TRUSTING: An International Multicenter Observational Study of Speech-Based Relapse Prediction in Psychosis Using Explainable AI
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Introduction
The course of psychotic disorders typically involves relapses. Early warning signs vary between individuals and are difficult to detect in clinical practice, especially in outpatient settings. Speech provides a quantitative clinical marker for detecting such early warning signs. The EU Horizon project TRUSTING (A TRUSTworthy speech-based AI monitoring system for the prediction of relapse in individuals with schizophrenia) aims to develop and evaluate a speech-based monitoring system for predicting imminent psychotic relapses. The study will examine the potential for prospective relapse prediction, and feasibility and usability of the monitoring system.
Methods and analysis
In this multicenter observational study, n = 240 remitted and at-risk-of-relapse adults with psychotic disorders and a comparison group with n = 120 healthy participants (matched by age and sex) will be examined at six sites and in six different languages (German, French, Dutch, English, Czech, and Turkish). The follow-up period is 6 months. The TRUSTING smartphone app will be used to collect weekly voice recordings through speech tasks; information on medication adherence, substance use, mood, anxiety, and sleep quality; and motor data from a tapping task. Primary endpoints encompass model performance for relapse prediction, user adherence, transcription quality, usability of recordings, and overall system usability. The primary analysis of user adherence, transcription quality, usability of recordings, and overall system usability will be an unadjusted description of the respective proportions using 95% Wilson confidence intervals. Regarding relapse prediction, the predictive value of the risk estimates for relapse occurrence will be assessed using the area under the receiver operating characteristic curve. Exploratory analysis will be performed on potential speech-based markers associated with relapse risk.
Ethics and dissemination
This study has been approved by swissethics (BASEC number: 2025-01177). Findings from this project will be disseminated through peer-reviewed journal publications and presentations at relevant scientific conferences, as well as public events related to mental health.
ARTICLE SUMMARY
Strengths and limitations of this study
International multicenter study spanning six sites, six languages, and five countries, enabling evaluation of the cross-linguistic generalizability of speech-based relapse prediction models in psychosis.
Human oversight enabling head-to-head comparison between human judgment and machine-generated predictions of relapse risk and ensuring the study’s safety and trustworthiness.
Involvement of people with lived experience of psychosis in both study and system design.
Inclusion of a matched control group to study intra- and interindividual variations in speech features over multiple measurements.
Insights into the feasibility of implementing artificial intelligence (AI)-based transcription and speech analysis in routine mental healthcare, and exploration of novel speech-based markers associated with relapse risk to enhance prediction, understanding, and prevention of relapse in the future.
