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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 
,
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
,
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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© 2024 Klos et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.
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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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| URI: | https://cronfa.swan.ac.uk/Record/cronfa65835 |
| first_indexed |
2024-03-13T11:59:47Z |
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2024-11-25T14:16:58Z |
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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
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2024-04-18T20:44:02.0490910 v2 65835 2024-03-13 Combining diaries and accelerometers to explain change in physical activity during a lifestyle intervention for adults with pre-diabetes: A PREVIEW sub-study 8b65bdcbf521c04fba915b3c6bbcb3d9 LEON KLOS LEON KLOS true false 6d62b2ed126961bed81a94a2beba8a01 0000-0001-5618-0803 Gareth Stratton Gareth Stratton true false bdb20e3f31bcccf95c7bc116070c4214 0000-0003-0355-6357 Kelly Mackintosh Kelly Mackintosh true false 062f5697ff59f004bc8c713955988398 0000-0003-0813-7477 Melitta McNarry Melitta McNarry true false 189d1ae79723a932dc37ae54fff6e4cd 0000-0003-3742-6139 Nils Joseph Swindell Nils Joseph Swindell true true 2024-03-13 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. Journal Article PLOS ONE 19 3 e0300646 Public Library of Science (PLoS) 1932-6203 21 3 2024 2024-03-21 10.1371/journal.pone.0300646 COLLEGE NANME COLLEGE CODE Swansea University SU Library paid the OA fee (TA Institutional Deal) 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. 2024-04-18T20:44:02.0490910 2024-03-13T11:53:31.2407810 Faculty of Science and Engineering School of Engineering and Applied Sciences - Sport and Exercise Sciences LEON KLOS 1 Gareth Stratton 0000-0001-5618-0803 2 Kelly Mackintosh 0000-0003-0355-6357 3 Melitta McNarry 0000-0003-0813-7477 4 Mikael Fogelholm 5 Mathijs Drummen 6 Ian Macdonald 7 J. Alfredo Martinez 0000-0001-5218-6941 8 Santiago Navas-Carretero 9 Teodora Handjieva-Darlenska 10 Georgi Bogdanov 11 Nicholas Gant 0000-0001-9740-0163 12 Sally D. Poppitt 13 Marta P. Silvestre 14 Jennie Brand-Miller 15 Roslyn Muirhead 0000-0002-4374-0362 16 Wolfgang Schlicht 17 Maija Huttunen-Lenz 0000-0002-1034-1613 18 Shannon Brodie 19 Elli Jalo 20 Margriet Westerterp-Plantenga 21 Tanja Adam 22 Pia Siig Vestentoft 23 Heikki Tikkanen 24 Jonas S. Quist 25 Anne Raben 26 Nils Joseph Swindell 0000-0003-3742-6139 27 65835__30062__110bbb287aaf4fb78883de7df87ce762.pdf 65835.VoR.pdf 2024-04-18T15:36:29.8915230 Output 1719142 application/pdf Version of Record true © 2024 Klos et al. This is an open access article distributed under the terms of the Creative Commons Attribution License. true eng http://creativecommons.org/licenses/by/4.0/ |
| title |
Combining diaries and accelerometers to explain change in physical activity during a lifestyle intervention for adults with pre-diabetes: A PREVIEW sub-study |
| spellingShingle |
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 Kelly Mackintosh Melitta McNarry Nils Joseph Swindell |
| title_short |
Combining diaries and accelerometers to explain change in physical activity during a lifestyle intervention for adults with pre-diabetes: A PREVIEW sub-study |
| title_full |
Combining diaries and accelerometers to explain change in physical activity during a lifestyle intervention for adults with pre-diabetes: A PREVIEW sub-study |
| title_fullStr |
Combining diaries and accelerometers to explain change in physical activity during a lifestyle intervention for adults with pre-diabetes: A PREVIEW sub-study |
| title_full_unstemmed |
Combining diaries and accelerometers to explain change in physical activity during a lifestyle intervention for adults with pre-diabetes: A PREVIEW sub-study |
| title_sort |
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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8b65bdcbf521c04fba915b3c6bbcb3d9 6d62b2ed126961bed81a94a2beba8a01 bdb20e3f31bcccf95c7bc116070c4214 062f5697ff59f004bc8c713955988398 189d1ae79723a932dc37ae54fff6e4cd |
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8b65bdcbf521c04fba915b3c6bbcb3d9_***_LEON KLOS 6d62b2ed126961bed81a94a2beba8a01_***_Gareth Stratton bdb20e3f31bcccf95c7bc116070c4214_***_Kelly Mackintosh 062f5697ff59f004bc8c713955988398_***_Melitta McNarry 189d1ae79723a932dc37ae54fff6e4cd_***_Nils Joseph Swindell |
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LEON KLOS Gareth Stratton Kelly Mackintosh Melitta McNarry Nils Joseph Swindell |
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LEON KLOS Gareth Stratton 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 |
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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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2024-03-21T08:23:04Z |
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