Dorothea Dumuid1, T Olds1, L K Lewis1,2, J A Martin-Fernández3, T Barreira4,5, S Broyles4, J-P Chaput6, M Fogelholm7, G Hu4, R Kuriyan8, A Kurpad8, E V Lambert9, J Maia10, V Matsudo11, V O Onywera12, O L Sarmiento13, M Standage14, M S Tremblay6, C Tudor-Locke15, P Zhao16, P Katzmarzyk4, F Gillison14, C Maher1. 1. School of Health Sciences, University of South Australia, Adelaide, Australia. 2. School of Health Sciences, Flinders University, Adelaide, Australia. 3. Facultat de Ciencies, Universitat de Girona, Girona, Spain. 4. Population Science, Pennington Biomedical Research Center, Baton Rouge, USA. 5. School of Education, Syracuse University, Syracuse, USA. 6. Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada. 7. Department of Food and Environmental Sciences, University of Helsinki, Helsinki, Finland. 8. Department of Nutrition, St John's Research Institute, Bengaluru, India. 9. Department of Human Biology, University of Cape Town, Cape Town, South Africa. 10. Faculty of Sport, University of Porto, Porto, Portugal. 11. Center of Studies of the Physical Fitness Research Laboratory from Sao Caetano du Sul (CELAFISCS), Sao Caetano do Sul, Brazil. 12. Department of Recreation Management and Exercise Science, Kenyatta University, Kenyatta, Kenya. 13. Facultad de Medicina, Universidad de los Andes, Bogotá, Colombia. 14. Department for Health, University of Bath, Bath, UK. 15. Department of Kinesiology, University of Massachusetts Amherst, Amherst, USA. 16. Tianjin Women's and Children's Health Center, Tianjin, China.
Abstract
BACKGROUND: The relationship between children's adiposity and lifestyle behaviour patterns is an area of growing interest. OBJECTIVES: The objectives of this study are to identify clusters of children based on lifestyle behaviours and compare children's adiposity among clusters. METHODS: Cross-sectional data from the International Study of Childhood Obesity, Lifestyle and the Environment were used. PARTICIPANTS: the participants were children (9-11 years) from 12 nations (n = 5710). MEASURES: 24-h accelerometry and self-reported diet and screen time were clustering input variables. Objectively measured adiposity indicators were waist-to-height ratio, percent body fat and body mass index z-scores. ANALYSIS: sex-stratified analyses were performed on the global sample and repeated on a site-wise basis. Cluster analysis (using isometric log ratios for compositional data) was used to identify common lifestyle behaviour patterns. Site representation and adiposity were compared across clusters using linear models. RESULTS: Four clusters emerged: (1) Junk Food Screenies, (2) Actives, (3) Sitters and (4) All-Rounders. Countries were represented differently among clusters. Chinese children were over-represented in Sitters and Colombian children in Actives. Adiposity varied across clusters, being highest in Sitters and lowest in Actives. CONCLUSIONS: Children from different sites clustered into groups of similar lifestyle behaviours. Cluster membership was linked with differing adiposity. Findings support the implementation of activity interventions in all countries, targeting both physical activity and sedentary time.
BACKGROUND: The relationship between children's adiposity and lifestyle behaviour patterns is an area of growing interest. OBJECTIVES: The objectives of this study are to identify clusters of children based on lifestyle behaviours and compare children's adiposity among clusters. METHODS: Cross-sectional data from the International Study of Childhood Obesity, Lifestyle and the Environment were used. PARTICIPANTS: the participants were children (9-11 years) from 12 nations (n = 5710). MEASURES: 24-h accelerometry and self-reported diet and screen time were clustering input variables. Objectively measured adiposity indicators were waist-to-height ratio, percent body fat and body mass index z-scores. ANALYSIS: sex-stratified analyses were performed on the global sample and repeated on a site-wise basis. Cluster analysis (using isometric log ratios for compositional data) was used to identify common lifestyle behaviour patterns. Site representation and adiposity were compared across clusters using linear models. RESULTS: Four clusters emerged: (1) Junk Food Screenies, (2) Actives, (3) Sitters and (4) All-Rounders. Countries were represented differently among clusters. Chinese children were over-represented in Sitters and Colombian children in Actives. Adiposity varied across clusters, being highest in Sitters and lowest in Actives. CONCLUSIONS:Children from different sites clustered into groups of similar lifestyle behaviours. Cluster membership was linked with differing adiposity. Findings support the implementation of activity interventions in all countries, targeting both physical activity and sedentary time.
Authors: Dorothea Dumuid; Tyman E Stanford; Željko Pedišić; Carol Maher; Lucy K Lewis; Josep-Antoni Martín-Fernández; Peter T Katzmarzyk; Jean-Philippe Chaput; Mikael Fogelholm; Martyn Standage; Mark S Tremblay; Timothy Olds Journal: BMC Public Health Date: 2018-03-02 Impact factor: 3.295
Authors: Jana Pelclová; Nikola Štefelová; Jana Hodonská; Jan Dygrýn; Aleš Gába; Izabela Zając-Gawlak Journal: Int J Environ Res Public Health Date: 2018-07-09 Impact factor: 3.390
Authors: Thiago Sousa Matias; Kelly Samara Silva; Jaqueline Aragoni da Silva; Gabrielli Thais de Mello; Jo Salmon Journal: BMC Public Health Date: 2018-11-21 Impact factor: 3.295
Authors: Javier Sevil-Serrano; Alberto Aibar-Solana; Ángel Abós; José Antonio Julián; Luis García-González Journal: Int J Environ Res Public Health Date: 2019-08-29 Impact factor: 3.390
Authors: Peter T Katzmarzyk; Jean-Philippe Chaput; Mikael Fogelholm; Gang Hu; Carol Maher; Jose Maia; Timothy Olds; Olga L Sarmiento; Martyn Standage; Mark S Tremblay; Catrine Tudor-Locke Journal: Nutrients Date: 2019-04-15 Impact factor: 5.717