Speech-based Assessment of Relapse Risk in People With Psychosis

ConditionPsychotic Disorder

Trial statusRecruiting

Trial phaseNot listed

Trial typeObservational

Biological sexAll

Age18-65

SponsorPhilipp Homan

About this trial

This observational, multinational study assesses the feasibility of speech and self-report data collection across six languages for Artificial Intelligence (AI)-driven relapse risk estimation in psychosis. Over 12 months, patients at risk of relapse and healthy controls will provide weekly speech recordings and self-report data for automated analysis. Risk scores will be stored but not shared with treating clinicians. Independent clinical evaluations ensure data quality and validation. The study lays the foundation for future Clinical Decision Support System (CDSS) research and explores novel speech markers for relapse prediction while minimizing participant burden.

Eligibility criteria

This trial accepts healthy volunteers

Qualifiers

Between 18 and 65 years old.

Subjects must be able to provide informed consent (IC).

Participants must have provided signed informed consent prior to enrollment.

Participants should not take prescription drugs regularly.

Disqualifiers

Any history of major diseases (e.g., cardiovascular, neurological, metabolic, renal, hepatic, respiratory or speech disorders).

Recreational drug use or psychiatric disorders due to use of alcohol.

Depression, anxiety, or other psychiatric disorders.

Family history of depression, anxiety, or other psychiatric disorders.

Trial population

Participants at risk of relapse will be recruited from clinical settings while in a stable phase of partial or full remission. Depending on the site, eligible individuals may be either inpatients nearing discharge or outpatients under ongoing care. If the treating clinician considers the patient's mental state sufficiently stable, the study information and consent process may be initiated during inpatient care, prior to discharge. For outpatients, participants may be contacted by phone, or the study can be presented during routine outpatient or day clinic visits. Healthy participants will be recruited through community outreach, advertisements, and volunteer registries from universities and hospitals. healthy participants will be selected to match individuals with psychosis based on key characteristics such as age and sex in each site.

Trial design

Design model

Cohort

Time perspective

Prospective

Treatments tested in this trial

Weekly online speech assessments via a smartphone app collect speech and self-report data. Recordings are securely transferred and analyzed by an AI-based backend to calculate relapse risk scores.
Other intervention
The intervention is a weekly online assessment of speech, to detect subtle characteristics of psychotic speech. These recordings are made through a speech data collection tool: a smartphone app with implemented tasks for the collection of behavioral response data (speech and self-report). Data is then transferred to a safe repository (TSD) to be analysed by an AI-based speech data analysis algorithm: a backend system will run the predictor code to calculate automatic relapse risk scores.

Treatment groups

360 Participants

are divided into 2 treatment groups

Group A: At risk of relapse1 intervention

Group B: Healthy controls1 intervention

Trial outcomes

Primary outcomes

Feasibility: User adherence

Proportion of users adhering to their allocated tasks. Task completion is automatically assessed for each user. A user is regarded as adherent, if they complete at least 33% of their tasks across all sessions.

Time frame

From enrollment to the end of the study at 12 months

Feasibility: Transcription quality

Proportion of users for which high quality transcripts are produced. Transcription quality is measured by the Word Error Rate (WER) between Automated Speech Recognition (ASR) and human corrected transcripts. A user with an overall WER of at most 35% is regarded as having high quality transcripts

Time frame

From enrollment to the end of the study at 12 months

Feasibility: Usability of recordings

Proportion of recordings that can be used by the AI algorithm. Usability of each record is assessed by HITL reviewers based on the three questions (i) has the user interpreted the question correctly, and (ii) is the audio (at least partially) audible and (iii) comprehensible. The three questions must both be answered with yes for the recording to be usable

Time frame

From enrollment to the end of the study at 12 months

Overall system usability

Proportion of users rating the app as usable. Usability is evaluated using the System Usability Scale (SUS), providing a standardized usability score (range 0-100) based on a 10-item questionnaire. We regard a score of at least 70 as usable.

Time frame

6 months and 12 months

Secondary outcomes

Provider-perceived usability and usefulness

Provider-perceived usability and usefulness of the app will be assessed using the mHealth App Usability Questionnaire (MAUQ - Provider version). Scores will be reported by domain and overall

Time frame

6 months and 12 months

Relapse Outcome 1

Relapse rate, defined as the proportion of participants experiencing at least one relapse (rehospitalization) during the study period.

Time frame

6 months and 12 months

Relapse Outcome 2

Human-in-the-loop (HITL) relapse risk estimates and relapse incidence: HITL risk scores (range 0-1) will be categorized into low (0-0.2), medium (\>0.2-0.6), and high (\>0.6) risk levels. Descriptive analyses will assess the proportion of relapses within each category.

Time frame

From enrollment to the end of the study at 12 months

Relapse Outcome 3

Clinical decision support system (CDSS) relapse risk estimates and relapse incidence: CDSS risk scores (range 0-1) will be categorized into low, medium, and high risk using the same thresholds. Descriptive analyses will assess relapse proportions per category.

Time frame

From enrollment to the end of the study at 12 months