Detection of subtle deficits in everyday functioning due to cognitive impairment is important for early detection of neurodegenerative diseases, particularly Alzheimer's disease. However, current standards for assessment of everyday functioning are based on qualitative, subjective ratings. Speech has been shown to provide good objective markers for cognitive impairments, but the association with cognition-relevant everyday functioning remains uninvestigated. In this study, we demonstrate the feasibility of using a smartwatch-based application to collect acoustic features as objective markers for detecting deficits in everyday functioning. We collected voice data during the performance of cognitive tasks and daily conversation, as possible application scenarios, from 54 older adults, along with a measure of everyday functioning. Machine learning models using acoustic features could detect individuals with deficits in everyday functioning with up to 77.8% accuracy, which was higher than the 68.5% accuracy with standard neuropsychological tests. We also identified common acoustic features for robustly discriminating deficits in everyday functioning across both types of voice data (cognitive tasks and daily conversation). Our results suggest that common acoustic features extracted from different types of voice data can be used as markers for deficits in everyday functioning.

翻译：检测由认知障碍引起的日常功能细微缺陷对于神经退行性疾病（尤其是阿尔茨海默病）的早期发现至关重要。然而，当前日常功能评估的标准依赖于定性、主观的评分。研究表明，语音可为认知障碍提供良好的客观标记，但其与认知相关日常功能的关联尚未得到探索。本研究展示了利用基于智能手表的应用程序采集声学特征作为客观标记，以检测日常功能缺陷的可行性。我们在认知任务执行和日常对话（作为可能的应用场景）中，从54名老年人中收集了语音数据，并同步采集了日常功能评估指标。采用声学特征的机器学习模型能够以高达77.8%的准确率检测日常功能缺陷个体，这一准确率高于标准神经心理学测试的68.5%。我们还识别出两类语音数据（认知任务与日常对话）中稳健区分日常功能缺陷的通用声学特征。结果表明，从不同类型语音数据中提取的通用声学特征可作为日常功能缺陷的标记。