Journal for the measurement of physical behaviour最新文献_第3页

Applying Average Real Variability to Quantifying Day–Day Physical Activity and Sedentary Postures Variability: A Comparison With Standard Deviation 应用平均真实变异性量化日常身体活动和久坐姿势变异性:与标准差的比较

Journal for the measurement of physical behaviour Pub Date : 2023-01-01 DOI: 10.1123/jmpb.2023-0021

Madeline E. Shivgulam, M. O'Brien

{"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}

引用次数: 0

Evolution of Public Health Physical Activity Applications of Accelerometers: A Personal Perspective 加速度计在公共健康运动应用的演变:个人视角

Journal for the measurement of physical behaviour Pub Date : 2023-01-01 DOI: 10.1123/jmpb.2022-0038

R. Troiano

引用次数: 1

The Stryd Foot Pod Is a Valid Measure of Stepping Cadence During Treadmill Walking and Running Stryd脚蹬是在跑步机上行走和跑步时测量步速的有效方法

Journal for the measurement of physical behaviour Pub Date : 2023-01-01 DOI: 10.1123/jmpb.2022-0031

Madeline E. Shivgulam, Jennifer L. Petterson, Liam P. Pellerine, D. Kimmerly, M. O'Brien

{"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}

引用次数: 0

Convergent Validity Between Epoch-Based activPAL and ActiGraph Methods for Measuring Moderate to Vigorous Physical Activity in Youth and Adults 基于时代的activPAL和ActiGraph测量青少年和成人中高强度体力活动的收敛效度

Journal for the measurement of physical behaviour Pub Date : 2023-01-01 DOI: 10.1123/jmpb.2022-0013

Adrian Ortega, B. Forseth, P. Hibbing, Chelsea Steel, J. Carlson

{"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}

引用次数: 1

Prediction Strength for Clustering Activity Patterns Using Accelerometer Data 利用加速度计数据聚类活动模式的预测强度

Journal for the measurement of physical behaviour Pub Date : 2023-01-01 DOI: 10.1123/jmpb.2022-0049

Jingzhi Yu, K. Kapphahn, Hyatt Moore, F. Haydel, Thomas Robinson, M. Desai

{"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}

引用次数: 0

Use of Accelerometers to Track Changes in Stepping Behavior With the Introduction of the 2020 COVID Pandemic Restrictions: A Case Study 随着2020年COVID大流行限制的引入，使用加速度计跟踪步进行为的变化:案例研究

Journal for the measurement of physical behaviour Pub Date : 2023-01-01 DOI: 10.1123/jmpb.2022-0015

Tiereny McGuire, Kirstie Devin, Victoria Patricks, Benjamin Griffiths, C. Speirs, M. Granat

{"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}

引用次数: 1

Comparative Analysis and Conversion Between Actiwatch and ActiGraph Open-Source Counts Actiwatch与ActiGraph开源计数的比较分析与转换

Journal for the measurement of physical behaviour Pub Date : 2023-01-01 DOI: 10.1123/jmpb.2022-0054

Paul H. Lee, Ali Neishabouri, Andy C. Y. Tse, Christine C. Guo

{"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}

引用次数: 0

Evaluation of Physical Activity Assessment Using a Triaxial Activity Monitor in Community-Dwelling Older Japanese Adults With and Without Lifestyle-Related Diseases 用三轴活动监测仪评价有或无生活方式相关疾病的日本社区老年人的身体活动评估

Journal for the measurement of physical behaviour Pub Date : 2023-01-01 DOI: 10.1123/jmpb.2022-0055

Sho Nagayoshi, Harukaze Yatsugi, Xin Liu, Takafumi Saito, Koji Yamatsu, Hiro Kishimoto

{"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}

引用次数: 0

Statistical Learning Methods to Identify Nonwear Periods From Accelerometer Data 从加速度计数据中识别非磨损期的统计学习方法

Journal for the measurement of physical behaviour Pub Date : 2023-01-01 DOI: 10.1123/jmpb.2022-0034

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

{"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}

引用次数: 0

Conceptualizing, Defining, and Measuring Before-School Physical Activity: A Review With Exploratory Analysis of Adolescent Data 学前体育活动的概念、定义和测量:青少年数据的探索性分析综述

Journal for the measurement of physical behaviour Pub Date : 2023-01-01 DOI: 10.1123/jmpb.2022-0051

J. Woodforde, S. Gomersall, A. Timperio, Venurs H. Y. Loh, H. Browning, Francisco Perales, J. Salmon, M. Stylianou

{"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}

引用次数: 0