Department of Head and Skin, Ghent University, University Hospital Ghent (UZ Ghent), Department of Psychiatry and Medical Psychology, Ghent, Belgium.
Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA.
Neurosci Biobehav Rev. 2023 May;148:105121. doi: 10.1016/j.neubiorev.2023.105121. Epub 2023 Mar 11.
Health research and health care alike are presently based on infrequent assessments that provide an incomplete picture of clinical functioning. Consequently, opportunities to identify and prevent health events before they occur are missed. New health technologies are addressing these critical issues by enabling the continual monitoring of health-related processes using speech. These technologies are a great match for the healthcare environment because they make high-frequency assessments non-invasive and highly scalable. Indeed, existing tools can now extract a wide variety of health-relevant biosignals from smartphones by analyzing a person's voice and speech. These biosignals are linked to health-relevant biological pathways and have shown promise in detecting several disorders, including depression and schizophrenia. However, more research is needed to identify the speech signals that matter most, validate these signals against ground-truth outcomes, and translate these data into biomarkers and just-in-time adaptive interventions. We discuss these issues herein by describing how assessing everyday psychological stress through speech can help both researchers and health care providers monitor the impact that stress has on a wide variety of mental and physical health outcomes, such as self-harm, suicide, substance abuse, depression, and disease recurrence. If done appropriately and securely, speech is a novel digital biosignal that could play a key role in predicting high-priority clinical outcomes and delivering tailored interventions that help people when they need it most.
健康研究和医疗保健目前都基于不频繁的评估，这些评估提供了临床功能的不完整画面。因此，错过了在健康事件发生之前识别和预防它们的机会。新的健康技术通过使用语音持续监测与健康相关的过程来解决这些关键问题。这些技术非常适合医疗保健环境，因为它们使高频评估变得非侵入性和高度可扩展。事实上，现有的工具现在可以通过分析一个人的语音来从智能手机中提取出各种与健康相关的生物信号。这些生物信号与健康相关的生物途径有关，并在检测几种疾病方面显示出了希望，包括抑郁症和精神分裂症。然而，需要更多的研究来确定最重要的语音信号，根据真实结果验证这些信号，并将这些数据转化为生物标志物和即时自适应干预措施。我们通过描述通过语音评估日常心理压力如何帮助研究人员和医疗保健提供者监测压力对各种心理健康和身体健康结果（如自残、自杀、药物滥用、抑郁和疾病复发）的影响来讨论这些问题。如果处理得当且安全，语音是一种新颖的数字生物信号，可以在预测高优先级临床结果和提供定制干预措施方面发挥关键作用，在人们最需要的时候为他们提供帮助。
