Swinton Paul A, Hemingway Ben Stephens, Saunders Bryan, Gualano Bruno, Dolan Eimear
School of Health Sciences, Robert Gordon University, Aberdeen, United Kingdom.
Applied Physiology & Nutrition Research Group, Rheumatology Division, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil.
Front Nutr. 2018 May 28;5:41. doi: 10.3389/fnut.2018.00041. eCollection 2018.
The concept of personalized nutrition and exercise prescription represents a topical and exciting progression for the discipline given the large inter-individual variability that exists in response to virtually all performance and health related interventions. Appropriate interpretation of intervention-based data from an individual or group of individuals requires practitioners and researchers to consider a range of concepts including the confounding influence of measurement error and biological variability. In addition, the means to quantify likely statistical and practical improvements are facilitated by concepts such as confidence intervals (CIs) and smallest worthwhile change (SWC). The purpose of this review is to provide accessible and applicable recommendations for practitioners and researchers that interpret, and report personalized data. To achieve this, the review is structured in three sections that progressively develop a statistical framework. Section 1 explores fundamental concepts related to measurement error and describes how typical error and CIs can be used to express uncertainty in baseline measurements. Section 2 builds upon these concepts and demonstrates how CIs can be combined with the concept of SWC to assess whether meaningful improvements occur post-intervention. Finally, section 3 introduces the concept of biological variability and discusses the subsequent challenges in identifying individual response and non-response to an intervention. Worked numerical examples and interactive Supplementary Material are incorporated to solidify concepts and assist with implementation in practice.
鉴于在几乎所有与表现和健康相关的干预措施的反应中都存在巨大的个体差异，个性化营养和运动处方的概念代表了该学科一个热门且令人兴奋的进展。对来自个体或一组个体的基于干预的数据进行恰当解读，要求从业者和研究人员考虑一系列概念，包括测量误差和生物变异性的混杂影响。此外，诸如置信区间（CIs）和最小有意义变化（SWC）等概念有助于量化可能的统计和实际改善情况。本综述的目的是为解读和报告个性化数据的从业者和研究人员提供易于理解且适用的建议。为实现这一目标，综述分为三个部分逐步构建一个统计框架。第1部分探讨与测量误差相关的基本概念，并描述如何使用典型误差和置信区间来表达基线测量中的不确定性。第2部分基于这些概念展开，展示如何将置信区间与最小有意义变化的概念相结合，以评估干预后是否发生了有意义的改善。最后，第3部分引入生物变异性的概念，并讨论在识别个体对干预的反应和无反应方面随之而来的挑战。文中纳入了数值示例和交互式补充材料，以巩固概念并协助在实践中实施。
