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Combining diaries and accelerometers to explain change in physical activity during a lifestyle intervention for adults with pre-diabetes: A PREVIEW sub-study

LEON KLOS, Gareth Stratton Orcid Logo, Kelly Mackintosh Orcid Logo, Melitta McNarry Orcid Logo, Mikael Fogelholm, Mathijs Drummen, Ian Macdonald, J. Alfredo Martinez Orcid Logo, Santiago Navas-Carretero, Teodora Handjieva-Darlenska, Georgi Bogdanov, Nicholas Gant Orcid Logo, Sally D. Poppitt, Marta P. Silvestre, Jennie Brand-Miller, Roslyn Muirhead Orcid Logo, Wolfgang Schlicht, Maija Huttunen-Lenz Orcid Logo, Shannon Brodie, Elli Jalo, Margriet Westerterp-Plantenga, Tanja Adam, Pia Siig Vestentoft, Heikki Tikkanen, Jonas S. Quist, Anne Raben, Nils Joseph Swindell Orcid Logo

PLOS ONE, Volume: 19, Issue: 3, Start page: e0300646

Swansea University Authors: LEON KLOS, Gareth Stratton Orcid Logo, Kelly Mackintosh Orcid Logo, Melitta McNarry Orcid Logo, Nils Joseph Swindell Orcid Logo

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Abstract

Self-report and device-based measures of physical activity (PA) both have unique strengths and limitations; combining these measures should provide complementary and comprehensive insights to PA behaviours. Therefore, we aim to 1) identify PA clusters and clusters of change in PA based on self-repor...

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Published in: PLOS ONE
ISSN: 1932-6203
Published: Public Library of Science (PLoS) 2024
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa65835
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Abstract: Self-report and device-based measures of physical activity (PA) both have unique strengths and limitations; combining these measures should provide complementary and comprehensive insights to PA behaviours. Therefore, we aim to 1) identify PA clusters and clusters of change in PA based on self-reported daily activities and 2) assess differences in device-based PA between clusters in a lifestyle intervention, the PREVIEW diabetes prevention study. In total, 232 participants with overweight and prediabetes (147 women; 55.9 ± 9.5yrs; BMI ≥25 kg·m-2; impaired fasting glucose and/or impaired glucose tolerance) were clustered using a partitioning around medoids algorithm based on self-reported daily activities before a lifestyle intervention and their changes after 6 and 12 months. Device-assessed PA levels (PAL), sedentary time (SED), light PA (LPA), and moderate-to-vigorous PA (MVPA) were assessed using ActiSleep+ accelerometers and compared between clusters using (multivariate) analyses of covariance. At baseline, the self-reported “walking and housework” cluster had significantly higher PAL, MVPA and LPA, and less SED than the “inactive” cluster. LPA was higher only among the “cycling” cluster. There was no difference in the device-based measures between the “social-sports” and “inactive” clusters. Looking at the changes after 6 months, the “increased walking” cluster showed the greatest increase in PAL while the “increased cycling” cluster accumulated the highest amount of LPA. The “increased housework” and “increased supervised sports” reported least favourable changes in device-based PA. After 12 months, there was only minor change in activities between the “increased walking and cycling”, “no change” and “increased supervised sports” clusters, with no significant differences in device-based measures. Combining self-report and device-based measures provides better insights into the behaviours that change during an intervention. Walking and cycling may be suitable activities to increase PA in adults with prediabetes.
College: Faculty of Science and Engineering
Funders: The PREVIEW study received grants from the EU 7th Framework Programme (FP7-KBBE2012), grant no: 312057; the New Zealand Health Research Council, grant no. 14/191; and the NHMRC-EU Collaborative Grant, Australia.This work was supported by a fellowship of the German Academic Exchange Service (DAAD; recipient: Leon Klos). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Issue: 3
Start Page: e0300646