Digital health technologies enable high-frequency collection of data in near-continuous time and capture rich information about the health of individuals. The raw data collected by these devices often have a hierarchical functional structure: repeated physiological functions are observed over time and on multiple time scales (seconds, days, weeks). While many summaries can be derived from digital data, typically, only a small subset of pre-defined scalars is validated as outcome measures in clinical trials. We explore data-driven summaries based on between-subject scores from Multilevel Functional Principal Component Analysis (MFPCA), which are low-dimensional representations of functional data with robust statistical properties. Specifically, we compute MFPCA projection scores with respect to a reference population, summarising how individuals differ from the dominant directions of variation at each hierarchical level. Through a simulation study based on smartwatch electrocardiogram (ECG) signals, we compare MFPCA scores with pre-specified summaries in terms of validation criteria, including test-retest reliability and known-groups discrimination. We demonstrate that MFPCA scores generally have high reliability and can discriminate between groups across simulated scenarios of change. This offers an advantage when digital tools enable the measurement of novel physiological signals and the characteristics of the change are not yet defined. Finally, using knee flexion-extension data from individuals living with Parkinson's disease, we demonstrate that one of the MFPCA scores more strongly correlates with established gold-standard metrics and can detect clinical change, compared to a pre-specified scalar. We conclude that MFPCA-derived scores retain more information than typical outcome measures and open the door to using learning representation strategies in clinical trial settings.

翻译：数字健康技术能够以近连续时间的高频方式采集数据，捕捉个体健康的丰富信息。这些设备采集的原始数据通常具有层次化函数结构：生理功能在多个时间尺度（秒、天、周）上随时间重复观测。尽管可从数字数据中衍生出多种摘要统计量，但在临床试验中，通常仅有一小部分预定义的标量被验证为结局指标。本文探索基于受试者间得分的多层级泛函主成分分析（MFPCA）数据驱动摘要，该方法可生成具有稳健统计性质的泛函数据低维表征。具体而言，我们计算相对于参考人群的MFPCA投影得分，总结个体在各层级主导变异方向上的差异。通过基于智能手表心电图（ECG）信号的仿真研究，我们从重测信度与已知组群区分效度等验证标准角度，比较了MFPCA得分与预定义摘要统计量。结果表明，MFPCA得分普遍具有高信度，且能在多种模拟变化场景中区分不同组群。当数字工具可测量新型生理信号而其特征变化尚未明确定义时，这一优势尤为突出。最后，基于帕金森病患者膝屈伸数据，我们发现与预定义标量相比，某个MFPCA得分与既定金标准指标的相关性更强，并能检测出临床变化。我们得出结论：MFPCA衍生得分比传统结局指标保留更多信息，为在临床试验场景中运用学习表征策略开辟了新路径。