Evidence map›Paper›PMID 32224966›Full record

ArticleInternational journal of environmental research and public health2020

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

Dorothea Dumuid, Željko Pedišić, Javier Palarea-Albaladejo, Josep Antoni Martín-Fernández, Karel Hron, Timothy Olds

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Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 182 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
182citing papers in PubMed, 6 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

182 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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  5. Exploring activity compensation amongst youth and adults: a systematic review.The international journal of behavioral nutrition and physical activity · 2022
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122 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Dorothea DumuidAlliance for Research in Exercise, Nutrition and Activity (ARENA), University of South Australia, Adelaide 5001, Australia.
Željko PedišićInstitute for Health and Sport, Victoria University, Melbourne 3000, Australia.
Javier Palarea-AlbaladejoBiomathematics and Statistics Scotland, EH9 3FD Edinburgh, Scotland, UK.ORCID 0000-0003-0162-669X
Josep Antoni Martín-FernándezDepartment of Computer Science, Applied Mathematics and Statistics, University of Girona, 17003 Girona, Spain.ORCID 0000-0003-2366-1592
Karel HronDepartment of Mathematical Analysis and Applications of Mathematics, Palacký University, 77146 Olomouc, Czech Republic.ORCID 0000-0002-1847-6598
Timothy OldsAlliance for Research in Exercise, Nutrition and Activity (ARENA), University of South Australia, Adelaide 5001, Australia.ORCID 0000-0001-6894-5519

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Data AnalysisExerciseSedentary BehaviorActivities of Daily LivingAdiposityAustraliaChildCohort StudiesHumansLongitudinal StudiesSleepcompositional dataphysical activitysedentary behaviorsleep

Identifiers

PMID32224966
PMCPMC7177981

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.