Journal for the measurement of physical behaviour最新文献

{"title":"Determinants of Consent in the SHARE Accelerometer Study","authors":"Fabio Franzese, Francesca Schrank, Michael Bergmann","doi":"10.1123/jmpb.2022-0046","DOIUrl":"https://doi.org/10.1123/jmpb.2022-0046","url":null,"abstract":"The eighth wave of the Survey of Health, Aging and Retirement in Europe comprises a subsample of respondents who were asked to participate in a measurement of physical activity using thigh-worn accelerometers. This paper describes the process for obtaining consent, identifies determinants of consent, and analyzes the aggregated results of the accelerometer measurements for bias due to sample selection. The overall consent rate in the Survey of Health, Aging and Retirement in Europe accelerometer study was 54%, with variations between countries ranging from 34% to 70%. Multivariate logistic regressions show that various factors are correlated with consent such as respondents’ age, self-reported moderate activity, computer literacy, willingness to answer questions, and the interviewers’ age. After introducing inverse probability weights, there appears to be only a small and insignificant influence of participant selection and consent.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135505593","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":"Context Matters: The Importance of Physical Activity Domains for Public Health","authors":"Tyler D. Quinn, Bethany Barone Gibbs","doi":"10.1123/jmpb.2023-0030","DOIUrl":"https://doi.org/10.1123/jmpb.2023-0030","url":null,"abstract":"Physical activity can be performed across several domains, including leisure, occupation, household, and transportation, but physical activity research, measurement, and surveillance have historically been focused on leisure-time physical activity. Emerging evidence suggests differential health effects across these domains. In particular, occupational physical activity may be associated with adverse health outcomes. We argue that to adequately consider and evaluate such impacts, physical activity researchers and public health practitioners engaging in measurement, surveillance, and guideline creation should measure and consider all relevant physical activity domains where possible. We describe why physical activity science is often limited to the leisure-time domain and provide a rationale for expanding research and public health efforts to include all physical activity domains.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135953827","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":"Methods to Estimate Energy Expenditure, Physical Activity, and Sedentary Time in Pregnant Women: A Validation Study Using Doubly Labeled Water","authors":"S. Alomairah, S. P. Knudsen, C. B. Roland, Ida-Marie Hergel, S. Molsted, Tine D. Clausen, E. Løkkegaard, J. Bendix, R. Maddison, M. Löf, J. E. Larsen, Gerrit van Hall, B. Stallknecht","doi":"10.1123/jmpb.2022-0033","DOIUrl":"https://doi.org/10.1123/jmpb.2022-0033","url":null,"abstract":"Background: Activity trackers and the Pregnancy Physical Activity Questionnaire (PPAQ) measures physical activity (PA) and sedentary time (SED). However, none of these tools have been validated against a criterion method in pregnancy. We aimed to compare a consumer activity tracker and the Danish version of PPAQ (PPAQ-DK) and to validate them using the doubly labeled water technique (DLW) as criterion method. Methods: A total of 220 healthy pregnant women participated. Total energy expenditure (TEE), PA energy expenditure (PAEE), and PA level were determined at gestational Weeks 28–29 using DLW and a Garmin Vivosport (Garmin, Olathe, KS) activity tracker. In addition, PAEE, moderate-to-vigorous intensity PA, and SED were determined using the activity tracker and PPAQ-DK during all three trimesters. Results: TEE from the activity tracker and DLW correlated (r = .63; p < .001), but the activity tracker overestimated TEE (503 kcal/day). Also, the activity tracker overestimated PAEE (303 kcal/day) and PA level compared with DLW. Likewise, PPAQ-DK overestimated PAEE (1,513 kcal/day) compared with DLW. Compared to PPAQ-DK, the activity tracker reported lower values of PAEE and moderate-to-vigorous intensity PA and higher values of SED during all three trimesters. Conclusions: When compared to DLW, we found better agreement of PAEE estimates from the activity tracker than from PPAQ-DK. TEE from the tracker and DLW correlated moderately well, but this was not the case for PAEE or PA level. The activity tracker measured lower PA and higher SED than PPAQ-DK throughout pregnancy. The consumer activity tracker performed better than the questionnaire, but both significantly overestimated PA compared to DLW.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85845490","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":"Impact of COVID-19 Pandemic on Physical Activity, Pain, Mood, and Sleep in Adults with Knee Osteoarthritis.","authors":"Michael J Rose,&nbsp;Michael P LaValley,&nbsp;S Reza Jafarzadeh,&nbsp;Kerry E Costello,&nbsp;Nirali Shah,&nbsp;Soyoung Lee,&nbsp;Belinda Borrelli,&nbsp;Stephen P Messier,&nbsp;Tuhina Neogi,&nbsp;Deepak Kumar","doi":"10.1123/jmpb.2022-0019","DOIUrl":"https://doi.org/10.1123/jmpb.2022-0019","url":null,"abstract":"<p><strong>Objective: </strong>To examine changes in physical activity, sleep, pain and mood in people with knee osteoarthritis (OA) during the ongoing COVID-19 pandemic by leveraging an ongoing randomized clinical trial (RCT).</p><p><strong>Methods: </strong>Participants enrolled in a 12-month parallel two-arm RCT (NCT03064139) interrupted by the COVID-19 pandemic wore an activity monitor (Fitbit Charge 3) and filled out custom weekly surveys rating knee pain, mood, and sleep as part of the study. Data from 30 weeks of the parent study were used for this analysis. Daily step count and sleep duration were extracted from activity monitor data, and participants self-reported knee pain, positive mood, and negative mood via surveys. Metrics were averaged within each participant and then across all participants for pre-pandemic, stay-at-home, and reopening periods, reflecting the phased re-opening in the state of Massachusetts.</p><p><strong>Results: </strong>Data from 28 participants showed small changes with inconclusive clinical significance during the stay-at-home and reopening periods compared to pre-pandemic for all outcomes. Summary statistics suggested substantial variability across participants with some participants showing persistent declines in physical activity during the observation period.</p><p><strong>Conclusion: </strong>Effects of the COVID-19 pandemic on physical activity, sleep, pain, and mood were variable across individuals with OA. Specific reasons for this variability could not be determined. Identifying factors that could affect individuals with knee OA who may exhibit reduced physical activity and/or worse symptoms during major lifestyle changes (such as the ongoing pandemic) is important for providing targeted healthcare services and management advice towards those that could benefit from it the most.</p>","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"5 4","pages":"294-298"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9918033/pdf/nihms-1839455.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9848992","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":"Agreement of Step-Based Metrics From ActiGraph and ActivPAL Accelerometers Worn Concurrently Among Older Adults.","authors":"Eric T Hyde, Steve Nguyen, Fatima Tuz-Zahra, Christopher C Moore, Mikael Anne Greenwood-Hickman, Rod L Walker, Loki Natarajan, Dori Rosenberg, John Bellettiere","doi":"10.1123/jmpb.2022-0001","DOIUrl":"10.1123/jmpb.2022-0001","url":null,"abstract":"<p><strong>Purpose: </strong>Our study evaluated the agreement of mean daily step counts, peak 1-min cadence, and peak 30-min cadence between the hip-worn ActiGraph GT3X+ accelerometer, using the normal filter (AG_N) and the low frequency extension (AG_LFE), and the thigh-worn activPAL3 micro (AP) accelerometer among older adults.</p><p><strong>Methods: </strong>Nine-hundred and fifty-three older adults (≥65 years) were recruited to wear the ActiGraph device concurrently with the AP for 4-7 days beginning in 2016. Using the AP as the reference measure, device agreement for each step-based metric was assessed using mean differences (AG_N - AP and AG_LFE - AP), mean absolute percentage error (MAPE), and Pearson and concordance correlation coefficients.</p><p><strong>Results: </strong>For AG_N - AP, the mean differences and MAPE were: daily steps -1,851 steps/day and 27.2%, peak 1-min cadence -16.2 steps/min and 16.3%, and peak 30-min cadence -17.7 steps/min and 24.0%. Pearson coefficients were .94, .85, and .91 and concordance coefficients were .81, .65, and .73, respectively. For AG_LFE - AP, the mean differences and MAPE were: daily steps 4,968 steps/day and 72.7%, peak 1-min cadence -1.4 steps/min and 4.7%, and peak 30-min cadence 1.4 steps/min and 7.0%. Pearson coefficients were .91, .91, and .95 and concordance coefficients were .49, .91, and .94, respectively.</p><p><strong>Conclusions: </strong>Compared with estimates from the AP, the AG_N underestimated daily step counts by approximately 1,800 steps/day, while the AG_LFE overestimated by approximately 5,000 steps/day. However, peak step cadence estimates generated from the AG_LFE and AP had high agreement (MAPE ≤ 7.0%). Additional convergent validation studies of step-based metrics from concurrently worn accelerometers are needed for improved understanding of between-device agreement.</p>","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"5 4","pages":"242-251"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9934009/pdf/nihms-1870840.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10806913","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":"Calibration of an Accelerometer Activity Index among Older Women and Its Association with Cardiometabolic Risk Factors.","authors":"Guangxing Wang,&nbsp;Sixuan Wu,&nbsp;Kelly R Evenson,&nbsp;Ilsuk Kang,&nbsp;Michael J LaMonte,&nbsp;John Bellettiere,&nbsp;I-Min Lee,&nbsp;Annie Green Howard,&nbsp;Andrea Z LaCroix,&nbsp;Chongzhi Di","doi":"10.1123/jmpb.2021-0031","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0031","url":null,"abstract":"<p><strong>Purpose: </strong>Traditional summary metrics provided by accelerometer device manufacturers, known as counts, are proprietary and manufacturer specific, making them difficult to compare studies using different devices. Alternative summary metrics based on raw accelerometry data have been introduced in recent years. However, they were often not calibrated on ground truth measures of activity-related energy expenditure for direct translation into continuous activity intensity levels. Our purpose is to calibrate, derive, and validate thresholds among women 60 years and older based on a recently proposed transparent raw data based accelerometer activity index (AAI), and to demonstrate its application in association with cardiometabolic risk factors.</p><p><strong>Methods: </strong>We first built calibration equations for estimating metabolic equivalents (METs) continuously using AAI and personal characteristics using internal calibration data (n=199). We then derived AAI cutpoints to classify epochs into sedentary behavior and intensity categories. The AAI cutpoints were applied to 4,655 data units in the main study. We then utilized linear models to investigate associations of AAI sedentary behavior and physical activity intensity with cardiometabolic risk factors.</p><p><strong>Results: </strong>We found that AAI demonstrated great predictive accuracy for METs (R²=0.74). AAI-based physical activity measures were associated in the expected directions with body mass index (BMI), blood glucose, and high density lipoprotein (HDL) cholesterol.</p><p><strong>Conclusion: </strong>The calibration framework for AAI and the cutpoints derived for women older than 60 years can be applied to ongoing epidemiologic studies to more accurately define sedentary behavior and physical activity intensity exposures which could improve accuracy of estimated associations with health outcomes.</p>","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"5 3","pages":"145-155"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9733915/pdf/nihms-1820509.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10136884","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":"Validation of Body-Worn Sensors for Gait Analysis During a 2-min Walk Test in Children.","authors":"Vincent Shieh, Cris Zampieri, Ashwini Sansare, John Collins, Thomas C Bulea, Minal Jain","doi":"10.1123/jmpb.2021-0035","DOIUrl":"10.1123/jmpb.2021-0035","url":null,"abstract":"<p><strong>Introduction: </strong>Instrumented gait mat systems have been regarded as one of the gold standard methods for measuring spatiotemporal gait parameters. However, their portable walkways confine walking to a restricted area and limit the number of gait cycles collected. Wearable inertial sensors are a potential alternative that allow more natural walking behavior and have fewer space restrictions. The objective of this pilot study was to establish the concurrent validity of body-worn sensors against the portable walkway system in older children.</p><p><strong>Methods: </strong>Twenty-one participants (10 males) 7-17 years old performed 2-min walk tests at a self-selected and fast pace in a 25-m-long hallway, while wearing three inertial sensors. Data collection were synchronized between devices and the portions of the walk when subjects passed on the walkway were used to compare gait speed, stride length, gait cycle duration, cadence, and double support time. Regression models and Bland-Altman analysis were completed to determine agreement between systems for the selected gait parameters.</p><p><strong>Results: </strong>Gait speed, cadence, gait cycle duration, and stride length as measured by inertial sensors demonstrated strong agreement overall. Double support time was found to have lower validity due to a combined bias of age, height, weight, and walking pace.</p><p><strong>Conclusion: </strong>These results support the validity of wearable inertial sensors in measuring gait speed, cadence, gait cycle duration, and stride length in children 7 years old and above during a 2-min walking test. Future studies are warranted with a broader age range to thoroughly represent the pediatric population.</p>","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"5 2","pages":"111-119"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10398795/pdf/nihms-1915200.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9936854","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":"Concurrent Agreement Between ActiGraph and activPAL for Measuring Physical Activity in Pregnant Women and Office Workers.","authors":"Melissa A Jones,&nbsp;Sara J Diesel,&nbsp;Bethany Barone Gibbs,&nbsp;Kara M Whitaker","doi":"10.1123/jmpb.2021-0050","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0050","url":null,"abstract":"<p><strong>Introduction: </strong>Current best practice for objective measurement of sedentary behavior and moderate-to-vigorous intensity physical activity (MVPA) requires two separate devices. This study assessed concurrent agreement between the ActiGraph GT3X and the activPAL3 micro for measuring MVPA to determine if activPAL can accurately measure MVPA in addition to its known capacity to measure sedentary behavior.</p><p><strong>Methods: </strong>Forty participants from two studies, including pregnant women (<i>n</i> = 20) and desk workers (<i>n</i> = 20), provided objective measurement of MVPA from waist-worn ActiGraph GT3X and thigh-worn activPAL micro3. MVPA from the GT3X was compared with MVPA from the activPAL using metabolic equivalents of task (MET)- and step-based data across three epochs. Intraclass correlation coefficient and Bland-Altman analyses, overall and by study sample, compared MVPA minutes per day across methods.</p><p><strong>Results: </strong>Mean estimates of activPAL MVPA ranged from 22.7 to 35.2 (MET based) and 19.7 to 25.8 (step based) minutes per day, compared with 31.4 min/day (GT3X). MET-based MVPA had high agreement with GT3X, intraclass correlation coefficient ranging from .831 to .875. Bland-Altman analyses revealed minimal bias between 15- and 30-s MET-based MVPA and GT3X MVPA (-3.77 to 8.63 min/day, <i>p</i> > .10) but with wide limits of agreement (greater than ±27 min). Step-based MVPA had moderate to high agreement (intraclass correlation coefficient: .681-.810), but consistently underestimated GT3X MVPA (bias: 5.62-11.74 min/day, <i>p</i> < .02). For all methods, activPAL appears to better estimate GT3X at lower quantities of MVPA. Results were similar when repeated separately by pregnant women and desk workers.</p><p><strong>Conclusion: </strong>activPAL can measure MVPA in addition to sedentary behavior, providing an option for concurrent, single device monitoring. MET-based MVPA using 30-s activPAL epochs provided the best estimate of GT3X MVPA in pregnant women and desk workers.</p>","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":" ","pages":"69-75"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9635580/pdf/nihms-1819674.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40451186","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":"COVID-19 Highlights the Potential for a More Dynamic Approach to Physical Activity Surveillance","authors":"A. Rowlands, P. Saint-Maurice, P. Dall","doi":"10.1123/jmpb.2022-0004","DOIUrl":"https://doi.org/10.1123/jmpb.2022-0004","url":null,"abstract":"The emergence of severe acute respiratory syndrome corona-virus 2, has urged the scienti fi c community and industry to obtain population snapshots of lifestyle behaviors to characterize changes in behaviors during relatively short windows of time (e","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74007858","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":"Processing of Accelerometry Data with GGIR in Motor Activity Research Consortium for Health","authors":"Wei Guo, A. Leroux, H. Shou, L. Cui, Sun J. Kang, M. Strippoli, M. Preisig, V. Zipunnikov, K. Merikangas","doi":"10.1123/jmpb.2022-0018","DOIUrl":"https://doi.org/10.1123/jmpb.2022-0018","url":null,"abstract":"The Mobile Motor Activity Research Consortium for Health (mMARCH) is a collaborative network of clinical and community studies that employ common digital mobile protocols and collect common clinical and biological measures across participating studies. At a high level, a key scientific goal which spans mMARCH studies is to develop a better understanding of the interrelationships between physical activity (PA), sleep (SL), and circadian rhythmicity (CR) and mental and physical health in children, adolescents, and adults. mMARCH studies employ wrist-worn accelerometry to obtain objective measures of PA/SL/CR. However, there is currently no consensus on a standard data processing pipeline for raw accelerometry data and few open-source tools which facilitate their development. The R package GGIR is the most prominent open-source software package for processing raw accelerometry data, offering great functionality and substantial user flexibility. However, even with GGIR, processing done in a harmonized and reproducible fashion across multiple analytical centers requires a nontrivial amount of expertise combined with a careful implementation. In addition, there are many statistical methods useful for analyzing PA/SL/CR patterns using accelerometry data which are implemented in non-GGIR R packages, including methods from multivariate statistics, functional data analysis, distributional data analysis, and time series analyses. To address the issues of multisite harmonization and additional feature creation, mMARCH developed a streamlined harmonized and reproducible pipeline for loading and cleaning raw accelerometry data via GGIR, merging GGIR, and non-GGIR features of PA/SL/CR together, implementing several additional data and feature quality checks, and performing multiple analyses including Joint and Individual Variation Explained, an unsupervised machine learning dimension reduction technique that identifies latent factors capturing joint across and individual to each of three domains of PA/SL/CR. The pipeline is easily modified to calculate additional features of interest, and allows for studies not affiliated with mMARCH to apply a pipeline which facilitates direct comparisons of scientific results in published work by mMARCH studies. This manuscript describes the pipeline and illustrates the use of combined GGIR and non-GGIR features by applying Joint and Individual Variation Explained to the accelerometry component of CoLaus|PsyCoLaus, one of mMARCH sites. The pipeline is publicly available via open-source R package mMARCH.AC.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"247 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77526500","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}