Journal for the measurement of physical behaviour最新文献

{"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}

{"title":"Comparison of Fitbit One and ActivPAL3TM in Adults With Multiple Sclerosis in a Free-Living Environment","authors":"G. Mehrabani, D. Gross, Saeideh Aminian, P. Manns","doi":"10.1123/jmpb.2020-0066","DOIUrl":"https://doi.org/10.1123/jmpb.2020-0066","url":null,"abstract":"Walking is the most common and preferred way for people with multiple sclerosis (MS) to be active. Consumer-grade wearable activity monitors may be used as a tool to assist people with MS to track their walking by counting the number of steps. The authors evaluated the validity of Fitbit One activity tracker in individuals with MS by comparing step counts measured over a 7-day period against ActivPAL3TM (AP). Twenty-five ambulatory adults with MS with an average age 51.7 (10.2) years and gait speed 0.98 (0.47) m/s, median Expanded Disability Status Scale 5.5 (2.5–6.5), and 15 years post-MS diagnosis wore Fitbit One (using both waist and ankle placement) and AP for 7 consecutive days. Validity of Fitbit One for measuring step counts against AP was assessed using intraclass correlation coefficients (ICCs), Bland–Altman plots, and t tests. Regardless of wearing location (waist or ankle), there was good agreement between steps recorded by Fitbit One and AP (ICC: .86 [.82, .90]). The ankle-worn Fitbit measured steps more accurately (ICC: .91 [.81, .95]) than the waist-worn Fitbit (ICC: .81 [.62, .85]) especially in individuals (n = 12) who walked slowly (gait speed = 0.74 m/s). Fitbit One as a user-friendly, inexpensive, consumer-grade activity tracker can accurately record steps in persons with MS in a free-living environment.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91189902","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":"Changes in Device-Measured Physical Activity Patterns in U.K. Adults Related to the First COVID-19 Lockdown","authors":"A. Kingsnorth, Mhairi Patience, E. Moltchanova, D. Esliger, Nicola J. Paine, M. Hobbs","doi":"10.1123/jmpb.2021-0005","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0005","url":null,"abstract":"The response to COVID-19 resulted in behavioral restrictions to tackle the spread of infection. Initial data indicates that step counts were impacted by lockdown restrictions; however, there is little evidence regarding changes of light and moderate to vigorous physical activity (MVPA) behavioral intensities. In this study, participants were asked to provide longitudinal wearable data from Fitbit devices over a period of 30 weeks, from December 2019 to June 2020. Self-assessed key worker status was captured, along with wearable estimates of steps, light activity, and MVPA. Bayesian change point analyses of data from 97 individuals found that there was a sharp decrease of 1,473 steps (95% credible interval [CI] [−2,218, −709]) and light activity minutes (41.9; 95% CI [−54.3, −29.3]), but an increase in MVPA minutes (11.7; 95% CI [2.9, 19.4]) in the mean weekly totals for nonkey workers. For the key workers, the total number of steps (207; 95% CI [−788, 1,456]) and MVPA minutes increased (20.5; 95% CI [12.6, 28.3]) but light activity decreased by an average of 46.9 min (95% CI [−61.2, −31.8]). Interestingly, the change in steps was commensurate with that observed during Christmas (1,458; 95% CI [−2,286, −554]) for nonkey workers and behavioral changes occurred at different time points and rates depending on key worker status. Results indicate that there were clear behavioral modifications before and during the initial COVID-19 lockdown period, and future research should assess whether any behavioral modifications were sustained over time.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85361458","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 of Individual Motor Abilities and Accelerometer-Derived Physical Activity Measures in Preschool-Aged Children","authors":"Becky Breau, Berit Brandes, Marvin N. Wright, C. Buck, L. Vallis, M. Brandes","doi":"10.1123/jmpb.2020-0065","DOIUrl":"https://doi.org/10.1123/jmpb.2020-0065","url":null,"abstract":"This study explored the relationship between motor abilities and accelerometer-derived measures of physical activity (PA) within preschool-aged children. A total of 193 children (101 girls, 4.2 ± 0.7 years) completed five tests to assess motor abilities, shuttle run (SR), standing long jump, lateral jumping, one-leg stand, and sit and reach. Four PA variables derived from 7-day wrist-worn GENEActiv accelerometers were analyzed including moderate to vigorous PA (in minutes), total PA (in minutes), percentage of total PA time in moderate to vigorous PA, and whether or not children met World Health Organization guidelines for PA. Linear regressions were conducted to explore associations between each PA variable (predictor) and motor ability (outcome). Models were adjusted for age, sex, height, parental education, time spent at sports clubs, and wear time. Models with percentage of total PA time in moderate to vigorous PA were adjusted for percentage of total PA time. Regression analyses indicated that no PA variables were associated with any of the motor abilities, but demographic factors such as age (e.g., SR: ß = −0.45; 95% confidence interval [−1.64, −0.66]), parental education (e.g., SR: ß = 0.25; 95% confidence interval [0.11, 1.87]), or sports club time (e.g., SR: ß = −0.08; 95% confidence interval [−0.98, 0.26]) showed substantial associations with motor abilities. Model strength varied depending on the PA variable and motor ability entered. Results demonstrate that total PA and meeting current PA guidelines may be of importance for motor ability development and should be investigated further. Other covariates showed stronger associations with motor abilities such as time spent at sports clubs and should be investigated in longitudinal settings to assess the associations with individual motor abilities.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74495661","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":"Validity of the iPhone M7 Motion Coprocessor to Estimate Physical Activity During Structured and Free-Living Activities in Healthy Adults","authors":"Nicola K. Thomson, L. McMichan, E. Macrae, J. Baker, D. Muggeridge, C. Easton","doi":"10.1123/jmpb.2020-0067","DOIUrl":"https://doi.org/10.1123/jmpb.2020-0067","url":null,"abstract":"Modern smartphones such as the iPhone contain an integrated accelerometer, which can be used to measure body movement and estimate the volume and intensity of physical activity. Objectives: The primary objective was to assess the validity of the iPhone to measure step count and energy expenditure during laboratory-based physical activities. A further objective was to compare free-living estimates of physical activity between the iPhone and the ActiGraph GT3X+ accelerometer. Methods: Twenty healthy adults wore the iPhone 5S and GT3X+ in a waist-mounted pouch during bouts of treadmill walking, jogging, and other physical activities in the laboratory. Step counts were manually counted, and energy expenditure was measured using indirect calorimetry. During two weeks of free-living, participants (n = 17) continuously wore a GT3X+ attached to their waist and were provided with an iPhone 5S to use as they would their own phone. Results: During treadmill walking, iPhone (703 ± 97 steps) and GT3X+ (675 ± 133 steps) provided accurate measurements of step count compared with the criterion method (700 ± 98 steps). Compared with indirect calorimetry (8 ± 3 kcal·min−1), the iPhone (5 ± 1 kcal·min−1) underestimated energy expenditure with poor agreement. During free-living, the iPhone (7,990 ± 4,673 steps·day−1) recorded a significantly lower (p < .05) daily step count compared with the GT3X+ (9,085 ± 4,647 steps·day−1). Conclusions: The iPhone accurately estimated step count during controlled laboratory walking but recorded a significantly lower volume of physical activity compared with the GT3X+ during free-living.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81887483","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":"Physical Activity, Sedentary Behavior, and Time in Bed Among Finnish Adults Measured 24/7 by Triaxial Accelerometry","authors":"P. Husu, K. Tokola, H. Vähä-Ypyä, H. Sievänen, J. Suni, O. Heinonen, J. Heiskanen, K. Kaikkonen, K. Savonen, S. Kokko, T. Vasankari","doi":"10.1123/JMPB.2020-0056","DOIUrl":"https://doi.org/10.1123/JMPB.2020-0056","url":null,"abstract":"Background: Studies measuring physical activity (PA) and sedentary behavior on a 24/7 basis are scarce. The present study assessed the feasibility of using an accelerometer at the hip while awake and at the wrist while sleeping to describe 24/7 patterns of physical behavior in working-aged adults by age, sex, and fitness. Methods: The study was based on the FinFit 2017 study where the physical behavior of 20- to 69-year-old Finns was assessed 24/7 by triaxial accelerometer (UKKRM42; UKK Terveyspalvelut Oy, Tampere, Finland). During waking hours, the accelerometer was kept at the right hip and, during time in bed, at the nondominant wrist. PA variables were based on 1-min exponential moving average of mean amplitude deviation of the resultant acceleration signal analyzed in 6-s epochs. The angle for the posture estimation algorithm was used to identify sedentary behavior and standing. Evaluation of time in bed was based on the wrist movement. Fitness was estimated by the 6-min walk test. Results: A total of 2,256 eligible participants (mean age 49.5 years, SD = 13.5, 59% women) wore the accelerometer at the hip 15.7 hr/day (SD = 1.4) and at the wrist 8.3 hr/day (SD = 1.4). Sedentary behavior covered 9 hr 18 min/day (SD = 1.8 hr/day), standing nearly 2 hr/day (SD = 0.9), light PA 3.7 hr/day (SD = 1.3), and moderate to vigorous PA 46 min/day (SD = 26). Participants took 7,451 steps per day (SD = 2,962) on average. Men were most active around noon, while women had activity peaks at noon and at early evening. The low-fit tertile took 1,186 and 1,747 fewer steps per day than the mid- and high-fit tertiles (both p < .001). Conclusions: One triaxial accelerometer with a two wear-site approach provides a feasible method to characterize hour-by-hour patterns of physical behavior among working-aged adults.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84870475","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":"Application of Convolutional Neural Network Algorithms for Advancing Sedentary and Activity Bout Classification.","authors":"Supun Nakandala,&nbsp;Marta M Jankowska,&nbsp;Fatima Tuz-Zahra,&nbsp;John Bellettiere,&nbsp;Jordan A Carlson,&nbsp;Andrea Z LaCroix,&nbsp;Sheri J Hartman,&nbsp;Dori E Rosenberg,&nbsp;Jingjing Zou,&nbsp;Arun Kumar,&nbsp;Loki Natarajan","doi":"10.1123/jmpb.2020-0016","DOIUrl":"https://doi.org/10.1123/jmpb.2020-0016","url":null,"abstract":"<p><strong>Background: </strong>Machine learning has been used for classification of physical behavior bouts from hip-worn accelerometers; however, this research has been limited due to the challenges of directly observing and coding human behavior \"in the wild.\" Deep learning algorithms, such as convolutional neural networks (CNNs), may offer better representation of data than other machine learning algorithms without the need for engineered features and may be better suited to dealing with free-living data. The purpose of this study was to develop a modeling pipeline for evaluation of a CNN model on a free-living data set and compare CNN inputs and results with the commonly used machine learning random forest and logistic regression algorithms.</p><p><strong>Method: </strong>Twenty-eight free-living women wore an ActiGraph GT3X+accelerometer on their right hip for 7 days. A concurrently worn thigh-mounted activPAL device captured ground truth activity labels. The authors evaluated logistic regression, random forest, and CNN models for classifying sitting, standing, and stepping bouts. The authors also assessed the benefit of performing feature engineering for this task.</p><p><strong>Results: </strong>The CNN classifier performed best (average balanced accuracy for bout classification of sitting, standing, and stepping was 84%) compared with the other methods (56% for logistic regression and 76% for random forest), even without performing any feature engineering.</p><p><strong>Conclusion: </strong>Using the recent advancements in deep neural networks, the authors showed that a CNN model can outperform other methods even without feature engineering. This has important implications for both the model's ability to deal with the complexity of free-living data and its potential transferability to new populations.</p>","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"4 2","pages":"102-110"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8389343/pdf/nihms-1715953.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39365925","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":"Sequential Activity Patterns and Outcome-Specific, Real-Time, and Target Group-Specific Feedback: The SPORT Algorithm","authors":"Nathalie M. Berninger, G. T. Hoor, G. Plasqui, R. Crutzen","doi":"10.1123/JMPB.2020-0043","DOIUrl":"https://doi.org/10.1123/JMPB.2020-0043","url":null,"abstract":"Purpose: Physical activity (PA) is crucial for health, but there is insufficient evidence about PA patterns and their operationalization. The authors developed two algorithms (SPORTconstant and SPORTlinear) to quantify PA patterns and check whether pattern information yields additional explained variance (compared with a compositional data approach [CoDA]). Methods: To measure PA, 397 (218 females) adolescents with a mean age of 12.4 (SD = 0.6) years wore an ActiGraph on their lower back for 1 week. The SPORT algorithms are based on a running value, each day starting with 0 and minutely adapting depending on the behavior being performed. The authors used linear regression models with a behavior-dependent constant (SPORTconstant) and a function of time-in-bout (SPORTlinear) as predictors and body mass index z scores (BMIz) and fat mass percentages (%FM) as exemplary outcomes. For generalizability, the models were validated using five-fold cross-validation where data were split up in five groups, and each of them was a test data set in one of five iterations. Results: The CoDA and the SPORTconstant models explained low variance in BMIz (2% and 1%) and low to moderate variance in %FM (both 5%). The variance being explained by the SPORTlinear models was 6% (BMIz) and 9% (%FM), which was significantly more than the CoDA models (p < .001) according to likelihood ratio tests. Conclusion: Among this group of adolescents, SPORTlinear explained more variance of BMIz and %FM than CoDA. These results suggest a way to enable research about PA patterns. Future research should apply the SPORTlinear algorithm in other target groups and with other health outcomes.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76234262","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":"Agreement of sedentary behaviour metrics derived from hip-worn and thigh-worn accelerometers among older adults: with implications for studying physical and cognitive health.","authors":"John Bellettiere,&nbsp;Fatima Tuz-Zahra,&nbsp;Jordan A Carlson,&nbsp;Nicola D Ridgers,&nbsp;Sandy Liles,&nbsp;Mikael Anne Greenwood-Hickman,&nbsp;Rod L Walker,&nbsp;Andrea Z LaCroix,&nbsp;Marta M Jankowska,&nbsp;Dori E Rosenberg,&nbsp;Loki Natarajan","doi":"10.1123/jmpb.2020-0036","DOIUrl":"https://doi.org/10.1123/jmpb.2020-0036","url":null,"abstract":"<p><p>Little is known about how sedentary behaviour (SB) metrics derived from hip-worn and thigh-worn accelerometers agree for older adults. Thigh-worn activPAL micro monitors were concurrently worn with hip-worn ActiGraph GT3X+ accelerometers (with SB measured using the 100 count-per-minute (cpm) cut-point; ActiGraph_100cpm) by 953 older adults (age 77±6.6, 54% women) for 4-to-7 days. Device agreement for sedentary time and 5 SB pattern metrics was assessed using mean error and correlations. Logistic regression tested associations with 4 health outcomes using standardized (i.e., z-scores) and unstandardized SB metrics. Mean errors (activPAL-ActiGraph_100cpm) and 95% limits of agreement were: sedentary time -54.7(-223.4,113.9) min/d; time in 30+ minute bouts 77.6(-74.8,230.1) min/d; mean bout duration 5.9(0.5,11.4) min; usual bout duration 15.2(0.4,30) min; breaks in sedentary time -35.4(-63.1,-7.6) breaks/d; and alpha -0.5(-0.6,-0.4). Respective Pearson correlations were: 0.66, 0.78, 0.73, 0.79, 0.51, 0.40. Concordance correlations were: 0.57, 0.67, 0.40, 0.50, 0.14, 0.02. The statistical significance and direction of associations was identical for ActiGraph_100cpm and activPAL metrics in 46 of 48 tests, though significant differences in the magnitude of odds ratios were observed among 9 of 24 tests for unstandardized and 2 of 24 for standardized SB metrics. Caution is needed when interpreting SB metrics and associations with health from ActiGraph_100cpm due to the tendency for it to overestimate breaks in sedentary time relative to activPAL. However, high correlations between activPAL and ActiGraph_100cpm measures and similar standardized associations with health outcomes suggest that studies using ActiGraph_100cpm are useful, though not ideal, for studying SB in older adults.</p>","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"4 1","pages":"79-88"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8547742/pdf/nihms-1686095.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10680597","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}