Levels and Correlates of Objectively Measured Sedentary Behaviour in Young Children: SUNRISE Study Results from 19 Countries.
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Kariippanon KE, Chong KH, Janssen X, Tomaz SA, Ribeiro EH, Munambah N, Chan CH, Chathurangana PWP, Draper CE, El Hamdouchi A, Florindo AA, Guan H, Ha AS, Hossain MS, Kim DH, Van Kim T, Koh DC, Löf M, Pham BN, Poh BK, Reilly JJ, Staiano AE, Suherman A, Tanaka C, Tang HK, Tremblay MS, Webster EK, Wickramasinghe VP, Wong JE, Okely AD
Med Sci Sports Exerc - (-) - [2022-02-08; online 2022-02-08]
There is a paucity of global data on sedentary behaviour during early childhood. The purpose of this study was to examine how device-measured sedentary behaviour in young children differed across geographically, economically, and socio-demographically diverse populations, in an international sample. This multinational, cross-sectional study included data from 1071 3-5-year-old children from 19 countries, collected between 2018 and 2020 (pre-COVID). Sedentary behaviour 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 behaviour variables between sex, country-level income groups, urban/rural settings, and population density. Children spent 56% (7.4 hours) 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/day 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, than children from low-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 behaviour measures. These data advance our understanding of young children's sedentary behaviour patterns globally. Country income levels and population density appear to be stronger drivers of the observed differences, than sex or rural/urban residential setting.
PubMed 35142711
DOI 10.1249/MSS.0000000000002886
Crossref 10.1249/MSS.0000000000002886
pii: 00005768-900000000-95866