Dorothea Dumuid

Compositional data analysis for physical activity, sedentary time and sleep research

The health effects of daily activity behaviours (physical activity, sedentary time and sleep) are widely studied. While previous research has largely examined activity behaviours in isolation, recent studies have adjusted for multiple behaviours. However, the inclusion of all activity behaviours in traditional multivariate analyses has not been possible due to the perfect multicollinearity of 24-h time budget data. The ensuing lack of adjustment for known effects on the outcome undermines the validity of study findings. We describe a statistical approach that enables the inclusion of all daily activity behaviours, based on the principles of compositional data analysis. Using data from the International Study of Childhood Obesity, Lifestyle and the Environment, we demonstrate the application of compositional multiple linear regression to estimate adiposity from children’s daily activity behaviours expressed as isometric log-ratio coordinates. We present a novel method for predicting change in a continuous outcome based on relative changes within a composition, and for calculating associated confidence intervals to allow for statistical inference. The compositional data analysis presented overcomes the lack of adjustment that has plagued traditional statistical methods in the field, and provides robust and reliable insights into the health effects of daily activity behaviours.

Health-related quality of life and lifestyle behavior clusters in school-aged children from 12 countries

Objective To evaluate the relationship between childrens lifestyles and health‐related quality of life and to explore whether this relationship varies among children from different world regions. Study design This study used cross‐sectional data from the International Study of Childhood Obesity, Lifestyle and the Environment. Children (9‐11 years) were recruited from sites in 12 nations (n = 5759). Clustering input variables were 24‐hour accelerometry and self‐reported diet and screen time. Health‐related quality of life was self‐reported with KIDSCREEN‐10. Cluster analyses (using compositional analysis techniques) were performed on a site‐wise basis. Lifestyle behavior cluster characteristics were compared between sites. The relationship between cluster membership and health‐related quality of life was assessed with the use of linear models. Results Lifestyle behavior clusters were similar across the 12 sites, with clusters commonly characterized by (1) high physical activity (actives); (2) high sedentary behavior (sitters); (3) high screen time/unhealthy eating pattern (junk‐food screenies); and (4) low screen time/healthy eating pattern and moderate physical activity/sedentary behavior (all‐rounders). Health‐related quality of life was greatest in the all‐rounders cluster. Conclusions Children from different world regions clustered into groups of similar lifestyle behaviors. Cluster membership was related to differing health‐related quality of life, with children from the all‐rounders cluster consistently reporting greatest health‐related quality of life at sites around the world. Findings support the importance of a healthy combination of lifestyle behaviors in childhood: low screen time, healthy eating pattern, and balanced daily activity behaviors (physical activity and sedentary behavior). Trial registration ClinicalTrials.gov: NCT01722500.

The compositional isotemporal substitution model: A method for estimating changes in a health outcome for reallocation of time between sleep, physical activity and sedentary behaviour:

How people use their time has been linked with their health. For example, spending more time being physically active is known to be beneficial for health, whereas long durations of sitting have been associated with unfavourable health outcomes. Accordingly, public health messages have advocated swapping strategies to promote the reallocation of time between parts of the time-use composition, such as “Move More, Sit Less”, with the aim of achieving optimal distribution of time for health. However, the majority of research underpinning these public health messages has not considered daily time use as a composition, and has ignored the relative nature of time-use data. We present a way of applying compositional data analysis to estimate change in a health outcome when fixed durations of time are reallocated from one part of a particular time-use composition to another, while the remaining parts are kept constant, based on a multiple linear regression model on isometric log ratio coordinates. In an example, we examine the expected differences in Body Mass Index z-scores for reallocations of time between sleep, physical activity and sedentary behaviour.

Time-Use Patterns and Health-Related Quality of Life in Adolescents

In this cross-sectional study of 1455 11- to 12-year-olds, we examined the association between 24-hour time-use patterns and HRQoL. OBJECTIVES: To describe 24-hour time-use patterns and their association with health-related quality of life (HRQoL) in early adolescence. METHODS: The Child Health CheckPoint was a cross-sectional study nested between Waves 6 and 7 of the Longitudinal Study of Australian Children. The participants were 1455 11- to 12-year-olds (39% of Wave 6; 51% boys). The exposure was 24-hour time use measured across 259 activities using the Multimedia Activity Recall for Children and Adolescents. “Average” days were generated from 1 school and 1 nonschool day. Time-use clusters were derived from cluster analysis with compositional inputs. The outcomes were self-reported HRQoL (Physical and Psychosocial Health [PedsQL] summary scores; Child Health Utility 9D [CHU9D] health utility). RESULTS: Four time-use clusters emerged: “studious actives” (22%; highest school-related time, low screen time), “techno-actives” (33%; highest physical activity, lowest school-related time), “stay home screenies” (23%; highest screen time, lowest passive transport), and “potterers” (21%; low physical activity). Linear regression models, adjusted for a priori confounders, showed that compared with the healthiest “studious actives” (mean [SD]: CHU9D 0.84 [0.14], PedsQL physical 86.8 [10.8], PedsQL psychosocial 79.9 [12.6]), HRQoL in “potterers” was 0.2 to 0.5 SDs lower (mean differences [95% confidence interval]: CHU9D −0.03 [−0.05 to −0.00], PedsQL physical −5.5 [−7.4 to −3.5], PedsQL psychosocial −5.8 [−8.0 to −3.5]). CONCLUSIONS: Discrete time-use patterns exist in Australian young adolescents. The cluster characterized by low physical activity and moderate screen time was associated with the lowest HRQoL. Whether this pattern translates into precursors of noncommunicable diseases remains to be determined.

The adiposity of children is associated with their lifestyle behaviours:a cluster analysis of school-aged children from 12 nations

The relationship between childrens adiposity and lifestyle behaviour patterns is an area of growing interest.

Past, present, and future: trends in sleep duration and implications for public health

&NA; Sleep is important for the physical, social and mental well‐being of both children and adults. Over the years, there has been a general presumption that sleep will inevitably decline with the increase in technology and a busy 24‐hour modern lifestyle. This narrative review discusses the empirical evidence for secular trends in sleep duration and the implications of these trends.

Academic Performance and Lifestyle Behaviors in Australian School Children: A Cluster Analysis.

Poor academic performance has been linked with particular lifestyle behaviors, such as unhealthy diet, short sleep duration, high screen time, and low physical activity. However, little is known about how lifestyle behavior patterns (or combinations of behaviors) contribute to children’s academic performance. We aimed to compare academic performance across clusters of children with common lifestyle behavior patterns. We clustered participants (Australian children aged 9-11 years, n = 284) into four mutually exclusive groups of distinct lifestyle behavior patterns, using the following lifestyle behaviors as cluster inputs: light, moderate, and vigorous physical activity; sedentary behavior and sleep, derived from 24-hour accelerometry; self-reported screen time and diet. Differences in academic performance (measured by a nationally administered standardized test) were detected across the clusters, with scores being lowest in the Junk Food Screenies cluster (unhealthy diet/high screen time) and highest in the Sitters cluster (high nonscreen sedentary behavior/low physical activity). These findings suggest that reduction in screen time and an improved diet may contribute positively to academic performance. While children with high nonscreen sedentary time performed better academically in this study, they also accumulated low levels of physical activity. This warrants further investigation, given the known physical and mental benefits of physical activity.

Human development index, children’s health-related quality of life and movement behaviors: a compositional data analysis

PurposeHealth-related quality of life has been related to physical activity, sedentary behavior, and sleep among children from developed nations. These relationships have rarely been assessed in developing nations, nor have behaviors been considered in their true context, as mutually exclusive and exhaustive parts of the movement behavior composition. This study aimed to explore whether children’s health-related quality of life is related to their movement behavior composition and if the relationship differs according to human development index.MethodsChildren aged 9–11 years (n = 5855), from the 12-nation cross-sectional observational International Study of Childhood Obesity, Lifestyle and the Environment 2011–2013, self-reported their health-related quality of life (KIDSCREEN-10). Daily movement behaviors were from 24-h, 7-day accelerometry. Isometric log-ratio mixed-effect linear models were used to calculate estimates for difference in health-related quality of life for the reallocation of time between daily movement behaviors.ResultsChildren from countries of higher human development index reported stronger positive relationships between health-related quality of life and moderate-to-vigorous physical activity, relative to the remaining behaviors (r = 0.75, p = 0.005) than those from lower human development index countries. In the very high human development index strata alone, health-related quality of life was significantly related to the movement behavior composition (p = 0.005), with moderate-to-vigorous physical activity (relative to remaining behaviors) being positively associated with health-related quality of life.ConclusionsThe relationship between children’s health-related quality of life and their movement behaviors is moderated by their country’s human development index. This should be considered when 24-h movement behavior guidelines are developed for children around the world.

Patterns of Time Use across the Chronic Obstructive Pulmonary Disease Severity Spectrum

Descriptions of time use patterns in people with chronic obstructive pulmonary disease (COPD) are scarce and the relationship between use-of-time and COPD severity remains unclear. This study aimed to describe a typical day for people with COPD and to explore the differences in time-use patterns across the Body Mass-Index, Airflow Obstruction, Dyspnoea and Exercise Capacity (BODE) index using compositional analyses. Using a cross-sectional design, 141 adults with clinically stable COPD had their demographics, objective measures of function (pulmonary, exercise capacity and physical activity), and self-reported COPD-related impairment recorded. Daily time-use compositions were derived from 24-h accelerometry and 24-h use-of-time recall interviews. Compositional multiple linear regression models were used to explore the relationship between the BODE index and 24-h time-use compositions. These models were used to predict daily time (min/d) that is spent in time-use components across the BODE index. The BODE index score was clearly associated with 24-h accelerometry (p < 0.0001) and 24-h use-of-time recall (p < 0.0001) compositions. Relative to the remaining time-use components, higher BODE index scores were associated with greater sedentary behaviour (p < 0.0001), Quiet time (p < 0.0001), Screen time (p = 0.001) and Self-care (p = 0.022), and less daily Chores (p < 0.0001) and Household administration (p = 0.015) time. As the BODE index scores increased, time-use predictions were strongly associated with decreases in Chores (up to 206 min/d), and increases in Screen (up to 156 min/d) and Quiet time (up to 131 min/d). Time–use patterns may provide a basis for planning interventions relative to the severity of COPD.

Rethinking the sleep-health link

Sleep is important for the physical, social and mental well-being of both children and adults. In this paper, we discuss the need to consider sleep as a multidimensional construct and as a component of total 24-hour activity. First, we make a case for considering sleep as a multidimensional construct, whereby all characteristics of sleep (including duration, quality, timing, and variability) and their links with health are examined. Second, we argue that sleep should also be conceptualized as part of the daily spectrum of time-use, along with other types of activity. We propose novel statistical models, in particular compositional data analysis (CoDA), as appropriate analytical methods for a new sleep paradigm.