Journal for the measurement of physical behaviour最新文献

{"title":"Validity of Two Awake Wear-Time Classification Algorithms for activPAL in Youth, Adults, and Older Adults.","authors":"Jordan A Carlson,&nbsp;Fatima Tuz-Zahra,&nbsp;John Bellettiere,&nbsp;Nicola D Ridgers,&nbsp;Chelsea Steel,&nbsp;Carolina Bejarano,&nbsp;Andrea Z LaCroix,&nbsp;Dori E Rosenberg,&nbsp;Mikael Anne Greenwood-Hickman,&nbsp;Marta M Jankowska,&nbsp;Loki Natarajan","doi":"10.1123/jmpb.2020-0045","DOIUrl":"https://doi.org/10.1123/jmpb.2020-0045","url":null,"abstract":"<p><strong>Background: </strong>The authors assessed agreement between participant diaries and two automated algorithms applied to activPAL (PAL Technologies Ltd, Glasgow, United Kingdom) data for classifying awake wear time in three age groups.</p><p><strong>Methods: </strong>Study 1 involved 20 youth and 23 adults who, by protocol, removed the activPAL occasionally to create nonwear periods. Study 2 involved 744 older adults who wore the activPAL continuously. Both studies involved multiple assessment days. In-bed, out-of-bed, and nonwear times were recorded in the participant diaries. The CREA (in PAL processing suite) and ProcessingPAL (secondary application) algorithms estimated out-of-bed wear time. Second- and day-level agreement between the algorithms and diary was investigated, as were associations of sedentary variables with self-rated health.</p><p><strong>Results: </strong>The overall accuracy for classifying out-of-bed wear time as compared with the diary was 89.7% (Study 1) to 95% (Study 2) for CREA and 89.4% (Study 1) to 93% (Study 2) for ProcessingPAL. Over 90% of the nonwear time occurring in nonwear periods >165 min was detected by both algorithms, while <11% occurring in periods ≤165 min was detected. For the daily variables, the mean absolute errors for each algorithm were generally within 0-15% of the diary mean. Most Spearman correlations were very large (≥.81). The mean absolute errors and correlations were less favorable for days on which any nonwear time had occurred. The associations between sedentary variables and self-rated health were similar across processing methods.</p><p><strong>Conclusion: </strong>The automated awake wear-time classification algorithms performed similarly to the diary information on days without short (≤2.5-2.75 hr) nonwear periods. Because both diary and algorithm data can have inaccuracies, best practices likely involve integrating diary and algorithm output.</p>","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"4 2","pages":"151-162"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8386818/pdf/nihms-1716010.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39356557","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":"Should We Use Activity Tracker Data From Smartphones and Wearables to Understand Population Physical Activity Patterns?","authors":"J. Mair, L. Hayes, A. Campbell, N. Sculthorpe","doi":"10.1123/jmpb.2021-0012","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0012","url":null,"abstract":"Researchers, practitioners, and public health organizations from around the world are becoming increasingly interested in using data from consumer-grade devices such as smartphones and wearable activity trackers to measure physical activity (PA). Indeed, large-scale, easily accessible, and autonomous data collection concerning PA as well as other health behaviors is becoming ever more attractive. There are several benefits of using consumer-grade devices to collect PA data including the ability to obtain big data, retrospectively as well as prospectively, and to understand individual-level PA patterns over time and in response to natural events. However, there are challenges related to representativeness, data access, and proprietary algorithms that, at present, limit the utility of this data in understanding population-level PA. In this brief report we aim to highlight the benefits, as well as the limitations, of using existing data from smartphones and wearable activity trackers to understand large-scale PA patterns and stimulate discussion among the scientific community on what the future holds with respect to PA measurement and surveillance.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73975717","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":"Correlates of the Adherence to a 24-hr Wrist-Worn Accelerometer Protocol in a Sample of High School Students","authors":"M. Lopes, B. Costa, L. Malheiros, R. Costa, Ana C. F. Souza, I. Crochemore-Silva, K. Silva","doi":"10.1123/jmpb.2020-0062","DOIUrl":"https://doi.org/10.1123/jmpb.2020-0062","url":null,"abstract":"This study (a) compared accelerometer wear time and compliance between distinct wrist-worn accelerometer data collection plans, (b) analyzed participants’ perception of using accelerometers, and (c) identified sociodemographic and behavioral correlates of accelerometer compliance. A sample of high school students (n = 143) wore accelerometers attached to the wrist by a disposable polyvinyl chloride (PVC) wristband or a reusable fabric wristband for 24 hr over 6 days. Those who wore the reusable fabric band, but not their peers, were instructed to remove the device during water-based activities. Participants answered a questionnaire about sociodemographic and behavioral characteristics and reported their experience wearing the accelerometer. We computed non-wear time and checked participants’ compliance with wear-time criteria (i.e., at least three valid weekdays and one valid weekend day) considering two valid day definitions separately (i.e., at least 16 and 23 hours of accelerometer data). Participants who wore a disposable band had greater compliance compared with those who wore a reusable band for both 16-hr (93% vs. 76%, respectively) and 23-hr valid day definitions (91% vs. 50%, respectively). High schoolers with the following characteristics were less likely to comply with wear time criteria if they (a) engaged in labor-intensive activities, (b) perceived that wearing the monitor hindered their daily activities, or (c) felt ashamed while wearing the accelerometer. In conclusion, the data collection plan composed of using disposable wristbands and not removing the monitor resulted in greater 24-hr accelerometer wear time and compliance. However, a negative experience in using the accelerometer may be a barrier to high schoolers’ adherence to rigorous protocols.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"89 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83400986","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":"Integration of Report-Based Methods to Enhance the Interpretation of Monitor-Based Research: Results From the Free-Living Activity Study for Health Project","authors":"Nicholas R. Lamoureux, P. Hibbing, Charles Matthews, G. Welk","doi":"10.1123/jmpb.2021-0029","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0029","url":null,"abstract":"Accelerometry-based monitors are commonly utilized to evaluate physical activity behavior, but the lack of contextual information limits the interpretability and value of the data. Integration of report-based with monitor-based data allows the complementary strengths of the two approaches to be used to triangulate information and to create a more complete picture of free-living physical behavior. This investigation utilizes data collected from the Free-Living Activity Study for Health to test the feasibility of annotating monitor data with contextual information from the Activities Completed Over Time in 24-hr (ACT24) previous-day recall. The evaluation includes data from 134 adults who completed the 24-hr free-living monitoring protocol and retrospective 24-hr recall. Analyses focused on the relative agreement of energy expenditure estimates between ACT24 and two monitor-based methods (ActiGraph and SenseWear Armband). Daily energy expenditure estimates from ACT24 were equivalent to the reference device-based estimate. Minute-level agreement of energy expenditure between ACT24 and device-based methods was moderate and was similar to the agreement between two different monitor-based methods. This minute-level agreement between ACT24 and device-based methods demonstrates the feasibility and utility of integrating self-report with accelerometer data to provide richer context on the monitored behaviors. This type of integration offers promise for advancing the assessment of physical behavior by aiding in data interpretation and providing opportunities to improve physical activity assessment methods under free-living conditions.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85973950","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 8th International Conference on Ambulatory Monitoring of Physical Activity and Movement","authors":"Emily W Flanagan, N. Broskey, R. Regterschot, M. Hellemons, J. Aerts, Sarah Richardson, L. Allan, A. Yarnall, X. Janssen, A. Okely, Mohammad Sorowar Hossain, Katherine L. McKee, K. Pfeiffer, Amber Pearson, Andrea Moosreiner, S. Burkart, R. Dugger, Hannah Parker, R. Weaver, B. Armstrong, E. Adams, Paul Jacob, R. Marchand, Andrew Meyer, E. Hampp, Elaine Justice, K. Taylor, Kelly Luttazi, M. Verstraete, Ricardo Antunes","doi":"10.1123/jmpb.2021-0036","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0036","url":null,"abstract":"The gold-standards for measuring energy expenditure (EE) under laboratory and free-living settings are whole-room indirect calorimeters and doubly labeled water (DLW), respectively These methods of measuring EE are generally used for quantifying differences in EE within individuals or across populations and can also be used as criterion measures to develop and validate wearable activity monitors for estimating EE Conversely, there can be added benefits of integrating wearable devices in EE studies using room calorimetry and DLW In EE studies aimed at measuring total EE, device-based measures add a dimension of context due to the fine temporal resolution and sensitivity to detect movement intensity which can be used to parse the individual contributors to total EE The focus of this workshop is to introduce the when, why, and how to integrate wearables to EE studies using room calorimeters and DLW For example, wearable monitors can be utilized during room calorimetry to better inform components of EE (resting, thermic effect of feeding, activity, etc ) Doubly labeled water studies give an average estimate of total daily energy expenditure over an assessment period Pairing wearable monitors with DLW, researchers can gain insight into day-to-day, weekday vs weekend, or inter-day variability in physical activity which may influence overall EE 1 Using wearable activity monitors in metabolic and nutritional studies This talk will cover the scope of how activity monitors have been used in different types of applications such as controlled trials and natural histories 2 Adding wearable activity monitors to whole-room indirect calorimetry studies This talk will present the methodology of room calorimetry, and the components of daily EE that wearables can help to quantify (e g , sleep, resting, activity, Detecting hotspots for physical activity using accelerometry, GPS and GIS BACKGROUND AND AIM: Daily physical activity is not one behavior that takes place in one location; it consists of many different behaviors occurring in different locations To get a better understanding of the correlates and determinants of physical activity behavior, knowing in which context it occurs can add valuable additional information With the emerging of methods to combine accelerometer and global positioning system (GPS) The aim of this presentation is to explain how the process of identifying physical activity hotspots works, and demonstrate the method using examples from several studies conducted in Australia and Denmark METHODS: Data were collected among school-children in Denmark and preschool children in Australia using an accelerometer (ActiGraph GT3X or Axivity) and a GPS (Qstarz BT-Q1000X) for 7 days (5 week days, 2 weekend days) to determine their level of activity and movement patterns The GPS position was recorded every 15 seconds and their activity level was recorded and 100Hz and compiled into 15 second epochs Data were merged and processed using HABITUS, an online","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78700112","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":"Converting Raw Accelerometer Data to Activity Counts Using Open-Source Code: Implementing a MATLAB Code in Python and R, and Comparing the Results to ActiLife","authors":"R. Brondeel, Y. Kestens, J. R. Anaraki, Kevin G. Stanley, B. Thierry, D. Fuller","doi":"10.1123/jmpb.2019-0063","DOIUrl":"https://doi.org/10.1123/jmpb.2019-0063","url":null,"abstract":"Background: Closed-source software for processing and analyzing accelerometer data provides little to no information about the algorithms used to transform acceleration data into physical activity indicators. Recently, an algorithm was developed in MATLAB that replicates the frequently used proprietary ActiLife activity counts. The aim of this software profile was (a) to translate the MATLAB algorithm into R and Python and (b) to test the accuracy of the algorithm on free-living data. Methods: As part of the INTErventions, Research, and Action in Cities Team, data were collected from 86 participants in Victoria (Canada). The participants were asked to wear an integrated global positioning system and accelerometer sensor (SenseDoc) for 10 days on the right hip. Raw accelerometer data were processed in ActiLife, MATLAB, R, and Python and compared using Pearson correlation, interclass correlation, and visual inspection. Results: Data were collected for a combined 749 valid days (>10 hr wear time). MATLAB, Python, and R counts per minute on the vertical axis had Pearson correlations with the ActiLife counts per minute of .998, .998, and .999, respectively. All three algorithms overestimated ActiLife counts per minute, some by up to 2.8%. Conclusions: A MATLAB algorithm for deriving ActiLife counts was implemented in R and Python. The different implementations provide similar results to ActiLife counts produced in the closed source software and can, for all practical purposes, be used interchangeably. This opens up possibilities to comparing studies using similar accelerometers from different suppliers, and to using free, open-source software.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86673011","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":"Twelve-Month Stability of Accelerometer-Measured Occupational and Leisure-Time Physical Activity and Compensation Effects","authors":"Jennifer L. Gay, D. Buchner","doi":"10.1123/jmpb.2021-0010","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0010","url":null,"abstract":"Introduction: Little is known about the stability of occupational physical activity (PA) and documented compensation effects over time. Study objectives were to (a) determine the stability of accelerometer estimates of occupational and nonoccupational PA over 6 months and 1 year in adults who do not change jobs, (b) examine PA stability in office workers relative to employees with nonoffice jobs who may be more susceptible to seasonal perturbations in work tasks, and (c) examine the stability data for compensation effects seen at baseline in this sample. Methods: City/county government workers from a variety of labor sectors wore an accelerometer at initial data collection, and at 6 (n = 98) and 12 months (n = 38) following initial data collection. Intraclass correlation coefficients (ICCs) were calculated for accelerometer counts and minutes by intensity, domain, and office worker status. Partial correlation coefficients were examined for compensation effects. Results: ICCs ranged from .19 to .91 for occupational and nonwork activity variables. ICCs were similar by office worker status. In both counts and minutes, greater occupational PA correlated with lower total nonwork PA. However, as minutes of occupational moderate to vigorous physical activity increased, nonoccupational moderate to vigorous physical activity did not decrease. Conclusions: There was moderate to high stability in occupational and nonoccupational PA over 6- and 12-month data collection. Occupational PA stability was greater in nonoffice workers, suggesting that those employees’ PA may be less prone to potential cyclical factors at the workplace. Confirmation of the compensation effect further supports the need for workplace intervention studies to examine changes in all intensities of activity during and outside of work time.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90078249","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":"Accelerometer Calibration: The Importance of Considering Functionality.","authors":"Scott J Strath,&nbsp;Taylor W Rowley,&nbsp;Chi C Cho,&nbsp;Allison Hyngstrom,&nbsp;Ann M Swartz,&nbsp;Kevin G Keenan,&nbsp;Julian Martinez,&nbsp;John W Staudenmayer","doi":"10.1123/jmpb.2020-0027","DOIUrl":"https://doi.org/10.1123/jmpb.2020-0027","url":null,"abstract":"<p><strong>Purpose: </strong>To compare the accuracy and precision of a hip-worn accelerometer to predict energy cost during structured activities across motor performance and disease conditions.</p><p><strong>Methods: </strong>118 adults self-identifying as healthy (<i>n</i> = 44) and those with arthritis (<i>n</i> = 23), multiple sclerosis (<i>n</i> = 18), Parkinson's disease (<i>n</i> = 17), and stroke (<i>n</i> =18) underwent measures of motor performance and were categorized into groups: Group 1, usual; Group 2, moderate impairment; and Group 3, severe impairment. The participants completed structured activities while wearing an accelerometer and a portable metabolic measurement system. Accelerometer-predicted energy cost (metabolic equivalent of tasks [METs]) were compared with measured METs and evaluated across functional impairment and disease conditions. Statistical significance was assessed using linear mixed effect models and Bayesian information criteria to assess model fit.</p><p><strong>Results: </strong>All activities' accelerometer counts per minute (CPM) were 29.5-72.6% less for those with disease compared with those who were healthy. The predicted MET bias was similar across disease, -0.49 (-0.71, -0.27) for arthritis, -0.38 (-0.53, -0.22) for healthy, -0.44 (-0.68, -0.20) for MS, -0.34 (-0.58, -0.09) for Parkinson's, and -0.30 (-0.54, -0.06) for stroke. For functional impairment, there was a graded reduction in CPM for all activities: Group 1, 1,215 CPM (1,129, 1,301); Group 2, 789 CPM (695, 884); and Group 3, 343 CPM (220, 466). The predicted MET bias revealed similar results across the Group 1, -0.37 METs (-0.52, -0.23); Group 2, -0.44 METs (-0.60, -0.28); and Group 3, -0.33 METs (-0.55, -0.13). The Bayesian information criteria showed a better model fit for functional impairment compared with disease condition.</p><p><strong>Conclusion: </strong>Using functionality to improve accelerometer calibration could decrease variability and warrants further exploration to improve accelerometer prediction of physical activity.</p>","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":" ","pages":"68-78"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8330493/pdf/nihms-1672920.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39281318","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":"Feasibility and Validity of Assessing Low-Income, African American Older Adults’ Physical Activity and Sedentary Behavior Through Ecological Momentary Assessment","authors":"Jaclyn P. Maher, Kourtney Sappenfield, Heidi Scheer, Christine Zecca, D. Hevel, L. Kennedy-Malone","doi":"10.1123/jmpb.2021-0024","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0024","url":null,"abstract":"Ecological momentary assessment (EMA) is a methodological tool that can provide novel insights into the prediction and modeling of physical behavior; however, EMA has not been used to study physical activity (PA) or sedentary behavior (SB) among racial minority older adults. This study aimed to determine the feasibility and validity of an EMA protocol to assess racial minority older adults’ PA and SB. For 8 days, older adults (n = 91; 89% African American; 70% earning <$20,000/year) received six randomly prompted, smartphone-based EMA questionnaires per day and wore an activPAL monitor to measure PA and SB. The PA and SB were also self-reported through EMA. Participants were compliant with the EMA protocol on 92.4% of occasions. Participants were more likely to miss an EMA prompt in the afternoon compared to morning and on weekend days compared to weekdays. Participants were less likely to miss an EMA prompt when engaged in more device-based SB in the 30 min around the prompt. When participants self-reported PA, they engaged in less device-based PA in the 15 min after compared to the 15 min before the EMA prompt, suggesting possible reactance or disruption of PA. EMA-reported PA and SB were positively associated with device-based PA and SB in the 30 min around the EMA prompt, supporting criterion validity. Overall, the assessment of low-income, African American older adults’ PA and SB through EMA is feasible and valid, though physical behaviors may influence compliance and prompting may create reactivity.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"121 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79442107","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":"Is the Polar M430 a Valid Tool for Estimating Maximal Oxygen Consumption in Adult Females?","authors":"K. Miller, T. Kempf, Brian C. Rider, S. Conger","doi":"10.1123/jmpb.2020-0050","DOIUrl":"https://doi.org/10.1123/jmpb.2020-0050","url":null,"abstract":"Background: Previous research studies have found that heart rate monitors that predict maximal oxygen consumption () are valid for males but overestimate in females. Inaccurate self-reported physical activity (PA) levels may affect the validity of the prediction algorithm used to predict . Purpose: To investigate the validity of the Polar M430 in predicting among females with varying PA levels. Methods: Polar M430 was used to predict () for 43 healthy female study participants (26.9 ± 1.3 years), under three conditions: the participant’s self-selected PA category (sPA), one PA category below the sPA (sPA − 1), and one category above the sPA (sPA + 1). Indirect calorimetry was utilized to measure () via a modified Astrand treadmill protocol. Repeated-measures analyses of covariance using age and percentage of body fat as covariates were used to detect differences between groups. Bland–Altman plots were used to assess the precision of the measurement. Results: was significantly correlated with (r = .695, p < .001). The mean values for and were 44.58 ± 9.29 and 43.98 ± 8.76, respectively. No significant differences were found between , , sPA – 1, and sPA + 1 (p = .492). However, the Bland–Altman plots indicated a low level of precision with the estimate. Conclusions: The Polar M430 was a valid method to predict across different sPA levels in females. Moreover, an under/overestimation in sPA had little effect on the predicted .","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84279507","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}