Abstract
OBJECTIVE: Depression is a common mental health disorder and a major public health concern, significantly interfering with the lives of those affected. The complex clinical presentation of depression complicates symptom assessments. Day-to-day fluctuations of depression symptoms within an individual bring an additional barrier, since infrequent testing may not reveal symptom fluctuation. Digital measures such as speech can facilitate daily objective symptom evaluation. Here, we evaluated the effectiveness of daily speech assessment in characterizing speech fluctuations in the context of depression symptoms, which can be completed remotely, at a low cost and with relatively low administrative resources. METHODS: Community volunteers (N = 16) completed a daily speech assessment, using the Winterlight Speech App, and Patient Health Questionnaire-9 (PHQ-9) for 30 consecutive business days. We calculated 230 acoustic and 290 linguistic features from individual's speech and investigated their relationship to depression symptoms at the intra-individual level through repeated measures analyses. RESULTS: We observed that depression symptoms were linked to linguistic features, such as less frequent use of dominant and positive words. Greater depression symptomatology was also significantly correlated with acoustic features: reduced variability in speech intensity and increased jitter. CONCLUSIONS: Our findings support the feasibility of using acoustic and linguistic features as a measure of depression symptoms and propose daily speech assessment as a tool for better characterization of symptom fluctuations.