Evidence map›Paper›PMID 30871114›Full record

ArticleInternational journal of environmental research and public health2019

Visualising Combined Time Use Patterns of Children's Activities and Their Association with Weight Status and Neighbourhood Context.

Jinfeng Zhao, Lisa Mackay, Kevin Chang, Suzanne Mavoa, Tom Stewart, Erika Ikeda, Niamh Donnellan, Melody Smith

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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

  1. Timing of physical activity within the 24-hour day and its influence on health: a systematic review.Health promotion and chronic disease prevention in Canada : research, policy and practice · 2022
    Pooled it
  2. Pooled it
  3. Article
  4. Article
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

8 authors.

Jinfeng ZhaoSchool of Nursing, The University of Auckland, Auckland 1023, New Zealand. jinfeng.zhao@auckland.ac.nz.ORCID 0000-0002-8458-8379
Lisa MackaySchool of Sport and Recreation, Auckland University of Technology, Auckland 0627, New Zealand. lisa.mackay@aut.ac.nz.ORCID 0000-0002-7344-5794
Kevin ChangDepartment of Statistics, The University of Auckland, Auckland 1010, New Zealand. k.chang@auckland.ac.nz.
Suzanne MavoaMelbourne School of Population and Global Health, The University of Melbourne, Melbourne 3010, Australia. suzanne.mavoa@unimelb.edu.au.ORCID 0000-0002-6071-2988
Tom StewartSchool of Sport and Recreation, Auckland University of Technology, Auckland 0627, New Zealand. tom.stewart@aut.ac.nz.ORCID 0000-0001-5915-3843
Erika IkedaSchool of Sport and Recreation, Auckland University of Technology, Auckland 0627, New Zealand. erika.ikeda@aut.ac.nz.ORCID 0000-0001-6999-3918
Niamh DonnellanSchool of Nursing, The University of Auckland, Auckland 1023, New Zealand. n.donnellan@auckland.ac.nz.ORCID 0000-0002-9411-7642
Melody SmithSchool of Nursing, The University of Auckland, Auckland 1023, New Zealand. melody.smith@auckland.ac.nz.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Compositional data techniques are an emerging method in physical activity research. These techniques account for the complexities of, and interrelationships between, behaviours that occur throughout a day (e.g., physical activity, sitting, and sleep). The field of health geography research is also developing rapidly. Novel spatial techniques and data visualisation approaches are increasingly being recognised for their utility in understanding health from a socio-ecological perspective. Linking compositional data approaches with geospatial datasets can yield insights into the role of environments in promoting or hindering the health implications of the daily time-use composition of behaviours. The 7-day behaviour data used in this study were derived from accelerometer data for 882 Auckland school children and linked to weight status and neighbourhood deprivation. We developed novel geospatial visualisation techniques to explore activity composition over a day and generated new insights into links between environments and child health behaviours and outcomes. Visualisation strategies that integrate compositional activities, time of day, weight status, and neighbourhood deprivation information were devised. They include a ringmap overview, small-multiple ringmaps, and individual and aggregated time⁻activity diagrams. Simultaneous visualisation of geospatial and compositional behaviour data can be useful for triangulating data from diverse disciplines, making sense of complex issues, and for effective knowledge translation.

Indexed as

Environment DesignExerciseResidence CharacteristicsAdolescentBody WeightChildFemaleHumansMaleNew ZealandSedentary BehaviorSleepaccelerometer datacompositional analysisneighbourhood contextphysical activityschool childrensedentary behavioursleeptime usevisualisationweight status

Identifiers

PMID30871114
PMCPMC6427195

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.