UCLA

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 persons 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.