Machine Learning Identifies Digital Phenotyping Measures Most Relevant to Negative Symptoms in Psychotic Disorders: Implications for Clinical Trials.

ABSTRACT:

Background

Digital phenotyping has been proposed as a novel assessment tool for clinical trials targeting negative symptoms in psychotic disorders (PDs). However, it is unclear which digital phenotyping measurements are most appropriate for this purpose.

Aims

Machine learning was used to address this gap in the literature and determine whether: (1) diagnostic status could be classified from digital phenotyping measures relevant to negative symptoms and (2) the 5 negative symptom domains (anhedonia, avolition, asociality, alogia, and blunted affect) were differentially classified by active and passive digital phenotyping variables.

Methods

Participants included 52 outpatients with a PD and 55 healthy controls (CN) who completed 6 days of active (ecological momentary as

SUBMITTER: Narkhede SM 

PROVIDER: S-EPMC8886590 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature