Dorothea Dumuid1, Timothy Olds2, Lucy K Lewis3, Josep Antoni Martin-Fernández4, Peter T Katzmarzyk5, Tiago Barreira6, Stephanie T Broyles5, Jean-Philippe Chaput7, Mikael Fogelholm8, Gang Hu5, Rebecca Kuriyan9, Anura Kurpad9, Estelle V Lambert10, José Maia11, Victor Matsudo12, Vincent O Onywera13, Olga L Sarmiento14, Martyn Standage15, Mark S Tremblay7, Catrine Tudor-Locke16, Pei Zhao17, Fiona Gillison15, Carol Maher2. 1. School of Health Sciences, University of South Australia. Electronic address: Dorothea.dumuid@mymail.unisa.edu.au. 2. School of Health Sciences, University of South Australia. 3. School of Health Sciences, University of South Australia; School of Health Sciences, Flinders University, Adelaide, Australia. 4. Department of Computer Science, Applied Mathematics and Statistics, University of Girona, Girona, Spain. 5. Population Science, Pennington Biomedical Research Center, Baton Rouge, LA. 6. Population Science, Pennington Biomedical Research Center, Baton Rouge, LA; School of Education, Syracuse University, Syracuse, NY. 7. Healthy Active Living and Obesity Research, Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada. 8. Department of Food and Environmental Sciences, University of Helsinki, Helsinki, Finland. 9. Department of Nutrition, St John's Research Institute, Karnataka, India. 10. Department of Human Biology, University of Cape Town, Cape Town, South Africa. 11. Faculty of Sport, University of Porto, Porto, Portugal. 12. Center for the Study of Physical Fitness Laboratory of São Caetano do Sul (CELAFISCS), São Caetano do Sul, Brazil. 13. Department of Recreation Management and Exercise Science, Kenyatta University, Nairobi City, Kenya. 14. School of Medicine, University of the Andes, Bogotá, Colombia. 15. Department for Health, University of Bath, Bath, UK. 16. Department of Kinesiology, University of Massachusetts Amherst, Amherst, MA. 17. Tianjin Women's and Children's Health Center, Tianjin, China.
Abstract
OBJECTIVE: To evaluate the relationship between children's lifestyles and health-related quality of life and to explore whether this relationship varies among children from different world regions. STUDY DESIGN: This study used cross-sectional data from the International Study of Childhood Obesity, Lifestyle and the Environment. Children (9-11 years) were recruited from sites in 12 nations (n = 5759). Clustering input variables were 24-hour accelerometry and self-reported diet and screen time. Health-related quality of life was self-reported with KIDSCREEN-10. Cluster analyses (using compositional analysis techniques) were performed on a site-wise basis. Lifestyle behavior cluster characteristics were compared between sites. The relationship between cluster membership and health-related quality of life was assessed with the use of linear models. RESULTS: Lifestyle behavior clusters were similar across the 12 sites, with clusters commonly characterized by (1) high physical activity (actives); (2) high sedentary behavior (sitters); (3) high screen time/unhealthy eating pattern (junk-food screenies); and (4) low screen time/healthy eating pattern and moderate physical activity/sedentary behavior (all-rounders). Health-related quality of life was greatest in the all-rounders cluster. CONCLUSIONS: Children from different world regions clustered into groups of similar lifestyle behaviors. Cluster membership was related to differing health-related quality of life, with children from the all-rounders cluster consistently reporting greatest health-related quality of life at sites around the world. Findings support the importance of a healthy combination of lifestyle behaviors in childhood: low screen time, healthy eating pattern, and balanced daily activity behaviors (physical activity and sedentary behavior). TRIAL REGISTRATION: ClinicalTrials.gov: NCT01722500.
OBJECTIVE: To evaluate the relationship between children's lifestyles and health-related quality of life and to explore whether this relationship varies among children from different world regions. STUDY DESIGN: This study used cross-sectional data from the International Study of Childhood Obesity, Lifestyle and the Environment. Children (9-11 years) were recruited from sites in 12 nations (n = 5759). Clustering input variables were 24-hour accelerometry and self-reported diet and screen time. Health-related quality of life was self-reported with KIDSCREEN-10. Cluster analyses (using compositional analysis techniques) were performed on a site-wise basis. Lifestyle behavior cluster characteristics were compared between sites. The relationship between cluster membership and health-related quality of life was assessed with the use of linear models. RESULTS: Lifestyle behavior clusters were similar across the 12 sites, with clusters commonly characterized by (1) high physical activity (actives); (2) high sedentary behavior (sitters); (3) high screen time/unhealthy eating pattern (junk-food screenies); and (4) low screen time/healthy eating pattern and moderate physical activity/sedentary behavior (all-rounders). Health-related quality of life was greatest in the all-rounders cluster. CONCLUSIONS:Children from different world regions clustered into groups of similar lifestyle behaviors. Cluster membership was related to differing health-related quality of life, with children from the all-rounders cluster consistently reporting greatest health-related quality of life at sites around the world. Findings support the importance of a healthy combination of lifestyle behaviors in childhood: low screen time, healthy eating pattern, and balanced daily activity behaviors (physical activity and sedentary behavior). TRIAL REGISTRATION: ClinicalTrials.gov: NCT01722500.
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Authors: Xiu Yun Wu; Li Hui Zhuang; Wei Li; Hong Wei Guo; Jian Hua Zhang; Yan Kui Zhao; Jin Wei Hu; Qian Qian Gao; Sheng Luo; Arto Ohinmaa; Paul J Veugelers Journal: Qual Life Res Date: 2019-03-14 Impact factor: 4.147
Authors: Dorothea Dumuid; Carol Maher; Lucy K Lewis; Tyman E Stanford; Josep Antoni Martín Fernández; Julie Ratcliffe; Peter T Katzmarzyk; Tiago V Barreira; Jean-Philippe Chaput; Mikael Fogelholm; Gang Hu; José Maia; Olga L Sarmiento; Martyn Standage; Mark S Tremblay; Catrine Tudor-Locke; Timothy Olds Journal: Qual Life Res Date: 2018-01-23 Impact factor: 4.147
Authors: Carlos K H Wong; Rosa S Wong; Jason P Y Cheung; Keith T S Tung; Jason C S Yam; Michael Rich; King-Wa Fu; Prudence W H Cheung; Nan Luo; Chi Ho Au; Ada Zhang; Wilfred H S Wong; Jiang Fan; Cindy L K Lam; Patrick Ip Journal: Health Qual Life Outcomes Date: 2021-05-12 Impact factor: 3.186