A Primer on Digital Health N-of-1 Studies and Single-Case Designs
Eric J. Daza
Abstract
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.
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