Monica Wong1,2, Tim Olds2,3, Lisa Gold2,4, Kate Lycett1,2, Dorothea Dumuid3, Josh Muller2, Fiona K Mensah1,2, David Burgner2,5, John B Carlin1,2,6, Ben Edwards7, Terence Dwyer1,2,8,9, Peter Azzopardi1,2,6, Melissa Wake10,2,6. 1. Melbourne School of Health Sciences, The University of Melbourne, Parkville, Victoria, Australia. 2. Murdoch Childrens Research Institute, Parkville, Victoria, Australia. 3. Alliance for Research in Exercise, Nutrition and Activity (ARENA), University of South Australia, Adelaide, South Australia, Australia. 4. School of Health and Social Development, Deakin University, Geelong, Victoria, Australia. 5. Department of Paediatrics, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia. 6. Department of Paediatrics and the Liggins Institute, The University of Auckland, Auckland, New Zealand. 7. Australian National University Centre for Social Research and Methods, Canberra, Australian Capital Territory, Australia. 8. The George Institute for Global Health, University of Oxford, Oxford, United Kingdom; and. 9. Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia. 10. Melbourne School of Health Sciences, The University of Melbourne, Parkville, Victoria, Australia; melissa.wake@auckland.ac.nz.
Abstract
OBJECTIVES: To describe 24-hour time-use patterns and their association with health-related quality of life (HRQoL) in early adolescence. METHODS: 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). RESULTS: 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]). CONCLUSIONS: 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.
OBJECTIVES: To describe 24-hour time-use patterns and their association with health-related quality of life (HRQoL) in early adolescence. METHODS: 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). RESULTS: 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]). CONCLUSIONS: 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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