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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
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Kelly Mackintosh ,
Melitta McNarry ,
Mikael Fogelholm,
Mathijs Drummen,
Ian Macdonald,
J. Alfredo Martinez ,
Santiago Navas-Carretero,
Teodora Handjieva-Darlenska,
Georgi Bogdanov,
Nicholas Gant ,
Sally D. Poppitt,
Marta P. Silvestre,
Jennie Brand-Miller,
Roslyn Muirhead ,
Wolfgang Schlicht,
Maija Huttunen-Lenz ,
Shannon Brodie,
Elli Jalo,
Margriet Westerterp-Plantenga,
Tanja Adam,
Pia Siig Vestentoft,
Heikki Tikkanen,
Jonas S. Quist,
Anne Raben,
Nils Joseph Swindell
PLOS ONE, Volume: 19, Issue: 3, Start page: e0300646
Swansea University Authors:
LEON KLOS, Gareth Stratton , Kelly Mackintosh , Melitta McNarry , Nils Joseph Swindell
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DOI (Published version): 10.1371/journal.pone.0300646
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...
| Published in: | PLOS ONE |
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| ISSN: | 1932-6203 |
| Published: |
Public Library of Science (PLoS)
2024
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| Online Access: |
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| 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. |
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| 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 |