AI-driven analyzes of open-ended responses to assess outcomes of internet-based cognitive behavioral therapy (ICBT) in adolescents with anxiety and depression comorbidity

Danilo Garcia; Alexandre Granjard; Loïs Vanhée; Matilda Berg; Gerhard Andersson; Marta Lasota; Sverker Sikström

doi:10.1016/j.jad.2025.04.003

AI-driven analyzes of open-ended responses to assess outcomes of internet-based cognitive behavioral therapy (ICBT) in adolescents with anxiety and depression comorbidity

J Affect Disord. 2025 Jul 15:381:659-668. doi: 10.1016/j.jad.2025.04.003. Epub 2025 Apr 3.

Authors

Danilo Garcia¹, Alexandre Granjard², Loïs Vanhée³, Matilda Berg⁴, Gerhard Andersson⁵, Marta Lasota⁶, Sverker Sikström⁷

Affiliations

¹ Department of Social Studies, University of Stavanger, Stavanger, Norway; Department of Behavioral Sciences and Learning, Linköping University, Linköping, Sweden; Promotion of Health and Innovation for Well-Being (PHI-WELL), Department of Social Studies, University of Stavanger, Stavanger, Norway; Lab for Biopsychosocial Personality Research (BPS-PR), International Network for Well-Being; Promotion of Health and Innovation (PHI) Lab, International Network for Well-Being; Centre for Ethics, Law and Mental Health (CELAM), University of Gothenburg, Gothenburg Sweden; Department of Psychology, University of Gothenburg, Gothenburg, Sweden. Electronic address: danilo.garcia@icloud.com.
² Promotion of Health and Innovation for Well-Being (PHI-WELL), Department of Social Studies, University of Stavanger, Stavanger, Norway; Lab for Biopsychosocial Personality Research (BPS-PR), International Network for Well-Being; Promotion of Health and Innovation (PHI) Lab, International Network for Well-Being; Department of Psychology, University of Gothenburg, Gothenburg, Sweden.
³ Department of Computing Science, Umeå University, Umeå, Sweden.
⁴ Department of Behavioral Sciences and Learning, Linköping University, Linköping, Sweden.
⁵ Department of Behavioral Sciences and Learning, Linköping University, Linköping, Sweden; Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden; Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
⁶ SWPS University of Social Sciences and Humanities.
⁷ Promotion of Health and Innovation for Well-Being (PHI-WELL), Department of Social Studies, University of Stavanger, Stavanger, Norway; Promotion of Health and Innovation (PHI) Lab, International Network for Well-Being; Department of Psychology, Lund University, Lund, Sweden. Electronic address: sverker.sikstrom@psy.lu.se.

PMID: 40187428
DOI: 10.1016/j.jad.2025.04.003

Abstract

Objective: Although patients prefer describing their problems using words, mental health interventions are commonly evaluated using rating scales. Fortunately, recent advances in natural language processing (i.e., AI-methods) yield new opportunities to quantify people's own mental health descriptions. Our aim was to explore whether responses to open-ended questions, quantified using AI, provide additional value in measuring intervention outcomes compared to traditional rating scales.

Method: Swedish adolescents (N = 44) who received Internet-based Cognitive Behavioral Therapy (ICBT) for eight weeks completed (pre/post) scales measuring anxiety and depression and three open-ended questions (related to depression, anxiety and general mental health). The language responses were quantified using a large language model and quantitative methods to predict mental health as measured by rating scales, valence (i.e., words' positive/negative affectivity), and semantic content (i.e., meaning).

Results: Similar to the rating scales, language measures revealed statistically significant health improvements between pre and post measures such as reduced depression and anxiety symptoms and an increase in the use of words conveying positive emotions and different meanings (e.g., pre-intervention: "anxious", depressed; post-intervention: "happy", "the future"). Notably, the health changes identified through semantic content measures remained statistically significant even after accounting for the changes captured by the rating scales.

Conclusion: Language responses analyzed using AI-methods assessed outcomes with fewer items, demonstrating effectiveness and accuracy comparable to traditional rating scales. Additionally, this approach provided valuable insights into patients' well-being beyond mere symptom reduction, thus highlighting areas of improvement that rating scales often overlook. Since patients often prefer using natural language to express their mental health, this method could complement, and address comprehension issues associated fixed-item questionnaires.

Keywords: Artificial intelligence; Internet-based cognitive behavioral therapy; Mental health interventions; Natural language; Outcome assessment.

MeSH terms

Adolescent
Anxiety Disorders* / epidemiology
Anxiety Disorders* / psychology
Anxiety Disorders* / therapy
Anxiety* / therapy
Cognitive Behavioral Therapy* / methods
Comorbidity
Depression* / therapy
Depressive Disorder* / epidemiology
Depressive Disorder* / psychology
Depressive Disorder* / therapy
Female
Humans
Internet*
Internet-Based Intervention*
Male
Psychiatric Status Rating Scales
Sweden
Treatment Outcome