Digital health metrics promise to advance the understanding of impaired body functions, for example in neurological disorders. However, their clinical integration is challenged by an insufficient validation of the many existing and often abstract metrics. Here, we propose a data-driven framework to select and validate a clinically relevant core set of digital health metrics extracted from a technology-aided assessment. As an exemplary use-case, the framework is applied to the Virtual Peg Insertion Test (VPIT), a technology-aided assessment of upper limb sensorimotor impairments. The framework builds on a use-case-specific pathophysiological motivation of metrics, models demographic confounds, and evaluates the most important clinimetric properties (discriminant validity, structural validity, reliability, measurement error, learning effects). Applied to 77 metrics of the VPIT collected from 120 neurologically intact and 89 affected individuals, the framework allowed selecting 10 clinically relevant core metrics. These assessed the severity of multiple sensorimotor impairments in a valid, reliable, and informative manner. These metrics provided added clinical value by detecting impairments in neurological subjects that did not show any deficits according to conventional scales, and by covering sensorimotor impairments of the arm and hand with a single assessment. The proposed framework provides a transparent, step-by-step selection procedure based on clinically relevant evidence. This creates an interesting alternative to established selection algorithms that optimize mathematical loss functions and are not always intuitive to retrace. This could help addressing the insufficient clinical integration of digital health metrics. For the VPIT, it allowed establishing validated core metrics, paving the way for their integration into neurorehabilitation trials.

数字健康指标有望增进对受损身体功能的理解，例如神经系统疾病。然而，它们的临床整合受到许多现有且通常抽象的指标验证不足的挑战。在这里，我们提出了一个数据驱动的框架来选择和验证从技术辅助评估中提取的临床相关核心数字健康指标集。作为示例性用例，该框架应用于虚拟钉插入测试（VPIT），这是一种上肢感觉运动障碍的技术辅助评估。该框架建立在特定用例的病理生理学动机的基础上，对人口统计学混杂因素进行建模，并评估最重要的临床特征（区分有效性、结构有效性、可靠性、测量误差、学习效果）。该框架适用于从 120 名神经系统完好者和 89 名受影响个体收集的 VPIT 的 77 项指标，允许选择 10 项临床相关的核心指标。这些以有效、可靠和信息丰富的方式评估了多种感觉运动障碍的严重程度。这些指标通过检测根据传统量表未显示任何缺陷的神经系统受试者的损伤，并通过单一评估涵盖手臂和手的感觉运动损伤，提供了附加的临床价值。拟议的框架提供了一个基于临床相关证据的透明、逐步的选择程序。这为已建立的选择算法创建了一个有趣的替代方案，该算法优化数学损失函数并且并不总是直观地回溯。这可能有助于解决数字健康指标临床整合不足的问题。对于 VPIT，它允许建立经过验证的核心指标，为将其纳入神经康复试验铺平道路。