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

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cris.lastimport.scopus2025-08-31T03:15:10Z
dc.abstract.enObjective: 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.
dc.affiliationWydział Psychologii i Prawa w Poznaniu
dc.contributor.authorGarcia, Danilo
dc.contributor.authorGranjard, Alexandre
dc.contributor.authorVanhée, Lois
dc.contributor.authorBerg, Matilda
dc.contributor.authorAndersson, Gerhard
dc.contributor.authorLasota, Marta
dc.contributor.authorSikström, Sverker
dc.date.access2025-04-19
dc.date.accessioned2025-05-27T09:56:03Z
dc.date.available2025-05-27T09:56:03Z
dc.date.created2025-04-01
dc.date.issued2025-04-19
dc.description.accesstimeat_publication
dc.description.physical659-668
dc.description.versionfinal_published
dc.description.volume381
dc.identifier.doi10.1016/j.jad.2025.04.003
dc.identifier.issn0165-0327
dc.identifier.urihttps://share.swps.edu.pl/handle/swps/1478
dc.identifier.weblinkhttps://pubmed.ncbi.nlm.nih.gov/40187428/
dc.languageen
dc.pbn.affiliationpsychologia
dc.rightsCC-BY
dc.rights.questionYes_rights
dc.share.articleOTHER
dc.subject.enArtificial intelligence
dc.subject.enOutcome assessment
dc.subject.enInternet-based cognitive behavioral therapy
dc.subject.enMental health interventions
dc.subject.enNatural language
dc.swps.sciencecloudsend
dc.titleAI-driven analyzes of open-ended responses to assess outcomes of internet-based cognitive behavioral therapy (ICBT) in adolescents with anxiety and depression comorbidity
dc.title.journalJournal of Affective Disorders
dc.typeJournalArticle
dspace.entity.typeArticle