153 research outputs found
Bayesian Multilevel Compositional Data Analysis: Introduction, Evaluation, and Application
Full text linkMultilevel compositional data commonly occur in various fields, particularly
in intensive, longitudinal studies using ecological momentary assessments.
Examples include data repeatedly measured over time that are non-negative and
sum to a constant value, such as sleep-wake movement behaviours in a 24-hour
day. This article presents a novel methodology for analysing multilevel
compositional data using a Bayesian inference approach. This method can be used
to investigate how reallocation of time between sleep-wake movement behaviours
may be associated with other phenomena (e.g., emotions, cognitions) at a daily
level. We explain the theoretical details of the data and the models, and
outline the steps necessary to implement this method. We introduce the R
package multilevelcoda to facilitate the application of this method and
illustrate using a real data example. An extensive parameter recovery
simulation study verified the robust performance of the method. Across all
simulation conditions investigated in the simulation study, the model had
minimal convergence issues (convergence rate > 99%) and achieved excellent
quality of parameter estimates and inference, with an average bias of 0.00
(range -0.09, 0.05) and coverage of 0.95 (range 0.93, 0.97). We conclude the
article with recommendations on the use of the Bayesian compositional
multilevel modelling approach, and hope to promote wider application of this
method to answer robust questions using the increasingly available data from
intensive, longitudinal studies
Cross-sectional and longitudinal associations between active commuting and patterns of movement behaviour during discretionary time: A compositional data analysis.
Get PDFBACKGROUND: Active living approaches seek to promote physical activity and reduce sedentary time across different domains, including through active travel. However, there is little information on how movement behaviours in different domains relate to each other. We used compositional data analysis to explore associations between active commuting and patterns of movement behaviour during discretionary time. METHODS AND FINDINGS: We analysed cross-sectional and longitudinal data from the UK Biobank study. At baseline (2006-2010) and follow up (2009-2013) participants reported their mode of travel to work, dichotomised as active (walking, cycling or public transport) or inactive (car). Participants also reported activities performed during discretionary time, categorised as (i) screen time; (ii) walking for pleasure; and (iii) sport and do-it-yourself (DIY) activities, summed to produce a total. We applied compositional data analysis to test for associations between active commuting and the composition and total amount of discretionary time, using linear regression models adjusted for covariates. Adverse events were not investigated in this observational analysis. The survey response rate was 5.5%. In the cross-sectional analysis (n = 182,406; mean age = 52 years; 51% female), active commuters engaged in relatively less screen time than those who used inactive modes (coefficient -0.12, 95% confidence interval [CI] -0.13 to -0.11), equating to approximately 60 minutes less screen time per week. Similarly, in the longitudinal analysis (n = 4,323; mean age = 51 years; 49% female) there were relative reductions in screen time in those who used active modes at both time points compared with those who used inactive modes at both time points (coefficient -0.15, 95% confidence interval [CI] -0.24 to -0.06), equating to a difference between these commute groups of approximately 30 minutes per week at follow up. However, as exposures and outcomes were measured concurrently, reverse causation is possible. CONCLUSIONS: Active commuting was associated with a more favourable pattern of movement behaviour during discretionary time. Active commuters accumulated 30-60 minutes less screen time per week than those using inactive modes. Though modest, this could have a cumulative effect on health over time.LF was funded by the Centre for Diet and Activity Research (CEDAR), a UKCRC Public Health Research Centre of Excellence. Funding from the British Heart Foundation, Cancer Research UK, Economic and Social Research Council, Medical Research Council, the National Institute for Health Research, and the Wellcome Trust, under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged (087636/Z/08/Z, ES/G007462/1, MR/K023187/1). DO (MC_UU_12015/6) and KW (MC_UU_12015/3) were supported by the Medical Research Council
Life on Holidays: Study Protocol for a 3-Year Longitudinal Study Tracking Changes in Children\u27s Fitness and Fatness during the In-School Versus Summer Holiday Period
Get PDFBackground: Emerging evidence suggests that children become fatter and less fit over the summer holidays but get leaner and fitter during the in-school period. This could be due to differences in diet and time use between these distinct periods. Few studies have tracked diet and time use across the summer holidays. This study will measure rates of change in fatness and fitness of children, initially in Grade 4 (age 9 years) across three successive years and relate these changes to changes in diet and time use between in-school and summer holiday periods.
Methods: Grade 4 Children attending Australian Government, Catholic and Independent schools in the Adelaide metropolitan area will be invited to participate, with the aim of recruiting 300 students in total. Diet will be reported by parents using the Automated Self-Administered 24-h Dietary Assessment Tool. Time use will be measured using 24-h wrist-worn accelerometry (GENEActiv) and self-reported by children using the Multimedia Activity Recall for Children and Adults (e.g. chores, reading, sport). Measurement of diet and time use will occur at the beginning (Term 1) and end (Term 4) of each school year and during the summer holiday period. Fitness (20-m shuttle run and standing broad jump) and fatness (body mass index z-score, waist circumference, %body fat) will be measured at the beginning and end of each school year. Differences in rates of change in fitness and fatness during in-school and summer holiday periods will be calculated using model parameter estimate contrasts from linear mixed effects model. Model parameter estimate contrasts will be used to calculate differences in rates of change in outcomes by socioeconomic position (SEP), sex and weight status. Differences in rates of change of outcomes will be regressed against differences between in-school and summer holiday period diet and time use, using compositional data analysis. Analyses will adjust for age, sex, SEP, parenting style, weight status, and pubertal status, where appropriate.
Discussion: Findings from this project may inform new, potent avenues for intervention efforts aimed at addressing childhood fitness and fatness. Interventions focused on the home environment, or alternatively extension of the school environment may be warranted.
Trial registration: Australia New Zealand Clinical Trials Registry, identifier ACTRN12618002008202. Retrospectively registered on 14 December 2018
Past, present, and future : trends in sleep duration and implications for public health
Get PDFSleep 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. (c) 2017 National Sleep Foundation. Published by Elsevier Inc. All rights reserved.Peer reviewe
Are reallocations of time between physical activity, sedentary behaviour and sleep associated with low back pain? A compositional data analysis
Get PDFObjectives The aim of this cross-sectional study was to explore the associations of reallocating time between moderate- to vigorous-intensity physical activity (MVPA), light-intensity physical activity (LPA), sedentary behaviour (SB) and sleep with occurrence, frequency and intensity of low back pain (LBP) among adults using compositional isotemporal substitution analysis.Methods A total of 2333 participants from the general adult population completed the Daily Activity Behaviours Questionnaire asking about their time-use composition consisting of sleep, SB, LPA and MVPA, and they self-reported their frequency and intensity of LBP in the past year.Results Regression analyses adjusted for age, sex, body mass index, smoking, stress, education and socioeconomic status found that the time-use composition is associated with the frequency (p=0.009) and intensity of LBP (p<0.001). Reallocating time from SB or LPA to sleep was associated with lower frequency and intensity of LBP (p<0.05). Reallocating time from MVPA to sleep, SB or LPA and from SB to LPA was associated with a lower intensity of LBP (p<0.05). For example, reallocating 30 min/day from SB to sleep was associated with 5% lower odds (95% CI: 2% to 8%, p=0.001) of experiencing LBP more frequently, and 2% lower LBP intensity (95% CI: 1% to 3%, p<0.001).Conclusion LBP sufferers may benefit from getting additional sleep and spending more time in LPA, while engaging less in SB and MVPA. These reallocations of time may be meaningful from clinical and public health perspectives
Adiposity, fitness, health-related quality of life and the reallocation of time between children’s school day activity behaviours: a compositional data analysis
Get PDFSedentary time (ST), light (LPA), and moderate-to-vigorous physical activity (MVPA) constitute the range of school day activity behaviours. This study investigated whether the composition of school activity behaviours was associated with health indicators, and the predicted changes in health when time was reallocated between activity behaviours. Accelerometers were worn for 7-days between October and December 2010 by 318 UK children aged 10–11, to provide estimates of school day ST, LPA, and MVPA. BMI z-scores and percent waist-to-height ratio were calculated as indicators of adiposity. Cardiorespiratory fitness (CRF) was assessed using the 20-m shuttle run test. The PedsQL™ questionnaire was completed to assess psychosocial and physical health-related quality of life (HRQL). Log-ratio multiple linear regression models predicted health indicators for the mean school day activity composition, and for new compositions where fixed durations of time were reallocated from one activity behaviour to another, while the remaining behaviours were unchanged. The school day activity composition significantly predicted adiposity and CRF (p = 0.04–0.002), but not HRQL. Replacing MVPA with ST or LPA around the mean activity composition predicted higher adiposity and lower CRF. When ST or LPA were substituted with MVPA, the relationships with adiposity and CRF were asymmetrical with favourable, but smaller predicted changes in adiposity and CRF than when MVPA was replaced. Predicted changes in HRQL were negligible. The school day activity composition significantly predicted adiposity and CRF but not HRQL. Reallocating time from ST and LPA to MVPA is advocated through comprehensive school physical activity promotion approaches. Trial registration: ISRCTN03863885.</p
Adolescent time use and mental health: a cross-sectional, compositional analysis in the Millennium Cohort Study
Get PDFOBJECTIVE: To examine the association of 24-hour time-use compositions with mental health in a large, geographically diverse sample of UK adolescents. DESIGN: Cross-sectional, secondary data analysis. SETTING: Millennium Cohort Study (sixth survey), a UK-based prospective birth cohort. PARTICIPANTS: Data were available from 4642 adolescents aged 14 years. Analytical samples for weekday and weekend analyses were n=3485 and n=3468, respectively (45% boys, 85% white ethnicity). PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcome measures were the Strengths and Difficulties Questionnaire (SDQ, socioemotional behaviour), Mood and Feelings Questionnaire (MFQ, depressive symptoms) and Rosenberg Self-Esteem Scale (RSE, self-esteem). Behavioural exposure data were derived from 24-hour time-use diaries. RESULTS: On weekdays, participants spent approximately 54% of their time in sleep, 3% in physical activity, 9% in school-related activities, 6% in hobbies, 11% using electronic media and 16% in domestic activities. Predicted differences in SDQ, MFQ and RSE were statistically significant for all models (weekday and weekend) that simulated the addition or removal of 15 min physical activity, with an increase in activity being associated with improved mental health and vice versa. Predicted differences in RSE were also significant for simulated changes in electronic media use; an increase in electronic media use was associated with reduced self-esteem. CONCLUSION: Small but consistent associations were observed between physical activity, electronic media use and selected markers of mental health. Findings support the delivery of physical activity interventions to promote mental health during adolescence, without the need to specifically target or protect time spent in other activities
Annual and Seasonal Patterns of Dietary Intake in Australian Adults: A Prospective Cohort Study
Get PDFPoor diet is a major risk factor for non-communicable disease. The aims of this study were to describe temporal patterns and seasonal changes in diet across the year in Australian adults. A total of 375 adults from a prospective cohort study conducted between 1 December 2019 and 31 December 2021 in Adelaide, Australia, were asked to complete the Dietary Questionnaire for Epidemiological Studies at eight timepoints over a year. Average intakes over the previous month of total energy, macronutrients, healthy food groups, and discretionary foods and beverages were derived. Temporal patterns in diet were analysed descriptively. Multilevel linear regression modelling was used to assess seasonal differences in diet. Of the 375 participants recruited, 358 provided sufficient data for analysis. Intake of total energy, all macronutrients, and most discretionary foods and beverages peaked in December. Total energy intake was higher in summer than in autumn, winter, and spring. Fruit intake was higher in summer than in winter. Consumption of alcoholic beverages was higher in summer than in autumn, winter, and spring. Consumption of non-alcoholic beverages was higher in summer than in autumn and winter. This study identified temporal differences in dietary intake among Australian adults. Seasonal effects appear to be driven largely by increases in consumption of foods and beverages over the December (summer) holiday period. These findings can inform the design and timing of dietary interventions.</p
Compositional functional regression and isotemporal substitution analysis: Methods and application in time-use epidemiology
Get PDFThe distribution of time that people spend in physical activity of various intensities has important health implications. Physical activity (commonly categorised by the intensity into light, moderate and vigorous physical activity), sedentary behaviour and sleep, should not be analysed separately, because they are parts of a time-use composition with a natural constraint of (Formula presented.) h/day. To find out how are relative reallocations of time between physical activity of various intensities associated with health, herewith we describe compositional scalar-on-function regression and a newly developed compositional functional isotemporal substitution analysis. Physical activity intensity data can be considered as probability density functions, which better reflects the continuous character of their measurement using accelerometers. These probability density functions are characterised by specific properties, such as scale invariance and relative scale, and they are geometrically represented using Bayes spaces with the Hilbert space structure. This makes possible to process them using standard methods of functional data analysis in the (Formula presented.) space, via centred logratio (clr) transformation. The scalar-on-function regression with clr transformation of the explanatory probability density functions and compositional functional isotemporal substitution analysis were applied to a dataset from a cross-sectional study on adiposity conducted among school-aged children in the Czech Republic. Theoretical reallocations of time to physical activity of higher intensities were found to be associated with larger and more progressive expected decreases in adiposity. We obtained a detailed insight into the dose–response relationship between physical activity intensity and adiposity, which was enabled by using the compositional functional approach
Longitudinal association between movement behaviours and depressive symptoms among adolescents using compositional data analysis
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