Text Topics and Treatment Response in Internet-Delivered Cognitive Behavioral Therapy for Generalized Anxiety Disorder: Text Mining Study
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....
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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JMIR Publications
2022-11-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2022/11/e38911 |
| _version_ | 1797734656412483584 |
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| author | Sanna Mylläri Suoma Eeva Saarni Ville Ritola Grigori Joffe Jan-Henry Stenberg Ole André Solbakken Nikolai Olavi Czajkowski Tom Rosenström |
| author_facet | Sanna Mylläri Suoma Eeva Saarni Ville Ritola Grigori Joffe Jan-Henry Stenberg Ole André Solbakken Nikolai Olavi Czajkowski Tom Rosenström |
| author_sort | Sanna Mylläri |
| collection | DOAJ |
| description |
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. |
| first_indexed | 2024-03-12T12:47:29Z |
| format | Article |
| id | doaj.art-84d3ff63ba994475b0dd542d9b4a225a |
| institution | Directory Open Access Journal |
| issn | 1438-8871 |
| language | English |
| last_indexed | 2024-03-12T12:47:29Z |
| publishDate | 2022-11-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj.art-84d3ff63ba994475b0dd542d9b4a225a2023-08-28T23:15:35ZengJMIR PublicationsJournal of Medical Internet Research1438-88712022-11-012411e3891110.2196/38911Text Topics and Treatment Response in Internet-Delivered Cognitive Behavioral Therapy for Generalized Anxiety Disorder: Text Mining StudySanna Myllärihttps://orcid.org/0000-0003-4059-9268Suoma Eeva Saarnihttps://orcid.org/0000-0003-3555-9958Ville Ritolahttps://orcid.org/0000-0001-9065-4347Grigori Joffehttps://orcid.org/0000-0002-0782-6812Jan-Henry Stenberghttps://orcid.org/0000-0003-1327-7757Ole André Solbakkenhttps://orcid.org/0000-0002-8341-0560Nikolai Olavi Czajkowskihttps://orcid.org/0000-0002-3713-653XTom Rosenströmhttps://orcid.org/0000-0001-8277-3776 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.https://www.jmir.org/2022/11/e38911 |
| spellingShingle | Sanna Mylläri Suoma Eeva Saarni Ville Ritola Grigori Joffe Jan-Henry Stenberg Ole André Solbakken Nikolai Olavi Czajkowski Tom Rosenström Text Topics and Treatment Response in Internet-Delivered Cognitive Behavioral Therapy for Generalized Anxiety Disorder: Text Mining Study Journal of Medical Internet Research |
| title | Text Topics and Treatment Response in Internet-Delivered Cognitive Behavioral Therapy for Generalized Anxiety Disorder: Text Mining Study |
| title_full | Text Topics and Treatment Response in Internet-Delivered Cognitive Behavioral Therapy for Generalized Anxiety Disorder: Text Mining Study |
| title_fullStr | Text Topics and Treatment Response in Internet-Delivered Cognitive Behavioral Therapy for Generalized Anxiety Disorder: Text Mining Study |
| title_full_unstemmed | Text Topics and Treatment Response in Internet-Delivered Cognitive Behavioral Therapy for Generalized Anxiety Disorder: Text Mining Study |
| title_short | Text Topics and Treatment Response in Internet-Delivered Cognitive Behavioral Therapy for Generalized Anxiety Disorder: Text Mining Study |
| title_sort | text topics and treatment response in internet delivered cognitive behavioral therapy for generalized anxiety disorder text mining study |
| url | https://www.jmir.org/2022/11/e38911 |
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