TRUSTING: An International Multicenter Observational Study of Speech-Based Relapse Prediction in Psychosis Using Explainable AI

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.

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. 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. Competing Interest Statement Philipp Homan has received grants and honoraria from Novartis, Lundbeck, Takeda, Mepha, Janssen, Boehringer Ingelheim, Neurolite, and OM Pharma outside of this work. No other conflicts of interest were reported. Funding Statement The TRUSTING project is supported by the European Union's Horizon Europe research and innovation programme under grant agreement No 101080251. Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or the European Health and Digital Executive Agency (HaDEA). Neither the European Union nor the granting authority can be held responsible for them. At the time of funding, Switzerland was not an associated country in Horizon Europe and Switzerland was not automatically eligible for EU funding. Therefore, the budgets for the University of Zurich and the University of Geneva (Geneva, Switzerland) were covered by the Swiss State Secretariat for Education, Research and Innovation (SERI) (ID: 23.00176). Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Kantonale Ethikkommission Zurich of Kanton Zurich gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Footnotes The author list has been updated. Data Availability Data collection has not started yet.