@Article{info:doi/10.2196/53562,
author="Pellemans, Mathijs
and Salmi, Salim
and M{\'e}relle, Saskia
and Janssen, Wilco
and van der Mei, Rob",
title="Automated Behavioral Coding to Enhance the Effectiveness of Motivational Interviewing in a Chat-Based Suicide Prevention Helpline: Secondary Analysis of a Clinical Trial",
journal="J Med Internet Res",
year="2024",
month="Aug",
day="1",
volume="26",
pages="e53562",
keywords="motivational interviewing; behavioral coding; suicide prevention; artificial intelligence; effectiveness; counseling; support tool; online help; mental health",
abstract="Background: With the rise of computer science and artificial intelligence, analyzing large data sets promises enormous potential in gaining insights for developing and improving evidence-based health interventions. One such intervention is the counseling strategy motivational interviewing (MI), which has been found effective in improving a wide range of health-related behaviors. Despite the simplicity of its principles, MI can be a challenging skill to learn and requires expertise to apply effectively. Objective: This study aims to investigate the performance of artificial intelligence models in classifying MI behavior and explore the feasibility of using these models in online helplines for mental health as an automated support tool for counselors in clinical practice. Methods: We used a coded data set of 253 MI counseling chat sessions from the 113 Suicide Prevention helpline. With 23,982 messages coded with the MI Sequential Code for Observing Process Exchanges codebook, we trained and evaluated 4 machine learning models and 1 deep learning model to classify client- and counselor MI behavior based on language use. Results: The deep learning model BERTje outperformed all machine learning models, accurately predicting counselor behavior (accuracy=0.72, area under the curve [AUC]=0.95, Cohen $\kappa$=0.69). It differentiated MI congruent and incongruent counselor behavior (AUC=0.92, $\kappa$=0.65) and evocative and nonevocative language (AUC=0.92, $\kappa$=0.66). For client behavior, the model achieved an accuracy of 0.70 (AUC=0.89, $\kappa$=0.55). The model's interpretable predictions discerned client change talk and sustain talk, counselor affirmations, and reflection types, facilitating valuable counselor feedback. Conclusions: The results of this study demonstrate that artificial intelligence techniques can accurately classify MI behavior, indicating their potential as a valuable tool for enhancing MI proficiency in online helplines for mental health. Provided that the data set size is sufficiently large with enough training samples for each behavioral code, these methods can be trained and applied to other domains and languages, offering a scalable and cost-effective way to evaluate MI adherence, accelerate behavioral coding, and provide therapists with personalized, quick, and objective feedback. ",
issn="1438-8871",
doi="10.2196/53562",
url="https://www.jmir.org/2024/1/e53562",
url="https://doi.org/10.2196/53562",
url="http://www.ncbi.nlm.nih.gov/pubmed/39088244"
}