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Considerations when using the activPAL monitor in field-based research with adult populations

Journal of Sport and Health Science 2017年第2期6卷 162-178页

作者： Charlotte L.Edwardson elisabeth A.H.winkler Danielle H.Bodicoat Tom Yates Melanie J.Davies David W.Dunstan Genevieve N.Healy diabetes research centre leicester general hospitaluniversity of leicesterLeicester LE5 4PWUK nihr leicester-loughborough diet lifestyleand physical activity biomedical research unitLeicester LE5 4PWUK school of population health the university of queenslandBrisbaneQLD 4006Australia baker idi heart and diabetes institute MelbourneVIC 3004Australia school of exercise and nutrition sciences deakin universityBurwoodVIC 3125Australia department of epidemiology and preventive medicine monash universityMelbourneVIC 3800Australia school of sport science exercise and healththe university of western australiaPerthWA 6009Australia central clinical school monash universityMelbourneVIC 3800Australia school of physiotherapy and exercise science curtin universityPerthWA 6102Australia

Research indicates that high levels of sedentary behavior(sitting or lying with low energy expenditure) are adversely associated with health. A key factor in improving our understanding of the impact of sedentary behavior(and patterns of sedentary time accumulation) on health is the use of objective measurement tools that collect date and time-stamped activity information. One such tool is the activP AL monitor. This thigh-worn device uses accelerometer-derived information about thigh position to determine the start and end of each period spent sitting/lying, standing, and stepping, as well as stepping speed, step counts, and postural transitions. The activP AL is increasingly being used within field-based research for its ability to measure sitting/lying via posture. We summarise key issues to consider when using the activP AL in physical activity and sedentary behavior field-based research with adult populations. It is intended that the findings and discussion points be informative for researchers who are currently using activP AL monitors or are intending to use them. Pre-data collection decisions, monitor preparation and distribution, data collection considerations, and manual and automated data processing possibilities are presented using examples from current literature and experiences from 2 research groups from the UK and Australia.

关键词： populations monitor lying collection accelerometer expenditure Australia stepping currently intended

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Using compositional data analysis to explore accumulation of sedentary behavior,physical activity and youth health

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Journal of Sport and Health Science 2022年第2期11卷 234-243页

作者： Simone J.J.M.Verswijveren Karen E.Lamb Josep A.Martín-Fernández elisabeth winkler Rebecca M.Leech Anna Timperio Jo Salmon Robin M.Daly Ester Cerin David W.Dunstan Rohan M.Telford Richard D.Telford Lisa S.Olive Nicola D.Ridgers Institute for Physical Activity and Nutrition School of Exercise and Nutrition SciencesDeakin UniversityGeelongVIC 3216Australia Melbourne School of Population and Global Health University of MelbourneParkvilleVIC 3053Australia Department of Computer Science Applied Mathematics and StatisticsUniversity of GironaGirona 17003Spain School of Public Health University of QueenslandHerstonQLD 4006Australia Mary MacKillop Institute for Health Research Australian Catholic UniversityFitzroyVIC 3065Australia School of Public Health University of Hong KongHong KongChina Baker Heart and Diabetes Institute MelbourneVIC 3004Australia Research Institute for Sport and Exercise University of CanberraBruceACT 2617Australia School of Psychology Deakin UniversityBurwoodVIC 3125Australia IMPACT Research Institute Deakin UniversityBurwoodVIC 3125Australia ANU Medical School Australian National UniversityGarranACT 2605Australia

Purpose:The study aimed to describe youth time-use compositions,focusing on time spent in shorter and longer bouts of sedentary behavior and physical activity(PA),and to examine associations of these time-use compositions with cardiometabolic ***:Accelerometer and cardiometabolic biomarker data from 2 Australian studies involving youths 7-13 years old were pooled(complete cases with accelerometry and adiposity marker data,n=782).A 9-component time-use composition was formed using compositional data analysis:time in shorter and longer bouts of sedentary behavior;time in shorter and longer bouts of light-,moderate-,or vigorous-intensity PA;and"other time"(i.e.,non-wear/sleep).Shorter and longer bouts of sedentary time were defined as5 min,*** bouts of light-,moderate-,and vigorous-intensity PA were defined as<1 min;longer bouts were defined as≥1 *** models examined associations between overall time-use composition and cardiometabolic ***,associations were derived between ratios of longer activity patterns relative to shorter activity patterns,and of each intensity level relative to the other intensity levels and"other time",and cardiometabolic ***:Confounder-adjusted models showed that the overall time-use composition was associated with adiposity,blood pressure,lipids,and the summary ***,more time in longer bouts of light-intensity PA relative to shorter bouts of light-intensity PA was significantly associated with greater body mass index z-score(zBMI)(β=1.79;SE=0.68)and waist circumference(β=18.35,SE=4.78).When each activity intensity was considered relative to all higher intensities and"other time",more time in light-and vigorous-intensity PA,and less time in sedentary behavior and moderate-intensity PA,were associated with lower waist ***:Accumulating PA,particularly light-intensity PA,in frequent short bursts may be more beneficial for limiting adi

关键词： Accumulation patterns Cardiometabolic health Children Compositional data analysis Time-use

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