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
{"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}
Wei Guo, A. Leroux, H. Shou, L. Cui, Sun J. Kang, M. Strippoli, M. Preisig, V. Zipunnikov, K. Merikangas
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.
{"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}
Hannah J. Coyle-Asbil, A. Bhatia, Andrew S. P. Lim, Mandeep Singh
Individuals suffering from neuropathic pain commonly report issues associated with sleep. To measure sleep in this population, researchers have used actigraphy. Historically, actigraphy data have been analyzed in the form of counts; however, due to the proprietary nature, many opt to quantify data in its raw form. Various processing techniques exist to accomplish this; however, it remains unclear how they compare to one another. This study sought to compare sleep measures derived using the GGIR R package versus the GENEActiv (GA) R Markdown tool in a neuropathic pain population. It was hypothesized that the processing techniques would yield significantly different sleep outcomes. One hundred and twelve individuals (mean age = 52.72 ± 13.01 years; 60 M) with neuropathic pain in their back and/or lower limbs were included. While simultaneously undergoing spinal cord stimulation, actigraphy devices were worn on the wrist for a minimum of 7 days (GA; 50 Hz). Upon completing the protocol, sleep outcome measures were calculated using (a) the GGIR R package and (b) the GA R Markdown tool. To compare these algorithms, paired-samples t tests and Bland–Altman plots were used to compare the total sleep time, sleep efficiency, wake after sleep onset, sleep onset time, and rise times. According to the paired-samples t test, the GA R Markdown yielded lower total sleep time and sleep efficiency and a greater wake after sleep onset, compared with the GGIR package. Furthermore, later sleep onset times and earlier rise times were reported by the GGIR package compared with the GA R Markdown.
患有神经性疼痛的人通常报告与睡眠有关的问题。为了测量这一人群的睡眠，研究人员使用了活动记录仪。历史上，活动记录仪数据以计数的形式进行分析;然而，由于专有性质，许多人选择以原始形式量化数据。存在各种加工技术来实现这一目标;然而，目前尚不清楚它们如何相互比较。本研究试图比较在神经性疼痛人群中使用GGIR R包和GENEActiv (GA) R Markdown工具得出的睡眠测量值。据推测，处理技术会产生显著不同的睡眠结果。112例，平均年龄52.72±13.01岁;60例患者为背部和/或下肢神经性疼痛患者。在接受脊髓刺激的同时，在手腕上佩戴活动记录仪至少7天(GA;50赫兹)。完成方案后，使用(a) GGIR R包和(b) GA R Markdown工具计算睡眠结果测量值。为了比较这些算法，使用配对样本t检验和Bland-Altman图来比较总睡眠时间、睡眠效率、睡眠开始后醒来、睡眠开始时间和起床时间。根据配对样本t检验，与GGIR包相比，GA R Markdown产生的总睡眠时间和睡眠效率更低，睡眠开始后更清醒。此外，与GA R Markdown相比，GGIR包报告了更晚的睡眠开始时间和更早的起床时间。
{"title":"Investigating the Effects of Applying Different Actigraphy Processing Approaches to Examine the Sleep Data of Patients With Neuropathic Pain","authors":"Hannah J. Coyle-Asbil, A. Bhatia, Andrew S. P. Lim, Mandeep Singh","doi":"10.1123/jmpb.2022-0017","DOIUrl":"https://doi.org/10.1123/jmpb.2022-0017","url":null,"abstract":"Individuals suffering from neuropathic pain commonly report issues associated with sleep. To measure sleep in this population, researchers have used actigraphy. Historically, actigraphy data have been analyzed in the form of counts; however, due to the proprietary nature, many opt to quantify data in its raw form. Various processing techniques exist to accomplish this; however, it remains unclear how they compare to one another. This study sought to compare sleep measures derived using the GGIR R package versus the GENEActiv (GA) R Markdown tool in a neuropathic pain population. It was hypothesized that the processing techniques would yield significantly different sleep outcomes. One hundred and twelve individuals (mean age = 52.72 ± 13.01 years; 60 M) with neuropathic pain in their back and/or lower limbs were included. While simultaneously undergoing spinal cord stimulation, actigraphy devices were worn on the wrist for a minimum of 7 days (GA; 50 Hz). Upon completing the protocol, sleep outcome measures were calculated using (a) the GGIR R package and (b) the GA R Markdown tool. To compare these algorithms, paired-samples t tests and Bland–Altman plots were used to compare the total sleep time, sleep efficiency, wake after sleep onset, sleep onset time, and rise times. According to the paired-samples t test, the GA R Markdown yielded lower total sleep time and sleep efficiency and a greater wake after sleep onset, compared with the GGIR package. Furthermore, later sleep onset times and earlier rise times were reported by the GGIR package compared with the GA R Markdown.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87127070","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}
Physical behaviors (e.g., sleep, sedentary behavior, and physical activity) often occur in sustained bouts that are punctuated with brief interruptions. To detect and classify these interrupted bouts, researchers commonly use wearable devices and specialized algorithms. Most algorithms examine the data in chronological order, initiating and terminating bouts whenever specific criteria are met. Consequently, the bouts may encapsulate or overlap with later periods that also meet the activation and termination criteria (i.e., alternative bout solutions). In some cases, it is desirable to compare these alternative bout solutions before making a final classification. Thus, comparison-focused algorithms are needed, which can be used in isolation or in concert with their chronology-focused counterparts. In this technical note, we present a comparison-focused algorithm called CRIB (Clustered Recognition of Interrupted Bouts). It uses agglomerative hierarchical clustering to facilitate the comparison of different bout solutions, with the final classification being made in favor of the smallest number of bouts that comply with user-specified criteria (i.e., limits on the number, individual duration, and cumulative duration of interruptions). For demonstration, we use CRIB to assess bouts of moderate to vigorous physical activity in accelerometer data from the National Health and Nutrition Examination Survey, and we include a comparison against results from two established chronology-focused algorithms. Our discussion explores strengths and limitations of CRIB, as well as potential considerations and applications for using it in future studies. An online vignette (https://github.com/paulhibbing/PBpatterns/blob/main/vignettes/CRIB.pdf) is available to assist users with implementing CRIB in R.
{"title":"CRIB: A Novel Method for Device-Based Physical Behavior Analysis","authors":"P. Hibbing, S. Creasy, J. Carlson","doi":"10.1123/jmpb.2021-0059","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0059","url":null,"abstract":"Physical behaviors (e.g., sleep, sedentary behavior, and physical activity) often occur in sustained bouts that are punctuated with brief interruptions. To detect and classify these interrupted bouts, researchers commonly use wearable devices and specialized algorithms. Most algorithms examine the data in chronological order, initiating and terminating bouts whenever specific criteria are met. Consequently, the bouts may encapsulate or overlap with later periods that also meet the activation and termination criteria (i.e., alternative bout solutions). In some cases, it is desirable to compare these alternative bout solutions before making a final classification. Thus, comparison-focused algorithms are needed, which can be used in isolation or in concert with their chronology-focused counterparts. In this technical note, we present a comparison-focused algorithm called CRIB (Clustered Recognition of Interrupted Bouts). It uses agglomerative hierarchical clustering to facilitate the comparison of different bout solutions, with the final classification being made in favor of the smallest number of bouts that comply with user-specified criteria (i.e., limits on the number, individual duration, and cumulative duration of interruptions). For demonstration, we use CRIB to assess bouts of moderate to vigorous physical activity in accelerometer data from the National Health and Nutrition Examination Survey, and we include a comparison against results from two established chronology-focused algorithms. Our discussion explores strengths and limitations of CRIB, as well as potential considerations and applications for using it in future studies. An online vignette (https://github.com/paulhibbing/PBpatterns/blob/main/vignettes/CRIB.pdf) is available to assist users with implementing CRIB in R.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86297302","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}
The purpose of this scoping review was to examine publications using accelerometers in children aged 6 months to <6 years and report on current methodologies used for data collection and analyses. We examined device make and model, device placement, sampling frequency, data collection protocol, definition of nonwear time, inclusion criteria, epoch duration, and cut points. Five online databases and three gray literature databases were searched. Studies were included if they were published in English between January 2009 and March 2021. A total of 627 articles were included for descriptive analyses. Of the reviewed articles, 75% used ActiGraph devices. The most common device placement was hip or waist. More than 80% of articles did not report a sampling frequency, and 7-day protocols during only waking hours were the most frequently reported. Fifteen-second epoch durations and the cut points developed by Pate et al. in 2006 were the most common. A total of 203 articles did not report which definition of nonwear time was used; when reported, “20 minutes of consecutive zeros” was the most frequently used. Finally, the most common inclusion criteria were “greater or equal to 10 hr/day for at least 3 days” for studies conducted in free-living environments and “greater than 50% of the school day” for studies conducted in preschool or childcare environments. Results demonstrated a major lack of reporting of methods used to analyze accelerometer data from young children. A list of recommended reporting practices was developed to encourage increased reporting of key methodological details for research in this area.
{"title":"The Use of Accelerometers in Young Children: A Methodological Scoping Review","authors":"Becky Breau, Hannah J. Coyle-Asbil, L. Vallis","doi":"10.1123/jmpb.2021-0049","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0049","url":null,"abstract":"The purpose of this scoping review was to examine publications using accelerometers in children aged 6 months to <6 years and report on current methodologies used for data collection and analyses. We examined device make and model, device placement, sampling frequency, data collection protocol, definition of nonwear time, inclusion criteria, epoch duration, and cut points. Five online databases and three gray literature databases were searched. Studies were included if they were published in English between January 2009 and March 2021. A total of 627 articles were included for descriptive analyses. Of the reviewed articles, 75% used ActiGraph devices. The most common device placement was hip or waist. More than 80% of articles did not report a sampling frequency, and 7-day protocols during only waking hours were the most frequently reported. Fifteen-second epoch durations and the cut points developed by Pate et al. in 2006 were the most common. A total of 203 articles did not report which definition of nonwear time was used; when reported, “20 minutes of consecutive zeros” was the most frequently used. Finally, the most common inclusion criteria were “greater or equal to 10 hr/day for at least 3 days” for studies conducted in free-living environments and “greater than 50% of the school day” for studies conducted in preschool or childcare environments. Results demonstrated a major lack of reporting of methods used to analyze accelerometer data from young children. A list of recommended reporting practices was developed to encourage increased reporting of key methodological details for research in this area.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91004756","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}
In the article title and the first paragraph of the conference abstracts, the conference was incorrectly referred to as the 8th International Conference on AmbulatoryMonitoring of Physical Activity. This has been to corrected to the 7th International Conference on Ambulatory Monitoring of Physical Activity and Movement. The article was corrected October 20, 2022. The authors apologize for the error.
{"title":"Erratum. The 7th International Conference on Ambulatory Monitoring of Physical Activity and Movement","authors":"","doi":"10.1123/jmpb.2022-0040","DOIUrl":"https://doi.org/10.1123/jmpb.2022-0040","url":null,"abstract":"In the article title and the first paragraph of the conference abstracts, the conference was incorrectly referred to as the 8th International Conference on AmbulatoryMonitoring of Physical Activity. This has been to corrected to the 7th International Conference on Ambulatory Monitoring of Physical Activity and Movement. The article was corrected October 20, 2022. The authors apologize for the error.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81013054","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}
Mary C. Hidde, K. Lyden, J. Broussard, Kim Henry, Julia Sharp, Elizabeth A Thomas, C. Rynders, H. Leach
Introduction: Patterns of physical activity (PA) and time in bed (TIB) across the 24-hr cycle have important implications for many health outcomes; therefore, wearable accelerometers are often implemented in behavioral research to measure free-living PA and TIB. Two accelerometers, the activPAL and Actiwatch, are common accelerometers for measuring PA (activPAL) and TIB (Actiwatch), respectively. Both accelerometers have the capacity to measure TIB, but the degree to which these accelerometers agree is not clear. Therefore, this study compared estimates of TIB between activPAL and the Actiwatch accelerometers. Methods: Participants (mean ± SDage = 39.8 ± 7.6 years) with overweight or obesity (N = 83) wore an activPAL and Actiwatch continuously for 7 days, 24 hr per day. TIB was assessed using manufacturer-specific algorithms. Repeated-measures mixed-effect models and Bland–Altman plots were used to compare the activPAL and Actiwatch TIB estimates. Results: Statistical differences between TIB assessed by activPAL versus Actiwatch (p < .001) were observed. There was not a significant interaction between accelerometer and day of wear (p = .87). The difference in TIB between accelerometers ranged from −72.9 ± 15.7 min (Day 7) to −98.6 ± 14.5 min (Day 3), with the Actiwatch consistently estimating longer TIB compared with the activPAL. Conclusion: Data generated by the activPAL and Actiwatch accelerometers resulted in divergent estimates of TIB. Future studies should continue to explore the validity of activity monitoring accelerometers for estimating TIB.
{"title":"Comparison of activPAL and Actiwatch for Estimations of Time in Bed in Free-Living Adults","authors":"Mary C. Hidde, K. Lyden, J. Broussard, Kim Henry, Julia Sharp, Elizabeth A Thomas, C. Rynders, H. Leach","doi":"10.1123/jmpb.2021-0047","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0047","url":null,"abstract":"Introduction: Patterns of physical activity (PA) and time in bed (TIB) across the 24-hr cycle have important implications for many health outcomes; therefore, wearable accelerometers are often implemented in behavioral research to measure free-living PA and TIB. Two accelerometers, the activPAL and Actiwatch, are common accelerometers for measuring PA (activPAL) and TIB (Actiwatch), respectively. Both accelerometers have the capacity to measure TIB, but the degree to which these accelerometers agree is not clear. Therefore, this study compared estimates of TIB between activPAL and the Actiwatch accelerometers. Methods: Participants (mean ± SDage = 39.8 ± 7.6 years) with overweight or obesity (N = 83) wore an activPAL and Actiwatch continuously for 7 days, 24 hr per day. TIB was assessed using manufacturer-specific algorithms. Repeated-measures mixed-effect models and Bland–Altman plots were used to compare the activPAL and Actiwatch TIB estimates. Results: Statistical differences between TIB assessed by activPAL versus Actiwatch (p < .001) were observed. There was not a significant interaction between accelerometer and day of wear (p = .87). The difference in TIB between accelerometers ranged from −72.9 ± 15.7 min (Day 7) to −98.6 ± 14.5 min (Day 3), with the Actiwatch consistently estimating longer TIB compared with the activPAL. Conclusion: Data generated by the activPAL and Actiwatch accelerometers resulted in divergent estimates of TIB. Future studies should continue to explore the validity of activity monitoring accelerometers for estimating TIB.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77647077","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}
Introduction: Wrist-worn accelerometer has gained popularity recently in commercial and research use for physical activity tracking. Yet, no consensus exists for standardized wrist-worn data processing, and physical activity data derived from wrist-worn accelerometer cannot be directly compared with data derived from the historically used hip-worn accelerometer. In this work, through a systematic review, we aim to identify and analyze discrepancies between wrist-worn versus hip-worn ActiGraph accelerometers in measuring adult physical activity. Methods: A systematic review was conducted on studies involving free-living data comparison between hip- and wrist-worn ActiGraph accelerometers among adult users. We assessed the population, study protocols, data processing criteria (axis, epoch, wear-time correction, etc.), and outcome measures (step count, sedentary activity time, moderate-to-vigorous physical activity, etc.). Step count and activity count discrepancy were analyzed using meta-analysis, while meta-analysis was not attempted for others due to heterogeneous data processing criteria among the studies. Results: We screened 235 studies with 19 studies qualifying for inclusion in the systematic review. Through meta-analysis, the wrist-worn sensor recorded, on average, 3,537 steps/day more than the hip-worn sensor. Regarding sedentary activity time and moderate-to-vigorous physical activity estimation, the wrist sensor consistently overestimates moderate-to-vigorous physical activity time while underestimating sedentary activity time, with discrepancies ranging from a dozen minutes to several hours. Discussions: Our findings quantified the substantial discrepancies between wrist and hip sensors. It calls attention to the need for a cautious approach to interpreting data from different wear locations. These results may also serve as a reference for data comparisons among studies using different sensor locations.
{"title":"A Comparison of Wrist- Versus Hip-Worn ActiGraph Sensors for Assessing Physical Activity in Adults: A Systematic Review","authors":"Nolan Gall, R. Sun, M. Smuck","doi":"10.1123/jmpb.2021-0045","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0045","url":null,"abstract":"Introduction: Wrist-worn accelerometer has gained popularity recently in commercial and research use for physical activity tracking. Yet, no consensus exists for standardized wrist-worn data processing, and physical activity data derived from wrist-worn accelerometer cannot be directly compared with data derived from the historically used hip-worn accelerometer. In this work, through a systematic review, we aim to identify and analyze discrepancies between wrist-worn versus hip-worn ActiGraph accelerometers in measuring adult physical activity. Methods: A systematic review was conducted on studies involving free-living data comparison between hip- and wrist-worn ActiGraph accelerometers among adult users. We assessed the population, study protocols, data processing criteria (axis, epoch, wear-time correction, etc.), and outcome measures (step count, sedentary activity time, moderate-to-vigorous physical activity, etc.). Step count and activity count discrepancy were analyzed using meta-analysis, while meta-analysis was not attempted for others due to heterogeneous data processing criteria among the studies. Results: We screened 235 studies with 19 studies qualifying for inclusion in the systematic review. Through meta-analysis, the wrist-worn sensor recorded, on average, 3,537 steps/day more than the hip-worn sensor. Regarding sedentary activity time and moderate-to-vigorous physical activity estimation, the wrist sensor consistently overestimates moderate-to-vigorous physical activity time while underestimating sedentary activity time, with discrepancies ranging from a dozen minutes to several hours. Discussions: Our findings quantified the substantial discrepancies between wrist and hip sensors. It calls attention to the need for a cautious approach to interpreting data from different wear locations. These results may also serve as a reference for data comparisons among studies using different sensor locations.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"120 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85762596","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}
Purpose: The purpose of this study was to evaluate the validity of the step count and Active Minutes features of the Garmin Vivofit Jr. 2 consumer activity monitor. Methods: Participants included 35 students (age 8–11) enrolled in an after-school physical activity (PA) and nutrition program. Participants wore an ActiGraph GT3x+ monitor on their waist and the Vivofit monitor on their wrist during the PA portion of the program. Data were collected across multiple sessions, resulting in 158 unique pairs of data. Pearson correlation, mean absolute percent error, and equivalence testing were performed to compare step count and minutes of activity (Vivofit Active Minutes vs ActiGraph moderate to vigorous PA) between the two monitors. Results: Moderate correlations were found between the monitors for steps (r = .65) and minutes (r = .43). Mean absolute percent error was 26% for steps and 43% for minutes, suggesting that there were high amounts of individual error. Equivalence testing showed significant agreement between the monitors for steps (p = .046), but not for minutes (p = .98). Conclusion: The Garmin Vivofit Jr. 2 shows acceptable validity for measurement of steps at a group level in a field-based setting, although the amount of individual variability must be considered. The Vivofit Jr. 2 was not valid for measurement of minutes of activity.
目的:本研究的目的是评估Garmin Vivofit Jr. 2消费者活动监测仪的步数和活动分钟特征的有效性。方法:参与者包括35名参加课后体育活动(PA)和营养计划的学生(8-11岁)。在项目的PA部分，参与者在腰上戴着ActiGraph GT3x+监测器，手腕上戴着Vivofit监测器。在多个会话中收集数据，产生158对独特的数据。进行Pearson相关性、平均绝对百分比误差和等效检验来比较两个监测器之间的步数和活动分钟数(Vivofit Active minutes vs ActiGraph中度至剧烈PA)。结果:步数(r = 0.65)与分钟数(r = 0.43)之间存在中度相关性。步数的平均绝对误差为26%，分钟数的平均绝对误差为43%，这表明存在很大的个人误差。等效性检验显示，监测器之间在步数(p = 0.046)上有显著的一致性，但在分钟数(p = 0.98)上没有显著的一致性。结论:Garmin Vivofit Jr. 2在以现场为基础的环境中，在群体水平上测量步数显示出可接受的有效性，尽管必须考虑个体变异的量。Vivofit Jr. 2不适用于测量活动分钟数。
{"title":"Validity of the Garmin Vivofit Jr. to Measure Physical Activity During a Youth After-School Program","authors":"K. Peyer, Kara C. Hamilton","doi":"10.1123/jmpb.2021-0039","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0039","url":null,"abstract":"Purpose: The purpose of this study was to evaluate the validity of the step count and Active Minutes features of the Garmin Vivofit Jr. 2 consumer activity monitor. Methods: Participants included 35 students (age 8–11) enrolled in an after-school physical activity (PA) and nutrition program. Participants wore an ActiGraph GT3x+ monitor on their waist and the Vivofit monitor on their wrist during the PA portion of the program. Data were collected across multiple sessions, resulting in 158 unique pairs of data. Pearson correlation, mean absolute percent error, and equivalence testing were performed to compare step count and minutes of activity (Vivofit Active Minutes vs ActiGraph moderate to vigorous PA) between the two monitors. Results: Moderate correlations were found between the monitors for steps (r = .65) and minutes (r = .43). Mean absolute percent error was 26% for steps and 43% for minutes, suggesting that there were high amounts of individual error. Equivalence testing showed significant agreement between the monitors for steps (p = .046), but not for minutes (p = .98). Conclusion: The Garmin Vivofit Jr. 2 shows acceptable validity for measurement of steps at a group level in a field-based setting, although the amount of individual variability must be considered. The Vivofit Jr. 2 was not valid for measurement of minutes of activity.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"77 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76085090","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}
Kimberly A. Clevenger, K. Mackintosh, M. McNarry, K. Pfeiffer, Alexander Montoye, J. Brønd
ActiGraph counts are commonly used for characterizing physical activity intensity and energy expenditure and are among the most well-studied accelerometer metrics. Researchers have recently replicated the counts processing method using a mechanical setup, now allowing users to generate counts from raw acceleration data. Purpose: The purpose of this study was to compare ActiGraph-generated counts to open-source counts and assess the impact on free-living physical activity levels derived from cut points, machine learning, and two-regression models. Methods: Children (n = 488, 13.0 ± 1.1 years of age) wore an ActiGraph wGT3X-BT on their right hip for 7 days during waking hours. ActiGraph counts and counts generated from raw acceleration data were compared at the epoch-level and as overall means. Seven methods were used to classify overall and epoch-level activity intensity. Outcomes were compared using weighted kappa, correlations, mean absolute deviation, and two one-sided equivalence testing. Results: All outcomes were statistically equivalent between ActiGraph and open-source counts; weighted kappa was ≥.971 and epoch-level correlations were ≥.992, indicating very high agreement. Bland–Altman plots indicated differences increased with activity intensity, but overall differences between ActiGraph and open-source counts were minimal (e.g., epoch-level mean absolute difference of 23.9 vector magnitude counts per minute). Regardless of classification model, average differences translated to 1.4–2.6 min/day for moderate- to vigorous-intensity physical activity. Conclusion: Open-source counts may be used to enhance comparability of future studies, streamline data analysis, and enable researchers to use existing developed models with alternative accelerometer brands. Future studies should verify the performance of open-source counts for other outcomes, like sleep.
{"title":"Comparison of Child and Adolescent Physical Activity Levels From Open-Source Versus ActiGraph Counts","authors":"Kimberly A. Clevenger, K. Mackintosh, M. McNarry, K. Pfeiffer, Alexander Montoye, J. Brønd","doi":"10.1123/jmpb.2021-0057","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0057","url":null,"abstract":"ActiGraph counts are commonly used for characterizing physical activity intensity and energy expenditure and are among the most well-studied accelerometer metrics. Researchers have recently replicated the counts processing method using a mechanical setup, now allowing users to generate counts from raw acceleration data. Purpose: The purpose of this study was to compare ActiGraph-generated counts to open-source counts and assess the impact on free-living physical activity levels derived from cut points, machine learning, and two-regression models. Methods: Children (n = 488, 13.0 ± 1.1 years of age) wore an ActiGraph wGT3X-BT on their right hip for 7 days during waking hours. ActiGraph counts and counts generated from raw acceleration data were compared at the epoch-level and as overall means. Seven methods were used to classify overall and epoch-level activity intensity. Outcomes were compared using weighted kappa, correlations, mean absolute deviation, and two one-sided equivalence testing. Results: All outcomes were statistically equivalent between ActiGraph and open-source counts; weighted kappa was ≥.971 and epoch-level correlations were ≥.992, indicating very high agreement. Bland–Altman plots indicated differences increased with activity intensity, but overall differences between ActiGraph and open-source counts were minimal (e.g., epoch-level mean absolute difference of 23.9 vector magnitude counts per minute). Regardless of classification model, average differences translated to 1.4–2.6 min/day for moderate- to vigorous-intensity physical activity. Conclusion: Open-source counts may be used to enhance comparability of future studies, streamline data analysis, and enable researchers to use existing developed models with alternative accelerometer brands. Future studies should verify the performance of open-source counts for other outcomes, like sleep.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90801584","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}
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