Evidence map›Paper›PMID 29108029›Full record

ArticlePloS one2017

Accelerometric estimates of physical activity vary unstably with data handling.

Maia P Smith, Marie Standl, Joachim Heinrich, Holger Schulz

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In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Maia P SmithInstitute of Epidemiology 1, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.ORCID http://orcid.org/0000-0002-1154-1158
Marie StandlInstitute of Epidemiology 1, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.
Joachim HeinrichInstitute of Epidemiology 1, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.
Holger SchulzInstitute of Epidemiology 1, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBecause of unreliable self-report, accelerometry is increasingly used to objectively monitor physical activity (PA). However, results of accelerometric studies vary depending on the chosen cutpoints between activity intensities. Population-specific activity patterns likely affect the size of these differences. To establish their size and stability we apply three sets of cutpoints, including two calibrated to a single reference, to our accelerometric data and compare PA estimates.

methods1402 German adolescents from the GINIplus and LISAplus cohorts wore triaxial accelerometers (Actigraph GT3x) for one week (mean 6.23 days, 14.7 hours per day) at the hip. After validation of wear, we applied three sets of cutpoints for youth, including the most common standard (Freedson, 2005) and two calibrated to a single reference, (Romanzini uni- and triaxial, from Romanzini, 2014) to these data, estimating daily sedentary, light, moderate, vigorous and moderate-to-vigorous PA (MPA, VPA, MVPA). Stability of differences was assessed by comparing Romanzini's two sets of cutpoints.

resultsRelative agreement between cutpoints was closer for activity of lower intensities (largest difference for sedentary behaviour 9%) but increased for higher intensities (largest difference for light activity 40%, MPA 102%, VPA 88%; all p<0.01). Romanzini's uniaxial and triaxial cutpoints agreed no more closely with each other than with Freedson's.

conclusionsEstimated PA differed significantly between different sets of cutpoints, even when those cutpoints agreed perfectly on another dataset (i.e. Romanzini's.) This suggests that the detected differences in estimated PA depend on population-specific activity patterns, which cannot be easily corrected for: converting activity estimates from one set of cutpoints to another may require access to raw data. This limits the utility of accelerometry for comparing populations in place and time. We suggest that accelerometric research adopt a standard for data processing, and apply and present the results of this standard in addition to those from any other method.

Indexed as

ExerciseAccelerometryAdolescentCohort StudiesFemaleHumansMale

Identifiers

PMID29108029
PMCPMC5673210

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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.