数字健康N-of-1研究与单案例设计入门
A Primer on Digital Health N-of-1 Studies and Single-Case Designs
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中文总结 AI 辅助
本文综述了数字健康领域N-of-1研究、单案例设计等方法的核心概念，探讨其与其他数字健康方法的关联，并展望了“esametry”的未来方向，旨在推动治疗个体化。
中文摘要 AI 辅助
临床研究通常假设组水平的平均值是指导每位患者临床护理中个体水平决策的有用量。精准医学通过高度精细化的亚组分析，显著缩小了向真正个体化护理迈进的差距。如今，数字健康技术及其他现代来源的密集、个人化“小数据”，为治疗个体化提供了一种不同的方法——该方法首要目标是描述单个人自身反复出现的健康模式，而非确定他们可能属于的最佳亚组。在本章中，我们综述了N-of-1研究、单案例设计及其他适用于数字健康应用的“多趋势（multitudinal）”方法的核心概念，并探讨它们与其他数字健康方法的关系。我们还分享了“esametry”（即我们每个人体内数字化群体的统计学）这一领域的一些有前景的未来方向。
英文摘要
Clinical studies generally assume that group-level averages are useful quantities for guiding individual-level decisions in the clinical care of each individual patient. Precision medicine has notably closed the gap towards truly individualized care through highly refined subgrouping. Today, digital health technologies and other modern sources of dense, personal "small data" enable a different approach to treatment individualization---one that seeks to characterize a single person's own recurring health patterns first and foremost, rather than identifying the best subgroup to which they might belong. In this chapter, we review the key concepts underlying n-of-1 studies, single-case designs, and other "multitudinal" approaches for digital health applications, and explore their relationships to other digital health methods. We also share some promising future directions for "esametry", the statistics of the digitized multitudes within each of us.
