No Cover Image

Journal article 530 views 433 downloads

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 Orcid Logo, Kelly Mackintosh Orcid Logo

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

Swansea University Authors: Melitta McNarry Orcid Logo, Kelly Mackintosh Orcid Logo

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...

Full description

Published in: Medicine & Science in Sports & Exercise
ISSN: 0195-9131
Published: Ovid Technologies (Wolters Kluwer Health) 2020
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa53251
Tags: Add Tag
No Tags, Be the first to tag this record!
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 ≥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.
Keywords: Physical activity, cardiometabolic risk factors, youth
College: Faculty of Science and Engineering
Issue: 7
Start Page: 1502
End Page: 1510