Data_Sheet_1_A Statistical Framework to Interpret Individual Response to Intervention: Paving the Way for Personalized Nutrition and Exercise Prescription.XLSX

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.

鉴于几乎所有与运动表现及健康相关的干预措施均存在显著的个体间差异，个性化营养与运动处方这一理念堪称该学科领域兼具前沿性与突破性的重要进展。对个体或群体的干预相关数据进行合理解读时，从业者与研究者需考量诸多维度，其中包括测量误差与生物学变异所带来的混杂影响。此外，置信区间（confidence intervals, CIs）与最小有意义变化（smallest worthwhile change, SWC）等概念，可为量化干预可能带来的统计学与实践层面改善提供方法支撑。本综述旨在为解读并报告个性化数据的从业者与研究者提供易于理解且具备实践指导价值的建议。为达成这一目标，本综述分为三个章节，逐步构建起一套统计学分析框架。第一章探讨与测量误差相关的基础概念，并阐释典型误差与置信区间如何用于表达基线测量中的不确定性。第二章基于上述概念展开，演示如何将置信区间与最小有意义变化相结合，以评估干预后是否出现具有实际意义的改善。最后，第三章介绍生物学变异的概念，并探讨在识别个体对干预措施的应答与无应答情况时所面临的后续挑战。本文辅以详实的数值示例与交互式补充材料，以强化对相关概念的理解，并助力其在实际工作中的应用。