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      24-h movement behaviors from infancy to preschool: cross-sectional and longitudinal relationships with body composition and bone health

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          Abstract

          Background

          New physical activity guidelines for children address all movement behaviors across the 24-h day (physical activity, sedentary behavior, sleep), but how each component relates to body composition when adjusted for the compositional nature of 24-h data is uncertain.

          Aims

          To i) describe 24-h movement behaviors from 1 to 5 years of age, ii) determine cross-sectional relationships with body mass index (BMI) z-score, iii) determine whether movement behaviors from 1 to 5 years of age predict body composition and bone health at 5 years.

          Methods

          24-h accelerometry data were collected in 380 children over 5–7 days at 1, 2, 3.5 and 5 years of age to determine the proportion of the day spent: sedentary (including wake after sleep onset), in light (LPA) and moderate-to-vigorous physical activity (MVPA), and asleep (including naps). BMI was determined at each age and a dual-energy x-ray absorptiometry (DXA) scan measured fat mass, bone mineral content (BMC) and bone mineral density (BMD) at 5 years of age. 24-h movement data were transformed into isometric log-ratio co-ordinates for multivariable regression analysis and effect sizes back-transformed.

          Results

          At age 1, children spent 49.6% of the 24-h day asleep, 38.2% sedentary, 12.1% in LPA, and 0.1% in MVPA, with corresponding figures of 44.4, 33.8, 19.8 and 1.9% at 5 years of age. Compositional time use was only related significantly to BMI z-score at 3.5 years in cross-sectional analyses. A 10% increase in mean sleep time (65 min) was associated with a lower BMI z-score (estimated difference, − 0.25; 95% CI, − 0.42 to − 0.08), whereas greater time spent sedentary (10%, 47 min) or in LPA (10%, 29 min) were associated with higher BMI z-scores (0.12 and 0.08 respectively, both p < 0.05). Compositional time use from 1 to 3.5 years was not related to future BMI z-score or percent fat. Although MVPA at 2 and 3.5 years was consistently associated with higher BMD and BMC at 5 years, actual differences were small.

          Conclusions

          Considerable changes in compositional time use occur from 1 to 5 years of age, but there is little association with adiposity. Although early MVPA predicted better bone health, the differences observed had little clinical relevance.

          Trial registration

          ClinicalTrials.gov number NCT00892983.

          Electronic supplementary material

          The online version of this article (10.1186/s12966-018-0753-6) contains supplementary material, which is available to authorized users.

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          Most cited references25

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          A six-year longitudinal study of the relationship of physical activity to bone mineral accrual in growing children: the university of Saskatchewan bone mineral accrual study.

          To investigate the influence of physical activity on bone mineral accrual during the adolescent years, we analyzed 6 years of data from 53 girls and 60 boys. Physical activity, dietary intakes, and anthropometry were measured every 6 months and dual-energy X-ray absorptiometry scans of the total body (TB), lumbar spine (LS), and proximal femur (Hologic 2000, array mode) were collected annually. Distance and velocity curves for height and bone mineral content (BMC) were fitted for each child at several skeletal sites using a cubic spline procedure, from which ages at peak height velocity (PHV) and peak BMC velocity (PBMCV) were identified. A mean age- and gender-specific standardized activity (Z) score was calculated for each subject based on multiple yearly activity assessments collected up until age of PHV. This score was used to identify active (top quartile), average (middle 2 quartiles), or inactive (bottom quartile) groups. Two-way analysis of covariance, with height and weight at PHV controlled for, demonstrated significant physical activity and gender main effects (but no interaction) for PBMCV, for BMC accrued for 2 years around peak velocity, and for BMC at 1 year post-PBMCV for the TB and femoral neck and for physical activity but not gender at the LS (all p < 0.05). Controlling for maturational and size differences between groups, we noted a 9% and 17% greater TB BMC for active boys and girls, respectively, over their inactive peers 1 year after the age of PBMCV. We also estimated that, on average, 26% of adult TB bone mineral was accrued during the 2 years around PBMCV.
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            Methodological considerations in using accelerometers to assess habitual physical activity in children aged 0-5 years.

            This paper reviews the evidence behind the methodological decisions accelerometer users make when assessing habitual physical activity in children aged 0-5 years. The purpose of the review is to outline an evidence-guided protocol for using accelerometry in young children and to identify gaps in the evidence base where further investigation is required. Studies evaluating accelerometry methodologies in young children were reviewed in two age groups (0-2 years and 3-5 years) to examine: (i) which accelerometer should be used, (ii) where the accelerometer should be placed, (iii) which epoch should be used, (iv) how many days of monitoring are required, (v) how many minutes of monitoring per day are required, (vi) how data should be reduced, (vii) which cut-point definitions for identifying activity intensity should be used, and (viii) which physical activity outcomes should be reported and how. Critique of the available evidence provided a basis for the development of a recommended users protocol in 3-5-year olds, although several issues require further research. Because of the absence of methodological studies in children under 3 years, a protocol for the use of accelerometers in this age range could not be specified. Formative studies examining the utility, feasibility and validity of accelerometer-based physical activity assessments are required in children under 3 years of age. Recommendations for further research are outlined, based on the above findings, which, if undertaken, will enhance the accuracy of accelerometer-based assessments of habitual physical activity in young children.
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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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                Author and article information

                Contributors
                +64 3 470 9180 , rachael.taylor@otago.ac.nz
                jill.haszard@otago.ac.nz
                kim.meredith-jones@otago.ac.nz
                barbara.galland@otago.ac.nz
                anne-louise.heath@otago.ac.nz
                julie.lawrence@otago.ac.nz
                andrew.gray@otago.ac.nz
                rachel.sayers@otago.ac.nz
                maha.hanna@otago.ac.nz
                barry.taylor@otago.ac.nz
                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
                26 November 2018
                26 November 2018
                2018
                : 15
                : 118
                Affiliations
                [1 ]ISNI 0000 0004 1936 7830, GRID grid.29980.3a, Departments of Medicine, Dunedin School of Medicine, , University of Otago, ; PO Box 56, Dunedin, New Zealand
                [2 ]ISNI 0000 0004 1936 7830, GRID grid.29980.3a, Departments of Women’s and Children’s Health, Dunedin School of Medicine, , University of Otago, ; Dunedin, New Zealand
                [3 ]ISNI 0000 0004 1936 7830, GRID grid.29980.3a, Departments of Human Nutrition, Dunedin School of Medicine, , University of Otago, ; Dunedin, New Zealand
                [4 ]ISNI 0000 0004 1936 7830, GRID grid.29980.3a, Departments of Biostatistics Unit, Dunedin School of Medicine, , University of Otago, ; Dunedin, New Zealand
                [5 ]ISNI 0000 0004 1936 7830, GRID grid.29980.3a, Departments of Office of the Dean, Dunedin School of Medicine, , University of Otago, ; Dunedin, New Zealand
                Article
                753
                10.1186/s12966-018-0753-6
                6260686
                30477518
                e5dfd6c7-7032-4ece-ba01-d65b10d7a6ee
                © The Author(s). 2018

                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
                : 26 July 2018
                : 14 November 2018
                Funding
                Funded by: Health Research Council of New Zealand
                Award ID: 08/374, 12/310
                Award ID: 12/281
                Award Recipient :
                Funded by: Southen District Health Board, New Zealand
                Award ID: N/A
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2018

                Nutrition & Dietetics
                physical activity,sedentary behavior,sleep,children,compositional time use
                Nutrition & Dietetics
                physical activity, sedentary behavior, sleep, children, compositional time use

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