Evidence map›Paper›PMID 33737239›Full record

ArticleJournal of sport and health science2022

Using compositional data analysis to explore accumulation of sedentary behavior, physical activity and youth health.

Simone J J M Verswijveren, Karen E Lamb, Josep A Martín-Fernández, Elisabeth Winkler, Rebecca M Leech, Anna Timperio, Jo Salmon, Robin M Daly, Ester Cerin, David W Dunstan and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of sport and health science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 2 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

23 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  11. Twenty-Four-Hour Compositional Data Analysis in Healthcare: Clinical Potential and Future Directions.International journal of environmental research and public health · 2025
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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

14 authors.

Simone J J M VerswijverenInstitute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC 3216, Australia. Electronic address: s.verswijveren@deakin.edu.au.
Karen E LambMelbourne School of Population and Global Health, University of Melbourne, Parkville, VIC 3053, Australia.
Josep A Martín-FernándezDepartment of Computer Science, Applied Mathematics and Statistics, University of Girona, Girona 17003, Spain.
Elisabeth WinklerSchool of Public Health, University of Queensland, Herston, QLD 4006, Australia.
Rebecca M LeechInstitute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC 3216, Australia.
Anna TimperioInstitute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC 3216, Australia.
Jo SalmonInstitute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC 3216, Australia.
Robin M DalyInstitute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC 3216, Australia.
Ester CerinMary MacKillop Institute for Health Research, Australian Catholic University, Fitzroy, VIC 3065, Australia; School of Public Health, University of Hong Kong, Hong Kong, China.
David W DunstanMary MacKillop Institute for Health Research, Australian Catholic University, Fitzroy, VIC 3065, Australia; Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia.
Rohan M TelfordResearch Institute for Sport and Exercise, University of Canberra, Bruce, ACT 2617, Australia.
Richard D TelfordResearch Institute for Sport and Exercise, University of Canberra, Bruce, ACT 2617, Australia.
Lisa S OliveSchool of Psychology, Deakin University, Burwood, VIC 3125, Australia; IMPACT Research Institute, Deakin University, Burwood, VIC 3125, Australia; ANU Medical School, Australian National University, Garran, ACT 2605, Australia.
Nicola D RidgersInstitute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC 3216, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe study aimed to describe youth time-use compositions, focusing on time spent in shorter and longer bouts of sedentary behavior and physical activity (PA), and to examine associations of these time-use compositions with cardiometabolic biomarkers.

methodsAccelerometer and cardiometabolic biomarker data from 2 Australian studies involving youths 7-13 years old were pooled (complete cases with accelerometry and adiposity marker data, n = 782). A 9-component time-use composition was formed using compositional data analysis: time in shorter and longer bouts of sedentary behavior; time in shorter and longer bouts of light-, moderate-, or vigorous-intensity PA; and "other time" (i.e., non-wear/sleep). Shorter and longer bouts of sedentary time were defined as <5 min and ≥5 min, respectively. Shorter bouts of light-, moderate-, and vigorous-intensity PA were defined as <1 min; longer bouts were defined as ≥1 min. Regression models examined associations between overall time-use composition and cardiometabolic biomarkers. Then, associations were derived between ratios of longer activity patterns relative to shorter activity patterns, and of each intensity level relative to the other intensity levels and "other time", and cardiometabolic biomarkers.

resultsConfounder-adjusted models showed that the overall time-use composition was associated with adiposity, blood pressure, lipids, and the summary score. Specifically, more time in longer bouts of light-intensity PA relative to shorter bouts of light-intensity PA was significantly associated with greater body mass index z-score (zBMI) (β = 1.79; SE = 0.68) and waist circumference (β = 18.35, SE = 4.78). When each activity intensity was considered relative to all higher intensities and "other time", more time in light- and vigorous-intensity PA, and less time in sedentary behavior and moderate-intensity PA, were associated with lower waist circumference.

conclusionAccumulating PA, particularly light-intensity PA, in frequent short bursts may be more beneficial for limiting adiposity compared to accumulating the same amount of PA at these intensities in longer bouts.

Indexed as

Data AnalysisSedentary BehaviorAdolescentAustraliaChildCross-Sectional StudiesExerciseHumansAccumulation patternsCardiometabolic healthChildrenCompositional data analysisTime-use

Identifiers

PMID33737239
PMCPMC9068553

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.