TY - JOUR
T1 - Sleep profiles of Australian children aged 11–12 years and their parents
T2 - sociodemographic characteristics and lifestyle correlates
AU - Matricciani, Lisa
AU - Paquet, Catherine
AU - Fraysse, François
AU - Wake, Melissa
AU - Olds, Tim
N1 - Funding Information:
The Child Health CheckPoint has been supported to date by the National Health and Medical Research Council (NHMRC) of Australia (Project Grants 1041352 , 1109355 ), The Royal Children's Hospital Foundation ( 2014-241 ), Murdoch Children's Research Institute, The University of Melbourne , National Heart Foundation of Australia ( 100660 ), Financial Markets Foundation for Children ( 2014-055 ) and Victorian Deaf Education Institute . The urinary albumin and creatinine quantification was funded through NHMRC Program Grant 633003 Screening and Test Evaluation Program.
Funding Information:
Senior Research Fellowships to MW (1046518). MW is supported by Cure Kids New Zealand . The MCRI administered the research grants for the study and provided infrastructural support (IT and biospecimen management) to its staff and the study, but played no role in the conduct or analysis of the trial. DSS played a role in study design; however, no other funding bodies had a role in the study design and conduct; data collection, management, analysis, and interpretation; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
PY - 2020/9
Y1 - 2020/9
N2 - Background: Good sleep is a growing public health focus. Given the multidimensional nature of sleep, it is of interest to examine population sleep profiles and determine sociodemographic and lifestyle correlates. Methods: This study uses actigraphy-measured sleep data collected between February 2015 and March 2016 in the Child Health CheckPoint study. Participants wore actigraphy monitors (GENEActiv Original, Cambs, UK) on their non-dominant wrist for seven days and sleep characteristics (duration, efficiency, timing, and variability) were derived from raw actigraphy data. Sleep profiles of 1043 Australian children aged 11–12 years and their parents were determined using K-means cluster analysis. The association between cluster membership and potential sociodemographic and lifestyle correlates were assessed using Generalised Estimating Equations, adjusting for geographic clustering. Results: Four sleep profiles were identified: Short sleepers, Late to bed, Long sleepers, and Overall good sleepers. Compared to Overall good sleepers, Late to bed cluster were of lower socioeconomic position and had the least favourable diet and activity patterns. Compared to Overall good sleepers, those in the Late to bed cluster had higher sedentary time, lower levels of moderate-vigorous physical activity and a higher consumption of savoury snacks. In contrast, sugary drink consumption was higher in Late to bed children and Long sleeper adults. Conclusion: Examining sleep profiles may provide a more holistic way of monitoring sleep at the population level. Future health promotion strategies may be best to consider sleep in terms of profiles, with emphasis on sleep timing and duration.
AB - Background: Good sleep is a growing public health focus. Given the multidimensional nature of sleep, it is of interest to examine population sleep profiles and determine sociodemographic and lifestyle correlates. Methods: This study uses actigraphy-measured sleep data collected between February 2015 and March 2016 in the Child Health CheckPoint study. Participants wore actigraphy monitors (GENEActiv Original, Cambs, UK) on their non-dominant wrist for seven days and sleep characteristics (duration, efficiency, timing, and variability) were derived from raw actigraphy data. Sleep profiles of 1043 Australian children aged 11–12 years and their parents were determined using K-means cluster analysis. The association between cluster membership and potential sociodemographic and lifestyle correlates were assessed using Generalised Estimating Equations, adjusting for geographic clustering. Results: Four sleep profiles were identified: Short sleepers, Late to bed, Long sleepers, and Overall good sleepers. Compared to Overall good sleepers, Late to bed cluster were of lower socioeconomic position and had the least favourable diet and activity patterns. Compared to Overall good sleepers, those in the Late to bed cluster had higher sedentary time, lower levels of moderate-vigorous physical activity and a higher consumption of savoury snacks. In contrast, sugary drink consumption was higher in Late to bed children and Long sleeper adults. Conclusion: Examining sleep profiles may provide a more holistic way of monitoring sleep at the population level. Future health promotion strategies may be best to consider sleep in terms of profiles, with emphasis on sleep timing and duration.
KW - Children
KW - Profiles
KW - Sleep
UR - http://www.scopus.com/inward/record.url?scp=85088948570&partnerID=8YFLogxK
U2 - 10.1016/j.sleep.2020.04.017
DO - 10.1016/j.sleep.2020.04.017
M3 - Article
AN - SCOPUS:85088948570
VL - 73
SP - 53
EP - 62
JO - Sleep Medicine
JF - Sleep Medicine
SN - 1389-9457
ER -