Original ArticleSleep and cardiometabolic health in children and adults: examining sleep as a component of the 24-h day
Introduction
Sleep is increasingly recognised as important for cardiometabolic health and has been associated with adiposity, hypertension, hyperglycaemia, inflammation and dyslipidemia [[1], [2], [3], [4], [5], [6]]. To date, most studies have examined the relationship between sleep duration and body mass index (BMI) [1,7]. In children, short sleep duration has been consistently associated with increased BMI [4], while some [8,9], but not all [[10], [11], [12]] studies in adults suggest a U-shaped association, whereby both long and short sleep durations are associated with increased BMI. Relatively few studies have examined the role of sleep duration on cardiometabolic phenotypes, particularly in children. However, available studies tend to suggest short sleep duration is associated with higher blood pressures [5,6], dyslipidemia [4,6] and metabolic risk [2] in both children and adults.
While lifestyle behaviours are widely recognised as important modifiable risk factors for the prevention and management of cardiometabolic health [1], guidelines [13,14] have largely overlooked sleep, and focus on advocating regular physical activity, a healthy diet, smoking cessation and reduced alcohol intake for the prevention and management of hypertension, cardiovascular disease and obesity. Perry [15] argues that sleep needs to be considered “as critical to health as diet and physical activity” while Heffron [16] suggests that “sleep is one of the three pillars (diet, exercise and sleep) of a healthy lifestyle”. In line with these suggestions, international guidelines are increasingly noting the importance of sleep [17].
Time-use epidemiologists suggest time spent in activity behaviours (ie sleep, physical activity, sedentary behaviours) should be considered as an ensemble of the 24-h day, rather than separately [18]. Activity behaviours are mutually exclusive and all-inclusive parts of the 24-h day, and consequently if the time devoted to one behaviour changes, there must be an equal and opposite change in the other behaviours collectively [19]. Accordingly, contemporary time-use epidemiology frameworks suggest a shift from traditional methods of exploring the relationship between just one activity behaviour (eg sleep) and a given health outcome, to exploring how activity patterns (called activity compositions) are associated with health outcomes. Considering time use in this way has become increasingly widespread, and 24-h activity guidelines have been incorporated into recent guidelines from Canada [20], Australia [21], New Zealand [22], Finland [23] and Croatia [24]. This paradigm shift has implications for measurement (the need to capture the entire 24-h day), conceptualising mechanisms linking exposures to outcomes (understanding the net effect of time-use changes on health), and intervention design (intervening to achieve the best possible reallocations of time from one domain to another).
Activity behaviours are perfectly multicollinear (always summing to 24 h) and so standard statistical regression techniques cannot be used [18]. Compositional Data Analysis (CoDA) has gained popularity as a feasible method for the statistical analysis of 24-h time use [7,18]. CoDA overcomes the problem of multicollinearity by expressing activity behaviours as a set of ratios that can then be used as variables in traditional statistical models to examine relative compensatory change [18]. CoDA allows us to model the effects of reallocating time from one activity behaviour to others, for example increasing sleep by one hour and reducing sedentary behaviour by the same amount [18]. Rather than exploring the health associations of changing to just one activity, CoDA allows us to explore the associations of reallocating time between activities. This reflects the real-world situation, because the 24-h daily window means that as one activity increases, other activities must decrease to compensate. That is, time must be reallocated between activities.
To date, few studies have examined the association between sleep duration and cardiometabolic health in this way. Chastin and colleagues [19] first examined the association between 24-h activity behaviours and cardiometabolic health markers (BMI, waist circumference, triglycerides, plasma glucose, plasma insulin, systolic and diastolic blood pressure, HDL and LDL). In 1937 adults they found the strongest associations were when sedentary time was reallocated to moderate-vigorous physical activity (MVPA). Similar findings were reported in a non-CoDA isotemporal substitution study by Buman and colleagues [25] who examined the association between cardiometabolic risks and the reallocation of time spent sleeping in a sample of 2185 adults. In 434 10-13 year-olds, Talarico and Jannsen [26] found that 24-h activity composition was associated with obesity for reallocations of time to and from MVPA and light physical activity (LPA), but not for other reallocations. In another study on children, Carson and colleagues [27], examined the association between the 24-h activity composition (consisting of subjectively measured sleep duration and objectively-measured physical activity and sedentary time) and a range of cardiometabolic health markers (ie body mass index, waist circumference, blood pressure, aerobic fitness, lipid levels). In this study of 4169 Canadian children aged 6–17 years, reallocating time from MVPA or sleep to any other movement behaviour had a negative association with health, although replacing MVPA had the largest associations.
The aim of this study was to further build on our understanding of sleep, as a component of time, and cardiometabolic health in both children and adults. Specifically, we aimed to:
- 1.
Determine the association between device-measured sleep, as a component of the 24-h time-use composition, and cardiometabolic health in children and adults
- 2.
Determine the association between device-measured sleep characteristics (timing, quality and variability) and cardiometabolic health, when sleep is considered a component of the 24-h time use composition.
- 3.
Examine the associations of reallocating time in sleep to and from MVPA, sedentary time and LPA.
Section snippets
Participant/design
Data examined in this study were collected between February 2015 and March 2016 as part of the Child Health CheckPoint (CheckPoint) study. The CheckPoint study was a one-off, comprehensive physical health and biomarker cross-sectional study nested between waves 6 and 7 of the Longitudinal Study of Australian Children (LSAC). Data were collected on 1874 child-parent dyads. LSAC commenced in 2004 with the recruitment of two cohorts (B and K – the latter not relevant to this paper), which have
Results
Of the 1874 parent-child CheckPoint participants, 1073 children and 1378 adults had complete 24-h activity behaviour data and were examined in this study (Fig. 1). Table 1 presents descriptive data for participants included for analysis. Differences in participants included and excluded for analysis (from the Checkpoint study) have been reported elsewhere [45]. In general, children included for analysis were younger, had less advanced pubertal status, were of higher SEP and lower BMI compared
Discussion
To our knowledge, this is the first study to determine the association between 24-h time-use composition and cardiometabolic health in both children and adults using CoDA. This is also the first study to explore cardiometabolic associations with actigraphy-derived measures of sleep characteristics (timing, quality and variability) together with all activity behaviours (sleep, physical activity and sedentary time).
Funding
The Child Health CheckPoint has been supported to date by the National Health and Medical Research Council 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, 2016-310), Cure Kids, New Zealand Ministry of Business, Innovation and Employment, University of Auckland Faculty
Disclosure statement
Non-financial Disclosure: none.
Credit author statement
Lisa Matricciani Conceptualization, Methodology, Writing (original draft), Investigation, Formal analysis; Dorothea Dumuid Conceptualization, Methodology, Writing (review and editing), Formal analysis, Supervision; Catherine Paquet Writing (review and editing), Supervision; François Fraysse Data curation, Software, Writing (review and editing); Yichao Wang Project administration, Writing (review and editing); Louise A Baur Funding acquisition, Project administration, Writing (review and
Acknowledgements
This paper uses data from Growing Up in Australia, the Longitudinal Study of Australian Children. The study is conducted in partnership between the Department of Social Services (DSS), the Australian Institute of Family Studies (AIFS) and the Australian Bureau of Statistics (ABS). The findings and views reported in this paper are those of the author and should not be attributed to DSS, AIFS or the ABS.
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