Journal for the measurement of physical behaviour最新文献

{"title":"Using Video Cameras as a Research Tool in Public Spaces: Addressing Ethical and Information Governance Challenges Under Data Protection Legislation","authors":"J. Benton, James Evans, M. Mourby, Mark J. Elliot, Jamie Anderson, J. Hipp, D. French","doi":"10.1123/jmpb.2022-0037","DOIUrl":"https://doi.org/10.1123/jmpb.2022-0037","url":null,"abstract":"Systematic observation is a promising unobtrusive method of assessing human behavior in urban environments without many issues typically associated with self-report measures (e.g., recall bias, low response rates). Improvements in video camera technologies make it more feasible for researchers to conduct systematic observation, which could reduce the time, labor, and cost to facilitate high-quality observational research in urban environments at scale. However, there are important ethical and information governance challenges driven by data protection laws, which discourage many researchers from using camera-based observation methods. The European Union General Data Protection Regulation is a leading global standard for data protection. Drawing on our experiences of conducting three studies using video cameras in public spaces, we discuss how to conduct this kind of research in line with General Data Protection Regulation requirements. The paper outlines issues concerning data protection, privacy, informed consent, and confidentiality, and how we addressed them. In doing this, the paper provides support for responsible use of camera-based observation methods, which will be of value to researchers, ethics committees, and funders. Outlining how to use video cameras responsibly will enable more research to be conducted that, in turn, will build the case for its benefits to researchers and society.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87204432","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 Continuous Focal Sampling Time Thresholds on Physical Activity Metrics When Using Video-Recorded Direct Observation","authors":"Julian Martinez, J. Staudenmayer, S. Strath","doi":"10.1123/jmpb.2023-0004","DOIUrl":"https://doi.org/10.1123/jmpb.2023-0004","url":null,"abstract":"Purpose: To determine differences in physical activity metrics between 1-, 5-, and 10-s direct observation (DO) time thresholds and compare annotation completion time between different time thresholds. Methods: Participants (n = 10, mean age 40.7 ± 22.3 years, five males) were video recorded for 2 hr within a free-living setting. DO videos were annotated by one experienced annotator with a priori developed Posture and Behavior schemas. The annotation order of video, time threshold, and schema used was randomized. For analysis, annotations were collapsed into posture and behavior domains. Total video time is reported. Time to code videos, overall percent agreement, and statistical bias of each posture and behavior domain for the 5-s time threshold and 10-s time threshold were compared to 1-s time threshold output. Results: 19.7 hr of DO were recorded. On average, the 1-s time threshold took 183.9 ± 34.2 min to annotate with the Posture schema and 118.8 ± 23.6 min with the Behavior schema. Under the Posture schema, the 5-s time threshold was 31.7% faster, had 91.5% agreement, and all biases were <±5 min, while the 10-s time threshold was 43.6% faster, had 89.2% agreement, and had biases ranging from −7.59 to 5.21 min. Under the Behavior schema, the 5-s time threshold was 16.0% faster, had 92.0% agreement, and had all biases <±2.1 min, while the 10-s time threshold was 27.6% faster, had 88.3% agreement, and had all biases <±3.9 min. Conclusion: Longer DO annotation time thresholds are accurate and faster but less precise for certain posture and behavior domains when compared to criterion 1-s time threshold in healthy adults.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79287076","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":"Evaluation of Within- and Between-Site Agreement for Direct Observation of Physical Behavior Across Four Research Groups","authors":"S. Keadle, Julian Martinez, S. Strath, J. Sirard, D. John, S. Intille, Diego Arguello, Marcos Amalbert-Birriel, Rachel Barnett, B. Thapa-Chhetry, Melanna Cox, John Chase, Erin E. Dooley, Robert Marcotte, Alex Tolas, John W. Staudemayer","doi":"10.1123/jmpb.2022-0048","DOIUrl":"https://doi.org/10.1123/jmpb.2022-0048","url":null,"abstract":"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.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81289836","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":"Validation of Smartphones and Different Low-Cost Activity Trackers for Step Counting Under Free-Living Conditions","authors":"C. Goh, Nan Xin Wang, A. Müller, Rowena Yap, S. Edney, F. Müller-Riemenschneider","doi":"10.1123/jmpb.2022-0022","DOIUrl":"https://doi.org/10.1123/jmpb.2022-0022","url":null,"abstract":"Background: Smartphones and wrist-worn activity trackers are increasingly popular for step counting purposes and physical activity promotion. Although trackers from popular brands have frequently been validated, the accuracy of low-cost devices under free-living conditions has not been adequately determined. Objective: To investigate the criterion validity of smartphones and low-cost wrist-worn activity trackers under free-living conditions. Methods: Participants wore a waist-worn Yamax pedometer and seven different low-cost wrist-worn activity trackers continuously over 3 days, and an activity log was completed at the end of each day. At the end of the study, the number of step counts reflected on the participants’ smartphone for each of the 3 days was also recorded. To establish criterion validity, step counts from smartphones and activity trackers were compared with the pedometers using Pearson’s correlation coefficient, mean absolute percentage error, and intraclass correlation coefficient. Results: Five of the seven activity trackers underestimated step counts and the remaining two and the smartphones overestimated step counts. Criterion validity was consistently higher for the activity trackers (r = .78–.92; mean absolute percentage error 14.5%–36.1%; intraclass correlation coefficient: .51–.91) than the smartphone (r = .37; mean absolute percentage error 55.7%; intraclass correlation coefficient: .36). Stratified analysis showed better validity of smartphones among female than for male participants. Phone wearing location also affected accuracy. Conclusions: Low-cost trackers demonstrated high accuracy in recording step counts and can be considered with confidence for research purposes or large-scale health promotion programs. The accuracy of using a smartphone for measuring step counts was substantially lower. Factors such as phone wear location and gender should also be considered when using smartphones to track step counts.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85564582","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 Six Accelerometer Metrics for Assessing the Temporal Patterns of Children’s Free-Play Physical Activity","authors":"Katherine L. McKee, K. Pfeiffer, A. Pearson, Kimberly A. Clevenger","doi":"10.1123/jmpb.2023-0007","DOIUrl":"https://doi.org/10.1123/jmpb.2023-0007","url":null,"abstract":"Accelerometers are frequently used to measure physical activity in children, but lack of uniformity in data processing methods, such as the metric used to summarize accelerometer data, limits comparability between studies. The objective was to compare six accelerometer metrics (raw: mean amplitude deviation, Euclidean norm minus one, activity index, monitor-independent movement summary units; count: vertical axis, vector magnitude) for characterizing the intensity and temporal patterns of first and second graders’ (n = 88; age = 7.8 ± 0.7 years) recess physical activity. At a 5-s epoch level, Pearson’s correlations (r) between metrics ranged from .66 to .98. When each epoch was classified into one of four intensity levels based on quartiles, agreement between metrics as indicated by weighted kappa ranged from .81 to .96. When collapsed to time spent in each intensity level, metrics were strongly correlated (r = .76–.99) and most often statistically equivalent for estimating time spent in Quartile 3 or 4. Children were ranked from least to most active, and agreement between metrics was strong (Spearman’s correlation ≥ .87). Temporal patterns were characterized using five fragmentation indices calculated using each of the six metrics, which were fair-to-strongly correlated (r = .53–.99), with the strongest associations for number of high-intensity activity bouts (r ≥ .89). Most fragmentation indices were not statistically equivalent between metrics. While metrics captured similar trends in activity intensity and temporal patterns, caution is warranted when making comparisons of point estimates derived from different metrics. However, all metrics were able to similarly capture higher intensity activity (i.e., Quartile 3 or 4), the most common outcome of interest in intervention studies.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84770900","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":"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}