Applied Sports, Technology, Exercise and Medicine (A-STEM) Research Centre, Faculty of Science and Engineering, Swansea University, Swansea, Wales, United Kingdom.
Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Karlsruhe, Germany.
PLoS One. 2024 Mar 21;19(3):e0300646. doi: 10.1371/journal.pone.0300646. eCollection 2024.
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.
自我报告和基于设备的身体活动 (PA) 测量都有其独特的优势和局限性;将这些测量方法结合起来应该可以提供对 PA 行为的互补和全面的了解。因此,我们的目的是:1) 根据自我报告的日常活动,确定基于自我报告的日常活动的 PA 聚类和 PA 变化聚类;2) 在生活方式干预(PREVIEW 糖尿病预防研究)中,评估基于设备的 PA 在不同聚类之间的差异。共有 232 名超重和糖尿病前期患者(147 名女性;55.9±9.5 岁;BMI≥25kg·m-2;空腹血糖受损和/或葡萄糖耐量受损)参与了这项研究,他们根据生活方式干预前的自我报告日常活动以及干预后 6 个月和 12 个月的活动变化,使用基于中位数的分区算法进行聚类。使用 ActiSleep+加速度计评估设备评估的 PA 水平 (PAL)、久坐时间 (SED)、低强度 PA (LPA) 和中高强度 PA (MVPA),并使用协方差分析 (多变量) 比较聚类之间的差异。在基线时,自我报告的“步行和家务”聚类的 PAL、MVPA 和 LPA 显著较高,SED 显著较低,而“不活跃”聚类则较低。仅在“骑自行车”聚类中 LPA 较高。在“社交运动”和“不活跃”聚类之间,设备测量值没有差异。观察 6 个月后的变化,“增加步行”聚类的 PAL 增加最大,而“增加骑自行车”聚类的 LPA 积累量最高。“增加家务”和“增加监督运动”报告的基于设备的 PA 变化最不理想。12 个月后,“增加步行和骑自行车”、“无变化”和“增加监督运动”聚类之间的活动仅有微小变化,基于设备的措施也没有显著差异。将自我报告和基于设备的测量方法相结合,可以更好地了解干预过程中发生变化的行为。对于糖尿病前期的成年人,步行和骑自行车可能是增加 PA 的合适活动。