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      A closer look at the relationship among accelerometer-based physical activity metrics: ICAD pooled data

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          Abstract

          Background

          Accelerometers are widely used to assess child physical activity (PA) levels. Using the accelerometer data, several PA metrics can be estimated. Knowledge about the relationships between these different metrics can improve our understanding of children’s PA behavioral patterns. It also has significant implications for comparing PA metrics across studies and fitting a statistical model to examine their health effects. The aim of this study was to examine the relationships among the metrics derived from accelerometers in children.

          Methods

          Accelerometer data from 24,316 children aged 5 to 18 years were extracted from the International Children’s Accelerometer Database (ICAD) 2.0. Correlation coefficients between wear time, sedentary behavior (SB), light-intensity PA (LPA), moderate-intensity PA (MPA), vigorous-intensity PA (VPA), moderate- and vigorous-intensity PA (MVPA), and total activity counts (TAC) were calculated.

          Results

          TAC was approximately 22X10 3 counts higher ( p < 0.01) with longer wear time (13 to 18 h/day) as compared to shorter wear time (8 to < 13 h/day), while MVPA was similar across the wear time categories. MVPA was very highly correlated with TAC ( r = .91; 99% CI = .91 to .91). Wear time-adjusted correlation between SB and LPA was also very high ( r = −.96; 99% CI = -.96, − 95). VPA was moderately correlated with MPA ( r = .58; 99% CI = .57, .59).

          Conclusions

          TAC is mostly explained by MVPA, while it could be more dependent on wear time, compared to MVPA. MVPA appears to be comparable across different wear durations and studies when wear time is ≥8 h/day. Due to the moderate to high correlation between some PA metrics, potential collinearity should be addressed when including multiple PA metrics together in statistical modeling.

          Electronic supplementary material

          The online version of this article (10.1186/s12966-019-0801-x) contains supplementary material, which is available to authorized users.

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          Most cited references10

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          Health outcomes associated with reallocations of time between sleep, sedentary behaviour, and physical activity: a systematic scoping review of isotemporal substitution studies

          Background During a 24-h day, each given period is spent in either sedentary behaviour, sleeping, light physical activity (LPA), or moderate-to-vigorous physical activity (MVPA). In epidemiological research most studies have traditionally analysed the associations of these behaviours in isolation from each other; that is, without taking into account the displacement of time spent in the remaining behaviours. In recent years, there has been a growing interest in exploring how all the behaviours across the energy expenditure spectrum influence health outcomes. A statistical model used to investigate these associations is termed an isotemporal substitution model (ISM). Considering the increasing number of ISM-based studies conducted in all age groups, the present paper aimed to: (i) review and summarise findings from studies that employed ISM in sleep, sedentary behaviour, and physical activity research; (ii) appraise the methodological quality of the studies; and (iii) suggest future research directions in this area. Methods A systematic search of ten databases was performed. The Newcastle–Ottawa scale was used to assess the methodological quality of the included studies. Results Fifty-six studies met the inclusion criteria, all being of moderate or high methodological quality. Associations were reported for exchanged time varying from one minute to 120 min/day across the studies, with 30 min/day being the most common amount of time reallocated. In total, three different ISM methodologies were used. The most commonly studied health outcomes in relation to isotemporal substitutions were mortality, general health, mental health, adiposity, fitness, and cardiometabolic biomarkers. It seems that reallocations of sedentary time to LPA or MVPA are associated with significant reduction in mortality risk. Current evidence appears to consistently suggest that reductions in mortality risk are greater when time spent sedentary is replaced with higher intensities of physical activity. For adiposity, it seems that reallocating sedentary time to physical activity may be associated with reduced body mass index, body fat percentage, and waist circumference in all age groups, with the magnitude of associations being greater for higher intensities of physical activity. While there is a relatively large body of evidence reporting beneficial associations between the reallocation of time from sedentary behaviour to LPA or MVPA and cardiometabolic biomarkers among adults, there is a lack of studies among children, adolescents, and older adults. Although some studies investigated general health, mental health, and fitness outcomes, further investigation of these topics is warranted. In general, it seems that the strongest association with health outcomes is observed when time is reallocated from sedentary behaviour to MVPA. Most studies did not account for sleep time, which is a major limitation of the current evidence. Conclusions The current evidence indicates that time reallocation between sleep, sedentary behaviour, LPA, and MVPA may be associated with a number of health outcomes. Future studies should employ longitudinal designs, take into account all movement behaviours, and examine a wider range of health, psychological, social, economic, and environmental outcomes. Electronic supplementary material The online version of this article (10.1186/s12966-018-0691-3) contains supplementary material, which is available to authorized users.
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            Fitness, fatness and the reallocation of time between children’s daily movement behaviours: an analysis of compositional data

            Background Movement behaviours performed over a finite period such as a 24 h day are compositional data. Compositional data exist in a constrained simplex geometry that is incongruent with traditional multivariate analytical techniques. However, the expression of compositional data as log-ratio co-ordinate systems transfers them to the unconstrained real space, where standard multivariate statistics can be used. This study aimed to use a compositional data analysis approach to examine the adiposity and cardiorespiratory fitness predictions of time reallocations between children’s daily movement behaviours. Methods This study used cross-sectional data from the Active Schools: Skelmersdale study, which involved Year 5 children from a low-income community in northwest England (n = 169). Measures included accelerometer-derived 24 h activity (sedentary time [ST], light physical activity [LPA], moderate-to-vigorous physical activity [MVPA], and sleep), cardiorespiratory fitness determined by the 20 m shuttle run test, objectively measured height, weight and waist circumference (from which zBMI and percent waist circumference-to-height ratio (%WHtR) were derived) and sociodemographic covariates. Log-ratio multiple linear regression models were used to predict adiposity and fitness for the mean movement behaviour composition, and for new compositions where fixed durations of time had been reallocated from one behaviour to another, while the remaining behaviours were unchanged. Predictions were also made for reallocations of fixed durations of time using the mean composition of three different weight status categories (underweight, normal-weight, and overweight/obese) as the starting point. Results Replacing MVPA with any other movement behaviour around the mean movement composition predicted higher adiposity and lower CRF. The log-ratio model predictions were asymmetrical: when time was reallocated to MVPA from sleep, ST, or LPA, the estimated detriments to fitness and adiposity were larger in magnitude than the estimated benefits of time reallocation from MVPA to sleep, ST or LPA. The greatest differences in fitness and fatness for reallocation of fixed duration of MVPA were predicted at the mean composition of overweight/obese children. Conclusions Findings reinforce the key role of MVPA for children’s health. Reallocating time from ST and LPA to MVPA in children is advocated in school, home, and community settings. Electronic supplementary material The online version of this article (doi:10.1186/s12966-017-0521-z) contains supplementary material, which is available to authorized users.
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              Accelerometer-Derived Sedentary and Physical Activity Time in Overweight/Obese Adults with Type 2 Diabetes: Cross-Sectional Associations with Cardiometabolic Biomarkers

              Objective To examine the associations of sedentary time and physical activity with biomarkers of cardiometabolic health, including the potential collective impact of shifting mean time use from less- to more-active behaviours (cross-sectionally, using isotemporal substitution), in adults with type 2 diabetes. Methods Participants with overweight/obese body mass index (BMI; ≥25 kg/m2) (n = 279; 158 men, mean [SD] age = 58.2 [8.6] years) wore Actigraph GT1M accelerometers (waking hours; seven days) to assess moderate- to vigorous-intensity physical activity (MVPA), light-intensity activity, and sedentary time (segregated into non-prolonged [accumulated in bouts <30min] and prolonged [accumulated in bouts ≥30 min]). Cross-sectional associations with waist circumference, BMI, fasting blood (HbA1c, glucose, triacylglycerols, high-density lipoprotein cholesterol), and blood pressure of these activity variables (30 min/day increments) were examined adjusted for confounders and wear then, if significant, examined using isotemporal substitution modelling. Results Waist circumference and BMI were significantly (p<0.05) associated with more prolonged sedentary time and less light-intensity activity. Light intensity activity was also significantly associated with lower fasting plasma glucose (relative rate: 0.98, 95% CI: 0.97, 1.00; p<0.05). No biomarker was significantly associated with non-prolonged sedentary time or MVPA. Lower mean prolonged sedentary time (−30 min/day) with higher mean light intensity time (+30 min/day) was significantly associated with lower waist circumference (β = −0.77, 95% CI: −1.33, −0.22 cm). Lower mean prolonged sedentary time (−30 min/day) with either 30 min/day higher mean non-prolonged sedentary time (β = −0.35, 95%CI: −0.70, −0.01 kg/m2) or light-intensity time (β = −0.36, −0.61, −0.11 kg/m2) was associated with significantly lower average BMI. Conclusions Significantly improved mean levels of waist circumference and BMI were observed when shifting time from prolonged sedentary to non-prolonged sedentary or light-intensity activity (cross-sectionally). Lifestyle interventions in overweight/obese adults with type 2 diabetes might consider targeting shifts in these non-MVPA activities to more rigorously evaluate their potential cardiometabolic benefit in this population.
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                Author and article information

                Contributors
                skwon@luriechildrens.org
                Lars.Bo.Andersen@hvl.no
                ncmoller@health.sdu.dk
                sigmund.anderssen@nih.no
                Greet.Cardon@UGent.be
                rachel.davey@canberra.edu.au
                susi.kriemler@ifspm.uzh.ch
                kate.northstone@bristol.ac.uk
                a.s.page@bristol.ac.uk
                jardena.puder@chuv.ch
                john.j.reilly@strath.ac.uk
                lsardinha@fmh.utl.pt
                esther.vansluijs@mrc-epid.cam.ac.uk
                kathleen-janz@uiowa.edu
                Journal
                Int J Behav Nutr Phys Act
                Int J Behav Nutr Phys Act
                The International Journal of Behavioral Nutrition and Physical Activity
                BioMed Central (London )
                1479-5868
                29 April 2019
                29 April 2019
                2019
                : 16
                : 40
                Affiliations
                [1 ]ISNI 0000 0004 0388 2248, GRID grid.413808.6, Ann & Robert H. Lurie Children’s Hospital of Chicago Stanley Manne Children’s Research Institute, ; 225 E Chicago Ave, Box 157, Chicago, IL 60611 USA
                [2 ]GRID grid.477239.c, Faculty of Education, Arts and Sport, , Western Norway University of Applied Sciences, ; Sogndal, Norway
                [3 ]ISNI 0000 0001 0728 0170, GRID grid.10825.3e, Department of Sports Science and Clinical Biomechanics, , University of Southern Denmark, ; Odense, Denmark
                [4 ]ISNI 0000 0000 8567 2092, GRID grid.412285.8, Norway, Norwegian School of Sport Science, ; Oslo, Norway
                [5 ]ISNI 0000 0001 2069 7798, GRID grid.5342.0, Department of Movement and Sports Sciences, , Ghent University, ; 9000 Ghent, Belgium
                [6 ]ISNI 0000 0004 0385 7472, GRID grid.1039.b, Centre for Research & Action in Public Health Health Research Institute, , University of Canberra, ; Canberra, Australia
                [7 ]ISNI 0000 0004 1937 0650, GRID grid.7400.3, Epidemiology, Biostatistics and Prevention Institute, , University of Zürich, ; Zürich, Switzerland
                [8 ]ISNI 0000 0004 1936 7603, GRID grid.5337.2, Bristol Medical School, , University of Bristol, ; Bristol, UK
                [9 ]ISNI 0000 0004 1936 7603, GRID grid.5337.2, Centre for Exercise, Nutrition and Health Sciences, , University of Bristol, ; Bristol, UK
                [10 ]ISNI 0000 0001 0423 4662, GRID grid.8515.9, Obstetric service, , Lausanne University Hospital, ; Lausanne, Switzerland
                [11 ]ISNI 0000000121138138, GRID grid.11984.35, Physical Activity for Health Group, School of Psychological Sciences and Health, , University of Strathclyde, ; Glasgow, UK
                [12 ]ISNI 0000 0001 2181 4263, GRID grid.9983.b, Exercise and Health Laboratory, CIPER, Faculdade de Motricidade Humana, , Universidade de Lisboa, ; Cruz-Quebrada, Portugal
                [13 ]ISNI 0000000121885934, GRID grid.5335.0, Centre for Diet and Activity Research (CEDAR) & MRC Epidemiology Unit, , University of Cambridge, ; Cambridge, UK
                [14 ]ISNI 0000 0004 1936 8294, GRID grid.214572.7, Department of Health and Human Physiology, , University of Iowa, ; Iowa City, IA USA
                Article
                801
                10.1186/s12966-019-0801-x
                6489360
                31036032
                ff40534a-42e4-4cf7-b2c8-edc611b8482e
                © The Author(s). 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 27 November 2018
                : 17 April 2019
                Categories
                Research
                Custom metadata
                © The Author(s) 2019

                Nutrition & Dietetics
                icad,children,adolescents,actigraph,total activity counts,sedentary,physical activity measurement

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