Foodwork in the United Kingdom from 1983 to 2014: A compositional data analysis of repeat cross-sectional time use surveys

Abstract Background Most studies on the effects of sleep, sedentary behavior (SB), and physical activity (PA) on mental health did not account for the intrinsically compositional nature of the time spent in several behaviors. Thus, we examined the cross-sectional and prospective associations of device-measured compositional time in sleep, SB, light PA (LPA) and moderate-to-vigorous PA (MVPA) with depression symptoms, loneliness, happiness, and global mental health in older people (≥ 65 years). Methods Data were taken from the Seniors-ENRICA-2 study, with assessments in 2015–2017 (wave 0) and 2018–2019 (wave 1). Time spent in sleep, SB, LPA and MVPA was assessed by wrist-worn accelerometers. Depression symptoms, loneliness, happiness, and global mental health were self-reported using validated questionnaires. Analyses were performed using a compositional data analysis (CoDA) paradigm and adjusted for potential confounders. Results In cross-sectional analyses at wave 0 (n = 2489), time-use composition as a whole was associated with depression and happiness (all p < 0.01). The time spent in MVPA relative to other behaviors was beneficially associated with depression (γ = -0.397, p < 0.001), loneliness (γ = -0.124, p = 0.017) and happiness (γ = 0.243, p < 0.001). Hypothetically, replacing 30-min of Sleep, SB or LPA with MVPA was beneficially cross-sectionally related with depression (effect size [ES] ranged -0.326 to -0.246), loneliness (ES ranged -0.118 to -0.073), and happiness (ES ranged 0.152 to 0.172). In prospective analyses (n = 1679), MVPA relative to other behaviors at baseline, was associated with favorable changes in global mental health (γ = 0.892, p = 0.049). We observed a beneficial prospective effect on global mental health when 30-min of sleep (ES = 0.521), SB (ES = 0.479) or LPA (ES = 0.755) were theoretically replaced for MVPA. Conclusions MVPA was cross-sectionally related with reduced depression symptoms and loneliness and elevated level of happiness, and prospectively related with enhanced global mental health. Compositional isotemporal analyses showed that hypothetically replacing sleep, SB or LPA with MVPA could result in modest but significantly improvements on mental health indicators. Our findings add evidence to the emerging body of research on 24-h time-use and health using CoDA and suggest an integrated role of daily behaviors on mental health in older people.

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The Gut Microbiota of Healthy Aged Chinese Is Similar to That of the Healthy Young

mSphere ◽

10.1128/msphere.00327-17 ◽

2017 ◽

Vol 2 (5) ◽

Cited By ~ 65

Author(s):

Gaorui Bian ◽

Gregory B. Gloor ◽

Aihua Gong ◽

Changsheng Jia ◽

Wei Zhang ◽

...

Keyword(s):

Data Analysis ◽

Gut Microbiota ◽

Large Scale ◽

Compositional Data ◽

Healthy Lifestyle ◽

Compositional Data Analysis ◽

Surprising Result ◽

Microbiota Composition ◽

Cross Sectional ◽

Age Cohorts

ABSTRACT We report the large-scale use of compositional data analysis to establish a baseline microbiota composition in an extremely healthy cohort of the Chinese population. This baseline will serve for comparison for future cohorts with chronic or acute disease. In addition to the expected difference in the microbiota of children and adults, we found that the microbiota of the elderly in this population was similar in almost all respects to that of healthy people in the same population who are scores of years younger. We speculate that this similarity is a consequence of an active healthy lifestyle and diet, although cause and effect cannot be ascribed in this (or any other) cross-sectional design. One surprising result was that the gut microbiota of persons in their 20s was distinct from those of other age cohorts, and this result was replicated, suggesting that it is a reproducible finding and distinct from those of other populations. The microbiota of the aged is variously described as being more or less diverse than that of younger cohorts, but the comparison groups used and the definitions of the aged population differ between experiments. The differences are often described by null hypothesis statistical tests, which are notoriously irreproducible when dealing with large multivariate samples. We collected and examined the gut microbiota of a cross-sectional cohort of more than 1,000 very healthy Chinese individuals who spanned ages from 3 to over 100 years. The analysis of 16S rRNA gene sequencing results used a compositional data analysis paradigm coupled with measures of effect size, where ordination, differential abundance, and correlation can be explored and analyzed in a unified and reproducible framework. Our analysis showed several surprising results compared to other cohorts. First, the overall microbiota composition of the healthy aged group was similar to that of people decades younger. Second, the major differences between groups in the gut microbiota profiles were found before age 20. Third, the gut microbiota differed little between individuals from the ages of 30 to >100. Fourth, the gut microbiota of males appeared to be more variable than that of females. Taken together, the present findings suggest that the microbiota of the healthy aged in this cross-sectional study differ little from that of the healthy young in the same population, although the minor variations that do exist depend upon the comparison cohort. IMPORTANCE We report the large-scale use of compositional data analysis to establish a baseline microbiota composition in an extremely healthy cohort of the Chinese population. This baseline will serve for comparison for future cohorts with chronic or acute disease. In addition to the expected difference in the microbiota of children and adults, we found that the microbiota of the elderly in this population was similar in almost all respects to that of healthy people in the same population who are scores of years younger. We speculate that this similarity is a consequence of an active healthy lifestyle and diet, although cause and effect cannot be ascribed in this (or any other) cross-sectional design. One surprising result was that the gut microbiota of persons in their 20s was distinct from those of other age cohorts, and this result was replicated, suggesting that it is a reproducible finding and distinct from those of other populations.

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Cross-sectional and longitudinal associations between active commuting and patterns of movement behaviour during discretionary time: A compositional data analysis

PLoS ONE ◽

10.1371/journal.pone.0216650 ◽

2019 ◽

Vol 14 (8) ◽

pp. e0216650 ◽

Cited By ~ 2

Author(s):

Louise Foley ◽

Dorothea Dumuid ◽

Andrew J. Atkin ◽

Katrien Wijndaele ◽

David Ogilvie ◽

...

Keyword(s):

Data Analysis ◽

Compositional Data ◽

Compositional Data Analysis ◽

Active Commuting ◽

Movement Behaviour ◽

Cross Sectional ◽

Discretionary Time ◽

Longitudinal Associations

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How Does Time Use Differ between Individuals Who Do More versus Less Foodwork? A Compositional Data Analysis of Time Use in the United Kingdom Time Use Survey 2014–2015

Nutrients ◽

10.3390/nu12082280 ◽

2020 ◽

Vol 12 (8) ◽

pp. 2280

Author(s):

Chloe Clifford Astbury ◽

Louise Foley ◽

Tarra L. Penney ◽

Jean Adams

Keyword(s):

Data Analysis ◽

Time Use ◽

Compositional Data ◽

Unintended Consequences ◽

Food Preparation ◽

Compositional Data Analysis ◽

Personal Care ◽

Home Food ◽

Time Use Survey ◽

Home Food Preparation

Background: Increased time spent on home food preparation is associated with higher diet quality, but a lack of time is often reported as a barrier to this practice. We compared time use in individuals who do more versus less foodwork (tasks required to feed ourselves and our households, including home food preparation). Methods: Cross-sectional analysis of the UK Time Use Survey 2014–15, participants aged 16+ (N = 6143). Time use over 24 h was attributed to seven compositional parts: personal care; sleep; eating; physical activity; leisure screen time; work (paid and unpaid); and socialising and hobbies. Participants were categorised as doing no, ‘some’ (<70 min), or ‘more’ foodwork (≥70 min). We used compositional data analysis to test whether time-use composition varied between these participant groups, determine which of the parts varied between groups, and test for differences across population subgroups. Results: Participants who spent more time on foodwork spent less time on sleep, eating, and personal care and more time on work. Women who did more foodwork spent less time on personal care, socialising, and hobbies, which was not the case for men. Conclusion: Those who seek to encourage home food preparation should be aware of the associations between foodwork and other activities and design their interventions to guard against unintended consequences.

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Compositional Data Analysis in Time-Use Epidemiology

Advances in Compositional Data Analysis ◽

10.1007/978-3-030-71175-7_20 ◽

2021 ◽

pp. 383-404

Author(s):

Dorothea Dumuid ◽

Željko Pedišić ◽

Javier Palarea-Albaladejo ◽

Josep Antoni Martín-Fernández ◽

Karel Hron ◽

...

Keyword(s):

Data Analysis ◽

Time Use ◽

Compositional Data ◽

Compositional Data Analysis

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Abstract P343: Associations of Adipose Tissue Distribution With Hypertension and Diabetes in African Ancestry Men: A Novel Approach Using Compositional Data Analysis

Circulation ◽

10.1161/circ.141.suppl_1.p343 ◽

2020 ◽

Vol 141 (Suppl_1) ◽

Author(s):

Curtis Tilves ◽

Allison Kuipers ◽

Joseph Zmuda ◽

J. J Carr ◽

James G Terry ◽

...

Keyword(s):

Adipose Tissue ◽

Data Analysis ◽

Compositional Data ◽

African Ancestry ◽

Disease Status ◽

The Other ◽

Compositional Data Analysis ◽

Cross Sectional ◽

Novel Approach ◽

African Caribbean

Background: Adipose tissue (AT) distribution, which differs by race/ethnicity, can differentially impact risk for cardiometabolic disease. Traditional analyses using AT volumes tend to place all ATs in a regression model, estimating an increase in one AT volume while “holding other volumes constant”; however, the compositional nature of the data suggests the other ATs cannot remain constant. Compositional data analysis (CODA) methods eliminate this concern by comparing logratios of components. CODA was designed for analyses of data like those of body composition , but has yet to be used in this field. We demonstrate the use of CODA for the comparison of visceral AT (VAT) and subcutaneous AT of the abdomen (ASAT) and thigh (TSAT) with prevalence of cardiometabolic diseases. Methods: This cross-sectional analysis used 615 African Caribbean men from Tobago aged 50-91 years (mean age 63.6 years, mean BMI 27.7 kg/m 2 ). AT volumes were obtained from CT scans of the abdomen (VAT and ASAT) and of the mid-thigh (TSAT). An additive log2 ratio transformation was applied to the ATs to generate the VAT, ASAT, and TSAT component effects relative to the other tissues captured in the respective CT scan. Ordinal logistic regression analyses were performed for increasing hypertension or glucose impairment categories (defined in Table) after adjustment for age, disease status, and lifestyle factors. Results: After full adjustment (Table), a 2-fold higher ASAT volume (relative to other abdominal tissues) was associated with higher odds, and a 2-fold higher TSAT volume (relative to other thigh tissues) was associated with lower odds, of being in a higher hypertension category. After BMI adjustment, no AT was associated with glucose impairment category. VAT was not a significant predictor for either outcome in African Ancestry men. Conclusion: CODA methods, which avoid problematic assumptions made in traditional analyses, found significant and opposing associations of TSAT and ASAT with hypertension categories in African Ancestry men.

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Zero problems with compositional data of physical behaviors: a comparison of three zero replacement methods

International Journal of Behavioral Nutrition and Physical Activity ◽

10.1186/s12966-020-01029-z ◽

2020 ◽

Vol 17 (1) ◽

Author(s):

Charlotte Lund Rasmussen ◽

Javier Palarea-Albaladejo ◽

Melker Staffan Johansson ◽

Patrick Crowley ◽

Matthew Leigh Stevens ◽

...

Keyword(s):

Data Analysis ◽

Time Use ◽

Compositional Data ◽

Real Data ◽

Compositional Data Analysis ◽

Accelerometer Data ◽

Relative Variation ◽

Physical Behavior ◽

Complete Dataset ◽

Log Ratio

Abstract Background Researchers applying compositional data analysis to time-use data (e.g., time spent in physical behaviors) often face the problem of zeros, that is, recordings of zero time spent in any of the studied behaviors. Zeros hinder the application of compositional data analysis because the analysis is based on log-ratios. One way to overcome this challenge is to replace the zeros with sensible small values. The aim of this study was to compare the performance of three existing replacement methods used within physical behavior time-use epidemiology: simple replacement, multiplicative replacement, and log-ratio expectation-maximization (lrEM) algorithm. Moreover, we assessed the consequence of choosing replacement values higher than the lowest observed value for a given behavior. Method Using a complete dataset based on accelerometer data from 1310 Danish adults as reference, multiple datasets were simulated across six scenarios of zeros (5–30% zeros in 5% increments). Moreover, four examples were produced based on real data, in which, 10 and 20% zeros were imposed and replaced using a replacement value of 0.5 min, 65% of the observation threshold, or an estimated value below the observation threshold. For the simulation study and the examples, the zeros were replaced using the three replacement methods and the degree of distortion introduced was assessed by comparison with the complete dataset. Results The lrEM method outperformed the other replacement methods as it had the smallest influence on the structure of relative variation of the datasets. Both the simple and multiplicative replacements introduced higher distortion, particularly in scenarios with more than 10% zeros; although the latter, like the lrEM, does preserve the ratios between behaviors with no zeros. The examples revealed that replacing zeros with a value higher than the observation threshold severely affected the structure of relative variation. Conclusions Given our findings, we encourage the use of replacement methods that preserve the relative structure of physical behavior data, as achieved by the multiplicative and lrEM replacements, and to avoid simple replacement. Moreover, we do not recommend replacing zeros with values higher than the lowest observed value for a behavior.

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Compositional Data Analysis in Time-Use Epidemiology: What, Why, How

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17072220 ◽

2020 ◽

Vol 17 (7) ◽

pp. 2220 ◽

Cited By ~ 9

Author(s):

Dorothea Dumuid ◽

Željko Pedišić ◽

Javier Palarea-Albaladejo ◽

Josep Antoni Martín-Fernández ◽

Karel Hron ◽

...

Keyword(s):

Physical Activity ◽

Data Analysis ◽

Sedentary Behavior ◽

Time Use ◽

Compositional Data ◽

Compositional Data Analysis ◽

Health It ◽

Future Directions ◽

The Relationship ◽

Daily Time

In recent years, the focus of activity behavior research has shifted away from univariate paradigms (e.g., physical activity, sedentary behavior and sleep) to a 24-h time-use paradigm that integrates all daily activity behaviors. Behaviors are analyzed relative to each other, rather than as individual entities. Compositional data analysis (CoDA) is increasingly used for the analysis of time-use data because it is intended for data that convey relative information. While CoDA has brought new understanding of how time use is associated with health, it has also raised challenges in how this methodology is applied, and how the findings are interpreted. In this paper we provide a brief overview of CoDA for time-use data, summarize current CoDA research in time-use epidemiology and discuss challenges and future directions. We use 24-h time-use diary data from Wave 6 of the Longitudinal Study of Australian Children (birth cohort, n = 3228, aged 10.9 ± 0.3 years) to demonstrate descriptive analyses of time-use compositions and how to explore the relationship between daily time use (sleep, sedentary behavior and physical activity) and a health outcome (in this example, adiposity). We illustrate how to comprehensively interpret the CoDA findings in a meaningful way.

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