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Activity Accumulation and Cardiometabolic Risk in Youth / Simone J. J. M. Verswijveren, Karen E. Lamb, Rebecca Leech, Jo Salmon, Anna Timperio, Rohan M. Telford, Melitta A. McNarry, Kelly A. Mackintosh, Robin M. Daly, David W. Dunstan, Clare Hume, Ester Cerin, Lisa S. Olive, Nicola D. Ridgers, Melitta McNarry, Kelly Mackintosh

Medicine & Science in Sports & Exercise, Volume: Publish Ahead of Print, Issue: 7, Pages: 1502 - 1510

Swansea University Authors: Melitta McNarry, Kelly Mackintosh

Abstract

Introduction This cross-sectional study aimed to: i) identify and characterize youth according to distinct physical activity (PA) and sedentary (SED) accumulation patterns; and ii) investigate associations of these derived patterns with cardiometabolic risk factors.Methods ActiGraph accelerometer da...

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Published in: Medicine & Science in Sports & Exercise
ISSN: 0195-9131
Published: Ovid Technologies (Wolters Kluwer Health) 2020
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fullrecord <?xml version="1.0"?><rfc1807><datestamp>2021-02-15T18:13:35.2571627</datestamp><bib-version>v2</bib-version><id>53251</id><entry>2020-01-14</entry><title>Activity Accumulation and Cardiometabolic Risk in Youth</title><swanseaauthors><author><sid>062f5697ff59f004bc8c713955988398</sid><ORCID>0000-0003-0813-7477</ORCID><firstname>Melitta</firstname><surname>McNarry</surname><name>Melitta McNarry</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>bdb20e3f31bcccf95c7bc116070c4214</sid><ORCID>0000-0003-0355-6357</ORCID><firstname>Kelly</firstname><surname>Mackintosh</surname><name>Kelly Mackintosh</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2020-01-14</date><deptcode>STSC</deptcode><abstract>Introduction This cross-sectional study aimed to: i) identify and characterize youth according to distinct physical activity (PA) and sedentary (SED) accumulation patterns; and ii) investigate associations of these derived patterns with cardiometabolic risk factors.Methods ActiGraph accelerometer data from 7-13 year olds from two studies were pooled (n=1,219; 843 [69%] with valid accelerometry included in analysis). Time accumulated in &#x2265;5-min and &#x2265;10-min SED bouts, &#x2265;1-min and &#x2265;5-min bouts of light (LPA), and &#x2265;1-min bouts of moderate (MPA) and vigorous (VPA) PA were calculated. Frequency of breaks in SED were also obtained. Latent profile analysis was used to identify groups of participants based on their distinct accumulation patterns. Linear and logistic regression models were used to test associations of group accumulation patterns with cardiometabolic risk factors, including adiposity indicators, blood pressure and lipids. Total PA and SED time were also compared between groups.Results Three distinct groups were identified: &#x201C;Prolonged sitters&#x201D; had the most time in prolonged SED bouts and the least time in VPA bouts; &#x201C;Breakers&#x201D; had the highest frequency of SED breaks and lowest engagement in sustained bouts across most PA intensities; &#x201C;Prolonged movers&#x201D; had the least time accumulated in SED bouts and the most in PA bouts across most intensities. Whilst &#x201C;Breakers&#x201D; engaged in less time in PA bouts compared to other groups, they had the healthiest adiposity indicators. No associations with the remaining cardiometabolic risk factors were found.Conclusion Youth accumulate their daily activity in three distinct patterns (prolonged sitters, breakers and prolonger movers), with those breaking up sitting and most time in sporadic PA across the day having a lower adiposity risk. No relationships with other cardiometabolic risk factors were identified.</abstract><type>Journal Article</type><journal>Medicine &amp; Science in Sports &amp; Exercise</journal><volume>Publish Ahead of Print</volume><journalNumber>7</journalNumber><paginationStart>1502</paginationStart><paginationEnd>1510</paginationEnd><publisher>Ovid Technologies (Wolters Kluwer Health)</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>0195-9131</issnPrint><issnElectronic/><keywords>Physical activity, cardiometabolic risk factors, youth</keywords><publishedDay>17</publishedDay><publishedMonth>1</publishedMonth><publishedYear>2020</publishedYear><publishedDate>2020-01-17</publishedDate><doi>10.1249/MSS.0000000000002275</doi><url>http://dx.doi.org/10.1249/MSS.0000000000002275</url><notes/><college>COLLEGE NANME</college><department>Sport and Exercise Sciences</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>STSC</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2021-02-15T18:13:35.2571627</lastEdited><Created>2020-01-14T09:19:22.8317812</Created><path><level id="1">College of Engineering</level><level id="2">Sports Science</level></path><authors><author><firstname>Simone J. J. M.</firstname><surname>Verswijveren</surname><order>1</order></author><author><firstname>Karen E.</firstname><surname>Lamb</surname><order>2</order></author><author><firstname>Rebecca</firstname><surname>Leech</surname><order>3</order></author><author><firstname>Jo</firstname><surname>Salmon</surname><order>4</order></author><author><firstname>Anna</firstname><surname>Timperio</surname><order>5</order></author><author><firstname>Rohan M.</firstname><surname>Telford</surname><order>6</order></author><author><firstname>Melitta A.</firstname><surname>McNarry</surname><order>7</order></author><author><firstname>Kelly A.</firstname><surname>Mackintosh</surname><order>8</order></author><author><firstname>Robin M.</firstname><surname>Daly</surname><order>9</order></author><author><firstname>David W.</firstname><surname>Dunstan</surname><order>10</order></author><author><firstname>Clare</firstname><surname>Hume</surname><order>11</order></author><author><firstname>Ester</firstname><surname>Cerin</surname><order>12</order></author><author><firstname>Lisa S.</firstname><surname>Olive</surname><order>13</order></author><author><firstname>Nicola D.</firstname><surname>Ridgers</surname><order>14</order></author><author><firstname>Melitta</firstname><surname>McNarry</surname><orcid>0000-0003-0813-7477</orcid><order>15</order></author><author><firstname>Kelly</firstname><surname>Mackintosh</surname><orcid>0000-0003-0355-6357</orcid><order>16</order></author></authors><documents><document><filename>53251__16286__e7d963095db444dcb9d420c722b06e71.pdf</filename><originalFilename>Verswijveren2020.pdf</originalFilename><uploaded>2020-01-14T09:24:51.0901843</uploaded><type>Output</type><contentLength>460179</contentLength><contentType>application/pdf</contentType><version>Accepted Manuscript</version><cronfaStatus>true</cronfaStatus><action/><embargoDate>2021-01-17T00:00:00.0000000</embargoDate><copyrightCorrect>true</copyrightCorrect><language>eng</language></document></documents><OutputDurs/></rfc1807>
spelling 2021-02-15T18:13:35.2571627 v2 53251 2020-01-14 Activity Accumulation and Cardiometabolic Risk in Youth 062f5697ff59f004bc8c713955988398 0000-0003-0813-7477 Melitta McNarry Melitta McNarry true false bdb20e3f31bcccf95c7bc116070c4214 0000-0003-0355-6357 Kelly Mackintosh Kelly Mackintosh true false 2020-01-14 STSC Introduction This cross-sectional study aimed to: i) identify and characterize youth according to distinct physical activity (PA) and sedentary (SED) accumulation patterns; and ii) investigate associations of these derived patterns with cardiometabolic risk factors.Methods ActiGraph accelerometer data from 7-13 year olds from two studies were pooled (n=1,219; 843 [69%] with valid accelerometry included in analysis). Time accumulated in ≥5-min and ≥10-min SED bouts, ≥1-min and ≥5-min bouts of light (LPA), and ≥1-min bouts of moderate (MPA) and vigorous (VPA) PA were calculated. Frequency of breaks in SED were also obtained. Latent profile analysis was used to identify groups of participants based on their distinct accumulation patterns. Linear and logistic regression models were used to test associations of group accumulation patterns with cardiometabolic risk factors, including adiposity indicators, blood pressure and lipids. Total PA and SED time were also compared between groups.Results Three distinct groups were identified: “Prolonged sitters” had the most time in prolonged SED bouts and the least time in VPA bouts; “Breakers” had the highest frequency of SED breaks and lowest engagement in sustained bouts across most PA intensities; “Prolonged movers” had the least time accumulated in SED bouts and the most in PA bouts across most intensities. Whilst “Breakers” engaged in less time in PA bouts compared to other groups, they had the healthiest adiposity indicators. No associations with the remaining cardiometabolic risk factors were found.Conclusion Youth accumulate their daily activity in three distinct patterns (prolonged sitters, breakers and prolonger movers), with those breaking up sitting and most time in sporadic PA across the day having a lower adiposity risk. No relationships with other cardiometabolic risk factors were identified. Journal Article Medicine & Science in Sports & Exercise Publish Ahead of Print 7 1502 1510 Ovid Technologies (Wolters Kluwer Health) 0195-9131 Physical activity, cardiometabolic risk factors, youth 17 1 2020 2020-01-17 10.1249/MSS.0000000000002275 http://dx.doi.org/10.1249/MSS.0000000000002275 COLLEGE NANME Sport and Exercise Sciences COLLEGE CODE STSC Swansea University 2021-02-15T18:13:35.2571627 2020-01-14T09:19:22.8317812 College of Engineering Sports Science Simone J. J. M. Verswijveren 1 Karen E. Lamb 2 Rebecca Leech 3 Jo Salmon 4 Anna Timperio 5 Rohan M. Telford 6 Melitta A. McNarry 7 Kelly A. Mackintosh 8 Robin M. Daly 9 David W. Dunstan 10 Clare Hume 11 Ester Cerin 12 Lisa S. Olive 13 Nicola D. Ridgers 14 Melitta McNarry 0000-0003-0813-7477 15 Kelly Mackintosh 0000-0003-0355-6357 16 53251__16286__e7d963095db444dcb9d420c722b06e71.pdf Verswijveren2020.pdf 2020-01-14T09:24:51.0901843 Output 460179 application/pdf Accepted Manuscript true 2021-01-17T00:00:00.0000000 true eng
title Activity Accumulation and Cardiometabolic Risk in Youth
spellingShingle Activity Accumulation and Cardiometabolic Risk in Youth
Melitta, McNarry
Kelly, Mackintosh
title_short Activity Accumulation and Cardiometabolic Risk in Youth
title_full Activity Accumulation and Cardiometabolic Risk in Youth
title_fullStr Activity Accumulation and Cardiometabolic Risk in Youth
title_full_unstemmed Activity Accumulation and Cardiometabolic Risk in Youth
title_sort Activity Accumulation and Cardiometabolic Risk in Youth
author_id_str_mv 062f5697ff59f004bc8c713955988398
bdb20e3f31bcccf95c7bc116070c4214
author_id_fullname_str_mv 062f5697ff59f004bc8c713955988398_***_Melitta, McNarry
bdb20e3f31bcccf95c7bc116070c4214_***_Kelly, Mackintosh
author Melitta, McNarry
Kelly, Mackintosh
author2 Simone J. J. M. Verswijveren
Karen E. Lamb
Rebecca Leech
Jo Salmon
Anna Timperio
Rohan M. Telford
Melitta A. McNarry
Kelly A. Mackintosh
Robin M. Daly
David W. Dunstan
Clare Hume
Ester Cerin
Lisa S. Olive
Nicola D. Ridgers
Melitta McNarry
Kelly Mackintosh
format Journal article
container_title Medicine & Science in Sports & Exercise
container_volume Publish Ahead of Print
container_issue 7
container_start_page 1502
publishDate 2020
institution Swansea University
issn 0195-9131
doi_str_mv 10.1249/MSS.0000000000002275
publisher Ovid Technologies (Wolters Kluwer Health)
college_str College of Engineering
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hierarchy_top_id collegeofengineering
hierarchy_top_title College of Engineering
hierarchy_parent_id collegeofengineering
hierarchy_parent_title College of Engineering
department_str Sports Science{{{_:::_}}}College of Engineering{{{_:::_}}}Sports Science
url http://dx.doi.org/10.1249/MSS.0000000000002275
document_store_str 1
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description Introduction This cross-sectional study aimed to: i) identify and characterize youth according to distinct physical activity (PA) and sedentary (SED) accumulation patterns; and ii) investigate associations of these derived patterns with cardiometabolic risk factors.Methods ActiGraph accelerometer data from 7-13 year olds from two studies were pooled (n=1,219; 843 [69%] with valid accelerometry included in analysis). Time accumulated in ≥5-min and ≥10-min SED bouts, ≥1-min and ≥5-min bouts of light (LPA), and ≥1-min bouts of moderate (MPA) and vigorous (VPA) PA were calculated. Frequency of breaks in SED were also obtained. Latent profile analysis was used to identify groups of participants based on their distinct accumulation patterns. Linear and logistic regression models were used to test associations of group accumulation patterns with cardiometabolic risk factors, including adiposity indicators, blood pressure and lipids. Total PA and SED time were also compared between groups.Results Three distinct groups were identified: “Prolonged sitters” had the most time in prolonged SED bouts and the least time in VPA bouts; “Breakers” had the highest frequency of SED breaks and lowest engagement in sustained bouts across most PA intensities; “Prolonged movers” had the least time accumulated in SED bouts and the most in PA bouts across most intensities. Whilst “Breakers” engaged in less time in PA bouts compared to other groups, they had the healthiest adiposity indicators. No associations with the remaining cardiometabolic risk factors were found.Conclusion Youth accumulate their daily activity in three distinct patterns (prolonged sitters, breakers and prolonger movers), with those breaking up sitting and most time in sporadic PA across the day having a lower adiposity risk. No relationships with other cardiometabolic risk factors were identified.
published_date 2020-01-17T04:13:55Z
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