ArticleJournal of medical Internet research2022
Text Topics and Treatment Response in Internet-Delivered Cognitive Behavioral Therapy for Generalized Anxiety Disorder: Text Mining Study.
Article in Journal of medical Internet research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review.Frontiers in psychiatry · 2024Pooled it
- Toward the automation of family semantic grids: a pilot exploration of text mining for evidence-informed systemic-relational psychotherapy.Frontiers in child and adolescent psychiatry · 2026Article
- Article
- Therapists' Role in Patient Adherence to Internet-Based Cognitive Behavioral Therapy: Qualitative Study.Journal of medical Internet research · 2025Article
- Integrating Artificial Intelligence and Smartphone Technology to Enhance Personalized Assessment and Treatment for Eating Disorders.The International journal of eating disorders · 2025Review
- Using natural language processing to explore differences in healthcare professionals' language on Functional Neurological Disorder: a comparative topic and sentiment analysis study.Frontiers in digital health · 2025Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundText mining methods such as topic modeling can offer valuable information on how and to whom internet-delivered cognitive behavioral therapies (iCBT) work. Although iCBT treatments provide convenient data for topic modeling, it has rarely been used in this context.
objectiveOur aims were to apply topic modeling to written assignment texts from iCBT for generalized anxiety disorder and explore the resulting topics' associations with treatment response. As predetermining the number of topics presents a considerable challenge in topic modeling, we also aimed to explore a novel method for topic number selection.
methodsWe defined 2 latent Dirichlet allocation (LDA) topic models using a novel data-driven and a more commonly used interpretability-based topic number selection approaches. We used multilevel models to associate the topics with continuous-valued treatment response, defined as the rate of per-session change in GAD-7 sum scores throughout the treatment.
resultsOur analyses included 1686 patients. We observed 2 topics that were associated with better than average treatment response: "well-being of family, pets, and loved ones" from the data-driven LDA model (B=-0.10 SD/session/∆topic; 95% CI -016 to -0.03) and "children, family issues" from the interpretability-based model (B=-0.18 SD/session/∆topic; 95% CI -0.31 to -0.05). Two topics were associated with worse treatment response: "monitoring of thoughts and worries" from the data-driven model (B=0.06 SD/session/∆topic; 95% CI 0.01 to 0.11) and "internet therapy" from the interpretability-based model (B=0.27 SD/session/∆topic; 95% CI 0.07 to 0.46).
conclusionsThe 2 LDA models were different in terms of their interpretability and broadness of topics but both contained topics that were associated with treatment response in an interpretable manner. Our work demonstrates that topic modeling is well suited for iCBT research and has potential to expose clinically relevant information in vast text data.
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