Journal for the measurement of physical behaviour最新文献

{"title":"ActiGraph Cutpoints Impact Physical Activity and Sedentary Behavior Outcomes in Young Children","authors":"Becky Breau, Hannah J. Coyle-Asbil, J. Haines, David W. L. Ma, L. Vallis, _","doi":"10.1123/jmpb.2021-0042","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0042","url":null,"abstract":"Purpose: Examine the effect of cutpoint selection on physical activity (PA) metrics calculated from young children’s accelerometer data and on the proportion of children meeting PA guidelines. Methods: A total of 262 children (3.6 ± 1.4 years, 126 males) wore ActiGraph wGT3X-BT accelerometers on their right hip for 7 days, 24 hr/day. Ten cutpoint sets were applied to the sample categorized by age, based on populations of the original cutpoint calibration studies using ActiLife software. Resulting sedentary behavior, light PA, moderate to vigorous PA, and total PA were compared using repeated-measures analysis of variance. Proportion of children meeting age-appropriate PA guidelines based on each cutpoint set was assessed using Cochran’s q tests. Results: Children wore the accelerometer for an average of 7.6 ± 1.2 days for an average of 11.9 ± 1.2 hr/day. Significant differences in time spent in each intensity were found across all cutpoints except for sedentary, and total PA for three comparisons (Trost vs. Butte Vertical Axis [VA], Pate vs. Puyau, and Costa VA vs. Evenson) and moderate to vigorous PA for four comparisons (Trost vs. Pate, Trost vs. Pate and Pfeiffer, Pate vs. Pate and Pfeiffer, and van Cauwenberghe vs. Evenson). When examined within age-appropriate groups, all sets of cutpoints resulted in significant differences across all intensities and in the number of children meeting PA guidelines. Conclusion: Choice of cutpoints applied to data from young children significantly affects times calculated for different movement intensities, which in turn impacts the proportion of children meeting guidelines. Thus, comparisons of movement intensities should not be made across studies using different sets of cutpoints.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81454368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Physical Behaviour Partnership From Heaven: The Prospective Physical Activity, Sitting, and Sleep Consortium and the International Society for the Measurement of Physical Behaviour","authors":"E. Stamatakis, B. Clark, Matthew N. Ahmadi, J. M. Blodgett, M. Granat, A. Donnelly, A. Atkin, Li-Tang Tsai, G. Mielke, Richard Pulsford, Nidhi Gupta, Patrick Crawley, Matthew Stevens, Peter J. Johansson, L. Brocklebank, L. Sherar, V. Rangul, A. Holtermann, M. Hamer, A. Koster","doi":"10.1123/jmpb.2022-0027","DOIUrl":"https://doi.org/10.1123/jmpb.2022-0027","url":null,"abstract":"","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"90 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83899630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Assessment of 24-Hr Physical Behavior in Children and Adolescents via Wearables: A Systematic Review of Laboratory Validation Studies","authors":"M. Giurgiu, C. Nigg, Janis Fiedler, Irina Timm, Ellen Rulf, J. Bussmann, C. Nigg, A. Woll, U. Ebner-Priemer","doi":"10.1123/jmpb.2022-0014","DOIUrl":"https://doi.org/10.1123/jmpb.2022-0014","url":null,"abstract":"Purpose: To raise attention to the quality of published validation protocols while comparing (in)consistencies and providing an overview on wearables, and whether they show promise or not. Methods: Searches from five electronic databases were included concerning the following eligibility criteria: (a) laboratory conditions with humans (<18 years), (b) device outcome must belong to one dimension of the 24-hr physical behavior construct (i.e., intensity, posture/activity type outcomes, biological state), (c) must include a criterion measure, and (d) published in a peer-reviewed English language journal between 1980 and 2021. Results: Out of 13,285 unique search results, 123 articles were included. In 86 studies, children <13 years were recruited, whereas in 26 studies adolescents (13–18 years) were recruited. Most studies (73.2%) validated an intensity outcome such as energy expenditure; only 20.3% and 13.8% of studies validated biological state or posture/activity type outcomes, respectively. We identified 14 wearables that had been used to validate outcomes from two or three different dimensions. Most (n = 72) of the identified 88 wearables were only validated once. Risk of bias assessment resulted in 7.3% of studies being classified as “low risk,” 28.5% as “some concerns,” and 71.5% as “high risk.” Conclusion: Overall, laboratory validation studies of wearables are characterized by low methodological quality, large variability in design, and a focus on intensity. No identified wearable provides valid results across all three dimensions of the 24-hr physical behavior construct. Future research should more strongly aim at biological state and posture/activity type outcomes, and strive for standardized protocols embedded in a validation framework.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86910396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tracking of Walking and Running for Exercise: Alignment Between Ecological Momentary Assessment and Accelerometer-Based Estimates","authors":"K. Strohacker, Lindsay P Toth, Lucas F. Sheridan, S. Crouter","doi":"10.1123/jmpb.2022-0016","DOIUrl":"https://doi.org/10.1123/jmpb.2022-0016","url":null,"abstract":"Ecological momentary assessment (EMA) and accelerometer-based devices can be used concurrently to better understand dimensions of physical activity. This study presents procedures for analyzing data derived from both methods to examine exercise-related walking and running, as well as determine evidence for alignment between these methods. The participants (N = 29) wore an ActiGraph GT3X+ and completed four EMA surveys/day across 2 weeks to report exercise (mode and duration). GT3X+ counts per 10 s were processed using the Crouter two-regression model to identify periods of walking/running (coefficient of variation in activity counts ≤10% and >0%). Two reviewers visually inspected Crouter two-regression model data and recorded durations of walking/running in time blocks corresponding to EMA reports of exercise. The data were classified as “aligned” if the duration of walking/running between methods were within 20% of one another. Frequency analyses determined the proportion of aligned versus nonaligned exercise durations. Reviewer reliability was examined by calculating interobserver agreement (classification of aligned vs. nonaligned) and intraclass correlation coefficients (ICC; duration based on coefficient of variation). Of the 139 self-reported bouts of walking and running exercise, 25% were classified as aligned with the Crouter two-regression model coefficient of variation. Initial interobserver agreement was 91, and ICCs across data classified as aligned (ICC = .992) and nonaligned (ICC = .960) were excellent. These novel procedures offer a means of isolating exercise-related physical activity for further analysis. Due to the inability to align evidence in most cases, we discuss key considerations for optimizing EMA survey questions, choice in accelerometer-based device, and future directions for visual analysis procedures.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90861401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effectiveness of Fitbit Activity Prompts in Reducing Sitting Time and Increasing Physical Activity in University Employees: A Randomized Controlled Trial","authors":"Benjamin D. Boudreaux, Julie A. Schenck, Zhixuan Chu, Michael D. Schmidt","doi":"10.1123/jmpb.2021-0060","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0060","url":null,"abstract":"Consumer activity devices use prompts to alter sedentary and physical activity (PA) behaviors. However, it is unclear if PA prompts are effective. Purpose: To evaluate the effectiveness of PA prompts from a consumer wearable device in reducing sitting time and increasing PA in university employees. Methods: Thirty-three university employees without a history of consumer activity device wear were randomly assigned a Fitbit Alta HR that administered PA prompts (Prompt group) or had the prompt feature deactivated (No Prompt group). Participants wore an activPAL for 5–7 days to measure baseline sitting time and PA behaviors. Participants then wore the Fitbit for 12 days during waking hours and an activPAL during the last 5–7 days of the Fitbit wear period. Changes in activPAL sitting time and PA were compared across groups. Mean Fitbit steps taken in the first 50 min and the last 10 min of each hour were calculated and compared across groups during “Inactive” hours (<250 steps in the first 50 min), where a prompt was given (Prompt group) or would have been given (No Prompt group). Results: Mean activPAL sitting time increased in the Prompt group (0.66 ± 1.70 hr/day) and remained stable in the No Prompt group (−0.04 ± 1.29 hr/day), with no statistically significant differences between groups (d = 0.33, p = .36). Moderate to vigorous PA increased modestly in both groups, but no significant differences were observed. Fitbit steps during the last 10 min of inactive hours did not differ across groups. Conclusion: Fitbit PA prompts did not alter sitting time or PA behaviors in university employees.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"417 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78107447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of the activPAL CREA and VANE Algorithms for Characterization of Posture and Activity in Free-Living Adults","authors":"A. Montoye, Joseph D. Vondrasek, Sylvia E. Neph, N. Basu, L. Paul, Eva-Maria Bachmair, K. Stefanov, S. Gray","doi":"10.1123/jmpb.2021-0053","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0053","url":null,"abstract":"Background: The activPAL accelerometer is used widely for assessment of free-living activity and postural data. Two algorithms, VANE (traditional) and CREA (new), are available to analyze activPAL data, but the comparability of metrics derived from these algorithms is unknown. Purpose: To determine the comparability of physical activity and sedentary behavior metrics from activPAL’s VANE and CREA algorithms. Methods: Individuals enrolled in the LIFT trial (n = 354) wore an activPAL accelerometer on the right thigh continuously for 7 days on four occasions, resulting in 5,851 valid days of data for analysis. Daily data were downloaded in the PALbatch software using the VANE and CREA algorithms. Correlations, mean absolute percentage error, effect sizes (ES), and equivalence (within 3%) were calculated to evaluate comparability of the algorithms. Results: Steps, activity score, stepping time, bouts of stepping, and upright time metrics were statistically equivalent, highly correlated (r ≥ .98), and had small mean absolute percentage errors (≤2.5%) and trivial ES (ES < 0.07) between algorithms. Stepping bouts also had good comparability. Conversely, sedentary-upright and upright-sedentary transitions and bouts of sitting were not equivalent, with large mean absolute percentage differences (17.4%–141.3%) and small to very large ES (ES = 0.45–3.80) between algorithms. Conclusions: Stepping and upright metrics are highly comparable between activPAL’s VANE and CREA algorithms, but sitting metrics had large differences as the VANE algorithm does not capture nonwear or differentiate between sitting and lying down. Researchers using the activPAL should explicitly describe the analytic algorithms used in their work to facilitate data pooling and comparability across studies.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87788494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Transparent Method for Step Detection using an Acceleration Threshold.","authors":"Scott W Ducharme,&nbsp;Jongil Lim,&nbsp;Michael A Busa,&nbsp;Elroy J Aguiar,&nbsp;Christopher C Moore,&nbsp;John M Schuna,&nbsp;Tiago V Barreira,&nbsp;John Staudenmayer,&nbsp;Stuart R Chipkin,&nbsp;Catrine Tudor-Locke","doi":"10.1123/jmpb.2021-0011","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0011","url":null,"abstract":"<p><p>Step-based metrics provide simple measures of ambulatory activity, yet device software either includes undisclosed proprietary step detection algorithms or simply do not compute step-based metrics. We aimed to develop and validate a simple algorithm to accurately detect steps across various ambulatory and non-ambulatory activities. Seventy-five adults (21-39 years) completed seven simulated activities of daily living (e.g., sitting, vacuuming, folding laundry) and an incremental treadmill protocol from 0.22-2.2ms^-1. Directly observed steps were hand-tallied. Participants wore GENEActiv and ActiGraph accelerometers, one of each on their waist and on their non-dominant wrist. Raw acceleration (g) signals from the anterior-posterior, medial-lateral, vertical, and vector magnitude (VM) directions were assessed separately for each device. Signals were demeaned across all activities and bandpass filtered [0.25, 2.5Hz]. Steps were detected via peak picking, with optimal thresholds (i.e., minimized absolute error from accumulated hand counted) determined by iterating minimum acceleration values to detect steps. Step counts were converted into cadence (steps/minute), and k-fold cross-validation quantified error (root mean squared error [RMSE]). We report optimal thresholds for use of either device on the waist (threshold=0.0267g) and wrist (threshold=0.0359g) using the VM signal. These thresholds yielded low error for the waist (RMSE<173 steps, ≤2.28 steps/minute) and wrist (RMSE<481 steps, ≤6.47 steps/minute) across all activities, and outperformed ActiLife's proprietary algorithm (RMSE=1312 and 2913 steps, 17.29 and 38.06 steps/minute for the waist and wrist, respectively). The thresholds reported herein provide a simple, transparent framework for step detection using accelerometers during treadmill ambulation and activities of daily living for waist- and wrist-worn locations.</p>","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"4 4","pages":"311-320"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9586317/pdf/nihms-1824859.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10617596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validity of a Global Positioning System-Based Algorithm and Consumer Wearables for Classifying Active Trips in Children and Adults.","authors":"Chelsea Steel, Katie Crist, Amanda Grimes, Carolina Bejarano, Adrian Ortega, Paul R Hibbing, Jasper Schipperijn, Jordan A Carlson","doi":"10.1123/jmpb.2021-0019","DOIUrl":"10.1123/jmpb.2021-0019","url":null,"abstract":"<p><strong>Objective: </strong>To investigate the convergent validity of a global positioning system (GPS)-based and two consumer-based measures with trip logs for classifying pedestrian, cycling, and vehicle trips in children and adults.</p><p><strong>Methods: </strong>Participants (<i>N</i> = 34) wore a Qstarz GPS tracker, Fitbit Alta, and Garmin vivosmart 3 on multiple days and logged their outdoor pedestrian, cycling, and vehicle trips. Logged trips were compared with device-measured trips using the Personal Activity Location Measurement System (PALMS) GPS-based algorithms, Fitbit's SmartTrack, and Garmin's Move IQ. Trip- and day-level agreement were tested.</p><p><strong>Results: </strong>The PALMS identified and correctly classified the mode of 75.6%, 94.5%, and 96.9% of pedestrian, cycling, and vehicle trips (84.5% of active trips, F1 = 0.84 and 0.87) as compared with the log. Fitbit and Garmin identified and correctly classified the mode of 26.8% and 17.8% (22.6% of active trips, F1 = 0.40 and 0.30) and 46.3% and 43.8% (45.2% of active trips, F1 = 0.58 and 0.59) of pedestrian and cycling trips. Garmin was more prone to false positives (false trips not logged). Day-level agreement for PALMS and Garmin versus logs was favorable across trip modes, though PALMS performed best. Fitbit significantly underestimated daily cycling. Results were similar but slightly less favorable for children than adults.</p><p><strong>Conclusions: </strong>The PALMS showed good convergent validity in children and adults and were about 50% and 27% more accurate than Fitbit and Garmin (based on F1). Empirically-based recommendations for improving PALMS' pedestrian classification are provided. Since the consumer devices capture both indoor and outdoor walking/running and cycling, they are less appropriate for trip-based research.</p>","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":" ","pages":"321-332"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9555805/pdf/nihms-1805244.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33511339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of ActiGraph Sampling Rate and Intermonitor Comparability on Measures of Physical Activity in Adults","authors":"Kimberly A. Clevenger, J. Brønd, D. Arvidsson, Alexander Montoye, K. Mackintosh, M. McNarry, K. Pfeiffer","doi":"10.1123/jmpb.2021-0016","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0016","url":null,"abstract":"Background: ActiGraph is a commonly used, research-grade accelerometer brand, but there is little information regarding intermonitor comparability of newer models. In addition, while sampling rate has been shown to influence accelerometer metrics, its influence on measures of free-living physical activity has not been directly studied. Purpose: To examine differences in physical activity metrics due to intermonitor variability and chosen sampling rate. Methods: Adults (n = 20) wore two hip-worn ActiGraph wGT3X-BT monitors for 1 week, with one accelerometer sampling at 30 Hz and the other at 100 Hz, which was downsampled to 30 Hz. Activity intensity was classified using vector magnitude, Euclidean Norm Minus One (ENMO), and mean amplitude deviation (MAD) cut points. Equivalence testing compared outcomes. Results: There was a lack of intermonitor equivalence for ENMO, time in sedentary/light- or moderate-intensity activity according to ENMO cut points, and time in moderate-intensity activity according to MAD cut points. Between sampling rates, differences existed for time in moderate-intensity activity according to vector magnitude, ENMO, and MAD cut points, and time in sedentary/light-intensity activity according to ENMO cut points. While mean differences were small (0.1–1.7 percentage points), this would equate to differences in moderate-to vigorous-intensity activity over a 10-hr wear day of 3.6 (MAD) to 10.8 (ENMO) min/day for intermonitor comparisons or 3.6 (vector magnitude) to 5.4 (ENMO) min/day for sampling rate. Conclusions: Epoch-level intermonitor differences were larger than differences due to sampling rate, but both may impact outcomes such as time spent in each activity intensity. ENMO was the least comparable metric between monitors or sampling rates.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"139 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77989035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association Between Accelerometer and Parental Reported Weekend and Weekday Sleeping Patterns and Adiposity Among Preschool-Aged Children","authors":"Bridget Coyle-Asbil, Hannah J. Coyle-Asbil, David W. L. Ma, J. Haines, L. Vallis","doi":"10.1123/jmpb.2021-0004","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0004","url":null,"abstract":"Sleep is vital for healthy development of young children; however, it is not understood how the quality and quantity vary between the weekends and weekdays (WE–WD). Research focused on older children has demonstrated that there is significant WE–WD variability and that this is associated with adiposity. It is unclear how this is experienced among preschoolers. This study explored: (a) the accuracy of WE–WD sleep as reported in parental logbooks compared with accelerometers; (b) the difference between WE and WD total sleep time, sleep efficiency, and timing, as assessed by accelerometers; and (c) the association between the variability of these metrics and adiposity. Eighty-seven preschoolers (M = 46; 4.48 ± 0.89 years) wore an accelerometer on their right hip for 7 days. Parents were given logbooks to track “lights out” times (sleep onset) and out of bed time (sleep offset). Compared with accelerometers, parental logbook reports indicated earlier sleep onset and later sleep offset times on both WEs and WDs. Accelerometer-derived total sleep time, sleep efficiency, and onset/offset were not significantly different on the WEs and WDs; however, a sex effect was observed, with males going to bed and waking up earlier than females. Correlation analyses revealed that variability of sleep onset times throughout the week was positively correlated with percentage of fat mass in children. Results suggest that variability of sleep onset may be associated with increased adiposity in preschool children. Additional research with larger and more socioeconomically and racially diverse samples is needed to confirm these findings.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"78 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77112439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}