Supplementary Material for: Harnessing Speech-Derived Digital Biomarkers to Detect and Quantify Cognitive Decline Severity in Older Adults

Introduction: Current cognitive assessments suffer from floor/ceiling and practice effects, poor psychometric performance in mild cases, and repeated assessment effects. This study explores the use of digital speech analysis as an alternative tool for determining cognitive impairment. The study specifically focuses on identifying the digital speech biomarkers associated with cognitive impairment and its severity.
Methods: We recruited older adults with varying cognitive health. Their speech data, recorded via a wearable-microphone during the reading aloud of a standard passage, were processed to derive digital biomarkers such as timing, pitch, and loudness. Cohen's D effect size highlighted group differences, and correlations were drawn to the Montreal Cognitive Assessment (MoCA). A stepwise approach using a Random Forest model was implemented to distinguish cognitive states using speech data and predict MoCA scores based on highly correlated features.
Results: The study comprised 59 participants, with 36 demonstrating cognitive impairment and 23 serving as cognitively intact controls. Among all assessed parameters, similarity, as determined by Dynamic Time Warping (DTW), exhibited the most substantial positive correlation (rho=0.529, p

引言：当前认知评估方法存在地板效应和天花板效应、轻微病例的心理测量性能不佳以及重复评估效应等问题。本研究旨在探讨数字语音分析作为确定认知障碍的替代工具的应用。研究特别聚焦于识别与认知障碍及其严重程度相关的数字语音生物标志物。方法：我们招募了认知健康状况各异的老年参与者。在朗读标准段落的过程中，通过佩戴式麦克风记录的语音数据被处理以推导出数字生物标志物，如时间、音调和响度。Cohen's D效应量突显了组间差异，并与其他蒙特利尔认知评估（MoCA）的相关性进行了分析。采用逐步方法，利用随机森林模型区分认知状态，并基于高度相关的特征预测MoCA分数。结果：研究包括59名参与者，其中36名表现出认知障碍，23名作为认知完整的对照组。在所有评估参数中，通过动态时间规整（DTW）确定的相似性显示出最大的正相关性（rho=0.529，p值未给出）。