Validity of a smartphone application for self-monitoring psychiatric symptoms in patients with schizophrenia - Sung-Wan Kim; Jae-Kyeong Kim; Min Jhon; Ju-Wan Kim; Seunghyong Ryu; Ju-Yeon Lee; Jae-Min Kim (Digital Health)
Objective Despite increasing research on digital technologies for psychiatric disorders, studies specifically examining self-monitoring of symptoms with smartphone applications by patients with schizophrenia remain limited. This study aims to evaluate the validity and reliability of self-monitoring psychiatric symptoms using a smartphone application among patients with schizophrenia at Mindlink, a community-based early intervention center. Methods Fifty-three young patients with schizophrenia spectrum disorders participated. They rated their psychiatric symptoms across five domains—delusions, hallucinations, anxiety, depression, and perceived stress—using an 11-point Likert scale at baseline, 1 week, 8 weeks, and 16 weeks. Test–retest reliability was assessed using intraclass correlation coefficients (ICCs) between baseline and 1-week ratings. Concurrent validity was determined by correlating app-based ratings with established self-report and clinician-administered scales, including the Eppendorf Schizophrenia Inventory, Hamilton Program for Schizophrenia Voices Questionnaire, Beck Depression Inventory, Generalized Anxiety Disorder-7, and Perceived Stress Scale. The accuracy of the app's depression rating was assessed using receiver operating characteristic (ROC) analysis. Results ICCs for test–retest reliability were high across all symptom domains, ranging from 0.741 to 0.876 ( p  < 0.001). Significant correlations were observed between app-based ratings and formal assessments at all time points. ROC analysis for single-item self-ratings using the app yielded an area under the curve of 0.829 ( p  = 0.002), indicating good accuracy. Conclusion This study demonstrates that self-monitoring of key symptoms and stress using a smartphone application is valid and reliable for patients with schizophrenia. These findings support the app's potential to enhance symptom management and enable early detection of relapse in this population.