Time-Use Patterns and Health-Related Quality of Life in Adolescents

被引:19
作者
Wong, Monica [1 ,2 ]
Olds, Tim [2 ,3 ]
Gold, Lisa [2 ,4 ]
Lycett, Kate [1 ,2 ]
Dumuid, Dorothea [3 ]
Muller, Josh [2 ]
Mensah, Fiona K. [1 ,2 ]
Burgner, David [2 ,7 ]
Carlin, John B. [1 ,2 ,5 ,6 ]
Edwards, Ben [8 ]
Dwyer, Terence [1 ,2 ,9 ,10 ]
Azzopardi, Peter [1 ,2 ,5 ,6 ]
Wake, Melissa [1 ,2 ,5 ,6 ]
机构
[1] Univ Melbourne, Melbourne Sch Hlth Sci, Parkville, Vic, Australia
[2] Murdoch Childrens Res Inst, Parkville, Vic, Australia
[3] Univ South Australia, Alliance Res Exercise Nutr & Act ARENA, Adelaide, SA, Australia
[4] Deakin Univ, Sch Hlth & Social Dev, Geelong, Vic, Australia
[5] Univ Auckland, Dept Paediat, Auckland, New Zealand
[6] Univ Auckland, Liggins Inst, Auckland, New Zealand
[7] Monash Univ, Sch Clin Sci, Dept Paediat, Clayton, Vic, Australia
[8] Australian Natl Univ, Ctr Social Res & Methods, Canberra, ACT, Australia
[9] Univ Oxford, George Inst Global Hlth, Oxford, England
[10] Univ Tasmania, Menzies Inst Med Res, Hobart, Tas, Australia
基金
英国医学研究理事会;
关键词
PHYSICAL-ACTIVITY; SELF-REPORT; UTILITY; 9D; CHILDREN; VALIDITY; PARTICIPATION; ASSOCIATIONS; RELIABILITY;
D O I
10.1542/peds.2016-3656
中图分类号
R72 [儿科学];
学科分类号
100202 ;
摘要
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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页数:9
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