Linguistic and non-linguistic markers of disorganization in psychotic illness

Einat Liebenthal; Michaela Ennis; Habiballah Rahimi-Eichi; Eric Lin; Yoonho Chung; Justin T Baker

doi:10.1016/j.schres.2022.12.003

Linguistic and non-linguistic markers of disorganization in psychotic illness

Schizophr Res. 2023 Sep:259:111-120. doi: 10.1016/j.schres.2022.12.003. Epub 2022 Dec 21.

Authors

Einat Liebenthal¹, Michaela Ennis², Habiballah Rahimi-Eichi³, Eric Lin⁴, Yoonho Chung³, Justin T Baker³

Affiliations

¹ McLean Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA. Electronic address: eliebenthal@mclean.harvard.edu.
² McLean Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA; Division of Medical Sciences, Harvard Medical School, Boston, MA, USA.
³ McLean Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
⁴ McLean Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA; Medical Informatics, Veterans Affairs Boston, Boston, MA, USA.

PMID: 36564239
PMCID: PMC10282106 (available on 2024-09-01)
DOI: 10.1016/j.schres.2022.12.003

Abstract

Background: Disorganization, presenting as impairment in thought, language and goal-directed behavior, is a core multidimensional syndrome of psychotic disorders. This study examined whether scalable computational measures of spoken language, and smartphone usage pattern, could serve as digital biomarkers of clinical disorganization symptoms.

Methods: We examined in a longitudinal cohort of adults with a psychotic disorder, the associations between clinical measures of disorganization and computational measures of 1) spoken language derived from monthly, semi-structured, recorded clinical interviews; and 2) smartphone usage pattern derived via passive sensing technologies over the month prior to the interview. The language features included speech quantity, rate, fluency, and semantic regularity. The smartphone features included data missingness and phone usage during sleep time. The clinical measures consisted of the Positive and Negative Symptom Scale (PANSS) conceptual disorganization, difficulty in abstract thinking, and poor attention, items. Mixed linear regression analyses were used to estimate both fixed and random effects.

Results: Greater severity of clinical symptoms of conceptual disorganization was associated with greater verbosity and more disfluent speech. Greater severity of conceptual disorganization was also associated with greater missingness of smartphone data, and greater smartphone usage during sleep time. While the observed associations were significant across the group, there was also significant variation between individuals.

Conclusions: The findings suggest that digital measures of speech disfluency may serve as scalable markers of conceptual disorganization. The findings warrant further investigation into the use of recorded interviews and passive sensing technologies to assist in the characterization and tracking of psychotic illness.

Keywords: Conceptual disorganization; Digital phenotyping; Psychosis; Smartphone-based passive sensing; Speech disfluency; Spoken language.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Adult
Cognition
Humans
Language
Psychotic Disorders* / diagnosis
Speech
Thinking

Abstract

Publication types

MeSH terms

Grants and funding