Faculty of Education, Arts and Sports, Department of Sport, Food and Natural Sciences, Campus Sogndal, Western Norway University of Applied Sciences, Box 133, 6851, Sogndal, Norway.
Department of Chemistry, University of Bergen, Box 7800, 5020, Bergen, Norway.
Int J Behav Nutr Phys Act. 2019 Aug 27;16(1):74. doi: 10.1186/s12966-019-0836-z.
The analysis of associations between accelerometer-derived physical activity (PA) intensities and cardiometabolic health is a major challenge due to multicollinearity between the explanatory variables. This challenge has facilitated the application of different analytic approaches within the field. The aim of the present study was to compare association patterns of PA intensities with cardiometabolic health in children obtained from multiple linear regression, compositional data analysis, and multivariate pattern analysis.
A sample of 841 children (age 10.2 ± 0.3 years; BMI 18.0 ± 3.0; 50% boys) provided valid accelerometry and cardiometabolic health data. Accelerometry (ActiGraph GT3X+) data were characterized into traditional (four PA intensity variables) and more detailed categories (23 PA intensity variables covering the intensity spectrum; 0-99 to ≥10,000 counts per minute). Several indices of cardiometabolic health were used to create a composite cardiometabolic health score. Multiple linear regression and multivariate pattern analyses were used to analyze both raw and compositional data.
Besides a consistent negative (favorable) association between vigorous PA and the cardiometabolic health measure using the traditional description of PA data, associations between PA intensities and cardiometabolic health differed substantially depending on the analytic approaches used. Multiple linear regression lead to instable and spurious associations, while compositional data analysis showed distorted association patterns. Multivariate pattern analysis appeared to handle the raw PA data correctly, leading to more plausible interpretations of the associations between PA intensities and cardiometabolic health.
Future studies should consider multivariate pattern analysis without any transformation of PA data when examining relationships between PA intensity patterns and health outcomes.
The study was registered in Clinicaltrials.gov 7th of April 2014 with identification number NCT02132494 .
由于解释变量之间存在多重共线性，因此分析加速度计得出的身体活动（PA）强度与心脏代谢健康之间的关联是一个主要挑战。这一挑战促进了该领域内不同分析方法的应用。本研究的目的是比较多元线性回归、成分数据分析和多变量模式分析中获得的 PA 强度与儿童心脏代谢健康之间的关联模式。
本研究纳入了 841 名儿童（年龄 10.2±0.3 岁；BMI 18.0±3.0；50%为男孩），这些儿童提供了有效的加速度计和心脏代谢健康数据。使用 ActiGraph GT3X+ 加速度计数据将身体活动分为传统（四个 PA 强度变量）和更详细的类别（23 个 PA 强度变量，涵盖整个强度范围；每分钟 0-99 到≥10,000 计数）。使用几种心脏代谢健康指标来创建一个综合心脏代谢健康评分。使用多元线性回归和多变量模式分析对原始数据和成分数据进行分析。
除了传统 PA 数据描述显示剧烈 PA 与心脏代谢健康测量值之间存在一致的负（有利）关联外，PA 强度与心脏代谢健康之间的关联因所使用的分析方法而异。多元线性回归导致不稳定和虚假关联，而成分数据分析则显示出扭曲的关联模式。多变量模式分析似乎正确地处理了原始 PA 数据，从而更合理地解释了 PA 强度与心脏代谢健康之间的关联。
未来的研究在检查 PA 强度模式与健康结果之间的关系时，应考虑使用多变量模式分析，且无需对 PA 数据进行任何转换。
该研究于 2014 年 4 月 7 日在 Clinicaltrials.gov 注册，注册号为 NCT02132494。
