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.