Med Sci Sports Exerc. 2023 May 1;55(5):837-846. doi: 10.1249/MSS.0000000000003108. Epub 2022 Dec 27.
This study aimed to identify physical activity patterns and examine their association with cardiometabolic biomarkers in a cross-sectional design.
Overall 6072 participants (mean age, 60.2 yr; SD 8.6 yr, 50% women) from The Maastricht Study provided daily physical activity data collected with thigh-worn activPAL3 accelerometers. The patterns of daily physical activity over weekdays and weekend days were identified by using Group Based Trajectory Modeling. Cardiometabolic biomarkers included body mass index, waist circumference, office blood pressure, glucose, HbA1c, and cholesterol levels. Associations between the physical activity patterns and cardiometabolic outcomes were examined using the analyses of covariance adjusted for sex, age, education, smoking, and diet. Because of statistically significant interaction, the analyses were stratified by type 2 diabetes status.
Overall, seven physical activity patterns were identified: consistently inactive (21% of participants), consistently low active (41%), active on weekdays (15%), early birds (2%), consistently moderately active (7%), weekend warriors (8%), and consistently highly active (6%). The consistently inactive and low active patterns had higher body mass index, waist, and glucose levels compared with the consistently moderately and highly active patterns, and these associations were more pronounced for participants with type 2 diabetes. The more irregular patterns accumulated moderate daily total activity levels but had rather similar cardiometabolic profiles compared with the consistently active groups.
The cardiometabolic profile was most favorable in the consistently highly active group. All patterns accumulating moderate to high levels of daily total physical activity had similar health profile suggesting that the amount of daily physical activity rather than the pattern is more important for cardiometabolic health.
本研究旨在通过横断面设计，确定身体活动模式，并研究其与心血管代谢生物标志物的相关性。
共有 6072 名（平均年龄 60.2 岁，标准差 8.6 岁，50%为女性）来自马斯特里赫特研究的参与者提供了使用大腿佩戴的 activPAL3 加速度计收集的日常体力活动数据。通过群组轨迹建模识别工作日和周末日常体力活动模式。使用协方差分析调整性别、年龄、教育、吸烟和饮食因素，检验体力活动模式与心血管代谢结局之间的关联。由于统计学上存在显著交互作用，因此对 2 型糖尿病患者进行了分层分析。
总体而言，确定了七种体力活动模式：持续不活跃（21%的参与者）、持续低活跃（41%）、工作日活跃（15%）、早起者（2%）、持续适度活跃（7%）、周末战士（8%）和持续高度活跃（6%）。与持续适度和高度活跃模式相比，持续不活跃和低活跃模式的体重指数、腰围和血糖水平更高，而对于 2 型糖尿病患者，这些关联更为明显。不规则模式的日常中等强度总活动量积累较多，但与持续活跃组相比，其心血管代谢特征相似。
持续高度活跃组的心血管代谢特征最佳。所有积累中等到高强度日常总体力活动的模式都具有相似的健康特征，这表明每日体力活动的量而不是模式对心血管代谢健康更为重要。
