Kate Lyden

Validation of Wearable Monitors for Assessing Sedentary Behavior

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.

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

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

Accelerometer Output and MET Values of Common Physical Activities

PURPOSE This article 1) provides the calibration procedures and methods for metabolic and activity monitor data collection, 2) compares measured MET values to the MET values from the compendium of physical activities, and 3) examines the relationship between accelerometer output and METs for a range of physical activities. METHODS Participants (N = 277) completed 11 activities for 7 min each from a menu of 23 physical activities. Oxygen consumption (V O2) was measured using a portable metabolic system, and an accelerometer was worn. MET values were defined as measured METs (V O2/measured resting metabolic rate) and standard METs (V O2/3.5 mL.kg.min). For the total sample and by subgroup (age [young < 40 yr], sex, and body mass index [normal weight < 25 kg.m]), measured METs and standard METs were compared with the compendium, using 95% confidence intervals to determine statistical significance (alpha = 0.05). Average counts per minute for each activity and the linear association between counts per minute and METs are presented. RESULTS Compendium METs were different than measured METs for 17/21 activities (81%). The number of activities different than the compendium was similar between subgroups or when standard METs were used. The average counts for the activities ranged from 11 counts per minute (dishes) to 7490 counts per minute (treadmill: 2.23 m.s, 3%). The r between counts and METs was 0.65. CONCLUSIONS This study provides valuable information about data collection, metabolic responses, and accelerometer output for common physical activities in a diverse participant sample. The compendium should be updated with additional empirical data, and linear regression models are inappropriate for accurately predicting METs from accelerometer output.

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

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

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

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

Changes in Sedentary Time and Physical Activity in Response to an Exercise Training and/or Lifestyle Intervention

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.

Discrete Features of Sedentary Behavior Impact Cardiometabolic Risk Factors

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

The independent and combined effects of exercise training and reducing sedentary behavior on cardiometabolic risk factors

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.

Validation of a previous day recall for measuring the location and purpose of active and sedentary behaviors compared to direct observation

PurposeGathering 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).MethodsFifteen 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.ResultsFor 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.ConclusionsThis 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.

Direct observation is a valid criterion for estimating physical activity and sedentary behavior.

BACKGROUND Physical activity and sedentary behavior measurement tools need to be validated in free-living settings. Direct observation (DO) may be an appropriate criterion for these studies. However, it is not known if trained observers can correctly judge the absolute intensity of free-living activities. PURPOSE To compare DO estimates of total MET-hours and time in activity intensity categories to a criterion measure from indirect calorimetry (IC). METHODS Fifteen participants were directly observed on three separate days for two hours each day. During this time participants wore an Oxycon Mobile indirect calorimeter and performed any activity of their choice within the reception area of the wireless metabolic equipment. Participants were provided with a desk for sedentary activities (writing, reading, computer use) and had access to exercise equipment (treadmill, bike). RESULTS DO accurately and precisely estimated MET-hours [% bias (95% CI) = -12.7% (-16.4, -7.3), ICC = 0.98], time in low intensity activity [% bias (95% CI) = 2.1% (1.1, 3.2), ICC = 1.00] and time in moderate to vigorous intensity activity [% bias (95% CI) -4.9% (-7.4, -2.5), ICC = 1.00]. CONCLUSION This study provides evidence that DO can be used as a criterion measure of absolute intensity in free-living validation studies.