Ecological momentary assessment (EMA) combined with unsupervised machine learning shows sensitivity to identify individuals in potential need for psychiatric assessment

Introduction: Ecological momentary assessment (EMA), a structured diary assessment technique, has shown feasibility to capture psychotic(-like) symptoms across different study groups. We investigated whether EMA combined with unsupervised machine learning can distinguish groups on the continuum of genetic risk towards psychotic illness and identify individuals with need for extended healthcare. Methods: Individuals with psychotic disorder (PD, N=55), healthy individuals (HC, N=25) and HC with first-degree relatives with psychosis (RE, N=20) were assessed at two sites over 7 days using EMA. Cluster analysis determined subgroups based on similarities in longitudinal trajectories of psychotic symptom ratings in EMA, agnostic of study group assignment. Psychotic symptom ratings were calculated as average of items related to hallucinations and paranoid ideas. Prior to EMA we assessed symptoms using the Positive and Negative Syndrome Scale (PANSS) and the Community Assessment of Psychic Experience (CAPE) to characterize the EMA subgroups. Results: We identified two clusters with distinct longitudinal EMA characteristics. Cluster 1 (NPD=12, NRE=1, NHC=2) showed higher mean EMA symptom ratings as compared to cluster 2 (NPD=43, NRE=19, NHC=23) (p < 0.001). Cluster 1 showed a higher burden on negative (p < 0.05) and positive (p < 0.05) psychotic symptoms in cross-sectional PANSS and CAPE ratings than cluster 2. Discussion: Findings indicate a separation of PD with high symptom burden (cluster 1) from PD with healthy-like rating patterns grouping together with HC and RE (cluster 2). Individuals in cluster 1 might particularly profit from exchange with a clinician underlining the idea of EMA as clinical monitoring tool