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}
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}
Sahej D. Randhawa, Manoj Sharma, M. Fiterau, J. Banda, F. Haydel, K. Kapphahn, Donna Matheson, Hyatt Moore, Robyn L. Ball, C. Kushida, S. Delp, Dennis P Wall, Thomas Robinson, M. Desai
Background: Accelerometers are used to objectively measure movement in free-living individuals. Distinguishing nonwear from sleep and sedentary behavior is important to derive accurate measures of physical activity, sedentary behavior, and sleep. We applied statistical learning approaches to examine their promise in detecting nonwear time and compared the results with commonly used wear time (WT) algorithms. Methods: Fifteen children, aged 4–17, wore an ActiGraph wGT3X-BT monitor on their hip during overnight polysomnography. We applied Hidden Markov Models (HMM) and Gaussian Mixture Models (GMM) to classify states of nonwear and wear in triaxial acceleration data. Performance of methods was compared with WT algorithms across two conditions with differing amounts of consecutive nonwear. Clinical scoring of polysomnography served as the gold standard. Results: When the length of nonwear was less than or equal to WT algorithms’ predefined thresholds for consecutive nonwear time, GMM methods yielded improved classification error, specificity, positive predictive value, and negative predictive value over commonly used algorithms. HMM was superior to one algorithm for sensitivity and negative predictive value. When the length of nonwear was longer, results were mixed, with the commonly used algorithms performing better on some parameters but GMM with the greatest specificity. However, all approached the upper limits of performance for almost all metrics. Conclusions: GMM and HMM demonstrated robust, consistently strong performance across multiple conditions, surpassing or remaining competitive with commonly used WT algorithms which had marked inaccuracy when nonwear time periods were shorter. Of the two statistical learning algorithms, GMM was superior to HMM.
{"title":"Statistical Learning Methods to Identify Nonwear Periods From Accelerometer Data","authors":"Sahej D. Randhawa, Manoj Sharma, M. Fiterau, J. Banda, F. Haydel, K. Kapphahn, Donna Matheson, Hyatt Moore, Robyn L. Ball, C. Kushida, S. Delp, Dennis P Wall, Thomas Robinson, M. Desai","doi":"10.1123/jmpb.2022-0034","DOIUrl":"https://doi.org/10.1123/jmpb.2022-0034","url":null,"abstract":"Background: Accelerometers are used to objectively measure movement in free-living individuals. Distinguishing nonwear from sleep and sedentary behavior is important to derive accurate measures of physical activity, sedentary behavior, and sleep. We applied statistical learning approaches to examine their promise in detecting nonwear time and compared the results with commonly used wear time (WT) algorithms. Methods: Fifteen children, aged 4–17, wore an ActiGraph wGT3X-BT monitor on their hip during overnight polysomnography. We applied Hidden Markov Models (HMM) and Gaussian Mixture Models (GMM) to classify states of nonwear and wear in triaxial acceleration data. Performance of methods was compared with WT algorithms across two conditions with differing amounts of consecutive nonwear. Clinical scoring of polysomnography served as the gold standard. Results: When the length of nonwear was less than or equal to WT algorithms’ predefined thresholds for consecutive nonwear time, GMM methods yielded improved classification error, specificity, positive predictive value, and negative predictive value over commonly used algorithms. HMM was superior to one algorithm for sensitivity and negative predictive value. When the length of nonwear was longer, results were mixed, with the commonly used algorithms performing better on some parameters but GMM with the greatest specificity. However, all approached the upper limits of performance for almost all metrics. Conclusions: GMM and HMM demonstrated robust, consistently strong performance across multiple conditions, surpassing or remaining competitive with commonly used WT algorithms which had marked inaccuracy when nonwear time periods were shorter. Of the two statistical learning algorithms, GMM was superior to HMM.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90575746","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}
J. Woodforde, S. Gomersall, A. Timperio, Venurs H. Y. Loh, H. Browning, Francisco Perales, J. Salmon, M. Stylianou
Physical activity (PA) among children and adolescents is often reported by time segments centered around the school day, including before school. However, there is no consistent approach to defining the before-school segment, to accurately capture PA levels and facilitate synthesis of results across studies. Therefore, this study aimed to (a) examine how studies with children and adolescents have defined the before-school segment, and (b) compare adolescents’ before-school PA using various segment definitions. We conducted a systematic search and review of literature from six databases, and subsequently analyzed accelerometer data from Australia (n = 472, mean age 14.9 years, 40% male), to compare PA across five before-school definitions. Our review found 69 studies reporting before-school PA, 59 of which used device-based measures. Definitions ranged widely, but justifications were rarely reported. Our empirical comparison of definitions resulted in a range of participants meeting wear time criteria (≥3 days at >50% of segment length) from the latest-starting definition (30 min prior to school; n = 443) to the earliest-starting definition (6:00 a.m.–school start; n = 155), implying that for many participants, accelerometer wear was low in the early hours due to sleep or noncompliance. Statistically significant differences in light and moderate-to-vigorous PA (mean minutes/school day, proportion of segment length, and proportion of wear time) were found between definitions, indicating that before-school PA could potentially be underestimated depending on definition choice. We recommend that future studies clearly report and justify segment definition, apply segment-specific wear time criteria, and collect wake time data to enable individualized segment start times and minimize risk of data misclassification.
{"title":"Conceptualizing, Defining, and Measuring Before-School Physical Activity: A Review With Exploratory Analysis of Adolescent Data","authors":"J. Woodforde, S. Gomersall, A. Timperio, Venurs H. Y. Loh, H. Browning, Francisco Perales, J. Salmon, M. Stylianou","doi":"10.1123/jmpb.2022-0051","DOIUrl":"https://doi.org/10.1123/jmpb.2022-0051","url":null,"abstract":"Physical activity (PA) among children and adolescents is often reported by time segments centered around the school day, including before school. However, there is no consistent approach to defining the before-school segment, to accurately capture PA levels and facilitate synthesis of results across studies. Therefore, this study aimed to (a) examine how studies with children and adolescents have defined the before-school segment, and (b) compare adolescents’ before-school PA using various segment definitions. We conducted a systematic search and review of literature from six databases, and subsequently analyzed accelerometer data from Australia (n = 472, mean age 14.9 years, 40% male), to compare PA across five before-school definitions. Our review found 69 studies reporting before-school PA, 59 of which used device-based measures. Definitions ranged widely, but justifications were rarely reported. Our empirical comparison of definitions resulted in a range of participants meeting wear time criteria (≥3 days at >50% of segment length) from the latest-starting definition (30 min prior to school; n = 443) to the earliest-starting definition (6:00 a.m.–school start; n = 155), implying that for many participants, accelerometer wear was low in the early hours due to sleep or noncompliance. Statistically significant differences in light and moderate-to-vigorous PA (mean minutes/school day, proportion of segment length, and proportion of wear time) were found between definitions, indicating that before-school PA could potentially be underestimated depending on definition choice. We recommend that future studies clearly report and justify segment definition, apply segment-specific wear time criteria, and collect wake time data to enable individualized segment start times and minimize risk of data misclassification.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"128 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89003769","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}
Pub Date : 2023-01-01Epub Date: 2023-01-11DOI: 10.1123/jmpb.2022-0035
I-Min Lee, Christopher C Moore, Kelly R Evenson
There is much evidence showing that physical activity is related to optimal health, including physical and mental function, and quality of life. Additionally, data are accumulating with regards to the detrimental health impacts of sedentary behavior. Much of the evidence related to long-term health outcomes, such as cardiovascular disease and cancer - the two leading causes of death in the United States and worldwide, comes from observational epidemiologic studies and, in particular, prospective cohort studies. Few data on these outcomes are derived from randomized controlled trials, conventionally regarded as the "gold standard" of research designs. Why is there a paucity of data from randomized trials on physical activity or sedentary behavior and long-term health outcomes? A further issue to consider is that prospective cohort studies investigating these outcomes can take a long time to accrue sufficient numbers of endpoints for robust and meaningful findings. This contrasts with the rapid pace at which technology advances. Thus, while the use of devices for measuring physical behaviors has been an important development in large-scale epidemiologic studies over the past decade, cohorts that are now publishing results on health outcomes related to accelerometer-assessed physical activity and sedentary behavior may have been initiated years ago, using "dated" technology. This paper, based on a keynote presentation at ICAMPAM 2022, discusses the issues of study design and slow pace of discovery in prospective cohort studies and suggests some possible ways to maximize the utility and comparability of "dated" device data from prospective cohort studies for research investigations, using the Women's Health Study as an example.
{"title":"Maximizing the Utility and Comparability of Accelerometer Data from Large-Scale Epidemiologic Studies.","authors":"I-Min Lee, Christopher C Moore, Kelly R Evenson","doi":"10.1123/jmpb.2022-0035","DOIUrl":"10.1123/jmpb.2022-0035","url":null,"abstract":"<p><p>There is much evidence showing that physical activity is related to optimal health, including physical and mental function, and quality of life. Additionally, data are accumulating with regards to the detrimental health impacts of sedentary behavior. Much of the evidence related to long-term health outcomes, such as cardiovascular disease and cancer - the two leading causes of death in the United States and worldwide, comes from observational epidemiologic studies and, in particular, prospective cohort studies. Few data on these outcomes are derived from randomized controlled trials, conventionally regarded as the \"gold standard\" of research designs. Why is there a paucity of data from randomized trials on physical activity or sedentary behavior and long-term health outcomes? A further issue to consider is that prospective cohort studies investigating these outcomes can take a long time to accrue sufficient numbers of endpoints for robust and meaningful findings. This contrasts with the rapid pace at which technology advances. Thus, while the use of devices for measuring physical behaviors has been an important development in large-scale epidemiologic studies over the past decade, cohorts that are now publishing results on health outcomes related to accelerometer-assessed physical activity and sedentary behavior may have been initiated years ago, using \"dated\" technology. This paper, based on a keynote presentation at ICAMPAM 2022, discusses the issues of study design and slow pace of discovery in prospective cohort studies and suggests some possible ways to maximize the utility and comparability of \"dated\" device data from prospective cohort studies for research investigations, using the Women's Health Study as an example.</p>","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"6 1","pages":"6-12"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10194828/pdf/nihms-1888319.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9496974","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}
Kirsten Dillon-Rossiter, Madison Hiemstra, Nina Bartmann, Wuyou Sui, Marc S Mitchell, Scott Rollo, P. Gardiner, H. Prapavessis
Office workers who transitioned to working from home are spending an even higher percentage of their workday sitting compared with being “in-office” and this is an emerging health concern. With many office workers continuing to work from home since the onset of the COVID-19 pandemic, it is imperative to have a validated self-report questionnaire to assess sedentary behavior, break frequency, and duration, to reduce the cost and burden of using device-based assessments. This secondary analysis study aimed to validate the modified Last 7-Day Sedentary Behavior Questionnaire (SIT-Q 7d) against an activPAL4™ device in full-time home-based “office” workers (n = 148; mean age = 44.90). Participants completed the modified SIT-Q 7d and wore an activPAL4 for a full work week. The findings showed that the modified SIT-Q 7d had low (ρ = .35–.37) and weak (ρ = .27–.28) criterion validity for accurate estimates of break frequency and break duration, respectively. The 95% limits of agreement were large for break frequency (26.85–29.01) and medium for break duration (5.81–8.47), indicating that the modified SIT-Q 7d may not be appropriate for measuring occupational sedentary behavior patterns at the individual level. Further validation is still required before confidently recommending this self-report questionnaire to be used among this population to assess breaks in sedentary time.
{"title":"Validity of the Modified SIT-Q 7d for Estimating Sedentary Break Frequency and Duration in Home-Based Office Workers During the COVID-19 Global Pandemic: A Secondary Analysis","authors":"Kirsten Dillon-Rossiter, Madison Hiemstra, Nina Bartmann, Wuyou Sui, Marc S Mitchell, Scott Rollo, P. Gardiner, H. Prapavessis","doi":"10.1123/jmpb.2022-0021","DOIUrl":"https://doi.org/10.1123/jmpb.2022-0021","url":null,"abstract":"Office workers who transitioned to working from home are spending an even higher percentage of their workday sitting compared with being “in-office” and this is an emerging health concern. With many office workers continuing to work from home since the onset of the COVID-19 pandemic, it is imperative to have a validated self-report questionnaire to assess sedentary behavior, break frequency, and duration, to reduce the cost and burden of using device-based assessments. This secondary analysis study aimed to validate the modified Last 7-Day Sedentary Behavior Questionnaire (SIT-Q 7d) against an activPAL4™ device in full-time home-based “office” workers (n = 148; mean age = 44.90). Participants completed the modified SIT-Q 7d and wore an activPAL4 for a full work week. The findings showed that the modified SIT-Q 7d had low (ρ = .35–.37) and weak (ρ = .27–.28) criterion validity for accurate estimates of break frequency and break duration, respectively. The 95% limits of agreement were large for break frequency (26.85–29.01) and medium for break duration (5.81–8.47), indicating that the modified SIT-Q 7d may not be appropriate for measuring occupational sedentary behavior patterns at the individual level. Further validation is still required before confidently recommending this self-report questionnaire to be used among this population to assess breaks in sedentary time.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77060384","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}
A. Kingsnorth, E. Moltchanova, Jonah J C Thomas, Maxine E. Whelan, M. Orme, D. Esliger, M. Hobbs
Introduction: While there is evidence on agreement, it is unknown whether commercial wearables can be used as surrogates for research-grade devices when investigating links with markers of cardiometabolic risk. Therefore, the aim of this study was to investigate whether data from a commercial wearable device could be used to assess associations between behavior and cardiometabolic risk markers, compared with physical activity from a research-grade monitor. Methods: Forty-five adults concurrently wore a wrist-worn Fitbit Charge 2 and a waist-worn ActiGraph wGT3X-BT during waking hours over 7 consecutive days. Log-linear regression models were fitted, and predictive fit via a one-out cross-validation was performed for each device between behavioral (steps, and light and moderate-to-vigorous physical activity) and cardiometabolic variables (body mass index, weight, body fat percentage, systolic and diastolic blood pressure, glycated haemoglobin, grip strength, estimated maximal oxygen uptake, and waist circumference). Results: Overall, step count was the most consistent predictor of cardiometabolic risk factors, with negative associations across both Fitbit and ActiGraph devices for body mass index (−0.017 vs. −0.020, p < .01), weight (−0.014 vs. −0.017, p < .05), body fat percentage (−0.021 vs. −0.022, p < .01), and waist circumference (−0.013 vs. −0.015, p < .01). Neither device was found to provide a consistently better prediction across all included cardiometabolic risk markers. Conclusions: Step count data from a commercial-grade wearable device showed similar associations and predictive relationships with cardiometabolic risk markers compared with a research-grade wearable device, providing preliminary support for their use in health research.
{"title":"Interchangeability of Research and Commercial Wearable Device Data for Assessing Associations With Cardiometabolic Risk Markers","authors":"A. Kingsnorth, E. Moltchanova, Jonah J C Thomas, Maxine E. Whelan, M. Orme, D. Esliger, M. Hobbs","doi":"10.1123/jmpb.2022-0050","DOIUrl":"https://doi.org/10.1123/jmpb.2022-0050","url":null,"abstract":"Introduction: While there is evidence on agreement, it is unknown whether commercial wearables can be used as surrogates for research-grade devices when investigating links with markers of cardiometabolic risk. Therefore, the aim of this study was to investigate whether data from a commercial wearable device could be used to assess associations between behavior and cardiometabolic risk markers, compared with physical activity from a research-grade monitor. Methods: Forty-five adults concurrently wore a wrist-worn Fitbit Charge 2 and a waist-worn ActiGraph wGT3X-BT during waking hours over 7 consecutive days. Log-linear regression models were fitted, and predictive fit via a one-out cross-validation was performed for each device between behavioral (steps, and light and moderate-to-vigorous physical activity) and cardiometabolic variables (body mass index, weight, body fat percentage, systolic and diastolic blood pressure, glycated haemoglobin, grip strength, estimated maximal oxygen uptake, and waist circumference). Results: Overall, step count was the most consistent predictor of cardiometabolic risk factors, with negative associations across both Fitbit and ActiGraph devices for body mass index (−0.017 vs. −0.020, p < .01), weight (−0.014 vs. −0.017, p < .05), body fat percentage (−0.021 vs. −0.022, p < .01), and waist circumference (−0.013 vs. −0.015, p < .01). Neither device was found to provide a consistently better prediction across all included cardiometabolic risk markers. Conclusions: Step count data from a commercial-grade wearable device showed similar associations and predictive relationships with cardiometabolic risk markers compared with a research-grade wearable device, providing preliminary support for their use in health research.","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":"78669106","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}
Measurement of 24-hr movement behaviors is important for assessing adherence to guidelines, participation trends over time, group differences, and whether health-promoting interventions are successful. For a measurement tool to be useful, it must be valid, reliable, and able to detect change, the latter being a measurement property called responsiveness, sensitivity to change, or longitudinal validity. We systematically reviewed literature on the responsiveness of accelerometers to detect change in 24-hr movement behaviors. Databases (PubMed, Scopus, and EBSCOHost) were searched for peer-reviewed papers published in English between 1998 and 2023. Quality/risk of bias was assessed using a customized tool. This study is registered at https://osf.io/qrn8a . Twenty-six papers met the inclusion/exclusion criteria with an overall sample of 1,939 participants. Narrative synthesis was used. Most studies focused on adults ( n = 21), and almost half ( n = 12) included individuals with specific medical conditions. Studies primarily took place in free-living settings ( n = 21) and used research-grade accelerometers ( n = 24) worn on the hip ( n = 18), thigh ( n = 7), or wrist ( n = 9). Outcomes included physical activity ( n = 19), sedentary time/behavior ( n = 12), or sleep ( n = 2) and were calculated using proprietary formulas (e.g., Fitbit algorithm), cut points, and/or count-based methods. Most studies calculated responsiveness by comparing before versus after an intervention ( n = 16). Six studies included a criterion measure to confirm that changes occurred. Limited research is available on the responsiveness of accelerometers for detecting change in 24-hr movement behaviors, particularly in youth populations, for sleep outcomes, and for commercial and thigh- or wrist-worn devices. Lack of a criterion measure precludes conclusions about the responsiveness even in more frequently studied outcomes/populations.
{"title":"Systematic Review of Accelerometer Responsiveness to Change for Measuring Physical Activity, Sedentary Behavior, or Sleep","authors":"Kimberly A. Clevenger, Alexander H.K. Montoye","doi":"10.1123/jmpb.2023-0025","DOIUrl":"https://doi.org/10.1123/jmpb.2023-0025","url":null,"abstract":"Measurement of 24-hr movement behaviors is important for assessing adherence to guidelines, participation trends over time, group differences, and whether health-promoting interventions are successful. For a measurement tool to be useful, it must be valid, reliable, and able to detect change, the latter being a measurement property called responsiveness, sensitivity to change, or longitudinal validity. We systematically reviewed literature on the responsiveness of accelerometers to detect change in 24-hr movement behaviors. Databases (PubMed, Scopus, and EBSCOHost) were searched for peer-reviewed papers published in English between 1998 and 2023. Quality/risk of bias was assessed using a customized tool. This study is registered at https://osf.io/qrn8a . Twenty-six papers met the inclusion/exclusion criteria with an overall sample of 1,939 participants. Narrative synthesis was used. Most studies focused on adults ( n = 21), and almost half ( n = 12) included individuals with specific medical conditions. Studies primarily took place in free-living settings ( n = 21) and used research-grade accelerometers ( n = 24) worn on the hip ( n = 18), thigh ( n = 7), or wrist ( n = 9). Outcomes included physical activity ( n = 19), sedentary time/behavior ( n = 12), or sleep ( n = 2) and were calculated using proprietary formulas (e.g., Fitbit algorithm), cut points, and/or count-based methods. Most studies calculated responsiveness by comparing before versus after an intervention ( n = 16). Six studies included a criterion measure to confirm that changes occurred. Limited research is available on the responsiveness of accelerometers for detecting change in 24-hr movement behaviors, particularly in youth populations, for sleep outcomes, and for commercial and thigh- or wrist-worn devices. Lack of a criterion measure precludes conclusions about the responsiveness even in more frequently studied outcomes/populations.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"12 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":"136207279","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}
M. O'Brien, Jennifer L. Petterson, Liam P. Pellerine, Madeline E. Shivgulam, D. Kimmerly, Ryan J. Frayne, P. Hettiarachchi, Peter J. Johansson
Wearable activity monitors provide objective estimates of time in different physical activity intensities. Each continuous stepping period is described by its length and a corresponding single intensity (in metabolic equivalents of task [METs]), creating square wave–shaped signals. We argue that physiological responses do not resemble square waves, with the purpose of this technical report to challenge this idea and use experimental data as a proof of concept and direct potential solutions to better characterize activity intensity. Healthy adults (n = 43, 19♀; 23 ± 5 years) completed 6-min treadmill stages (five walking and five jogging/running) where oxygen consumption (3.5 ml O2·kg−1·min−1 = 1 MET) was recorded throughout and following the cessation of stepping. The time to steady state was ∼1–1.5 min, and time back to baseline following exercise was ∼1–2 min, with faster stepping stages generally exhibiting longer durations. Instead of square waves, the duration intensity signal reflected a trapezoid shape for each stage. The METs per minute during the rise to steady state (upstroke slopes; average: 1.7–6.3 METs/min for slow walking to running) may be used to better characterize activity intensity for shorter activity bouts where steady state is not achieved (within ∼90 s). While treating each activity bout as a single intensity is a much simpler analytical procedure, characterizing each bout in a continuous manner may better reflect the true physiological responses to movement. The information provided herein may be used to improve the characterization of activity intensity, definition of bout breaks, and act as a starting point for researchers and software developers interested in using wearables to measure activity intensity.
{"title":"Moving Beyond the Characterization of Activity Intensity Bouts as Square Waves Signals","authors":"M. O'Brien, Jennifer L. Petterson, Liam P. Pellerine, Madeline E. Shivgulam, D. Kimmerly, Ryan J. Frayne, P. Hettiarachchi, Peter J. Johansson","doi":"10.1123/jmpb.2022-0041","DOIUrl":"https://doi.org/10.1123/jmpb.2022-0041","url":null,"abstract":"Wearable activity monitors provide objective estimates of time in different physical activity intensities. Each continuous stepping period is described by its length and a corresponding single intensity (in metabolic equivalents of task [METs]), creating square wave–shaped signals. We argue that physiological responses do not resemble square waves, with the purpose of this technical report to challenge this idea and use experimental data as a proof of concept and direct potential solutions to better characterize activity intensity. Healthy adults (n = 43, 19♀; 23 ± 5 years) completed 6-min treadmill stages (five walking and five jogging/running) where oxygen consumption (3.5 ml O2·kg−1·min−1 = 1 MET) was recorded throughout and following the cessation of stepping. The time to steady state was ∼1–1.5 min, and time back to baseline following exercise was ∼1–2 min, with faster stepping stages generally exhibiting longer durations. Instead of square waves, the duration intensity signal reflected a trapezoid shape for each stage. The METs per minute during the rise to steady state (upstroke slopes; average: 1.7–6.3 METs/min for slow walking to running) may be used to better characterize activity intensity for shorter activity bouts where steady state is not achieved (within ∼90 s). While treating each activity bout as a single intensity is a much simpler analytical procedure, characterizing each bout in a continuous manner may better reflect the true physiological responses to movement. The information provided herein may be used to improve the characterization of activity intensity, definition of bout breaks, and act as a starting point for researchers and software developers interested in using wearables to measure activity intensity.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80418098","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：