TY - JOUR
T1 - Combining diaries and accelerometers to explain change in physical activity during a lifestyle intervention for adults with prediabetes
T2 - A PREVIEW sub-study
AU - Klos, Leon
AU - Stratton, Gareth
AU - Mackintosh, Kelly A.
AU - McNarry, Melitta A.
AU - Fogelholm, Mikael
AU - Drummen, Mathijs
AU - Macdonald, Ian
AU - Martinez, J. Alfredo
AU - Navas-Carretero, Santiago
AU - Handjieva-Darlenska, Teodora
AU - Bogdanov, Georgi
AU - Gant, Nicholas
AU - Poppitt, Sally D.
AU - Silvestre, Marta P.
AU - Brand-Miller, Jennie
AU - Muirhead, Roslyn
AU - Schlicht, Wolfgang
AU - Huttunen-Lenz, Maija
AU - Brodie, Shannon
AU - Jalo, Elli
AU - Westerterp-Plantenga, Margriet
AU - Adam, Tanja
AU - Vestentoft, Pia Siig
AU - Tikkanen, Heikki
AU - Quist, Jonas S.
AU - Raben, Anne
AU - Swindell, Nils
N1 - Publisher Copyright:
© 2024 Klos et.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
U2 - 10.1371/journal.pone.0300646
DO - 10.1371/journal.pone.0300646
M3 - Journal article
C2 - 38512828
AN - SCOPUS:85188292660
VL - 19
JO - PLoS ONE
JF - PLoS ONE
SN - 1932-6203
IS - 3
M1 - e0300646
ER -