Affiliations 

  • 1 Early Start, School of Health and Society, Faculty of the Arts, Social Science and Humanities, University of Wollongong, NSW, AUSTRALIA
  • 2 Physical Activity for Health Group, School of Psychological Sciences and Health, University of Strathclyde, Glasgow, Scotland, UNITED KINGDOM
  • 3 College of Medical, Veterinary and Life Sciences, University of Glasgow, Scotland, UNITED KINGDOM
  • 4 Universidade de São Paulo, São Paulo, BRAZIL
  • 5 Rehabilitation Unit, Faculty of Medicine and Health Sciences, University of Zimbabwe, Harare, ZIMBABWE
  • 6 Faculty of Education, The Chinese University of Hong Kong, Hong Kong, THE PEOPLE'S REPUBLIC OF CHINA
  • 7 Department of Paediatrics, Faculty of Medicine, University of Colombo, Colombo, SRI LANKA
  • 8 SAMRC/Wits Developmental Pathways for Health Research Unit, University of the Witwatersrand, Johannesburg, SOUTH AFRICA
  • 9 Unité Mixte de Recherche Nutrition et Alimentation, CNESTEN-Université Ibn Tofail URAC 39, Regional Designated Center of Nutrition Associated with AFRA/IAEA, Rabat, MOROCCO
  • 10 Department of Early Childhood Development, Beijing Municipal Key Laboratory of Child Development and Nutriomics, Capital Institute of Pediatrics, Beijing, CHINA
  • 11 Biomedical Research Foundation, Dhaka, BANGLADESH
  • 12 Korea Institute of Child Care and Education, Seoul, REPUBLIC OF KOREA
  • 13 Department of Epidemiology, Faculty of Public Health, Pham Ngoc Thach University of Medicine, Ho Chi Minh City, VIETNAM
  • 14 Centre of Community Education and Well-being, Faculty of Education, Universiti Kebangsaan Malaysia, Kuala Lumpur, MALAYSIA
  • 15 Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, SWEDEN
  • 16 Papua New Guinea Institute of Medical Research, Goroka, PAPUA NEW GUINEA
  • 17 Centre for Community Health Studies (ReaCH), Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, MALAYSIA
  • 18 Pennington Biomedical Research Center, Baton Rouge, LA
  • 19 Faculty of Sport and Health Education, Universitas Pendidikan Indonesia, Bandung, INDONESIA
  • 20 Department of Human Nutrition, Tokyo Kasei Gakuin University, Tokyo, JAPAN
  • 21 Healthy Active Lifestyle and Obesity (HALO) Research Group, Children's Hospital of Eastern Ontario Research Institute, Ottawa, CANADA
  • 22 Institute of Public and Preventive Health, Augusta University, Augusta, GA
Med Sci Sports Exerc, 2022 Jul 01;54(7):1123-1130.
PMID: 35142711 DOI: 10.1249/MSS.0000000000002886

Abstract

PURPOSE: There is a paucity of global data on sedentary behavior during early childhood. The purpose of this study was to examine how device-measured sedentary behavior in young children differed across geographically, economically, and sociodemographically diverse populations, in an international sample.

METHODS: This multinational, cross-sectional study included data from 1071 children 3-5 yr old from 19 countries, collected between 2018 and 2020 (pre-COVID). Sedentary behavior was measured for three consecutive days using activPAL accelerometers. Sedentary time, sedentary fragmentation, and seated transport duration were calculated. Linear mixed models were used to examine the differences in sedentary behavior variables between sex, country-level income groups, urban/rural settings, and population density.

RESULTS: Children spent 56% (7.4 h) of their waking time sedentary. The longest average bout duration was 81.1 ± 45.4 min, and an average of 61.1 ± 50.1 min·d-1 was spent in seated transport. Children from upper-middle-income and high-income countries spent a greater proportion of the day sedentary, accrued more sedentary bouts, had shorter breaks between sedentary bouts, and spent significantly more time in seated transport, compared with children from low-income and lower-middle-income countries. Sex and urban/rural residential setting were not associated with any outcomes. Higher population density was associated with several higher sedentary behavior measures.

CONCLUSIONS: These data advance our understanding of young children's sedentary behavior patterns globally. Country income levels and population density appear to be stronger drivers of the observed differences, than sex or rural/urban residential setting.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.