LPA), moderate-to-vigorous intensity PA (MVPA), and steps. Data were examined to determine differences in patterns of PA across the week and across the day using mixed effect models. RESULTS: Across the week, WLM engaged in ≥60 minutes of MVPA on 73% of days, significantly more than OC (36%, p

Background and objectives We tested the feasibility of reducing sedentary behavior common in CKD. Design, setting, participants, & measurements We carried out a Sit Less, Interact, Move More intervention in a 24-week parallel-group, randomized controlled trial in patients with stages 2–5 CKD. In the intervention group (n=54), accelerometry performed at baseline and repeated every 4 weeks was used to develop and monitor adherence to individualized plans targeting sedentary and stepping durations. The control group (n=52) was provided national physical activity recommendations; accelerometry was performed at baseline and every 8 weeks. Between-groups changes from baseline to the average follow-up values at weeks 8, 16, and 24 of the sedentary and stepping durations were the coprimary end points. Results The mean age was 69±13 years. Fourteen percent were on dialysis or received a kidney transplant. Eight percent of the control group and 17% of the intervention group were lost to follow-up. Sedentary and stepping durations did not change in the control group. Within the intervention group, the maximum decrease in sedentary duration (−43; 95% confidence interval, −69 to −17 min/d) and increase in stepping duration (16; 95% confidence interval, 7 to 24 min/d) and the number of steps per day (1265; 95% confidence interval, 518 to 2012) were seen at week 20. These attenuated at week 24. In mixed effects models, overall treatment effects between groups on sedentary (−17; 95% confidence interval, −43 to 8 min/d) and stepping (6; 95% confidence interval, −3 to 15 min/d) durations and the number of steps per day, a secondary end point (652; 95% confidence interval, −146 to 1449), were not significantly different. The intervention significantly reduced secondary end points of body mass index (−1.1; 95% confidence interval, −1.9 to −0.3 kg/m2) and body fat percentage (−2.1%; 95% confidence interval, −4.4% to −0.2%). Conclusions It is feasible to reduce sedentary duration and increase stepping duration in patients with CKD, but these were not sustained. Clinical Trial registry name and registration number: National Health and Nutrition Examination Survey (NHANES), NCT02970123

PURPOSE: To determine the effects of 5 months of ovarian hormone suppression in pre-menopausal women on objectively measured physical activity (PA). METHODS: Participants (age = 35±8 yr; body mass index = 27±6 kg·m(−2)) received monthly intramuscular injections of gonadotropin releasing hormone agonist therapy (GnRH(AG)) which suppresses pituitary gonadotropins and results in suppression of ovarian sex hormones. Women were randomized to receive concurrent transdermal E(2) (GnRH(AG)+E(2); n=30) or placebo (GnRH(AG)+PL, n=31). PA was assessed for 1 week before and during each month of the 5-month intervention using a hip-worn accelerometer (Actical, Mini Mitter Co., Inc., Bend, OR). Estimates of time spent in sedentary, light, and moderate-to-vigorous physical activity (MVPA) were derived using a previously published equation. Subsets of participants in each group were also randomized to a supervised progressive resistance exercise training program. RESULTS: Total MVPA tended towards being higher (p=0.08) in the GnRH(AG)+E(2) group at month 4. There were no significant effects of intervention or time in sedentary or light PA. In the subset of women who did not participate in structured exercise training for which Actical data were obtained (N=16 in each group), total MVPA was higher at month 4 (p=0.01). CONCLUSION: Physical activity levels appear to be maintained at a higher level in women undergoing pharmacological suppression of ovarian function with E(2) add back when compared with women treated with placebo. These data provide proof of concept data that E(2) contributes to the regulation of PA in humans. However, given the exploratory nature of this study, future confirmatory investigations will be necessary.

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

A tri-axial accelerometer worn on the thigh can provide information on the angle of rotation of the thigh. These data may be used to estimate periods of lying versus sitting. Purpose: To develop and test a classification algorithm to identify sedentary events as either lying or sitting events using a thigh-worn, tri-axial accelerometer. Methods: Seven day free-living activity from 14 sedentary workers was recorded using the activPAL3TM monitor. Participants recorded when they went to bed and when they got up in a diary. All “in bed” sedentary events were assumed to be lying and all “not in bed” sedentary events as sitting. An algorithm computed the angle of rotation of the y-axis, which was used to detect orientation of the thigh. Crossing a rotational threshold in the transverse plane of ±65was used to classify a sedentary event as lying. The classification accuracy of the algorithm was compared to self-reported classification from the diary. Results: The algorithm classified 96.7% of the sedentary time “in bed” (sensitivity) as lying and 92.9% of the time “not in bed” as not lying (specificity). Conclusion: Tri-axial accelerometer data recorded from a single site on the thigh can be used to classify sedentary events as sitting and lying. The automated method developed in this study will allow objective measurement of diurnal lying time and that while sleeping without relying on self-report. This will help advance the understanding of the impact of different types of sedentary behaviours on various health outcomes.

The objective of this study was to compare physical activity energy expenditure (PAEE) and total daily energy expenditure (TDEE) in successful weight loss maintainers (WLM) with normal weight controls (NC) and controls with overweight/obesity (OC).Participants were recruited in three groups: WLM (n = 25, BMI 24.1 ± 2.3 kg/mPAEE in WLM (812 ± 268 kcal/d, mean ± SD) was significantly higher compared with that in both NC (621 ± 285 kcal/d, P 0.01) and OC (637 ± 271 kcal/d, P = 0.02). As a result, TDEE in WLM (2,495 ± 366 kcal/d) was higher compared with that in NC (2,195 ± 521 kcal/d, P = 0.01) but was not significantly different from that in OC (2,573 ± 391 kcal/d).The high levels of PAEE and TDEE observed in individuals maintaining a substantial weight loss (-26.2 ± 9.8 kg maintained for 9.0 ± 10.2 years) suggest that this group relies on high levels of energy expended in physical activity to remain in energy balance (and avoid weight regain) at a reduced body weight.

Office workers are vulnerable to the adverse health effects of sedentary behavior (i.e., sitting time). Increasing physical activity and preventing time spent sitting is an occupational health priority. This randomized crossover design study compared the short-term (3-days) effects of hourly interruptions of sedentary time with 5-min micrrobouts of activity for 9 hours (MICRO) to a sedentary control condition (SED) and a duration-matched continuous single bout of physical activity (45-min/d, ONE) condition on inclinometer-derived sitting-time on work and non-work days in sedentary overweight/obese adults. Differences in sitting/lying, standing, stepping, number of sit/stand transitions, time spent in moderate and vigorous activity (MVPA), energy expenditure, self-perceived vigor and fatigue, and insulin sensitivity were also examined. Twenty-two participants (10M/12F, 31.7 &plusmn, 1.3 year old BMI 30.4 &plusmn, 0.5 kg/m2) completed all conditions. No between-condition effects were observed in sitting-time and sit/stand transitions. Both interventions increased daily steps, MVPA and energy expenditure with increases being greater in ONE than MICRO. Feelings of vigor and fasting insulin sensitivity were also improved. Participants reported less fatigue with MICRO than SED and ONE. Both interventions increase physical activity and energy expenditure in occupational and leisure-time contexts. The sustainability of these effects over the long term and on health outcomes will need to be tested in future studies.

The objective of this study was to compare patterns of objectively measured moderate-to-vigorous physical activity (MVPA, ≥ 3.00 metabolic equivalents [METs]), light-intensity physical activity (LPA, 1.50-2.99 METs), and sedentary behavior (SB, 1.50 METs) in successful weight loss maintainers (WLMs), normal weight controls (NC), and controls with overweight/obesity (OC).Participants (18-65 y) were recruited in three groups: WLM (maintaining ≥ 13.6-kg weight loss for ≥ 1 year, n = 30), NC (BMI matched to current BMI of WLM, n = 33), and OC (BMI matched to pre-weight loss BMI of WLM, n = 27). All participants wore the activPAL for 1 week.Compared with OC and NC, WLM spent more awake time in total MVPA (WLM: 9.6 ± 3.9%, NC: 7.1 ± 2.1%, OC: 5.9 ± 2.0%; P 0.01) and more time in sustained (≥ 10 min) bouts of MVPA (WLM: 39 ± 33, NC: 17 ± 14, OC: 9 ± 11 min/d; P 0.01). Compared with OC, WLM and NC spent more awake time in LPA (WLM: 29.6 ± 7.9%, NC: 29.1 ± 8.3%, OC: 24.8 ± 6.7%; P = 0.04) and less awake time sedentary (WLM: 60.8 ± 9.3%, NC: 63.8 ± 9.5%, OC: 69.3 ± 7.5%; P 0.01).Results provide additional data supporting the important role of MVPA in weight loss maintenance and suggest notable differences in LPA and SB between normal weight individuals and those with overweight/obesity. Increasing LPA and/or decreasing SB may be additional potential targets for weight management interventions.

Background:Individuals may compensate for exercise training by modifying nonexercise behavior (ie, increase sedentary time (ST) and decrease nonexercise physical activity [NEPA]).Purpose:To compare ST and NEPA during a 12-week exercise training and/or lifestyle intervention.Methods:Fifty-seven overweight/obese participants (19 M/39 F) completed the study (mean ± SD; age 43.6 ± 9.9 y, BMI 35.1 ± 4.6 kg/m2). There were no between-group differences in activity levels at baseline. Four-arm quasi-experimental intervention study 1) EX: exercise 5 days per week at a moderate intensity (40% to 65% VO2peak) 2) rST: reduce ST and increase NEPA, 3) EX-rST: combination of EX and rST and 4) CON: maintain habitual behavior.Results:For the EX group, ST did not decrease significantly (mean ((95% confidence interval) 0.48 (–2.2 to 3.1)% and there was no changes in NEPA at week-12 compared with baseline. The changes were variable, with approximately 50% of participants increasing ST and decreasing NEPA. The rST group decreased ST (–4.8 (0.8 to 7.9)% and increased NEPA. EX-rST significantly decreased ST (–5.1 (–2.2 to 7.9)% and increased time in NEPA at week-12 compared with baseline. The control group increased ST by 4.3 (0.8 to 7.9)%.Conclusions:Changes in nonexercise ST and NEPA are variable among participants in an exercise-training program, with nearly half decreasing NEPA compared with baseline. Interventions targeting multiple behaviors (ST and NEPA) may effectively reduce compensation and increase daily activity.

AB 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.

Background:The Compendium of Energy Expenditures for Youth assigns MET values to a wide range of activities. However, only 35% of activity MET values were derived from energy cost data measured in youth; the remaining activities were estimated from adult values.Purpose:To determine the energy cost of common activities performed by children and adolescents and compare these data to similar activities reported in the compendium.Methods:Thirty-two children (8−11 years old) and 28 adolescents (12−16 years) completed 4 locomotion activities on a treadmill (TRD) and 5 age-specific activities of daily living (ADL). Oxygen consumption was measured using a portable metabolic analyzer.Results:In children, measured METs were significantly lower than compendium METs for 3 activities [basketball, bike riding, and Wii tennis (1.1−3.5 METs lower)]. In adolescents, measured METs were significantly lower than compendium METs for 4 ADLs [basketball, bike riding, board games, and Wii tennis (0.3−2.5 METs lower)] and 3 TRDs [2.24 m·s-1, 1.56 m·s-1, and 1.34 m·s-1 (0.4−0.8 METs lower)].Conclusion:The Compendium of Energy Expenditures for Youth is an invaluable resource to applied researchers. Inclusion of empirically derived data would improve the validity of the Compendium of Energy Expenditures for Youth.

Background:Thirty-five percent of the activities assigned MET values in the Compendium of Energy Expenditures for Youth were obtained from direct measurement of energy expenditure (EE). The aim of this study was to provide directly measured EE for several different activities in youth.Methods:Resting metabolic rate (RMR) of 178 youths (80 females, 98 males) was first measured. Participants then performed structured activity bouts while wearing a portable metabolic system to directly measure EE. Steady-state oxygen consumption data were used to compute activity METstandard (activity VO2/3.5) and METmeasured (activity VO2/measured RMR) for the different activities.Results:Rates of EE were measured for 70 different activities and ranged from 1.9 to 12.0 METstandard and 1.5 to 10.0 METmeasured.Conclusion:This study provides directly measured energy cost values for 70 activities in children and adolescents. It contributes empirical data to support the expansion of the Compendium of Energy Expenditures for Youth.

Previous work from our laboratory provided a “proof of concept” for use of artificial neural networks (nnets) to estimate metabolic equivalents (METs) and identify activity type from accelerometer data (Staudenmayer J, Pober D, Crouter S, Bassett D, Freedson P, J Appl Physiol 107: 1330–1307, 2009). The purpose of this study was to develop new nnets based on a larger, more diverse, training data set and apply these nnet prediction models to an independent sample to evaluate the robustness and flexibility of this machine-learning modeling technique. The nnet training data set (University of Massachusetts) included 277 participants who each completed 11 activities. The independent validation sample ( n = 65) (University of Tennessee) completed one of three activity routines. Criterion measures were 1) measured METs assessed using open-circuit indirect calorimetry; and 2) observed activity to identify activity type. The nnet input variables included five accelerometer count distribution features and the lag-1 autocorrelation. The bias and root mean square errors for the nnet MET trained on University of Massachusetts and applied to University of Tennessee were +0.32 and 1.90 METs, respectively. Seventy-seven percent of the activities were correctly classified as sedentary/light, moderate, or vigorous intensity. For activity type, household and locomotion activities were correctly classified by the nnet activity type 98.1 and 89.5% of the time, respectively, and sport was correctly classified 23.7% of the time. Use of this machine-learning technique operates reasonably well when applied to an independent sample. We propose the creation of an open-access activity dictionary, including accelerometer data from a broad array of activities, leading to further improvements in prediction accuracy for METs, activity intensity, and activity type.

Numerous accelerometers and prediction methods are used to estimate energy expenditure (EE). Validation studies have been limited to small sample sizes in which participants complete a narrow range of activities and typically validate only one or two prediction models for one particular accelerometer. The purpose of this study was to evaluate the validity of nine published and two proprietary EE prediction equations for three different accelerometers. Two hundred and seventy-seven participants completed an average of six treadmill (TRD) (1.34, 1.56, 2.23 ms(-1) each at 0 and 3% grade) and five self-paced activities of daily living (ADLs). EE estimates were compared with indirect calorimetry. Accelerometers were worn while EE was measured using a portable metabolic unit. To estimate EE, 4 ActiGraph prediction models were used, 5 Actical models, and 2 RT3 proprietary models. Across all activities, each equation underestimated EE (bias -0.1 to -1.4 METs and -0.5 to -1.3 kcal, respectively). For ADLs EE was underestimated by all prediction models (bias -0.2 to -2.0 and -0.2 to -2.8, respectively), while TRD activities were underestimated by seven equations, and overestimated by four equations (bias -0.8 to 0.2 METs and -0.4 to 0.5 kcal, respectively). Misclassification rates ranged from 21.7 (95% CI 20.4, 24.2%) to 34.3% (95% CI 32.3, 36.3%), with vigorous intensity activities being most often misclassified. Prediction equations did not yield accurate point estimates of EE across a broad range of activities nor were they accurate at classifying activities across a range of intensities (light3 METs, moderate 3-5.99 METs, vigorous ≥ 6 METs). Current prediction techniques have many limitations when translating accelerometer counts to EE.

This pilot study examined if the combination of exercise training and reducing sedentary time (ST) results in greater changes to health markers than either intervention alone. Fifty-seven overweight/obese participants (19 males/39 females) (mean ± SD; age, 43.6 ± 9.9 years; body mass index (BMI), 35.1 ± 4.6 kg·m–2) completed the 12-week study and were randomly assigned to (i) EX: exercise 5 days·week–1 for 40 min·session–1 at moderate intensity; (ii) rST: reduce ST and increase nonexercise physical activity; (iii) EX-rST: combination of EX and rST; and (iv) CON: maintain behavior. Fasting lipids, blood pressure (BP), peak oxygen uptake, BMI, and 2-h oral glucose tolerance tests were completed pre- and post-intervention. EX and EX-rST increased peak oxygen uptake by ∼10% and decreased systolic BP (both p < 0.001). BMI decreased by –3.3% (95% confidence interval: –4.6% to –1.9%) for EX-rST and –2.2% (–3.5% to 0.0%) for EX. EX-rST significantly increased composite insulin-sensitivity index by 17.8% (2.8% to 32.8%) and decreased insulin area under the curve by 19.4% (–31.4% to –7.3%). No other groups improved in insulin action variables. rST group decreased ST by 7% (∼50 min·day–1); however, BP was the only health-related outcome that improved. EX and EX-rST improved peak oxygen uptake and BMI, providing further evidence that moderate-intensity exercise is beneficial. The within-group analysis provides preliminary evidence that exercising and reducing ST may result in improvements in metabolic biomarkers that are not seen with exercise alone, though between-group differences did not reach statistical significance. Future studies, with larger samples, should examine health-related outcomes resulting from greater reductions in ST over longer intervention periods.

AB 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, [beta]1 = 0.80-1.13) and active time ([beta]1 = 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 ([beta]1 = 0.63-0.73) and active time ([beta]1 = 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. (C) 2013 American College of Sports Medicine

AB 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[middle dot]sed-h-1)). Results: Break rate was the only metric sensitive to changes in behavior between baseline (5.1 [3.3-6.8] brk[middle dot]sed-h-1) and treatment conditions (7.3 [4.7-9.8] brk[middle dot]sed-h-1) (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[middle dot]sed-h-1; treatment, 8.0 [5.8-10.2] brk[middle dot]sed-h-1). 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. (C)2012The American College of Sports Medicine

Purpose: A primary barrier to elucidating the association between sedentary behavior (SB) and health outcomes is the lack of valid monitors to assess SB in a free-living environment. The purpose of this study was to examine the validity of commercially available monitors to assess SB. Methods: Twenty overweight (mean ± SD: body mass index = 33.7 ± 5.7 kg·m-2) inactive, office workers age 46.5 ± 10.7 yr were directly observed for two 6-h periods while wearing an activPAL (AP) and an ActiGraph GT3X (AG). During the second observation, participants were instructed to reduce sitting time. We assessed the validity of the commonly used cut point of 100 counts per minute (AG100) and several additional AG cut points for defining SB. We used direct observation (DO) using focal sampling with duration coding to record either sedentary (sitting/lying) or nonsedentary behavior. The accuracy and precision of the monitors and the sensitivity of the monitors to detect reductions in sitting time were assessed using mixed-model repeated-measures analyses. Results: On average, the AP and the AG100 underestimated sitting time by 2.8% and 4.9%, respectively. The correlation between the AP and DO was R2 = 0.94, and the AG100 and DO sedentary minutes was R2 = 0.39. Only the AP was able to detect reductions in sitting time. The AG 150-counts-per-minute threshold demonstrated the lowest bias (1.8%) of the AG cut points. Conclusions: The AP was more precise and more sensitive to reductions in sitting time than the AG, and thus, studies designed to assess SB should consider using the AP. When the AG monitor is used, 150 counts per minute may be the most appropriate cut point to define SB.

Purpose:To compare intensity misclassification and activity MET values using measured RMR (measMET) compared with 3.5 ml·kg−1·min−1 (standMET) and corrected METs [corrMET = mean standMET × (3.5 ÷ Harris-Benedict RMR)] in subgroups.Methods:RMR was measured for 252 subjects following a 4-hr fast and before completion of 11 activities. VO2 was measured during activity using indirect calorimetry (n = 2555 activities). Subjects were classified by BMI category (normal-weight or overweight/obese), sex, age (decade 20, 30, 40, or 50 y), and fitness quintiles (low to high). Activities were classified into low, moderate, and vigorous intensity categories.Results:The (mean ± SD) measMET was 6.1 ± 2.64 METs. StandMET [mean (95% CI)] was (0.51(0.42, 0.59) METs) less than measMET. CorrMET was not statistically different from measMET (−0.02 (−0.11, 0.06) METs). 12.2% of the activities were misclassified using standMETs compared with an 8.6% misclassification rate for METs based on predicted RMR (P < .0001). StandMET differences and misclassification rates were highest for low fit, overweight, and older individuals while there were no differences when corrMETs were used.Conclusion:Using 3.5 ml·kg−1·min−1 to calculate activity METs causes higher misclassification of activities and inaccurate point estimates of METs than a corrected baseline which considers individual height, weight, and age. These errors disproportionally impact subgroups of the population with the lowest activity levels.

Purpose: Gathering contextual information (i.e., location and purpose) about active and sedentary behaviors is an advantage of self-report tools such as previous day recalls (PDR). However, the validity of PDR’s for measuring context has not been empirically tested. The purpose of this paper was to compare PDR estimates of location and purpose to direct observation (DO). Methods: Fifteen adult (18–75 y) and 15 adolescent (12–17 y) participants were directly observed during at least one segment of the day (i.e., morning, afternoon or evening). Participants completed their normal daily routine while trained observers recorded the location (i.e., home, community, work/school), purpose (e.g., leisure, transportation) and whether the behavior was sedentary or active. The day following the observation, participants completed an unannounced PDR. Estimates of time in each context were compared between PDR and DO. Intra-class correlations (ICC), percent agreement and Kappa statistics were calculated. Results: For adults, percent agreement was 85% or greater for each location and ICC values ranged from 0.71 to 0.96. The PDR-reported purpose of adults’ behaviors were highly correlated with DO for household activities and work (ICCs of 0.84 and 0.88, respectively). Transportation was not significantly correlated with DO (ICC = �0.08). For adolescents, reported classification of activity location was 80.8% or greater. The ICCs for purpose of adolescents’ behaviors ranged from 0.46 to 0.78. Participants were most accurate in classifying the location and purpose of the behaviors in which they spent the most time. Conclusions: This study suggests that adults and adolescents can accurately report where and why they spend time in behaviors using a PDR. This information on behavioral context is essential for translating the evidence for specific behavior-disease associations to health interventions and public policy.

This study examined the validity of commonly used regression equations for the Actigraph and Actical accelerometers in predicting energy expenditure (EE) in children and adolescents. Sixty healthy (8–16 yrs) participants completed four treadmill (TM) and five self-paced activities of daily living (ADL). Four Actigraph (AG) and three Actical (AC) regression equations were used to estimate EE. Bias (±95% CI) and root mean squared errors were used to assess the validity of the regression equations compared with indirect calorimetry. For children, the Freedson (AG) model accurately predicted EE for all activities combined and the Treuth (AG) model accurately predicted EE for TM activities. For adolescents, the Freedson model accurately predicted EE for TM activities and the Treuth model accurately predicted EE for all activities and for TM activities. No other equation accurately estimated EE. The percent agreement for the AG and AC equations were better for light and vigorous compared with moderate intensity activities. The Trost (AG) equation most accurately classified all activity intensity categories. Overall, equations yield inconsistent point estimates of EE. The health benefits of regular physical activity (PA) have been well established in adults (26). Less data are available on these associations in children and adolescents and what has been reported is inconsistent (26). It has been suggested that the inconsistent results may be due to variations in the methods used to characterize PA dose (22). The variations in the methods used to assess PA impedes researchers and policy makers ability to validly document the prevalence of PA, determine if children are meeting PA recommendations, and test the effectiveness of interventions to increase PA. In children, PA can be assessed by subjective measures (e.g., self-report diaries or questionnaires) or objective measures (e.g., direct observation, doubly-labeled water, heart rate monitoring, and accelerometry; 16, 17, 20). Large-scale prospective