Introduction: Mobility restriction following limb loss might lead to a sedentary lifestyle, impacting health. Daily activity monitoring of amputees has focused on prosthetic steps, neglecting overall activity. Purpose: To assess daily activity in individuals with an established amputation and to explore the amount of activity recorded from the prosthesis as compared to the overall activity. Methods: Individuals with a unilateral transfemoral amputation or knee disarticulation who had used a prosthesis in daily life for >1 year and could walk 100 m (unsupported or single aided) were recruited. Descriptive information and prosthetic mobility were collected. Two activPAL™ accelerometers were attached to the nonamputated thigh and the prosthesis, respectively. The mean daily activity over 7 days was compared between the nonamputated limb and the prosthesis. Results: Thirty-nine participants (22 men/17 women; mean age 54 [14.5] years) with amputation mainly due to trauma (59%) or tumor (28%) were included. Overall, participants took 6,125 steps and spent 10.2 hr sedentary, 5.0 hr upright, and 8.7 hr laying per day. Compared to recordings from the nonamputated limb, 85% of sit-to-stand transitions (32/38), 73% of steps (4,449/6,125), and 68% of walking time (1.0/1.5 hr) were recorded from the prosthesis. Recordings seemed to be less adequate for incidental prosthetic steps than for walks. Conclusions: Sedentary behavior accounted for most of the day demonstrating the importance to encourage physical activity among established prosthetic users. The prosthesis is used for daily activity to a great extent. However, noted pitfalls in the recordings call for further refinement of the measurements.
{"title":"Daily Activity of Individuals With an Amputation Above the Knee as Recorded From the Nonamputated Limb and the Prosthetic Limb","authors":"K. Hagberg, R. Zügner, P. Thomsen, R. Tranberg","doi":"10.1123/jmpb.2022-0053","DOIUrl":"https://doi.org/10.1123/jmpb.2022-0053","url":null,"abstract":"Introduction: Mobility restriction following limb loss might lead to a sedentary lifestyle, impacting health. Daily activity monitoring of amputees has focused on prosthetic steps, neglecting overall activity. Purpose: To assess daily activity in individuals with an established amputation and to explore the amount of activity recorded from the prosthesis as compared to the overall activity. Methods: Individuals with a unilateral transfemoral amputation or knee disarticulation who had used a prosthesis in daily life for >1 year and could walk 100 m (unsupported or single aided) were recruited. Descriptive information and prosthetic mobility were collected. Two activPAL™ accelerometers were attached to the nonamputated thigh and the prosthesis, respectively. The mean daily activity over 7 days was compared between the nonamputated limb and the prosthesis. Results: Thirty-nine participants (22 men/17 women; mean age 54 [14.5] years) with amputation mainly due to trauma (59%) or tumor (28%) were included. Overall, participants took 6,125 steps and spent 10.2 hr sedentary, 5.0 hr upright, and 8.7 hr laying per day. Compared to recordings from the nonamputated limb, 85% of sit-to-stand transitions (32/38), 73% of steps (4,449/6,125), and 68% of walking time (1.0/1.5 hr) were recorded from the prosthesis. Recordings seemed to be less adequate for incidental prosthetic steps than for walks. Conclusions: Sedentary behavior accounted for most of the day demonstrating the importance to encourage physical activity among established prosthetic users. The prosthesis is used for daily activity to a great extent. However, noted pitfalls in the recordings call for further refinement of the measurements.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84762155","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}
We strongly believe that diversity, equity, and inclusion in research lead to better science, more innovations and more relevant outcomes that better serve society at large. Historically, scientific research is quite WEIRD, meaning that it is dominated by researchers and study samples from Western, Educated, Industrialized, Rich, and Democratic countries. Such WEIRD research leads to results that better serve a small, privileged group of WEIRD people, widening health inequalities. Research among a selective group with similar backgrounds and perspectives results in bias and hinders innovation. As a result, we end up missing out on the valuable holistic viewpoint that more inclusive research would gain. In this invited commentary based on the International Conference on Ambulatory Monitoring of Physical Activity and Movement (ICAMPAM) 2022 keynote presentation by Prof. ChinAPaw, we discuss the importance of diversity, equity, and inclusion in research and introduce our vision for AWESOME science—All-inclusive, Worldwide ranging, Equitable, Sincere, Open-minded, Mindful of our own implicit bias, and Essential—that is more inclusive and relevant for everyone regardless of who they are and where they live. More diversity, equity, and inclusion make our collective dance toward healthy societies more beautiful and impactful!
{"title":"Let us Dance Around the World! Toward More Diversity, Equity, and Inclusion in Research","authors":"M. Chinapaw, Manou Anselma","doi":"10.1123/jmpb.2022-0043","DOIUrl":"https://doi.org/10.1123/jmpb.2022-0043","url":null,"abstract":"We strongly believe that diversity, equity, and inclusion in research lead to better science, more innovations and more relevant outcomes that better serve society at large. Historically, scientific research is quite WEIRD, meaning that it is dominated by researchers and study samples from Western, Educated, Industrialized, Rich, and Democratic countries. Such WEIRD research leads to results that better serve a small, privileged group of WEIRD people, widening health inequalities. Research among a selective group with similar backgrounds and perspectives results in bias and hinders innovation. As a result, we end up missing out on the valuable holistic viewpoint that more inclusive research would gain. In this invited commentary based on the International Conference on Ambulatory Monitoring of Physical Activity and Movement (ICAMPAM) 2022 keynote presentation by Prof. ChinAPaw, we discuss the importance of diversity, equity, and inclusion in research and introduce our vision for AWESOME science—All-inclusive, Worldwide ranging, Equitable, Sincere, Open-minded, Mindful of our own implicit bias, and Essential—that is more inclusive and relevant for everyone regardless of who they are and where they live. More diversity, equity, and inclusion make our collective dance toward healthy societies more beautiful and impactful!","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87589034","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}
Intraindividual activity variability is often overlooked, with some existing work using SD as a variability metric. However, average real variability (ARV) may be a more suitable metric as it accounts for temporal variability. The purpose of this exploratory study was to (a) apply ARV analyses to habitual activity outcomes; (b) assess the agreement between ARV and SD for habitual step counts, standing time, and sedentary time; and (c) determine the relationship between activity variability (SD and ARV) with average activity values. One hundred and eighty-nine participants (37 ± 22 years, 109 females) wore the activPAL inclinometer on their thigh 24 hr/day for 6.4 ± 0.9 days. SD and ARV were calculated for each participant across their wear time. A Wilcoxon signed-rank test revealed that ARV was significantly higher than SD for step count, standing time, and sedentary time (all, p < .001). Equivalence testing demonstrated mixed equivalence for step counts (10%), standing time (12%), and sedentary time (14%). SD and ARV were highly correlated to each other for all activity metrics (all, ρ > .857, p < .001). SD was moderately (ρ = .601, p < .001) and weakly (ρ = .296, p < .001) correlated with average step count and standing time, respectively. ARV was weakly correlated with average step count and standing time (both: ρ < .499, p < .001). However, average sedentary time was not associated with SD or ARV (both, p > .177). While the two measurements of variability were strongly correlated, they cannot be used interchangeably. More monitoring research should consider intraindividual activity variability and use methods, such as ARV, that consider the temporal nature of day–day activity.
个体内部活动的可变性经常被忽视，一些现有的工作使用SD作为可变性度量。然而，平均实际变率(ARV)可能是一个更合适的度量，因为它说明了时间变率。本探索性研究的目的是:(a)将ARV分析应用于习惯性活动的结果;(b)评估ARV和SD在习惯步数、站立时间和久坐时间方面的一致性;(c)确定活度变异(SD和ARV)与平均活度值之间的关系。189名参与者(37±22岁，109名女性)每天24小时在大腿上佩戴activPAL倾角仪，持续6.4±0.9天。计算每位参与者在整个佩戴时间内的SD和ARV。Wilcoxon sign -rank检验显示，在步数、站立时间和久坐时间方面，ARV显著高于SD。857, p .177)。虽然这两种测量变异性的方法密切相关，但它们不能互换使用。更多的监测研究应考虑到个人活动的可变性和使用方法，例如考虑到日常活动的时间性质的抗逆转录病毒药物。
{"title":"Applying Average Real Variability to Quantifying Day–Day Physical Activity and Sedentary Postures Variability: A Comparison With Standard Deviation","authors":"Madeline E. Shivgulam, M. O'Brien","doi":"10.1123/jmpb.2023-0021","DOIUrl":"https://doi.org/10.1123/jmpb.2023-0021","url":null,"abstract":"Intraindividual activity variability is often overlooked, with some existing work using SD as a variability metric. However, average real variability (ARV) may be a more suitable metric as it accounts for temporal variability. The purpose of this exploratory study was to (a) apply ARV analyses to habitual activity outcomes; (b) assess the agreement between ARV and SD for habitual step counts, standing time, and sedentary time; and (c) determine the relationship between activity variability (SD and ARV) with average activity values. One hundred and eighty-nine participants (37 ± 22 years, 109 females) wore the activPAL inclinometer on their thigh 24 hr/day for 6.4 ± 0.9 days. SD and ARV were calculated for each participant across their wear time. A Wilcoxon signed-rank test revealed that ARV was significantly higher than SD for step count, standing time, and sedentary time (all, p < .001). Equivalence testing demonstrated mixed equivalence for step counts (10%), standing time (12%), and sedentary time (14%). SD and ARV were highly correlated to each other for all activity metrics (all, ρ > .857, p < .001). SD was moderately (ρ = .601, p < .001) and weakly (ρ = .296, p < .001) correlated with average step count and standing time, respectively. ARV was weakly correlated with average step count and standing time (both: ρ < .499, p < .001). However, average sedentary time was not associated with SD or ARV (both, p > .177). While the two measurements of variability were strongly correlated, they cannot be used interchangeably. More monitoring research should consider intraindividual activity variability and use methods, such as ARV, that consider the temporal nature of day–day activity.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85999131","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}
Adrian Ortega, B. Forseth, P. Hibbing, Chelsea Steel, J. Carlson
Purpose: We investigated convergent validity of commonly used ActiGraph scoring methods with various activPAL scoring methods in youth and adults. Methods: Youth and adults wore an ActiGraph and activPAL simultaneously for 1–3 days. We compared moderate to vigorous physical activity (MVPA) estimates from the ActiGraph Evenson 15-s (youth) and Freedson 60-s (adult) cut-point scoring methods and four activPAL scoring methods based on metabolic equivalents (METs), step counts, vertical axis counts, and vector magnitude counts. All activPAL methods were applied to 15-s epochs for youth and 60-s epochs for adults, and the METs method was also applied to 1-s epochs. Epoch-level agreement was examined with classification tests (sensitivity, positive predictive value, and F1) using the ActiGraph methods as the referent. Day-level agreement was examined using tests of mean error, mean absolute error, and Spearman correlations. Results: Relative to ActiGraph methods, which indicated a mean MVPA of 41 min/day for youth and 24 min/day for adults, the activPAL METs method applied to 15-s epochs in youth and 60-s epochs in adults yielded the most comparable estimates of MVPA. Daily MVPA estimated from all other activPAL scoring methods generally had poor agreement with ActiGraph methods in youth and adults. Conclusion: When using the same epoch lengths between monitors, MVPA estimation via the activPAL METs scoring method appears to have good comparability to ActiGraph cut points at the group-level and moderate comparability at the individual-level in youth and adults. When using this scoring method, the activPAL appears to be appropriate for measuring daily minutes of MVPA in youth and adults.
{"title":"Convergent Validity Between Epoch-Based activPAL and ActiGraph Methods for Measuring Moderate to Vigorous Physical Activity in Youth and Adults","authors":"Adrian Ortega, B. Forseth, P. Hibbing, Chelsea Steel, J. Carlson","doi":"10.1123/jmpb.2022-0013","DOIUrl":"https://doi.org/10.1123/jmpb.2022-0013","url":null,"abstract":"Purpose: We investigated convergent validity of commonly used ActiGraph scoring methods with various activPAL scoring methods in youth and adults. Methods: Youth and adults wore an ActiGraph and activPAL simultaneously for 1–3 days. We compared moderate to vigorous physical activity (MVPA) estimates from the ActiGraph Evenson 15-s (youth) and Freedson 60-s (adult) cut-point scoring methods and four activPAL scoring methods based on metabolic equivalents (METs), step counts, vertical axis counts, and vector magnitude counts. All activPAL methods were applied to 15-s epochs for youth and 60-s epochs for adults, and the METs method was also applied to 1-s epochs. Epoch-level agreement was examined with classification tests (sensitivity, positive predictive value, and F1) using the ActiGraph methods as the referent. Day-level agreement was examined using tests of mean error, mean absolute error, and Spearman correlations. Results: Relative to ActiGraph methods, which indicated a mean MVPA of 41 min/day for youth and 24 min/day for adults, the activPAL METs method applied to 15-s epochs in youth and 60-s epochs in adults yielded the most comparable estimates of MVPA. Daily MVPA estimated from all other activPAL scoring methods generally had poor agreement with ActiGraph methods in youth and adults. Conclusion: When using the same epoch lengths between monitors, MVPA estimation via the activPAL METs scoring method appears to have good comparability to ActiGraph cut points at the group-level and moderate comparability at the individual-level in youth and adults. When using this scoring method, the activPAL appears to be appropriate for measuring daily minutes of MVPA in youth and adults.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"225 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85988227","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}
Jingzhi Yu, K. Kapphahn, Hyatt Moore, F. Haydel, Thomas Robinson, M. Desai
Background: Clustering, a class of unsupervised machine learning methods, has been applied to physical activity data recorded by accelerometers to discover unique patterns of physical activity and health outcomes. The prediction strength metric provides a criterion to determine the optimal number of clusters for clustering methods. The aim of this study is to provide specific guidance for applying prediction strength to time series accelerometer data. Methods: For this purpose, we designed an extensive simulation study. We created a synthetic data set of accelerometer data using data from a childhood obesity management trial. We evaluated the role of a prespecified threshold of the prediction strength metric as a key input parameter. We compared the recommended threshold (between 0.8 and 0.9) with an approach we developed (Local Maxima). Results: The choice of threshold had a large impact on performance. When the noise level increased (greater overlap between true clusters), lower thresholds outperformed the recommended threshold, which tended to underestimate the true number of clusters. In addition, we found that sorting the data by magnitude of intensity in windows within the time series of interest prior to clustering alleviated sensitivity to threshold choice. Furthermore, for accelerometer data, we recommend that the Local Maxima approach be utilized together with a graphical evaluation of the prediction strength metric function over values of k. Finally, we strongly suggest sorting of the data prior to clustering if sorting retains meaning for the research question at hand. Conclusion: Our recommendations can help future researchers discover more robust patterns from accelerometer data.
{"title":"Prediction Strength for Clustering Activity Patterns Using Accelerometer Data","authors":"Jingzhi Yu, K. Kapphahn, Hyatt Moore, F. Haydel, Thomas Robinson, M. Desai","doi":"10.1123/jmpb.2022-0049","DOIUrl":"https://doi.org/10.1123/jmpb.2022-0049","url":null,"abstract":"Background: Clustering, a class of unsupervised machine learning methods, has been applied to physical activity data recorded by accelerometers to discover unique patterns of physical activity and health outcomes. The prediction strength metric provides a criterion to determine the optimal number of clusters for clustering methods. The aim of this study is to provide specific guidance for applying prediction strength to time series accelerometer data. Methods: For this purpose, we designed an extensive simulation study. We created a synthetic data set of accelerometer data using data from a childhood obesity management trial. We evaluated the role of a prespecified threshold of the prediction strength metric as a key input parameter. We compared the recommended threshold (between 0.8 and 0.9) with an approach we developed (Local Maxima). Results: The choice of threshold had a large impact on performance. When the noise level increased (greater overlap between true clusters), lower thresholds outperformed the recommended threshold, which tended to underestimate the true number of clusters. In addition, we found that sorting the data by magnitude of intensity in windows within the time series of interest prior to clustering alleviated sensitivity to threshold choice. Furthermore, for accelerometer data, we recommend that the Local Maxima approach be utilized together with a graphical evaluation of the prediction strength metric function over values of k. Finally, we strongly suggest sorting of the data prior to clustering if sorting retains meaning for the research question at hand. Conclusion: Our recommendations can help future researchers discover more robust patterns from accelerometer data.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86721974","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}
Tiereny McGuire, Kirstie Devin, Victoria Patricks, Benjamin Griffiths, C. Speirs, M. Granat
Introduction: The COVID-19 lockdown introduced restrictions to free-living activities. Changes to these activities can be accurately quantified using combined measurement. Using activPAL3 and self-reports to collect activity data, the study aimed to quantify changes that occurred in physical activity and sedentary behavior between prelockdown and lockdown. The study also sought to determine changes in indoor and outdoor stepping. Methods: Using activPAL3, four participants recorded physical activity data prelockdown and during lockdown restrictions (February–June 2020). Single events (sitting, standing, stepping, lying) were recorded and analyzed by the CREA algorithm using an event-based approach. The analysis focused on step count, sedentary time, and lying (in bed) time; median and interquartile range were calculated. Daily steps classified as taking place indoors and outdoors were calculated separately. Results: 33 prelockdown and 92 in-lockdown days of valid data were captured. Median daily step count across all participants reduced by 14.8% (from 5,828 prelockdown to 4,963 in-lockdown), while sedentary and lying time increased by 4% and 8%, respectively (sedentary: 9.98–10.30 hr; lying: 9.33–10.05 hr). Individual variations were observed in hours spent sedentary (001: 8.44–8.66, 002: 7.41–8.66, 003: 11.97–10.59, 004: 6.29–7.94, and lying (001: 9.69–9.49, 002: 11.46–11.66, 003: 7.63–9.34, 004: 9.7–11.12) pre- and in-lockdown. Discrepancies in self-report versus algorithm classification of indoor/outdoor stepping were observed for three participants. Conclusion: The study quantitively showed lockdown restrictions negatively impacted physical activity and sedentary behavior; two variables closely linked to health outcomes. This has important implications for public health policies to help develop targeted interventions and mandates that encourage additional physical activity and lower sedentary behavior.
{"title":"Use of Accelerometers to Track Changes in Stepping Behavior With the Introduction of the 2020 COVID Pandemic Restrictions: A Case Study","authors":"Tiereny McGuire, Kirstie Devin, Victoria Patricks, Benjamin Griffiths, C. Speirs, M. Granat","doi":"10.1123/jmpb.2022-0015","DOIUrl":"https://doi.org/10.1123/jmpb.2022-0015","url":null,"abstract":"Introduction: The COVID-19 lockdown introduced restrictions to free-living activities. Changes to these activities can be accurately quantified using combined measurement. Using activPAL3 and self-reports to collect activity data, the study aimed to quantify changes that occurred in physical activity and sedentary behavior between prelockdown and lockdown. The study also sought to determine changes in indoor and outdoor stepping. Methods: Using activPAL3, four participants recorded physical activity data prelockdown and during lockdown restrictions (February–June 2020). Single events (sitting, standing, stepping, lying) were recorded and analyzed by the CREA algorithm using an event-based approach. The analysis focused on step count, sedentary time, and lying (in bed) time; median and interquartile range were calculated. Daily steps classified as taking place indoors and outdoors were calculated separately. Results: 33 prelockdown and 92 in-lockdown days of valid data were captured. Median daily step count across all participants reduced by 14.8% (from 5,828 prelockdown to 4,963 in-lockdown), while sedentary and lying time increased by 4% and 8%, respectively (sedentary: 9.98–10.30 hr; lying: 9.33–10.05 hr). Individual variations were observed in hours spent sedentary (001: 8.44–8.66, 002: 7.41–8.66, 003: 11.97–10.59, 004: 6.29–7.94, and lying (001: 9.69–9.49, 002: 11.46–11.66, 003: 7.63–9.34, 004: 9.7–11.12) pre- and in-lockdown. Discrepancies in self-report versus algorithm classification of indoor/outdoor stepping were observed for three participants. Conclusion: The study quantitively showed lockdown restrictions negatively impacted physical activity and sedentary behavior; two variables closely linked to health outcomes. This has important implications for public health policies to help develop targeted interventions and mandates that encourage additional physical activity and lower sedentary behavior.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90480800","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}
Paul H. Lee, Ali Neishabouri, Andy C. Y. Tse, Christine C. Guo
Body-worn sensors have contributed to a rich and growing body of literature in public health and clinical research in the last decades. A major challenge in sensor research is the lack of consistency and standardization of the collection and reporting of the sensor data. The algorithms used to derive these activity counts can be vastly different between manufactures and not always transparent to the researchers. With Philips, one of the major research-grade wearable device manufacturers, discontinuing this product line, many researchers are left in need of alternative solutions and at the risk of not being able to relate their historical data using the Philips Actiwatch 2 devices to future findings with other devices. We herein provide a comparison analysis and conversion method that can be used to convert activity counts from Philips to those from ActiGraph, another major manufacturer who provide both raw acceleration data and count data based on their open-source algorithm to the research community. This work provides an approach to maximize the scientific value of historical actigraphy data collected by the Actiwatch devices to support research continuity in this community. The conversion, however, is not perfect and only offers an approximation, due to the intrinsic difference in the count algorithms between the two accelerometers, and the permanent information loss during data reduction. We encourage future research using body-worn sensors to retain the raw sensor data to ensure data consistency, comparability, and the ability to leverage future algorithm improvement.
{"title":"Comparative Analysis and Conversion Between Actiwatch and ActiGraph Open-Source Counts","authors":"Paul H. Lee, Ali Neishabouri, Andy C. Y. Tse, Christine C. Guo","doi":"10.1123/jmpb.2022-0054","DOIUrl":"https://doi.org/10.1123/jmpb.2022-0054","url":null,"abstract":"Body-worn sensors have contributed to a rich and growing body of literature in public health and clinical research in the last decades. A major challenge in sensor research is the lack of consistency and standardization of the collection and reporting of the sensor data. The algorithms used to derive these activity counts can be vastly different between manufactures and not always transparent to the researchers. With Philips, one of the major research-grade wearable device manufacturers, discontinuing this product line, many researchers are left in need of alternative solutions and at the risk of not being able to relate their historical data using the Philips Actiwatch 2 devices to future findings with other devices. We herein provide a comparison analysis and conversion method that can be used to convert activity counts from Philips to those from ActiGraph, another major manufacturer who provide both raw acceleration data and count data based on their open-source algorithm to the research community. This work provides an approach to maximize the scientific value of historical actigraphy data collected by the Actiwatch devices to support research continuity in this community. The conversion, however, is not perfect and only offers an approximation, due to the intrinsic difference in the count algorithms between the two accelerometers, and the permanent information loss during data reduction. We encourage future research using body-worn sensors to retain the raw sensor data to ensure data consistency, comparability, and the ability to leverage future algorithm improvement.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78163776","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}
Madeline E. Shivgulam, Jennifer L. Petterson, Liam P. Pellerine, D. Kimmerly, M. O'Brien
Stepping cadence is an important determinant of activity intensity, with faster stepping associated with the most health benefits. The Stryd monitor provides real-time feedback on stepping cadence. The limited existing literature has neither validated the Stryd across slow walking to fast running speeds nor strictly followed statistical guidelines for monitor validation studies. We assessed the criterion validity of the Stryd monitor to detect stepping cadence across multiple walking and jogging/running speeds. It was hypothesized that the Stryd monitor would be an accurate measure of stepping cadence across all measured speeds. Forty-six participants (23 ± 5 years, 26 females) wore the Stryd monitor on their shoelaces during a 10-stage progressive treadmill walking (Speeds 1–5) and jogging/running (Speeds 6–10) protocol (criterion: manually counted video-recorded cadence; total stages: 438). Standardized guidelines for physical activity monitor statistical analyses were followed. A two-way repeated-measure analysis of variance revealed the Stryd monitor recorded a slightly higher cadence (<1 steps/min difference, all p < .001) at 2 miles/hr (92.1 ± 6.2 steps/min vs. 91.5 ± 6.4 steps/min, p < .001), 2.5 miles/hr (101.3 ± 6.1 steps/min vs. 100.7 ± 6.4 steps/min), and 3.5 miles/hr (117.4 ± 5.9 steps/min vs. 117.0 ± 6.0 steps/min). However, equivalence testing demonstrated high equivalence of the Stryd and manually counted cadence (equivalence zone required: ≤± 2.6%) across all speeds. The Stryd activity monitor is a valid measure of stepping cadence across walking, jogging, and running speeds. By providing real-time cadence feedback, the Stryd monitor has strong potential to help guide the general public monitor their stepping intensity to promote more habitual activity at faster cadences.
{"title":"The Stryd Foot Pod Is a Valid Measure of Stepping Cadence During Treadmill Walking and Running","authors":"Madeline E. Shivgulam, Jennifer L. Petterson, Liam P. Pellerine, D. Kimmerly, M. O'Brien","doi":"10.1123/jmpb.2022-0031","DOIUrl":"https://doi.org/10.1123/jmpb.2022-0031","url":null,"abstract":"Stepping cadence is an important determinant of activity intensity, with faster stepping associated with the most health benefits. The Stryd monitor provides real-time feedback on stepping cadence. The limited existing literature has neither validated the Stryd across slow walking to fast running speeds nor strictly followed statistical guidelines for monitor validation studies. We assessed the criterion validity of the Stryd monitor to detect stepping cadence across multiple walking and jogging/running speeds. It was hypothesized that the Stryd monitor would be an accurate measure of stepping cadence across all measured speeds. Forty-six participants (23 ± 5 years, 26 females) wore the Stryd monitor on their shoelaces during a 10-stage progressive treadmill walking (Speeds 1–5) and jogging/running (Speeds 6–10) protocol (criterion: manually counted video-recorded cadence; total stages: 438). Standardized guidelines for physical activity monitor statistical analyses were followed. A two-way repeated-measure analysis of variance revealed the Stryd monitor recorded a slightly higher cadence (<1 steps/min difference, all p < .001) at 2 miles/hr (92.1 ± 6.2 steps/min vs. 91.5 ± 6.4 steps/min, p < .001), 2.5 miles/hr (101.3 ± 6.1 steps/min vs. 100.7 ± 6.4 steps/min), and 3.5 miles/hr (117.4 ± 5.9 steps/min vs. 117.0 ± 6.0 steps/min). However, equivalence testing demonstrated high equivalence of the Stryd and manually counted cadence (equivalence zone required: ≤± 2.6%) across all speeds. The Stryd activity monitor is a valid measure of stepping cadence across walking, jogging, and running speeds. By providing real-time cadence feedback, the Stryd monitor has strong potential to help guide the general public monitor their stepping intensity to promote more habitual activity at faster cadences.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"10 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72411229","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}
Accelerometer technology and applications have expanded and evolved rapidly over approximately the past two decades. This commentary, which reflects content presented at a keynote presentation at 8th International Conference on Ambulatory Monitoring of Physical Activity and Movement (ICAMPAM 2022), discusses aspects of this evolution from the author’s perspective. The goal is to provide historical context for newer investigators working with device-based measures of physical activity. The presentation includes discussion of the fielding of accelerometer devices in the 2003–2006 National Health and Nutrition Examination Survey, selected recommendations from relevant workshops between 2004 and 2010, and the author’s perspective on the current status of accelerometer use in population surveillance and public health. The important role of collaboration is emphasized.
{"title":"Evolution of Public Health Physical Activity Applications of Accelerometers: A Personal Perspective","authors":"R. Troiano","doi":"10.1123/jmpb.2022-0038","DOIUrl":"https://doi.org/10.1123/jmpb.2022-0038","url":null,"abstract":"Accelerometer technology and applications have expanded and evolved rapidly over approximately the past two decades. This commentary, which reflects content presented at a keynote presentation at 8th International Conference on Ambulatory Monitoring of Physical Activity and Movement (ICAMPAM 2022), discusses aspects of this evolution from the author’s perspective. The goal is to provide historical context for newer investigators working with device-based measures of physical activity. The presentation includes discussion of the fielding of accelerometer devices in the 2003–2006 National Health and Nutrition Examination Survey, selected recommendations from relevant workshops between 2004 and 2010, and the author’s perspective on the current status of accelerometer use in population surveillance and public health. The important role of collaboration is emphasized.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73639277","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}
Background : Several previous studies investigated physical activity of older adults using wearable devices, but more studies need to develop normative values for chronic disease conditions. This study aimed to investigate physical activity using a triaxial activity monitor in community-dwelling older Japanese adults with and without lifestyle-related diseases. Methods : Data from a total of 732 community-dwelling older Japanese men and women were collected and analyzed in a cross-sectional study. The participants’ physical activity was assessed for seven consecutive days by a triaxial accelerometer. Physical activity was assessed by number of lifestyle-related diseases and six lifestyle-related diseases categories by gender. Physical activity was assessed separately for total, locomotive, and nonlocomotive physical activity. Results : Participants with multiple (two or more) diseases had significantly lower total light-intensity physical activity (LPA; 278.5 ± 8.4 min/day) and nonlocomotive LPA (226.4 ± 7.0 min/day) versus without diseases in men. Compared in each disease category, total LPA and nonlocomotive LPA was significantly lower in men with hypertension and diabetes. Total sedentary time was significantly higher in men with hypertension, diabetes, and heart disease. Locomotive LPA was significantly lower in men with diabetes. In women, locomotive moderate- to vigorous-intensity physical activity was significantly higher in women with diabetes, and nonlocomotive moderate- to vigorous-intensity physical activity was significantly lower in women with heart disease. Conclusion : This study demonstrated that older Japanese men with multiple lifestyle-related diseases had lower physical activity. In each disease category, hypertension, diabetes, and heart disease affected lower physical activity, especially in men.
{"title":"Evaluation of Physical Activity Assessment Using a Triaxial Activity Monitor in Community-Dwelling Older Japanese Adults With and Without Lifestyle-Related Diseases","authors":"Sho Nagayoshi, Harukaze Yatsugi, Xin Liu, Takafumi Saito, Koji Yamatsu, Hiro Kishimoto","doi":"10.1123/jmpb.2022-0055","DOIUrl":"https://doi.org/10.1123/jmpb.2022-0055","url":null,"abstract":"Background : Several previous studies investigated physical activity of older adults using wearable devices, but more studies need to develop normative values for chronic disease conditions. This study aimed to investigate physical activity using a triaxial activity monitor in community-dwelling older Japanese adults with and without lifestyle-related diseases. Methods : Data from a total of 732 community-dwelling older Japanese men and women were collected and analyzed in a cross-sectional study. The participants’ physical activity was assessed for seven consecutive days by a triaxial accelerometer. Physical activity was assessed by number of lifestyle-related diseases and six lifestyle-related diseases categories by gender. Physical activity was assessed separately for total, locomotive, and nonlocomotive physical activity. Results : Participants with multiple (two or more) diseases had significantly lower total light-intensity physical activity (LPA; 278.5 ± 8.4 min/day) and nonlocomotive LPA (226.4 ± 7.0 min/day) versus without diseases in men. Compared in each disease category, total LPA and nonlocomotive LPA was significantly lower in men with hypertension and diabetes. Total sedentary time was significantly higher in men with hypertension, diabetes, and heart disease. Locomotive LPA was significantly lower in men with diabetes. In women, locomotive moderate- to vigorous-intensity physical activity was significantly higher in women with diabetes, and nonlocomotive moderate- to vigorous-intensity physical activity was significantly lower in women with heart disease. Conclusion : This study demonstrated that older Japanese men with multiple lifestyle-related diseases had lower physical activity. In each disease category, hypertension, diabetes, and heart disease affected lower physical activity, especially in men.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"43 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":"135953910","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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