PsychosisMOTS+NCT06978894
Predicting Psychotic Relapse Using Speech-Based Early Detection
An observational study of Psychosis, run by Douglas Mental Health University Institute at 3 sites in Canada.
Trial summary
Psychotic disorders, including schizophrenia and affective psychosis, are severe mental health conditions marked by recurrent episodes that contribute to long-term disability. Relapses, characterized by the re-emergence of psychotic symptoms after remission, are a critical factor in the progression of these disorders… Read more about this study
- Who is it for?
- Ages 16 years and older
- Conditions the study is for
- Psychosis
- How many people
- 250 participants
- Where
- 3 sitesCanada
- Study dates
- 27 May 2024to 1 Jul 2029
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 16 years and older
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: This study population will consist of approximately 250 patients experiencing or having experienced psychosis, enrolled in the EPI programs.
Who may be able to join
- Age must be 16 years and older
- Diagnosis must meet DSM-5 criteria for psychotic disorders, including schizophrenia, schizoaffective disorder, or related conditions
- Fluency in English or French
- Must be currently receiving treatment through an EPI program
Who cannot join
- Severe comorbid speech or language disorders (e.g., aphasia)
- Primary diagnosis of non-psychotic disorders
- Inability to provide consent or complete assessments
Trial locations and contacts
3 sites in 1 country. Contact the study team or a site near you to ask about taking part.
Canada3 sites, 3 recruiting
- Recruiting
Robarts Research Institute
London, Ontario, N6A 5B7
Michael MacKinley, PHD, 519.931.5777, michael.mackinley@lhsc.on.ca
- Recruiting
Douglas Mental Health University Institute
Montreal, Quebec, H4H 1A8
Lena Palaniyappan, MD, PHD, 5147616131 ext. 6116, lena.palaniyappan@mcgill.ca
- Recruiting
Vitam
Québec, Quebec, G1J 2G1
Amélie M Achim, Phd, 418 663-5741, amelie.achim@fmed.ulaval.ca
About this study
Psychotic disorders, including schizophrenia and affective psychosis, are severe mental health conditions marked by recurrent episodes that contribute to long-term disability. Relapses, characterized by the re-emergence of psychotic symptoms after remission, are a critical factor in the progression of these disorders, increasing risks such as suicide, cognitive impairment, and unemployment. This study aims to develop a novel, speech-based digital model to predict relapses in individuals with psychosis. Building on previous research into language abnormalities in schizophrenia, the study will employ a longitudinal design across Early Psychosis Intervention (EPI) clinics in Ontario and Quebec to advance relapse prediction
Read the detailed description
OBJECTIVES: The primary goal of this study is to develop and validate a speech-based digital model to predict psychotic relapses in individuals with early psychosis. The study specifically aims to:
Test the hypothesis that within-subject changes in speech coherence, connectedness, and complexity, as measured by natural language processing (NLP) tools, will accurately identify imminent relapse, up to four weeks before clinical relapse in individuals receiving care in Early Psychosis Intervention (EPI) programs.
Investigate whether these speech-based relapse prediction models generalize across different languages (English and French) and are equally predictive in both males and females, addressing potential sociodemographic and linguistic influences on model performance.
Explore whether combining acoustic and prosodic features with core NLP-based speech measures improves the model's sensitivity and specificity for relapse prediction.
METHODS:
This study will employ a longitudinal, prospective design involving 250 first-episode psychosis (FEP) patients recruited from three Early Psychosis Intervention (EPI) clinics in Ontario and Quebec. The study aims to develop and evaluate a speech-based relapse prediction model, with a particular focus on generalizing results across different languages (English and French) and genders.
Participant Recruitment and Stratification:
Participants: A total of 250 FEP patients, including both English- and French-speaking individuals, will be enrolled to ensure linguistic diversity. The sample will be stratified by sex to evaluate model performance across genders.
Language groups: Approximately 60% of the participants will be English speakers and 40% French speakers, reflecting the population served by the EPI clinics.
Gender representation: The study aims to ensure that at least 40% of participants are female to assess gender-based differences in model prediction performance.
Baseline Assessments:
At baseline, participants will undergo a comprehensive in-person assessment to collect a detailed profile for each patient. This will include psychiatric symptomatology using the Positive and Negative Syndrome Scale (PANSS), Calgary Depression Scale and the Personal and Social Performance (PSP) scale, and cognitive functioning. Additionally, socioeconomic variables, historical and current medication usage, substance use (e.g., cannabis), and treatment adherence will also be recorded to provide a full clinical and treatment profile for each participant.
Speech Sampling and Data Collection:
Monthly Speech Samples: After the baseline assessment, participants will provide monthly speech samples over the course of 24 months. These speech samples will be collected using web-based prompts that include open-ended tasks, such as picture description or recall narratives, designed to elicit spontaneous speech.
Attrition and Speech Sample Estimates: Given an expected attrition rate of 35-50%, it is estimated that by the end of the study, 840-960 speech samples will be obtained from English-speaking participants and 660-870 speech samples from French-speaking participants.
Speech Analysis:
The collected speech samples will be analyzed using natural language processing (NLP) methods to extract key features associated with psychosis, including coherence (Measured by lexical predictability), Connectedness (Assessed using speech graph analysis) and Complexity (evaluated using the Analytic Thinking Index (ATI)). These NLP-derived speech metrics will be tracked over time to predict imminent psychotic relapses and compared across subgroups to assess the impact of language and gender on the predictive accuracy of the relapse model.
Data Analysis and Generalization:
The primary objective is to determine whether speech-based relapse prediction models generalize across different languages and genders. To achieve this, model performance will be evaluated across subgroups:
Linguistic subgroup analysis will compare the model's performance in English- and French-speaking participants.
Gender-based analysis will assess whether the predictive power of the speech-based model varies between male and female participants.
This analysis will ensure that the final model can be generalized across diverse populations and adapted for use in different clinical settings.
- Conditions
- Psychosis
- Keywords
- psychosis, natural language processing, relapse, speech, relapse prediction
- Medical subject headings
- Psychotic Disorders, Recurrence, Speech
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Early Psychosis Patients | This study will participants recruited from three Early Psychosis Intervention (EPI) programs across Ontario and Quebec. This group will consist of approximately 250 patients experiencing or having experienced psychosis, enrolled in the EPI programs. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Likelihood of relapse estimated using Speech-NLP Metrics
This primary outcome will assess the ability of speech-based NLP metrics (coherence, connectedness, and complexity) to predict impending relapses in psychosis. Monthly speech samples will be analyzed to determine if changes in these metrics can distinguish timepoints preceding relapses from those not followed by relapse, with the aim of predicting relapses up to four weeks in advance. The likelihood of relapse is a numerical probabilistic estimate without any units. Outcome definition: Occurrence of relapse (i.e., psychiatric hospitalization, an increase in the level of psychiatric care, or substantial clinical deterioration >1wk that requires >25% increase in Defined Daily Dose equivalents of antipsychotics)
Measured: Monthly, up to 24 months
Generalization of Speech-Based Relapse Prediction Models Across Languages and Genders
This outcome will assess whether the speech-based relapse prediction models are valid and perform equally well across different languages (English and French) and genders (male and female). The study will evaluate how sociodemographic factors such as language and sex impact the predictive accuracy of the models. NLP metrics (coherence, connectedness, complexity) will be correlated with clinical outcomes, and model performance will be compared across linguistic and gender subgroups to ensure generalizability.
Measured: Monthly, up to 24 months
Secondary outcomes (1)
Likelihood of relapse estimated using multi-level speech features
This exploratory outcome will assess the likelihood of relapse prediction by integrating acoustic features (e.g., speech rate, intonation), prosodic features (e.g., rhythm, pitch), and sentiment analysis into the primary measures (coherence, connectedness, complexity). Accuracy (the proportion of all relapse classifications that were correct, whether positive or negative) will be reported for the predictive models.
Measured: Monthly, up to 24 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 May 2025
- Study start27 May 2024
- Primary completion1 Jul 2028 (estimated)
- Study completion1 Jul 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for PsychosisStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Prospective
- Enrolment
- 250 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Douglas Mental Health University Institute
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Lena Palaniyappan, Douglas Mental Health University Institute
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06978894
- Sponsor's protocol ID
- 2024-979
- Acronym
- MOTS+
- Status last verified
- Nov 2024
- First submitted
- 13 Jan 2025
- Last updated
- 18 May 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: We propose to use a clinical linguistic archiving system called the TalkBank (https://www.talkbank.org/) for data sharing purposes. TalkBank registry/database is located at Carnegie Mellon University, Pittsburgh, Pennsylvania-USA. Several filters are used to de-identify the recorded speech data before storage in the databank. Firstly, the DISCOURSE protocol explicitly avoids using proper nouns and names/addresses. Second, the transcribed data is checked, and any proper names are replaced by common names (e.g., 'McGill' will be changed to 'University') or bleeped out (for audio data). If requested, we will play back the recorded responses to check if the participant is comfortable with the degree of anonymity. Identifying demographics will not be stored alongside the speech data to reduce triangulation. Finally, sharing is controlled by password protection, and a re-review is done to remove identifying information before sharing is initiated.
Publications
Zaher F, Diallo M, Achim AM, Joober R, Roy MA, Demers MF, Subramanian P, Lavigne KM, Lepage M, Gonzalez D, Zeljkovic I, Davis K, Mackinley M, Sabesan P, Lal S, Voppel A, Palaniyappan L. Speech markers to predict and prevent recurrent episodes of psychosis: A narrative overview and emerging opportunities. Schizophr Res. 2024 Apr;266:205-215. doi: 10.1016/j.schres.2024.02.036. Epub 2024 Feb 29.
PubMed 38428118
Study at a glance
From the registry record, last updated by the sponsor on 18 May 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Predicting Psychotic Relapse Using Speech-Based Early Detection
- ClinicalTrials.gov ID
- NCT06978894
- Acronym / sponsor ID
- MOTS+ / 2024-979
- Conditions
- Psychosis
- Keywords
- psychosis, natural language processing, relapse, speech, relapse prediction
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 250 participants (estimated)
- Lead sponsor
- Douglas Mental Health University InstituteUniversity, hospital or other
- Study start
- 27 May 2024 (actual)
- Primary completion
- 1 Jul 2028 (estimated)
- Study completion
- 1 Jul 2029 (estimated)
- First posted
- 18 May 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Psychotic disorders, including schizophrenia and affective psychosis, are severe mental health conditions marked by recurrent episodes that contribute to long-term disability. Relapses, characterized by the re-emergence of psychotic symptoms after remission, are a critical factor in the progression of these disorders, increasing risks such as suicide, cognitive impairment, and unemployment. This study aims to develop a novel, speech-based digital model to predict relapses in individuals with psychosis. Building on previous research into language abnormalities in schizophrenia, the study will employ a longitudinal design across Early Psychosis Intervention (EPI) clinics in Ontario and Quebec to advance relapse prediction
Detailed description
OBJECTIVES: The primary goal of this study is to develop and validate a speech-based digital model to predict psychotic relapses in individuals with early psychosis. The study specifically aims to:
Test the hypothesis that within-subject changes in speech coherence, connectedness, and complexity, as measured by natural language processing (NLP) tools, will accurately identify imminent relapse, up to four weeks before clinical relapse in individuals receiving care in Early Psychosis Intervention (EPI) programs.
Investigate whether these speech-based relapse prediction models generalize across different languages (English and French) and are equally predictive in both males and females, addressing potential sociodemographic and linguistic influences on model performance.
Explore whether combining acoustic and prosodic features with core NLP-based speech measures improves the model's sensitivity and specificity for relapse prediction.
METHODS:
This study will employ a longitudinal, prospective design involving 250 first-episode psychosis (FEP) patients recruited from three Early Psychosis Intervention (EPI) clinics in Ontario and Quebec. The study aims to develop and evaluate a speech-based relapse prediction model, with a particular focus on generalizing results across different languages (English and French) and genders.
Participant Recruitment and Stratification:
Participants: A total of 250 FEP patients, including both English- and French-speaking individuals, will be enrolled to ensure linguistic diversity. The sample will be stratified by sex to evaluate model performance across genders.
Language groups: Approximately 60% of the participants will be English speakers and 40% French speakers, reflecting the population served by the EPI clinics.
Gender representation: The study aims to ensure that at least 40% of participants are female to assess gender-based differences in model prediction performance.
Baseline Assessments:
At baseline, participants will undergo a comprehensive in-person assessment to collect a detailed profile for each patient. This will include psychiatric symptomatology using the Positive and Negative Syndrome Scale (PANSS), Calgary Depression Scale and the Personal and Social Performance (PSP) scale, and cognitive functioning. Additionally, socioeconomic variables, historical and current medication usage, substance use (e.g., cannabis), and treatment adherence will also be recorded to provide a full clinical and treatment profile for each participant.
Speech Sampling and Data Collection:
Monthly Speech Samples: After the baseline assessment, participants will provide monthly speech samples over the course of 24 months. These speech samples will be collected using web-based prompts that include open-ended tasks, such as picture description or recall narratives, designed to elicit spontaneous speech.
Attrition and Speech Sample Estimates: Given an expected attrition rate of 35-50%, it is estimated that by the end of the study, 840-960 speech samples will be obtained from English-speaking participants and 660-870 speech samples from French-speaking participants.
Speech Analysis:
The collected speech samples will be analyzed using natural language processing (NLP) methods to extract key features associated with psychosis, including coherence (Measured by lexical predictability), Connectedness (Assessed using speech graph analysis) and Complexity (evaluated using the Analytic Thinking Index (ATI)). These NLP-derived speech metrics will be tracked over time to predict imminent psychotic relapses and compared across subgroups to assess the impact of language and gender on the predictive accuracy of the relapse model.
Data Analysis and Generalization:
The primary objective is to determine whether speech-based relapse prediction models generalize across different languages and genders. To achieve this, model performance will be evaluated across subgroups:
Linguistic subgroup analysis will compare the model's performance in English- and French-speaking participants.
Gender-based analysis will assess whether the predictive power of the speech-based model varies between male and female participants.
This analysis will ensure that the final model can be generalized across diverse populations and adapted for use in different clinical settings.
Study design
- Observational model
- Case only
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Early Psychosis Patients | – | –This study will participants recruited from three Early Psychosis Intervention (EPI) programs across Ontario and Quebec. This group will consist of approximately 250 patients experiencing or having experienced psychosis, enrolled in the EPI programs. |
Who may qualify
- Ages
- 16 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- This study population will consist of approximately 250 patients experiencing or having experienced psychosis, enrolled in the EPI programs.
Key inclusion criteria (4 listed)
- Age must be 16 years and older
- Diagnosis must meet DSM-5 criteria for psychotic disorders, including schizophrenia, schizoaffective disorder, or related conditions
- Fluency in English or French
- Must be currently receiving treatment through an EPI program
Key exclusion criteria (3 listed)
- Severe comorbid speech or language disorders (e.g., aphasia)
- Primary diagnosis of non-psychotic disorders
- Inability to provide consent or complete assessments
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Likelihood of relapse estimated using Speech-NLP Metrics
Time frame: Monthly, up to 24 months
This primary outcome will assess the ability of speech-based NLP metrics (coherence, connectedness, and complexity) to predict impending relapses in psychosis. Monthly speech samples will be analyzed to determine if changes in these metrics can distinguish timepoints preceding relapses from those not followed by relapse, with the aim of predicting relapses up to four weeks in advance. The likelihood of relapse is a numerical probabilistic estimate without any units. Outcome definition: Occurrence of relapse (i.e., psychiatric hospitalization, an increase in the level of psychiatric care, or substantial clinical deterioration >1wk that requires >25% increase in Defined Daily Dose equivalents of antipsychotics)
Generalization of Speech-Based Relapse Prediction Models Across Languages and Genders
Time frame: Monthly, up to 24 months
This outcome will assess whether the speech-based relapse prediction models are valid and perform equally well across different languages (English and French) and genders (male and female). The study will evaluate how sociodemographic factors such as language and sex impact the predictive accuracy of the models. NLP metrics (coherence, connectedness, complexity) will be correlated with clinical outcomes, and model performance will be compared across linguistic and gender subgroups to ensure generalizability.
1 secondary outcome
Likelihood of relapse estimated using multi-level speech features
Time frame: Monthly, up to 24 months
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 18 May 2025.
- Results publication: PubMed 38428118Zaher F, Diallo M, Achim AM, Joober R, Roy MA, Demers MF, Subramanian P, Lavigne KM, Lepage M, Gonzalez D, Zeljkovic I, Davis K, Mackinley M, Sabesan P, Lal S, Voppel A, Palaniyappan L. Speech markers to predict and prevent recurrent episodes of psychosis: A narrative overview and emerging opportunities. Schizophr Res. 2024 Apr;266:205-215. doi: 10.1016/j.schres.2024.02.036. Epub 2024 Feb 29.
- Individual participant dataSharing planned: Yes. We propose to use a clinical linguistic archiving system called the TalkBank (https://www.talkbank.org/) for data sharing purposes. TalkBank registry/database is located at Carnegie Mellon University, Pittsburgh, Pennsylvania-USA. Several filters are used to de-identify the recorded speech data before storage in the databank. Firstly, the DISCOURSE protocol explicitly avoids using proper nouns and names/addresses. Second, the transcribed data is checked, and any proper names are replaced by common names (e.g., 'McGill' will be changed to 'University') or bleeped out (for audio data). If requested, we will play back the recorded responses to check if the participant is comfortable with the degree of anonymity. Identifying demographics will not be stored alongside the speech data to reduce triangulation. Finally, sharing is controlled by password protection, and a re-review is done to remove identifying information before sharing is initiated.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
3 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Psychosis, run by Douglas Mental Health University Institute at 3 sites in Canada.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 18 May 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 sites in Canada. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Lena Palaniyappan, MD, PHD (5147616131 ext. 6116, lena.palaniyappan@mcgill.ca), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Likelihood of relapse estimated using Speech-NLP Metrics (time frame: Monthly, up to 24 months). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06978894, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 18 May 2025.