Ecological assessment of transdiagnostic clinical symptoms in serious mental illness with daily smartphone surveys

Abstract Clinical symptoms in serious mental illness (SMI) fluctuate dynamically, yet standard interview-based assessments often fail to capture these daily changes. Smartphone-based ecological surveys offer a scalable approach to monitoring symptoms in naturalistic settings. We analyzed longitudinal data from 56 outpatients with psychotic or affective disorders who completed 12,984 daily surveys and 1,028 clinical assessments over one year. Machine learning models showed that smartphone surveys moderately estimated Montgomery–Åsberg Depression Rating Scale (r rm = 0.57; p < 0.001) and Young Mania Rating Scale (r rm = 0.39; p < 0.001) and reliably captured within-person fluctuations. Positive symptoms measured by the Positive and Negative Syndrome Scale were also correlated (r rm = 0.24, p < 0.001), though with variable accuracy across participants. Factor modeling showed strongest convergence in negative affective domains, with symptom severity not affecting adherence. These findings highlight smartphone surveys as an ecologically valid tool for real-time symptom monitoring in SMI.