BACKGROUND 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. METHODS 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. RESULTS 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. CONCLUSIONS Future studies should consider multivariate pattern analysis without any transformation of PA data when examining relationships between PA intensity patterns and health outcomes. TRIAL REGISTRATION The study was registered in Clinicaltrials.gov 7th of April 2014 with identification number NCT02132494 .

中文翻译：

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多共线体育活动加速度计数据以及与心脏代谢健康的关联：挑战，陷阱和潜在解决方案。

背景技术由于解释变量之间的多重共线性，对加速度计派生的体力活动（PA）强度与心脏代谢健康之间的关联性进行分析是一项重大挑战。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从多元线性回归，成分数据分析和多元模式分析获得的儿童PA强度与心脏代谢健康的关联模式。方法抽取了841名儿童（年龄10.2±0.3岁； BMI 18.0±3.0； 50％男孩）作为样本，提供了有效的加速度计和心脏代谢健康数据。加速度计（ActiGraph GT3X +）数据的特征分为传统的（四个PA强度变量）和更详细的类别（23个PA强度变量，覆盖强度谱；每分钟0-99到≥10,000个计数）。使用几种心脏代谢健康指数来创建复合心脏代谢健康评分。多元线性回归和多元模式分析用于分析原始数据和成分数据。结果除了使用传统的PA数据描述，剧烈的PA与心脏代谢健康指标之间始终存在负（有利）关联外，根据所使用的分析方法，PA强度与心脏代谢健康之间的联系也存在很大差异。多元线性回归会导致不稳定和虚假的关联，而成分数据分析显示扭曲的关联模式。多变量模式分析似乎可以正确处理原始的PA数据，从而可以更合理地解释PA强度与心脏代谢健康之间的关系。结论未来的研究应在检查PA强度模式与健康结果之间的关系时考虑多模式分析，而无需对PA数据进行任何转换。试验注册该研究于2014年4月7日在Clinicaltrials.gov上进行了注册，标识号为NCT02132494。结论未来的研究应在检查PA强度模式与健康结果之间的关系时考虑多模式分析，而无需对PA数据进行任何转换。试验注册该研究于2014年4月7日在Clinicaltrials.gov上进行了注册，标识号为NCT02132494。结论未来的研究应在检查PA强度模式与健康结果之间的关系时考虑多模式分析，而无需对PA数据进行任何转换。试验注册该研究于2014年4月7日在Clinicaltrials.gov上进行了注册，标识号为NCT02132494。