Cited by 415Physical Activity and HealthContext-Aware Activity Recognition SystemsCardiovascular and exercise physiology

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A systematic literature review of reviews on techniques for physical activity measurement in adults: a DEDIPAC study

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Dowd, Kieran P., Szeklicki, Robert, Minetto, Marco Alessandro, Murphy, Marie H., Polito, Angela, Ghigo, Ezio, van der Ploeg, Hidde, Ekelund, Ulf, Maciaszek, Janusz, Stemplewski, Rafal, Tomczak, Maciej, Donnelly, Alan E.·2018·International Journal of Behavioral Nutrition and Physical Activity

DOI 10.1186/s12966-017-0636-2PMC PMC5806271

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Abstract

The links between increased participation in Physical Activity (PA) and improvements in health are well established. As this body of evidence has grown, so too has the search for measures of PA with high levels of methodological effectiveness (i.e. validity, reliability and responsiveness to change). The aim of this "review of reviews" was to provide a comprehensive overview of the methodological effectiveness of currently employed measures of PA, to aid researchers in their selection of an appropriate tool. A total of 63 review articles were included in this review, and the original articles cited by these reviews were included in order to extract detailed information on methodological effectiveness.Self-report measures of PA have been most frequently examined for methodological effectiveness, with highly variable findings identified across a broad range of behaviours. The evidence-base for the methodological effectiveness of objective monitors, particularly accelerometers/activity monitors, is increasing, with lower levels of variability observed for validity and reliability when compared to subjective measures. Unfortunately, responsiveness to change across all measures and behaviours remains under-researched, with limited information available.Other criteria beyond methodological effectiveness often influence tool selection, including cost and feasibility. However, researchers must be aware of the methodological effectiveness of any measure selected for use when examining PA. Although no "perfect" tool for the examination of PA in adults exists, it is suggested that researchers aim to incorporate appropriate objective measures, specific to the behaviours of interests, when examining PA in free-living environments.

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Fig. 1 PRISMA flow diagram for search and inclusion process for identification of review articlesCC BY · PubMed Central

Fig. 2 Forest plot of percentage mean difference between self-reported energy expenditure (TEE, PAEE, PAL) compared to criterion measure of energy expenditure (doubly labelled water)CC BY · PubMed Central

Fig. 3 a Forest plot of percentage mean difference between accelerometer determined energy expenditure (TEE, PAEE, PAL) compared to criterion measure of energy expenditure (doubly labelled water). b Forest plot of percentage mean difference between accelerometer determined steps, distance walked and activity type compared to criterion measure of direct observationCC BY · PubMed Central

Fig. 4 Forest plot of mean percentage difference between pedometer determined step count/energy expenditure compared to criterion measure (direct observation/doubly labelled water respectively). * denotes multiple devices compared in the same studyCC BY · PubMed Central

Fig. 5 Forest plot of percentage mean difference between heart rate monitor determined energy expenditure/physical activity level compared to criterion measure (doubly labelled water)CC BY · PubMed Central

Fig. 6 Forest plot of percentage mean difference between energy expenditure/body posture determined by combined sensors compared to criterion measure (doubly labelled water/direct observation)CC BY · PubMed Central

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Authors: Dowd, K. P., Szeklicki, R., Minetto, M. A., Murphy, M. H., Polito, A., Ghigo, E., van der Ploeg, H., Ekelund, U., Maciaszek, J., Stemplewski, R., Tomczak, M., & Donnelly, A. E.
Affiliations: Athlone Institute of Technology (IE)
University of Turin (IT)
University of Ulster (GB)
Istituto Nazionale di Ricerca per gli Alimenti e la Nutrizione (IT)
University of Cambridge (GB)
University of Limerick (IE)
Corresponding author: Donnelly, Alan E.
ORCID: Dowd, Kieran P.
Szeklicki, Robert
Minetto, Marco Alessandro
Murphy, Marie H.
Polito, Angela
Ghigo, Ezio
Ekelund, Ulf
Maciaszek, Janusz
Tomczak, Maciej
Donnelly, Alan E.
Venue: International Journal of Behavioral Nutrition and Physical Activity
Publisher: Springer Science and Business Media LLC
Published: 2018-02-08
Volume: 15
Issue: 1
Pages: 15-15
ISSN: 1479-5868 (ISSN-L)
DOI: 10.1186/s12966-017-0636-2
PMC: PMC5806271
OpenAlex: W2787504438
Cited by: 385
References: 490
Citations by year: 2026: 33 · 2025: 31 · 2024: 42 · 2023: 49 · 2022: 63 · 2021: 70 · 2020: 45 · 2019: 40 · 2018: 10
FWCI: 5.40
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
Locations: International Journal of Behavioral Nutrition and Physical Activity (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Pure Amsterdam UMC (publishedVersion, cc-by)
Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion, other-oa)
Ulster University Research Portal (Ulster University) (publishedVersion, other-oa)
Arrow@dit (Dublin Institute of Technology) (acceptedVersion)
Institutional Research Information System University of Turin (University of Turin) (submittedVersion, other-oa)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Figshare (submittedVersion, cc-by-nc-sa)
Research@THEA (submittedVersion, cc-by)
University of Limerick Institutional Repository (University of Limerick) (submittedVersion, cc-by-nc-sa)
Apollo (University of Cambridge) (submittedVersion, cc-by)
Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
Apollo
Apollo (cc-by)
Funders: Health Research Board
Topics: Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Physical Activity and Health
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
Health Sciences › Medicine › Physiology (medical) › Cardiovascular and exercise physiology
Keywords: physical activity measurement, validity, reliability statistics, responsiveness to change, self-report questionnaire, accelerometer, wearable activity trackers, adults, free-living physical activity, tool selection
MeSH terms: Adult, Health Surveys, Humans, Male, Reproducibility of Results, Exercise, Self Report, Accelerometry

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A systematic literature review of reviews on techniques for physical activity measurement in adults: a DEDIPAC study

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