Evidence map›Paper›PMID 32607544›Full record

ArticleAnnals of work exposures and health2020

Time-Based Data in Occupational Studies: The Whys, the Hows, and Some Remaining Challenges in Compositional Data Analysis (CoDA).

Nidhi Gupta, Charlotte Lund Rasmussen, Andreas Holtermann, Svend Erik Mathiassen

Abstract read
In one paragraph

Article in Annals of work exposures and health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed
–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

40 citing papers in PubMed.

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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

4 authors.

Nidhi GuptaNational Research Centre for the Working Environment, Department of Musculoskeletal Disorders and Physical Work Demands, Copenhagen Ø, Denmark.
Charlotte Lund RasmussenNational Research Centre for the Working Environment, Department of Musculoskeletal Disorders and Physical Work Demands, Copenhagen Ø, Denmark.
Andreas HoltermannNational Research Centre for the Working Environment, Department of Musculoskeletal Disorders and Physical Work Demands, Copenhagen Ø, Denmark.
Svend Erik MathiassenCentre for Musculoskeletal Research, Department of Occupational Health Sciences and Psychology, University of Gävle, Gävle, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Data on the use of time in different exposures, behaviors, and work tasks are common in occupational research. Such data are most often expressed in hours, minutes, or percentage of work time. Thus, they are constrained or 'compositional', in that they add up to a finite sum (e.g. 8 h of work or 100% work time). Due to their properties, compositional data need to be processed and analyzed using specifically adapted methods. Compositional data analysis (CoDA) has become a particularly established framework to handle such data in various scientific fields such as nutritional epidemiology, geology, and chemistry, but has only recently gained attention in public and occupational health sciences. In this paper, we introduce the reader to CoDA by explaining why CoDA should be used when dealing with compositional time-use data, showing how to perform CoDA, including a worked example, and pointing at some remaining challenges in CoDA. The paper concludes by emphasizing that CoDA in occupational research is still in its infancy, and stresses the need for further development and experience in the use of CoDA for time-based occupational exposures. We hope that the paper will encourage researchers to adopt and apply CoDA in studies of work exposures and health.

Indexed as

Data AnalysisHumansOccupational ExposureOccupational Healthconstrained dataergonomicsisometric log-ratiowork environment

Identifiers

PMID32607544
PMCPMC7544002

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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.