02971nas a2200433   4500000000100000008004100001653001100042653001100053653000900064653002800073653001500101653001700116653002500133653001300158653001400171653002000185653001000205653001500215653003100230653002400261100001200285700001600297700001700313700001600330700001300346700001400359700001600373700002000389700001600409700001700425700001800442700001600460700002000476700005600496245007300552490000800625520189000633022001402523       2017                            d10aFemale10aHumans10aMale10aCross-Sectional Studies10aAdolescent10aTime Factors10aLongitudinal Studies10aExercise10aAustralia10aQuality of Life10aChild10aLife Style10aSurveys and Questionnaires10aAdolescent Behavior1 aDwyer T1 aCarlin John1 aWake Melissa1 aWong Monica1 aOlds Tim1 aGold Lisa1 aLycett Kate1 aDumuid Dorothea1 aMuller Josh1 aMensah Fiona1 aBurgner David1 aEdwards Ben1 aAzzopardi Peter1 aLSAC’s Child Health CheckPoint Investigator Group00aTime-Use Patterns and Health-Related Quality of Life in Adolescents.0 v1403 a<p>
<b>OBJECTIVES: </b>To describe 24-hour time-use patterns and their association with health-related quality of life (HRQoL) in early adolescence.
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<b>METHODS: </b>The Child Health CheckPoint was a cross-sectional study nested between Waves 6 and 7 of the Longitudinal Study of Australian Children. The participants were 1455 11- to 12-year-olds (39% of Wave 6; 51% boys). The exposure was 24-hour time use measured across 259 activities using the Multimedia Activity Recall for Children and Adolescents. "Average" days were generated from 1 school and 1 nonschool day. Time-use clusters were derived from cluster analysis with compositional inputs. The outcomes were self-reported HRQoL (Physical and Psychosocial Health [PedsQL] summary scores; Child Health Utility 9D [CHU9D] health utility).
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<b>RESULTS: </b>Four time-use clusters emerged: "studious actives" (22%; highest school-related time, low screen time), "techno-actives" (33%; highest physical activity, lowest school-related time), "stay home screenies" (23%; highest screen time, lowest passive transport), and "potterers" (21%; low physical activity). Linear regression models, adjusted for a priori confounders, showed that compared with the healthiest "studious actives" (mean [SD]: CHU9D 0.84 [0.14], PedsQL physical 86.8 [10.8], PedsQL psychosocial 79.9 [12.6]), HRQoL in "potterers" was 0.2 to 0.5 SDs lower (mean differences [95% confidence interval]: CHU9D -0.03 [-0.05 to -0.00], PedsQL physical -5.5 [-7.4 to -3.5], PedsQL psychosocial -5.8 [-8.0 to -3.5]).
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<b>CONCLUSIONS: </b>Discrete time-use patterns exist in Australian young adolescents. The cluster characterized by low physical activity and moderate screen time was associated with the lowest HRQoL. Whether this pattern translates into precursors of noncommunicable diseases remains to be determined.
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  a1098-4275