In Journal of affective disorders ; h5-index 79.0
BACKGROUND : We examined whether digital phenotyping of spontaneous speech, such as the use of specific word categories during speech samples, was associated with depressive symptoms in youth who were at familial and clinical risk for mood disorders.
METHODS : Participants (ages 12-21) had active mood symptoms, mood instability, and at least one parent with bipolar or major depressive disorder. During a randomized trial of family-focused therapy, participants were instructed to make weekly calls to a central voice server and leave speech samples in response to automated prompts. We coded youths' speech samples with the Linguistic Inquiry and Word Count system and used machine learning to identify the combination of speech features that were most closely associated with the course of depressive symptoms over 18 weeks.
RESULTS : A total of 253 speech samples were collected from 44 adolescents (mean age = 15.8 years; SD = 1.6) over 18 weeks. Speech that included more words indicative of affective processes, social processes, drives toward risk or reward, nonfluencies, and time orientation was correlated with depressive symptoms at concurrent time periods (ps < 0.01). Machine learning analyses revealed that affective processes, nonfluencies, and drives and risk words combined to most strongly predict changes in depressive symptoms over 18 weeks of treatment.
LIMITATIONS : Study results were limited by the small sample and the exclusion of paralinguistic or contextual variables in analyzing speech samples.
CONCLUSIONS : In youth at high risk for mood disorders, knowledge of speech patterns may inform prognosis during outpatient psychosocial treatment.
Weintraub Marc J, Posta Filippo, Ichinose Megan C, Arevian Armen C, Miklowitz David J
2022-Dec-14
Adolescents, Bipolar, Depression, LIWC, Linguistic, Machine learning
