Smartphone monitoring and digital phenotyping apps for schizophrenia: A review of the academic literature

Background: Monitoring-based applications are increasingly administered in mental health research to detect relapses and track symptoms by using digital phenotyping. This review systematically examines unique datasets generated from unique apps for schizophrenia-spectrum disorders, including identifying patterns in study design, sample size, duration, comparison groups, device usage, incentives, and eligibility criteria. Methods: In January 2025, we conducted a systematic review with a narrative/qualitative synthesis of research for schizophrenia-related apps and coded them for demographics, eligibility, outcomes and experiences, engagement and features, and app availability. We focused specifically on apps related to monitoring schizophrenia and psychosis symptoms in patients. Results: The academic literature search yielded 3902 articles, of which 54 were included. Across these, 27 unique monitoring apps related to schizophrenia and psychosis were featured. The average study sample size was N = 78, and the average study duration was 26 weeks. The use of smartphone sensor data and digital phenotyping was common: GPS (18 of 27 apps), accelerometer (10 of 27 apps), screentime (5 of 27 apps), or phone logs (7 of 27 apps). However, nearly all apps supported self-report measures, the majority (21/27) in a survey format. Twenty-six percent of studies (14/54) focused on relapse prevention, but many were secondary analyses. There were only two apps that had replication studies. Conclusion: This review identifies a shift towards scalable digital phenotyping and relapse monitoring in mental health using apps. It underscores the necessity for standardized methodologies and longitudinal studies to evaluate the validity of these results. These findings inform future research directions, emphasizing the potential for personalized digital mental health solutions and early intervention strategies.