Activity Intensity Categories Research Articles

Evaluation of Within- and Between-Site Agreement for Direct Observation of Physical Behavior Across Four Research Groups

Direct observation (DO) is a widely accepted ground-truth measure, but the field lacks standard operational definitions. Research groups develop project-specific annotation platforms, limiting the utility of DO if labels are not consistent. Purpose: The purpose was to evaluate within- and between-site agreement for DO taxonomies (e.g., activity intensity category) across four independent research groups who have used video-recorded DO. Methods: Each site contributed video files (508 min) and had two trained research assistants annotate the shared video files according to their existing annotation protocols. The authors calculated (a) within-site agreement for the two coders at the same site expressed as intraclass correlation and (b) between-site agreement, the proportion of seconds that agree between any two coders regardless of site. Results: Within-site agreement at all sites was good–excellent for both activity intensity categories (intraclass correlation range: .82–.9) and posture/whole-body movement (intraclass correlation range: .77–.98). Between-site agreement for intensity categories was 94.6% for sedentary, 80.9% for light, and 82.8% for moderate–vigorous. Three of the four sites had common labels for eight posture/whole-body movements and had within-site agreements of 94.5% and between-site agreements of 86.1%. Conclusions: Distinct research groups can annotate key features of physical behavior with good-to-excellent interrater reliability. Operational definitions are provided for core metrics for researchers to consider in future studies to facilitate between-study comparisons and data pooling, enabling the deployment of deep learning approaches to wearable device algorithm calibration.

US Population-referenced Percentiles for Wrist-Worn Accelerometer-derived Activity.

This study aimed to present age- and sex-specific percentiles for daily wrist-worn movement metrics in US youth and adults. This metric represents a summary of all recorded movement, regardless of the purpose, context, or intensity. Wrist-worn accelerometer data from the combined 2011-2014 National Health and Nutrition Examination Survey cycles and the 2012 National Health and Nutrition Examination Survey National Youth Fitness Survey were used for this analysis. Monitor-Independent Movement Summary units (MIMS-units) from raw triaxial accelerometer data were used. We removed the partial first and last assessment days and days with ≥5% nonwear time. Participants with ≥1 valid day were included. Mean MIMS-units were calculated across all valid days. Percentile tables and smoothed curves of daily MIMS-units were calculated for each age and sex using the Generalized Additive Models for Location Shape and Scale. The analytical sample included 14,705 participants age ≥3 yr. The MIMS-unit activity among youth was similar for both sexes, whereas adult females generally had higher MIMS-unit activity than did males. Median daily MIMS-units peaked at age 6 yr for both sexes (males, 20,613; females, 20,706). Lowest activity was observed for males and females 80+ yr of age: 8799 and 9503, respectively. Population referenced MIMS-unit percentiles for US youth and adults are a novel means of characterizing total activity volume. By using MIMS-units, we provide a standardized reference that can be applied across various wrist-worn accelerometer devices. Further work is needed to link these metrics to activity intensity categories and health outcomes.

Associations of accelerometer-determined sedentary behavior and physical activity with physical performance outcomes by race/ethnicity in older women.

To determine the cross-sectional associations of accelerometer-measured time spent in physical activity intensity categories (sedentary, low and high light intensity, or moderate to vigorous intensity physical activity (MVPA) with physical performance outcomes [stair climb ascent, 40 foot walk test, and short physical performance battery (SPPB)] in older women and examine differences by race/ethnicity. Data were from 1,256 Study of Women's Health Across the Nation (SWAN) participants [aged 64.9 (2.7) years at Visit 15 (2015-16); 54.1% non-White]. Three sets of adjusted multivariable linear or logistic regression models were built to test the study objectives using the backward elimination approach to identify relevant covariates. In the full analytic sample, a 10min increment in MVPA was related to faster performance on the stair climb [β=-0.023 (95% CI: -0.04, -0.005) seconds] and 40 foot walk test [β=-0.066 (95% CI: -0.133, -0.038) seconds], and a 9% lower odds [OR: 0.91; 95% CI: 0.87, 0.96; p=0.004] of limitations based on the SPPB. Statistically significant differences by race/ethnicity were found for the stair climb ascent time as MVPA was associated with better performance for White, Chinese, and Japanese participants while high light intensity physical activity, but not MVPA, was deemed beneficial in Black women. Findings from the isotemporal substitution models were consistent. Findings further support the importance of MVPA on physical performance outcomes in older women. Further research is needed to examine the complex associations between physical (in)activity and physical performance outcomes by race/ethnicity to provide more targeted recommendations.

Concurrent Validity of ActiGraph GT3X+ and Axivity AX3 Accelerometers for Estimating Physical Activity and Sedentary Behavior

Introduction: Accelerometers are commonly used to assess time-use behaviors related to physical activity, sedentary behavior, and sleep; however, as new accelerometer technologies emerge, it is important to ensure consistency with previous devices. This study aimed to evaluate the concurrent validity of the commonly used accelerometer, ActiGraph GT3X+, and the relatively new Axivity AX3 (fastened to the lower back) for detecting physical activity intensity and body postures when using direct observation as the criterion measure. Methods: A total of 41 children (aged 6–16 years) and 33 adults (aged 28–59 years) wore both monitors concurrently while performing 10 prescribed activities under laboratory conditions. The GT3X+ data were categorized into different physical activity intensity and posture categories using intensity-based cut points and ActiGraph proprietary inclinometer algorithms, respectively. The AX3 data were first converted to ActiGraph counts before being categorized into different physical activity intensity categories, while activity recognition models were used to detect the target postures. Sensitivity, specificity, and the balanced accuracy for intensity and posture category classification were calculated for each accelerometer. Differences in balanced accuracy between the devices and between children and adults were also calculated. Results: Both accelerometers obtained 74–96% balanced accuracy, with the AX3 performing slightly better (∼4% higher, p < .01) for detecting postures and physical activity intensity. Error in both devices was greatest when contrasting sitting/standing, sedentary/light intensity, and moderate/light intensity. Conclusion: In comparison with the GT3X+ accelerometer, AX3 was able to detect various postures and activity intensities with slightly higher balanced accuracy in children and adults.

A Method To Estimate Free-living Behaviors Using High-frequency Wrist Accelerometer Data

PURPOSE: To develop a novel method to estimate free-living sedentary behavior and activity intensity using high-frequency wrist accelerometer data. METHODS: Forty-nine participants (mean ± SD; age: 20.4±1.3 yrs, 45.8% male) completed four, 1-hour sessions of free-living behaviors in home, school, community, and exercise environments. Each session was video-recorded (DO) and participants wore an ActiGraph wGT3X-BT (AG) accelerometer on the non-dominant wrist. Videos were coded for whole body movement, contextual activity type, and activity intensity from the Compendium of Physical Activities (e.g. walking, shopping, 2.8 METs). The novel two-step method (SojWrist) first segments AG data into bouts, or “sojourns”, of inactivity (i.e. sedentary and standing behaviors) or activity using an acceleration standard deviation threshold and random forest model. The second step estimates the intensity of inactive (sedentary, light) and active (light, moderate, vigorous) sojourns. Separate inactive and active sojourn RF models were fit to estimate intensity using bout duration and time- and frequency-domain AG signal characteristics. A 90-10 sample split was used for SojWrist development (N = 44) and cross-validation (N=5). Percent agreement between DO and SojWrist was evaluated at each step using second-by-second data. RESULTS: In the cross-validation sample, 91.8% [95%CI: 87.0%, 96.5%] of inactive and active periods were classified correctly from step 1 of SojWrist. After step 2, overall percent agreement between DO and SojWrist was 86.9% [95%CI: 78.9%, 95.0%] across all intensity categories (Table). CONCLUSION: The new SojWrist performed well at estimating free-living activity intensity categories from a wrist worn accelerometer. Future work should strive to improve method performance for predicting activity intensity categories and test validity on a diverse, independent, free-living sample. Supported by NIH NIDDK 1R01DK110148

A Functional Data Analysis Framework For Modeling Physical Activity Intensity Continuously Using Accelerometer Data

PURPOSE: Cutpoints approaches are often used to define physical activity (PA) intensity categories for accelerometry. We proposed methods to characterize the full continuum of PA intensity, free of cutpoints, and to quantify associations between PA accumulated at varying intensity levels and health outcomes. METHODS: During 2012-2014, 6,379 women aged 63-99 wore accelerometers on their waist for 7 consecutive days. Accelerometer counts data were analyzed in 15-second epochs. Complementary cumulative distribution functions (CCDF) were used to characterize how each subject distributes PA across all intensity levels. Functional linear regression models were used to estimate flexible dose-response relationships between PA intensity and health outcomes, including body mass index (BMI) and fasting glucose levels. RESULTS: The CCDFs of PA intensity showed that age-related decline in PA occurred at all intensity levels. The dose-response relationships between PA intensity and BMI and glucose were shown in Figure 1. Neither effects were constant, indicating that intensity mattered after controlling for volume (count-min or MET-min). The relationship between BMI and intensity was complicated, while PA accumulated at higher intensity was found to be associated with lower fasting glucose levels, after controlling for volume. CONCLUSION: We provided a cutpoint-free analytic framework to model PA intensity continuously. The effect of PA on BMI and fasting glucose varied across intensity levels, even when fixing PA volume. The proposed methods are applicable not only to counts data, but also to other acceleration-based metrics calculated using raw data.Figure 1: Association of PA intensity (counts/15-sec or equivalent METs) with BMI and fasting glucose. The adjusted analysis included age, race-ethnicity and education for BMI, and for glucose additionally adjusted for BMI. Corresponding METs were calculated based on an internal calibration study.

Sedentary Time and Physical Activity Across Occupational Classifications.

To examine differences in activity patterns across employment and occupational classifications. Cross-sectional. A 2005-2006 Coronary Artery Risk Development in Young Adults (CARDIA) study. Participants with valid accelerometry data (n = 2068). Uniaxial accelerometry data (ActiGraph 7164), accumulated during waking hours, were summarized as mean activity counts (counts/min) and time spent (min/d) in long-bout sedentary (≥30 minutes, SED≥30), short-bout sedentary (<30 minutes, SED<30), light physical activity (LPA), short-bout moderate-to-vigorous physical activity (<10 minutes, MVPA<10), and long-bout MVPA (≥10 minutes, MVPA≥10) using Freedson cut-points. Employment status was self-reported as full time, part time, unemployed, keeping house, or raising children. Self-reported job duties were categorized into 23 major groups using the 2010 Standard Occupational Classification. Omnibus differences were analyzed using adjusted analysis of covariance and repeated after stratification by race (black/white) and sex (female/male). SED≥30, SED<30, LPA, and MVPA<10 differed significantly by employment and occupational categories (P ≤ .05), while MVPA≥10 did not (P ≥ .50). SED≥30, SED<30, and LPA differed by occupational classification in men, women, blacks, and whites (P < .05). Mean activity counts, MVPA<10, and MVPA≥10 were significantly different across occupational classifications in whites (P ≤ .05), but not in blacks (P > .05). Significant differences in mean activity counts and MVPA<10 across occupational classifications were found in males (P ≤ .001), but not in females (P > .05). Time within activity intensity categories differs across employment and occupational classifications and by race and sex.

Evaluating and Enhancing the Generalization Performance of Machine Learning Models for Physical Activity Intensity Prediction From Raw Acceleration Data.

To evaluate and enhance the generalization performance of machine learning physical activity intensity prediction models developed with raw acceleration data on populations monitored by different activity monitors. Five datasets from four studies, each containing only hip- or wrist-based raw acceleration data (two hip- and three wrist-based) were extracted. The five datasets were then used to develop and validate artificial neural networks (ANN) in three setups to classify activity intensity categories (sedentary behavior, light, and moderate-to-vigorous). To examine generalizability, the ANN models were developed using within dataset (leave-one-subject-out) cross validation, and then cross tested to other datasets with different accelerometers. To enhance the models' generalizability, a combination of four of the five datasets was used for training and the fifth dataset for validation. Finally, all the five datasets were merged to develop a single model that is generalizable across the datasets (50% of the subjects from each dataset for training, the remaining for validation). The datasets showed high performance in within dataset cross validation (accuracy 71.9-95.4%, Kappa K = 0.63-0.94). The performance of the within dataset validated models decreased when applied to datasets with different accelerometers (41.2-59.9%, K = 0.21-0.48). The trained models on merged datasets consisting hip and wrist data predicted the left-out dataset with acceptable performance (65.9-83.7%, K = 0.61-0.79). The model trained with all five datasets performed with acceptable performance across the datasets (80.4-90.7%, K = 0.68-0.89). Integrating heterogeneous datasets in training sets seems a viable approach for enhancing the generalization performance of the models. Instead, within dataset validation is not sufficient to understand the models' performance on other populations with different accelerometers.

Convergent validity of ActiGraph and Actical accelerometers for estimating physical activity in adults.

PurposeThe aim of the present study was to examine the convergent validity of two commonly-used accelerometers for estimating time spent in various physical activity intensities in adults.MethodsThe sample comprised 37 adults (26 males) with a mean (SD) age of 37.6 (12.2) years from San Diego, USA. Participants wore ActiGraph GT3X+ and Actical accelerometers for three consecutive days. Percent agreement was used to compare time spent within four physical activity intensity categories under three counts per minute (CPM) threshold protocols: (1) using thresholds developed specifically for each accelerometer, (2) applying ActiGraph thresholds to regression-rectified Actical CPM data, and (3) developing new ‘optimal’ Actical thresholds.ResultsUsing Protocol 1, the Actical estimated significantly less time spent in light (-16.3%), moderate (-2.8%), and vigorous (-0.4%) activity than the ActiGraph, but greater time spent sedentary (+20.5%). Differences were slightly more pronounced when the low frequency extension filter on the ActiGraph was enabled. The two adjustment methods (Protocols 2 and 3) improved agreement in this sample.ConclusionsOur findings show that ActiGraph and Actical accelerometers provide significantly different estimates of time spent in various physical activity intensities. Regression and threshold adjustment were able to reduce these differences, although some level of non-agreement persisted. Researchers should be aware of the inherent limitations of count-based physical activity assessment when reporting and interpreting study findings.

The Development And Testing Of A Direct Observation Protocol For Children’S Free-play Activity

Traditional real-time direct observation (DO) systems have been used for decades to assess children’s free-living physical activity (PA). Using video-taped DO would overcome several methodological issues and allow for more precise assessments of behaviors. PURPOSE: To develop and test a novel video-based DO system for children’s free-play activity. METHODS: Following iterative DO system development (The Observer XT, Noldus), 28 children (age=8.4±1.5 years) participated in a 30-minute indoor free-play session. The participants were recorded using a GoPro camera and wore an accelerometer on the hip (AG-H) and non-dominant wrist (AG-W). Researchers coded videos for the main Whole-Body Movement and four modifiers: 1) Locomotion, 2) Limb Movement, 3) Activity Type, and 4) MET value. For intrarater reliability, percent agreement was calculated from six randomly selected videos, using duplicate entries by an expert coder one-week apart. For inter-rater reliability, three videos were used to calculate percent agreement between entries from trained, novice coders (n=6) and the expert coder. To assess construct validity, time spent in activity intensity categories from expert-coded DO MET values were compared with accelerometer estimates using Wilcoxon Rank-Sum tests. RESULTS: Percent agreement for intra-rater reliability was above 80% except for Locomotion (47%; video 4, 26%; video 3) and Limb Movement, and MET value (19%, 78%, respectively; video 3). Across all variables, percent agreement for inter-rater reliability ranged widely from 12%-96%, 0-100%, and 36%-97% for videos 1, 2 and 3, respectively. Mean estimated time spent in PA intensity categories from AG-H overestimated sedentary (SED; p=0.008), moderate (MPA; p<0.001), and moderate-to-vigorous PA (MVPA; p=0.017) and underestimated light, (LPA; p< 0.001). The AG-W underestimated SED (p=0.03) and LPA (p<0.001) but overestimated MPA (p<0.001) and MVPA (p<0.001). CONCLUSIONS: The current DO system is feasible for observing detailed changes in children’s free-play activity. However, refinement to the system must be made to improve reliability before it is adopted as a criterion measure for free-play activity in children. Supported by: University of Massachusetts Amherst Commonwealth Honors College

Testing of a Novel Direct Observation Method for Children’s Free Play Activity

PURPOSE: Direct Observation (DO) has been used as a criterion measure of children’s physical activity (PA) since the early 1990’s. A limitation of these DO systems include researchers instantaneously recording only the highest activity level performed in a given time interval. Therefore, the purpose of this study is to compare the estimates of time spent in activity intensity categories using a novel video recorded DO methodology, compared to accelerometer data. METHODS: Children ages 6-10 years old participated in a simulated free play session. Toys, equipment, and a sibling or friend were available to facilitate a full range of activity intensities; sedentary (SED), light (LPA), moderate (MPA), and vigorous (VPA) for 30 minutes. Participants wore an ActiGraph GT3X+ (AG) over their right hip (AG-H) and non-dominant wrist (AG-W). The sessions were recorded with a GoPro™ video camera and, using Noldus™ behavioral observation software, an activity intensity category was assigned each time the child changed their activity. Two observers independently coded every 30-minute session. The percent of time spent in each PA category (calculated from the Noldus software) was compared between AG and DO using Spearman-Rank Correlations and Wilcoxon Rank-Sum Tests. RESULTS: Twelve children completed this study (8.9±1.2 yrs; 33% female). DO and AG-H were moderately correlated for all intensity categories (r=0.32 to 0.7), except for MPA (r=0.15). Mean values of percent of time spent in activity intensity categories were similar between DO and AG-H (p=0.11 to 0.83), except for MPA (DO 12%+5%, AG-H 18%+8%, p=0.02). DO and AG-W showed weak or negative associates for all intensity categories (r=-0.6 to 0.09). The AG-W underestimated percent of time spent in SED, LPA, and MPA (p<0.001 for all) but overestimated VPA (DO 13%±5%, AG-W 81%±10%, p=0.0001), compared to DO. CONCLUSIONS: Based on these results, the novel DO system tested here is promising but needs to be further refined to better distinguish moderate intensity activities from light or vigorous movement. Caution is warranted when interpreting wrist accelerometer data from free-living children.

The activPALTM Accurately Classifies Activity Intensity Categories in Healthy Adults.

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.

Abstract MP026: Effects of Substituting Sedentary Behavior With Light and Moderate-to-vigorous Physical Activity on Obesity Indices in Adults

Sedentary behavior (SB) has emerged as an independent risk factor for the occurrence of cardiovascular problems and all-cause mortality. However, the deleterious effect of SB may be a consequence of replacing time spent in light and moderate-to-vigorous (MVPA) physical activities. Few studies have explored this topic, especially in prospective follow-up studies with adults. We hypothesized that substituting SB with light and MVPA may be associated with 1-year change in body weight, body mass index (BMI) and fat body mass (FBM) in asymptomatic adults. We aimed to use isotemporal substitution modeling to investigate cross-sectional and longitudinal effects of reallocating SB with light and MVPA on obesity indices in asymptomatic adults. Also, we examined the correlations between 1-year changes in physical activity intensity categories (i.e., sedentary, light, moderate, vigorous, very vigorous, and MVPA) and changes in FBM. We included 780 participants over the age of 20-yr. Body weight, BMI, and FBM (bioelectrical impedance), as well as its 1-year changes, were the outcomes. We objectively measured physical activity using triaxial accelerometers (ActiGraph GT3X) worn above the dominant hip during waking hours for at least four consecutive days (4-7 days). After 1-year follow-up, 242 completed all the assessments. We reallocated time spent in SB by light or MVPA and assessed the cross-sectional and prospective associations with the outcomes. The cross-sectional isotemporal substitution analysis showed that substituting 10-min blocks of SB with MVPA was significantly related to decreases of 1.23 kg in body weight, 0.30 kg/m2 in BMI and 0.38% in FBM. Substituting 10-min blocks of SB with light physical activity produced significant lower values of body weight (1 kg) and BMI (0.1 kg/m2). We found no effects of substituting SB with light physical activity on FBM. As for the longitudinal analysis, we observed that reallocating SB with MVPA was only associated with a significant decline in FBM (-0.31%). We found no other significant effects of substituting SB with light or MVPA. We observed significant correlations between change in FBM and changes in sedentary (r = 0.147), light (r = -0.290) and vigorous (r = -0.277) physical activity. After stepwise multiple regressions adjusted for all physical activity intensities and covariates, change in vigorous physical activity was the only intensity selected as a significant predictor for 1-year absolute change in FBM (β = -0.312; ΔR 2 = 0.102). We may conclude that SB presents non-consistent influence on obesity indices in asymptomatic adults and its substitution with MVPA is associated with significant improvement in body composition over a short-term follow-up. Our results suggest that vigorous physical activities play the most important role in obesity in adults.

Comparison of Two ActiGraph Accelerometers in Laboratory and Free-Living Conditions

Objective: Evaluate the agreement between the GT3X+ and GT9X Link with respect to the vertical axis under laboratory and free-living conditions using the Normal Filter and LFE Filter, respectively and to evaluate the agreement between the minutes spent in different activity intensities between the devices under free-living conditions using both the Normal and LFE Filter, respectively. Methods: Eight participants (mean age = 28.3 ± 3.8) were enrolled. Participants completed 4 walking trials of 2 different speeds (2 and 3 miles per hour, 3-min stages) while wearing 2 GT3X+ and 2 GT9X Link accelerometers on an elastic belt on the right sides of the hip. They continued wearing them for another 6-10 hours in the free-living setting. Results: There were no significant differences between the mean CPM between the devices for the walking trials and free-living assessment when both filters were applied. There were also no significant differences in the different physical activity intensity categories. There was strong evidence of agreement between the GT3X+ and GT9X Link. Conclusion: Data collected with the GT3X+ and GT9X Link should be comparable across past and future studies.

Associations of Low- and High-Intensity Light Activity with Cardiometabolic Biomarkers.

Light-intensity physical activity (LIPA) accounts for much of adults' waking hours (≈40%) and substantially contributes to overall daily energy expenditure. Encompassing activity behaviors of low intensity (standing with little movement) to those of higher intensity (slow walking), LIPA is ubiquitous, yet little is known about how associations with health may vary depending on its intensity. We examined the associations of objectively assessed LIPA (categorized as either low LIPA [LLPA] or high LIPA [HLPA]) and moderate- to vigorous-intensity activity with cardiometabolic risk biomarkers. Cardiometabolic biomarkers were measured in 4614 US adults (47 ± 17 yr) who participated in the 2003-2004 and 2005-2006 National Health and Nutrition Examination Survey cycles. Multiple linear regression analyses examined the associations of three accelerometer-derived physical activity (SD increment per day) intensity categories (LLPA, 100-761 counts per minute; HLPA, 762-1951 counts per minute; moderate-intensity physical activity [MPA], 1952-5724 counts per minute; vigorous-intensity physical activity [VPA], ≥5725 counts per minute) with cardiometabolic biomarkers, adjusting for potential sociodemographic, behavioral, and medical confounders. All intensities of physical activity were beneficially associated with waist circumference, C-reactive protein, triglycerides, fasting insulin, β-cell function, and insulin sensitivity (P < 0.05); only some activity intensities showed significant associations with systolic blood pressure (LLPA), body mass index, HDL cholesterol, fasting glucose, and 2-h plasma glucose (HLPA, MPA, and VPA). Generally, effect size increased with intensity of physical activity. Overall, further adjustment for waist circumference attenuated associations with MPA and VPA to a greater extent than associations with LLPA and HLPA. The cross-sectional findings provide novel evidence for the potential benefits of increasing both LLPA and HLPA. They further reinforce the established importance of moderate- to vigorous-intensity activity, the mainstay of public health recommendations.

Body Mass Index: Accounting for Full Time Sedentary Occupation and 24-Hr Self-Reported Time Use

ObjectivesWe used linked existing data from the 2006–2008 American Time Use Survey (ATUS), the Current Population Survey (CPS, a federal survey that provides on-going U.S. vital statistics, including employment rates) and self-reported body mass index (BMI) to answer: How does BMI vary across full time occupations dichotomized as sedentary/non-sedentary, accounting for time spent in sleep, other sedentary behaviors, and light, moderate, and vigorous intensity activities?MethodsWe classified time spent engaged at a primary job (sedentary or non-sedentary), sleep, and other non-work, non-sleep intensity-defined behaviors, specifically, sedentary behavior, light, moderate, and vigorous intensity activities. Age groups were defined by 20–29, 30–39, 40–49, and 50–64 years. BMI groups were defined by 18.5–24.9, 25.0–27.4, 27.5–29.9, 30.0–34.9, and ≥35.0 kg/m2. Logistic and linear regression were used to examine the association between BMI and employment in a sedentary occupation, considering time spent in sleep, other non-work time spent in sedentary behaviors, and light, moderate, and vigorous intensity activities, sex, age race/ethnicity, and household income.ResultsThe analysis data set comprised 4,092 non-pregnant, non-underweight individuals 20–64 years of age who also reported working more than 7 hours at their primary jobs on their designated time use reporting day. Logistic and linear regression analyses failed to reveal any associations between BMI and the sedentary/non-sedentary occupation dichotomy considering time spent in sleep, other non-work time spent in sedentary behaviors, and light, moderate, and vigorous intensity activities, sex, age, race/ethnicity, and household income.ConclusionsWe found no evidence of a relationship between self-reported full time sedentary occupation classification and BMI after accounting for sex, age, race/ethnicity, and household income and 24-hours of time use including non-work related physical activity and sedentary behaviors. The various sources of error associated with self-report methods and assignment of generalized activity and occupational intensity categories could compound to obscure any real relationships.

Is Physical Behavior Affected in Fatigued Persons With Multiple Sclerosis?

ObjectiveTo study physical behavior in detail in fatigued persons with multiple sclerosis (MS). DesignCase-control explorative study. SettingOutpatient rehabilitation department and participants' daily environment. ParticipantsFatigued persons with MS (n=23) were selected from a randomized controlled trial. Cases were matched by age and sex to healthy, nonfatigued controls (n=23). Eligible persons with MS were severely fatigued (Checklist Individual Strength fatigue domain mean score, 43.2±6.6) and ambulatory (Expanded Disability Status Scale mean score, 2.5±1.5). InterventionsNot applicable. Main Outcome MeasuresMeasurements were performed using an accelerometer over 7 days. Outcomes included the following: amount of physical activity expressed in counts per day, counts per minute (CPM), and counts per day period (morning, afternoon, evening); duration of activity intensity categories (sedentary, light physical activity, moderate-to-vigorous physical activity [MVPA]); and distribution of MVPA and sedentary periods over the day. ResultsPersons with MS had fewer counts per day (mean difference, −156×103; 95% confidence interval [CI], −273×103 to −39×103; P=.010), had fewer CPM (mean difference, −135; 95% CI, −256 to −14; P=.030), and were less physically active in the morning (mean difference, −200; 95% CI, −389 to −11; P=.039) and evening (mean difference, −175; 95% CI, −336 to −14; P=.034) than controls. Persons with MS spent a higher percentage of their time sedentary (mean difference, 5.6; 95% CI, .1–11.1; P=.045) and spent less time at the higher MVPA intensity (mean difference, −2.4; 95% CI, −4.7 to −0.09; P=.042). They had fewer MVPA periods (mean difference, 29; 95% CI, −56.2 to −2.6; P=.032) and a different distribution of sedentary (mean difference, .033; 95% CI, .002 to .064; P=.039) and MVPA periods (mean difference, −.08; 95% CI, −.15 to −.01; P=.023). ConclusionsDetailed analyses of physical behavior showed that ambulatory fatigued persons with MS do differ from healthy controls not only in physical activity level, but also in other physical behavior dimensions (eg, day patterns, intensity, distribution).

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

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. To compare DO estimates of total MET-hours and time in activity intensity categories to a criterion measure from indirect calorimetry (IC). 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). 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]. This study provides evidence that DO can be used as a criterion measure of absolute intensity in free-living validation studies.

Calibration and cross‐validation of a wrist‐worn Actigraph in young preschoolers

To calibrate the Actigraph GT3X+ accelerometer for wrist-worn placement in young preschoolers by developing intensity thresholds for sedentary, low- and high-intensity physical activity. Furthermore, to cross-validate the developed thresholds in young preschoolers. Actigraph GT3X+ was used to measure physical activity during structured activities and free play in 38 children (15-36 months). Activity was video recorded and scored into sedentary, low- and high-intensity physical activity based on Children's Activity Rating Scale (CARS) and combined with accelerometer data using a 5 s epoch. Receiver operating characteristic analysis was used to develop intensity thresholds in 26 randomly selected children. The remaining 12 children were used for cross-validation. Intensity thresholds for sedentary were ≤89 vertical counts (Y) and ≤221 vector magnitude (VM) counts per 5 s and ≥440 Y counts and ≥730 VM counts per 5 s for high-intensity physical activity. Sensitivity and specificity were 60-100% for the developed intensity thresholds. Strong correlations (Spearman rank correlation 0.69-0.91) were found in the cross-validation sample between the developed thresholds for the accelerometer and CARS scoring time in all intensity categories. The developed intensity thresholds appear valid to categorize sedentary behaviour and physical activity intensity categories in children 2 years of age.

Effect of BMI on Prediction of Accelerometry-Based Energy Expenditure in Youth

The objective of this study is to determine the effect of body mass index (BMI) on level of agreement between six previously established prediction equations for three commonly used accelerometers to predict summary measures of energy expenditure (EE) in youth. One hundred and thirty-one youth between the ages of 10-17 yr and BMI from 15 to 44 kg·m were outfitted with hip-worn ActiGraph GT1M (Pensacola, FL), Actical (MiniMiter/Respironics, Bend, OR), and RT3 (StayHealthy, Monrovia, CA) accelerometers and spent approximately 24 h in a whole-room indirect calorimeter while performing structured and self-selected activities. Five commonly used regression and one propriety equations for each device were used to predict the minute-to-minute EE (normalized to METs), daily physical activity level (PAL), and time spent in sedentary, light, moderate, and vigorous physical activity intensity categories. The calculated values were compared with criterion measurements obtained from the room calorimeter. All predictive equations, except RT3, significantly over- or underpredicted daily PAL (P < 0.001), with large discrepancies observed in the estimate of sedentary and light activity. Discrepancies between actual and estimated PAL ranged from 0.05 to 0.68. In addition, BMI represented a modifier for two ActiGraph predictive equations (AG1 and AG2), affecting the accuracy of physical activity-related EE predictions. ActiGraph (AG3) and the RT3 closely predicted overall PAL (within 4.2% and 6.8%, respectively) as a group. When adjusting for age, sex, and ethnicity, Actical (AC1 and AC2) and ActiGraph (AG3) were not influenced by BMI. However, a gap between some hip-worn accelerometer predictive and regression equations was demonstrated compared with both criterion measurement and each other, which poses a potential difficulty for interstudy (e.g., different accelerometers) and intrastudy (e.g., BMI and adiposity) comparisons.