Measurement of Active and Sedentary Behavior in Context of Large Epidemiologic Studies.
ABSTRACT: INTRODUCTION/PURPOSE:To assess the utility of measurement methods that may be more accurate and precise than traditional questionnaire-based estimates of habitual physical activity and sedentary behavior we compared the measurement properties of a past year questionnaire (AARP) and more comprehensive measures: an internet-based 24-h recall (ACT24), and a variety of estimates from an accelerometer (ActiGraph). METHODS:Participants were 932 adults (50-74 yr) in a 12-month study that included reference measures of energy expenditure from doubly labeled water (DLW) and active and sedentary time via activPAL. RESULTS:Accuracy at the group level (mean differences) was generally better for both ACT24 and ActiGraph than the AARP questionnaire. The AARP accuracy for energy expenditure ranged from -4% to -13% lower than DLW, but its accuracy was poorer for physical activity duration (-48%) and sedentary time (-18%) versus activPAL. In contrast, ACT24 accuracy was within 3% to 10% of DLW expenditure measures and within 1% to 3% of active and sedentary time from activPAL. For ActiGraph, accuracy for energy expenditure was best for the Crouter 2-regression method (-2% to -7%), and for active and sedentary time the 100 counts per minute cutpoint was most accurate (-1% to 2%) at the group level. One administration of the AARP questionnaire was significantly correlated with long-term average from the reference measures (?TX = 0.16-0.34) overall, but four ACT24 recalls had higher correlations (?TX = 0.48-0.60), as did 4 d of ActiGraph assessment (?TX = 0.54-0.87). CONCLUSIONS:New exposure assessments suitable for use in large epidemiologic studies (ACT24, ActiGraph) were more accurate and had higher correlations than a traditional questionnaire. Use of better more comprehensive measures in future epidemiologic studies could yield new etiologic discoveries and possibly new opportunities for prevention.
Project description: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).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.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, ?1 = 0.80-1.13) and active time (?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 (?1 = 0.63-0.73) and active time (?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).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.
Project description:PURPOSE:Sedentary behavior is an emerging independent health risk factor. The accuracy of measuring sedentary time using accelerometers may depend on the wear location. This study in older adults evaluated the accuracy of various hip- and wrist-worn ActiGraph accelerometer cutoff points to define sedentary time using the activPAL as the reference method. METHODS:Data from 62 adults (mean age, 78.4 yr) of the Aging Research Evaluating Accelerometry study were used. Participants simultaneously wore an activPAL accelerometer on the thigh and ActiGraph accelerometers on the hip, dominant, and nondominant wrist for 7 d in a free-living environment. Using the activPAL as the reference criteria, we compared classification of sedentary time to hip-worn and wrist-worn ActiGraph accelerometers over a range of cutoff points for both 60-s and 15-s epochs. RESULTS:The optimal cutoff point for the hip vertical axis was <22 counts per minute with an area under the curve (AUC) of 0.85; the optimal hip vector magnitude cutoff point was <174 counts per minute with an AUC of 0.89. For the dominant wrist, the optimal vector magnitude cutoff point to define sedentary time was <2303 counts per minute (AUC, 0.86) and for the nondominant wrist <1853 counts per minute (AUC, 0.86). The optimal 15-s cutoff points resulted in lower agreements compared with activPAL. CONCLUSIONS:Hip- and wrist-worn ActiGraph data may be used to define sedentary time with a moderate to high accuracy when compared with activPAL. The observed optimal cutoff point for hip vertical axis <22 counts per minute is substantially lower than the standard <100 counts per minute. It is unknown how these optimal cutoff points perform in different populations. Results on an individual basis should therefore be interpreted with caution.
Project description:Accurate, nonintrusive, and feasible methods are needed to predict energy expenditure (EE) and physical activity (PA) levels in preschoolers. Herein, we validated cross-sectional time series (CSTS) and multivariate adaptive regression splines (MARS) models based on accelerometry and heart rate (HR) for the prediction of EE using room calorimetry and doubly labeled water (DLW) and established accelerometry cut points for PA levels.Fifty preschoolers, mean ± SD age of 4.5 ± 0.8 yr, participated in room calorimetry for minute-by-minute measurements of EE, accelerometer counts (AC) (Actiheart and ActiGraph GT3X+), and HR (Actiheart). Free-living 105 children, ages 4.6 ± 0.9 yr, completed the 7-d DLW procedure while wearing the devices. AC cut points for PA levels were established using smoothing splines and receiver operating characteristic curves.On the basis of calorimetry, mean percent errors for EE were -2.9% ± 10.8% and -1.1% ± 7.4% for CSTS models and -1.9% ± 9.6% and 1.3% ± 8.1% for MARS models using the Actiheart and ActiGraph+HR devices, respectively. On the basis of DLW, mean percent errors were -0.5% ± 9.7% and 4.1% ± 8.5% for CSTS models and 3.2% ± 10.1% and 7.5% ± 10.0% for MARS models using the Actiheart and ActiGraph+HR devices, respectively. Applying activity EE thresholds, final accelerometer cut points were determined: 41, 449, and 1297 cpm for Actiheart x-axis; 820, 3908, and 6112 cpm for ActiGraph vector magnitude; and 240, 2120, and 4450 cpm for ActiGraph x-axis for sedentary/light, light/moderate, and moderate/vigorous PA (MVPA), respectively. On the basis of confusion matrices, correctly classified rates were 81%-83% for sedentary PA, 58%-64% for light PA, and 62%-73% for MVPA.The lack of bias and acceptable limits of agreement affirms the validity of the CSTS and MARS models for the prediction of EE in preschool-aged children. Accelerometer cut points are satisfactory for the classification of sedentary, light, and moderate/vigorous levels of PA in preschoolers.
Project description:Improving sedentary measurement is critical to understanding sedentary-health associations in youth. This study assessed agreement between the thigh-worn activPAL and commonly used hip-worn ActiGraph accelerometer methods for assessing sedentary patterns in children. Both devices were worn by 8-12-year-olds (N = 195) for 4.6 ± 1.9 days. Two ActiGraph cut-points were applied to two epoch durations: ?25 counts (c)/15 s, ?75c/15s, ?100c/60s, and ?300c/60s. Bias, mean absolute deviation (MAD), and intraclass correlation coefficients (ICCs) tested agreement between devices for total sedentary time and 11 sedentary pattern variables (usual bout duration, sedentary time accumulated in various bout durations, breaks/day, break rate, and alpha). For most sedentary pattern variables, ActiGraph 25c/15s, 75c/15s, and 100c/60s had poor ICCs, with bias and MAD >20%. ActiGraph 300c/60s had a better agreement than the other cut-points, but all ICCs were <0.587. ActiGraph underestimated sedentary time in longer bouts and usual bout duration, and overestimated sedentary time in shorter bouts, breaks/day, and alpha. For total sedentary time, ActiGraph 25c/15s, 300c/60s, and 75c/15s had good/fair ICCs, with bias and MAD <20%. Sedentary patterns derived from two commonly used ActiGraph cut-points did not appear to reflect postural changes. These differences between measurement devices should be considered when interpreting findings from sedentary pattern studies.
Project description:We determined measurement properties of the Sedentary Time and Activity Reporting Questionnaire (STAR-Q), which was designed to estimate past-month activity energy expenditure (AEE). STAR-Q validity and reliability were assessed in 102 adults in Alberta, Canada (2009-2011), who completed 14-day doubly labeled water (DLW) protocols, 7-day activity diaries on day 15, and the STAR-Q on day 14 and again at 3 and 6 months. Three-month reliability was substantial for total energy expenditure (TEE) and AEE (intraclass correlation coefficients of 0.84 and 0.73, respectively), while 6-month reliability was moderate. STAR-Q-derived TEE and AEE were moderately correlated with DLW estimates (Spearman's ?s of 0.53 and 0.40, respectively; P < 0.001), and on average, the STAR-Q overestimated TEE and AEE (median differences were 367 kcal/day and 293 kcal/day, respectively). Body mass index-, age-, sex-, and season-adjusted concordance correlation coefficients (CCCs) were 0.24 (95% confidence interval (CI): 0.07, 0.36) and 0.21 (95% CI: 0.11, 0.32) for STAR-Q-derived versus DLW-derived TEE and AEE, respectively. Agreement between the diaries and STAR-Q (metabolic equivalent-hours/day) was strongest for occupational sedentary time (adjusted CCC = 0.76, 95% CI: 0.64, 0.85) and overall strenuous activity (adjusted CCC = 0.64, 95% CI: 0.49, 0.76). The STAR-Q demonstrated substantial validity for estimating occupational sedentary time and strenuous activity and fair validity for ranking individuals by AEE.
Project description:PURPOSE:To compare the degree to which four accelerometer metrics-total activity counts per day (TAC per day), steps per day (steps per day), physical activity energy expenditure (PAEE) (kcal·kg·d), and moderate- to vigorous-intensity physical activity (MVPA) (min·d)-were correlated with PAEE measured by doubly labeled water (DLW). Additionally, accelerometer metrics based on vertical axis counts and triaxial counts were compared. METHODS:This analysis included 684 women and 611 men age 43 to 83 yr. Participants wore the Actigraph GT3X on the hip for 7 d twice during the study and the average of the two measurements was used. Each participant also completed one DLW measurement, with a subset having a repeat. PAEE was estimated by subtracting resting metabolic rate and the thermic effect of food from total daily energy expenditure estimated by DLW. Partial Spearman correlations were used to estimate associations between PAEE and each accelerometer metric. RESULTS:Correlations between the accelerometer metrics and DLW-determined PAEE were higher for triaxial counts than vertical axis counts. After adjusting for weight, age, accelerometer wear time, and fat free mass, the correlation between TAC per day based on triaxial counts and DLW-determined PAEE was 0.44 in women and 0.41 in men. Correlations for steps per day and accelerometer-estimated PAEE with DLW-determined PAEE were similar. After adjustment for within-person variation in DLW-determined PAEE, the correlations for TAC per day increased to 0.61 and 0.49, respectively. Correlations between MVPA and DLW-determined PAEE were lower, particularly for modified bouts of ?10 min. CONCLUSIONS:Accelerometer measures that represent total activity volume, including TAC per day, steps per day, and PAEE, were more highly correlated with DLW-determined PAEE than MVPA using traditional thresholds and should be considered by researchers seeking to reduce accelerometer data to a single metric.
Project description:BACKGROUND:Sedentary behavior (SED) is a potential risk factor for poor pregnancy outcomes. We evaluated the validity of several common and one new method to assess SED across three trimesters of pregnancy. METHODS:This cohort study of pregnant women measured objective and self-reported SED each trimester via thigh-worn activPAL3 micro (criterion), waist-worn Actigraph GT3X, and self-report from the Pregnancy Physical Activity Questionnaire (PPAQ) and the de novo Sedentary Behavior Two Domain Questionnaire (SB2D). SED (hours per day) and percent time in SED (SED%) from activPAL were compared to GT3X, SB2D, and PPAQ using Pearson's r, ICC, Bland-Altman analysis, and comparison of criterion SED and SED% across tertiles of alternative methods. RESULTS:Fifty-eight women (mean age 31.5?±?4.8?years; pre-pregnancy BMI 25.1?±?5.6?kg/m2; 76% white) provided three trimesters of valid activPAL data. Compared to activPAL, GT3X had agreement ranging from r?=?0.54-0.66 and ICC?=?0.52-0.65. Bland-Altman plots revealed small mean differences and unpatterned errors, but wide limits of agreement (greater than ±2?h and?±?15%). The SB2D and PPAQ had r?<?0.5 and ICC?<?0.3 vs. activPAL SED, with lower agreement during the 2nd and 3rd trimesters, and performed poorly in Bland-Altman analyses. SED% from the modified SB2D performed best of the self-reported instruments with modest mean differences, r ranging from 0.55 to 0.60, and ICCs from 0.31-0.33; though, limits of agreement were greater than ±35%. Significant trends in activPAL SED were observed across increasing tertiles of SB2D SED in the 1st and 3rd trimesters (both p ? 0.001), but not the 2nd trimester (p?=?0.425); and for PPAQ SED in the 1st and 2nd trimesters (both p?<?0.05), but not the 3rd trimester (p?=?0.158). AcitvPAL SED and SED% increased significantly across tertiles of GT3X SED and SED% as well as SB2D SED% (all p-for-trend???0.001). CONCLUSIONS:Compared to activPAL, waist-worn GT3X produced moderate agreement, though similar mean estimates of SED across pregnancy. Self-report questionnaires had large absolute error and wide limits of agreement for SED hr./day; SB2D measurement of SED% was the best self-report method. These data suggest activPAL be used to measure SED when possible, followed by GT3X, and - when necessary - SB2D assessing SED% in pregnancy. TRIAL REGISTRATION:www.clinicaltrials.gov NCT03084302 on 3/20/2017.
Project description:PURPOSE:We evaluated the validity and sensitivity to change of a workplace questionnaire to assess sedentary behavior (SB) during and outside work. METHODS:Participants wore an activPAL and completed an SB questionnaire at two time points (baseline and 3-month follow-up). Ecological momentary assessments were used to assess workplace location (at desk vs. away from desk). Intraclass correlation coefficients, mean difference, root of mean square error, kappa agreement, and Bland-Altman plots assessed validity. Sensitivity to change after 3 months of intervention was assessed using the standardized effect size. RESULTS:Data from 546 participants (age = 45.1 ± 16.4 years, 24.9% males, 72.7% white) were analyzed. Intraclass correlation coefficients ranged from 0.08 to 0.23. SB was overestimated d¯(95%CI) by 47.9 (39.2, 56.6) min during work hours but underestimated for both non-work hours and nonworkdays by -38.3 (-47.4, 29.1) and -106.7 (124.0, -89.5) min, respectively. Participants slightly underestimated SB by -3.4 (-12.6, 5.7)% when at their desk but overestimated SB by 2.8 (-2.4, 8.0)% when not at their desk. The questionnaire demonstrated similar standardized effect size (>0.6) to the activPAL for sedentary and standing time. CONCLUSIONS:Agreement between the questionnaire and activPAL was on par with other self-report measures. The questionnaire yielded valid estimates of at/away from desk SB and was sensitive to change.
Project description:This study aimed to: (1) compare acceleration output between ActiGraph (AG) hip and wrist monitors and GENEActiv (GA) wrist monitors; (2) identify raw acceleration sedentary and stationary thresholds for the two brands and placements; and (3) validate the thresholds during a free-living period. Twenty-seven from 9- to 10-year-old children wore AG accelerometers on the right hip, dominant- and non-dominant wrists, GA accelerometers on both wrists, and an activPAL on the thigh, while completing seven sedentary and light-intensity physical activities, followed by 10 minutes of school recess. In a subsequent study, 21 children wore AG and GA wrist monitors and activPAL for two days of free-living. The main effects of activity and brand and a significant activity × brand × placement interaction were observed (all p < 0.0001). Output from the AG hip was lower than the AG wrist monitors (both p < 0.0001). Receiver operating characteristic (ROC) curves established AG sedentary thresholds of 32.6 mg for the hip, 55.6 mg and 48.1 mg for dominant and non-dominant wrists respectively. GA wrist thresholds were 56.5 mg (dominant) and 51.6 mg (non-dominant). Similar thresholds were observed for stationary behaviours. The AG non-dominant threshold came closest to achieving equivalency with activPAL during free-living.
Project description:Research indicates that high levels of sedentary behavior (sitting or lying with low energy expenditure) are adversely associated with health. A key factor in improving our understanding of the impact of sedentary behavior (and patterns of sedentary time accumulation) on health is the use of objective measurement tools that collect date and time-stamped activity information. One such tool is the activPAL monitor. This thigh-worn device uses accelerometer-derived information about thigh position to determine the start and end of each period spent sitting/lying, standing, and stepping, as well as stepping speed, step counts, and postural transitions. The activPAL is increasingly being used within field-based research for its ability to measure sitting/lying via posture. We summarise key issues to consider when using the activPAL in physical activity and sedentary behavior field-based research with adult populations. It is intended that the findings and discussion points be informative for researchers who are currently using activPAL monitors or are intending to use them. Pre-data collection decisions, monitor preparation and distribution, data collection considerations, and manual and automated data processing possibilities are presented using examples from current literature and experiences from 2 research groups from the UK and Australia.