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Fitness, fatness and the reallocation of time between children's daily movement behaviours: an analysis of compositional data
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 sys...
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Published in: | The international journal of behavioral nutrition and physical activity 2017-05, Vol.14 (1), p.64-64, Article 64 |
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description | 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.
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.
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.
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. |
doi_str_mv | 10.1186/s12966-017-0521-z |
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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.
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.
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.</description><identifier>ISSN: 1479-5868</identifier><identifier>EISSN: 1479-5868</identifier><identifier>DOI: 10.1186/s12966-017-0521-z</identifier><identifier>PMID: 28486972</identifier><language>eng</language><publisher>England: BioMed Central</publisher><subject>Accelerometer ; Adiposity ; Behavior ; Body mass index ; Body Weight ; Child ; Children & youth ; Cross-Sectional Studies ; Data analysis ; England ; Exercise ; Exercise Test ; Female ; Geometry ; Humans ; LPA ; Male ; Motor Activity ; MVPA ; Obesity ; Obesity - etiology ; Obesity - prevention & control ; Overweight ; Physical activity ; Physical Exertion ; Physical Fitness ; Schools ; Sedentary behavior ; Sedentary Lifestyle ; Sedentary time ; Sleep ; Socioeconomic factors ; Studies ; Thinness ; Waist Circumference</subject><ispartof>The international journal of behavioral nutrition and physical activity, 2017-05, Vol.14 (1), p.64-64, Article 64</ispartof><rights>Copyright BioMed Central 2017</rights><rights>The Author(s). 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c493t-3ac8fa1db4d4fd90fe397750fd05194c60fab945d853d459af18b51ad7fe7873</citedby><cites>FETCH-LOGICAL-c493t-3ac8fa1db4d4fd90fe397750fd05194c60fab945d853d459af18b51ad7fe7873</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5424384/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1906062287?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28486972$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Fairclough, Stuart J</creatorcontrib><creatorcontrib>Dumuid, Dorothea</creatorcontrib><creatorcontrib>Taylor, Sarah</creatorcontrib><creatorcontrib>Curry, Whitney</creatorcontrib><creatorcontrib>McGrane, Bronagh</creatorcontrib><creatorcontrib>Stratton, Gareth</creatorcontrib><creatorcontrib>Maher, Carol</creatorcontrib><creatorcontrib>Olds, Timothy</creatorcontrib><title>Fitness, fatness and the reallocation of time between children's daily movement behaviours: an analysis of compositional data</title><title>The international journal of behavioral nutrition and physical activity</title><addtitle>Int J Behav Nutr Phys Act</addtitle><description>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.
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.
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.
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.</description><subject>Accelerometer</subject><subject>Adiposity</subject><subject>Behavior</subject><subject>Body mass index</subject><subject>Body Weight</subject><subject>Child</subject><subject>Children & youth</subject><subject>Cross-Sectional Studies</subject><subject>Data analysis</subject><subject>England</subject><subject>Exercise</subject><subject>Exercise Test</subject><subject>Female</subject><subject>Geometry</subject><subject>Humans</subject><subject>LPA</subject><subject>Male</subject><subject>Motor Activity</subject><subject>MVPA</subject><subject>Obesity</subject><subject>Obesity - etiology</subject><subject>Obesity - prevention & control</subject><subject>Overweight</subject><subject>Physical activity</subject><subject>Physical Exertion</subject><subject>Physical Fitness</subject><subject>Schools</subject><subject>Sedentary behavior</subject><subject>Sedentary Lifestyle</subject><subject>Sedentary time</subject><subject>Sleep</subject><subject>Socioeconomic factors</subject><subject>Studies</subject><subject>Thinness</subject><subject>Waist Circumference</subject><issn>1479-5868</issn><issn>1479-5868</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpdkl9rFDEUxQdRbK1-AF9kwAd9cDSZ_JnEB0GK1ULBl76Hu8lNN0tmsiazK1vwu5vdraUVArkk5_y493Ka5jUlHylV8lOhvZayI3ToiOhpd_ukOaV80J1QUj19UJ80L0pZEcKoIuJ5c9IrrqQe-tPmz0WYJyzlQ-vhULQwuXZeYpsRYkwW5pCmNvl2DiO2C5x_I06tXYboMk7vSusgxF07pi2OOM1VsYRtSJtcPldUPRB3JZQ9waZxnUrYAyFW3wwvm2ceYsFXd_dZc33x7fr8R3f18_vl-derznLN5o6BVR6oW3DHvdPEI9PDIIh3RFDNrSQeFpoLpwRzXGjwVC0EBTd4HNTAzprLI9YlWJl1DiPknUkQzOEh5RsDeQ42olHIsXdAvLeOq4EoySRx3iPV0gtmK-vLkbXeLEZ0ts6cIT6CPv6ZwtLcpK0RvOdM8Qp4fwfI6dcGy2zGUCzGCBOmTTFUaU2pkERW6dv_pKu62Lq8qtKkKvr-MB09qmxOpWT0981QYvY5McecmJoTs8-Jua2eNw-nuHf8Cwb7C2czvH0</recordid><startdate>20170510</startdate><enddate>20170510</enddate><creator>Fairclough, Stuart J</creator><creator>Dumuid, Dorothea</creator><creator>Taylor, Sarah</creator><creator>Curry, Whitney</creator><creator>McGrane, Bronagh</creator><creator>Stratton, Gareth</creator><creator>Maher, Carol</creator><creator>Olds, Timothy</creator><general>BioMed Central</general><general>BMC</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RQ</scope><scope>7RV</scope><scope>7TS</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8C1</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>K9.</scope><scope>KB0</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20170510</creationdate><title>Fitness, fatness and the reallocation of time between children's daily movement behaviours: an analysis of compositional data</title><author>Fairclough, Stuart J ; Dumuid, Dorothea ; Taylor, Sarah ; Curry, Whitney ; McGrane, Bronagh ; Stratton, Gareth ; Maher, Carol ; Olds, Timothy</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c493t-3ac8fa1db4d4fd90fe397750fd05194c60fab945d853d459af18b51ad7fe7873</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Accelerometer</topic><topic>Adiposity</topic><topic>Behavior</topic><topic>Body mass index</topic><topic>Body Weight</topic><topic>Child</topic><topic>Children & youth</topic><topic>Cross-Sectional Studies</topic><topic>Data analysis</topic><topic>England</topic><topic>Exercise</topic><topic>Exercise Test</topic><topic>Female</topic><topic>Geometry</topic><topic>Humans</topic><topic>LPA</topic><topic>Male</topic><topic>Motor Activity</topic><topic>MVPA</topic><topic>Obesity</topic><topic>Obesity - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>The international journal of behavioral nutrition and physical activity</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fairclough, Stuart J</au><au>Dumuid, Dorothea</au><au>Taylor, Sarah</au><au>Curry, Whitney</au><au>McGrane, Bronagh</au><au>Stratton, Gareth</au><au>Maher, Carol</au><au>Olds, Timothy</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fitness, fatness and the reallocation of time between children's daily movement behaviours: an analysis of compositional data</atitle><jtitle>The international journal of behavioral nutrition and physical activity</jtitle><addtitle>Int J Behav Nutr Phys Act</addtitle><date>2017-05-10</date><risdate>2017</risdate><volume>14</volume><issue>1</issue><spage>64</spage><epage>64</epage><pages>64-64</pages><artnum>64</artnum><issn>1479-5868</issn><eissn>1479-5868</eissn><abstract>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.
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.
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.
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.</abstract><cop>England</cop><pub>BioMed Central</pub><pmid>28486972</pmid><doi>10.1186/s12966-017-0521-z</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Accelerometer Adiposity Behavior Body mass index Body Weight Child Children & youth Cross-Sectional Studies Data analysis England Exercise Exercise Test Female Geometry Humans LPA Male Motor Activity MVPA Obesity Obesity - etiology Obesity - prevention & control Overweight Physical activity Physical Exertion Physical Fitness Schools Sedentary behavior Sedentary Lifestyle Sedentary time Sleep Socioeconomic factors Studies Thinness Waist Circumference |
title | Fitness, fatness and the reallocation of time between children's daily movement behaviours: an analysis of compositional data |
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