AI-driven analyzes of open-ended responses to assess outcomes of internet-based cognitive behavioral therapy (ICBT) in adolescents with anxiety and depression comorbidity
AI-driven analyzes of open-ended responses to assess outcomes of internet-based cognitive behavioral therapy (ICBT) in adolescents with anxiety and depression comorbidity
StatusVoR
Alternative title
Authors
Garcia, Danilo
Granjard, Alexandre
Vanhée, Lois
Berg, Matilda
Andersson, Gerhard
Lasota, Marta
Sikström, Sverker
Monograph
Monograph (alternative title)
Date
2025-04-19
Publisher
Journal title
Journal of Affective Disorders
Issue
Volume
381
Pages
Pages
659-668
ISSN
0165-0327
ISSN of series
Access date
2025-04-19
Abstract PL
Abstract EN
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.
Abstract other
Keywords PL
Keywords EN
Artificial intelligence
Outcome assessment
Internet-based cognitive behavioral therapy
Mental health interventions
Natural language
Outcome assessment
Internet-based cognitive behavioral therapy
Mental health interventions
Natural language
Keywords other
Exhibition title
Place of exhibition (institution)
Exhibition curator
Type
License type
Funder
Time range from
Time range to
Contact person name
Related publication
Related publication
Grant/project name
Collections
Views
7Acquisition Date14.07.2025
Downloads
6Acquisition Date14.07.2025
Altmetrics©
Dimensions
Google Scholar
