Prediction of outcome in internet‐delivered cognitive behaviour therapy for paediatric obsessive‐compulsive disorder: A machine learning approach
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Background: There are no consistent predictors of treatment outcome in paediatric obsessive–compulsive disorder (OCD). One reason for this might be the use of suboptimal statistical methodology. Machine learning is an approach to efficiently analyse complex data. Machine learning has been widely used within other fields, but has rarely been tested in the prediction of paediatric mental health treatment outcomes. Objective: To test four different machine learning methods in the prediction of treatment response in a sample of paediatric OCD patients who had received Internet‐delivered cognitive behaviour therapy (ICBT). Methods: Participants were 61 adolescents (12–17 years) who enrolled in a randomized controlled trial and received ICBT. All clinical baseline variables were used to predict strictly defined treatment response status three months after ICBT. Four machine learning algorithms were implemented. For comparison, we also employed a traditional logistic regression approach. Results: Multivariate logistic regression could not detect any significant predictors. In contrast, all four machine learning algorithms performed well in the prediction of treatment response, with 75 to 83% accuracy. Conclusions: The results suggest that machine learning algorithms can successfully be applied to predict paediatric OCD treatment outcome. Validation studies and studies in other disorders are warranted.
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Studies that ranked high for selective reporting bias tended to report only user satisfaction data and treatment adherence, despite their methods considering collection and assessment of psychiatric scale data and other effectiveness outcomes.
“The studies that ranked high for selective reporting bias tended to report only user satisfaction data and treatment adherence, despite their methods considering the collection and assessment of psychiatric scale data and other effectiveness outcomes (29, 76, 79, 85–88, 92).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [88]
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Experimental — accuracy not yet measuredLenhard et al. (2018) suggested that eCBT for OCD may be better suited for individuals with mild to moderate symptoms.
“Based on these results they suggested that eCBT for OCD may be better suited for individuals with mild to moderate symptoms (88).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [88]
The Discussion (page unavailable) suggests that internet-delivered CBT may suit patients with moderate OCD symptom severity better than those with severe symptoms. The study concerns paediatric OCD and does not specifically recommend treatment for mild symptoms, so the claim extends beyond what the paper says. (LLM, conf=0.84)
Experimental — accuracy not yet measuredLenhard et al. (2018) identified younger age of symptom onset, and duration and severity of symptoms in patients participating in eCBT for OCD as predictors of poorer treatment response with 75% to 83% accuracy.
“Lenhard et al. (2018) identified younger age of symptom onset, and duration and severity of symptoms in patients participating in eCBT for OCD were predictors of poorer treatment response with 75% to 83% accuracy.”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [88]
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- Authors
- Lenhard, F., Sauer, S., Andersson, E., Månsson, K. N., Mataix‐Cols, D., Rück, C., & Serlachius, E.
- Affiliations
- Region Stockholm (SE)
- FOM University of Applied Sciences for Economics and Management (DE)
- Karolinska Institutet (SE)
- Stockholm University (SE)
- Corresponding author
- Lenhard, Fabian
- ORCID
- Venue
- International Journal of Methods in Psychiatric Research
- Publisher
- Wiley
- Published
- 2017-07-28
- Volume
- 27
- Issue
- 1
- ISSN
- 1049-8931 (ISSN-L)
- 1557-0657
- PMC
- PMC6877165
- OpenAlex
- W2742084467
- Cited by
- 84
- References
- 59
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 15 · 2023: 16 · 2022: 9 · 2021: 8 · 2020: 10 · 2019: 10 · 2018: 5
- FWCI
- 8.48
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Journal of Methods in Psychiatric Research (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Forskningsrådet om Hälsa, Arbetsliv och Välfärd (2014‐4052)
- Vetenskapsrådet (K2013‐61P‐22168)
- Stockholms Läns Landsting (20120167, 20140085)
- Topics
- Social Sciences › Psychology › Clinical Psychology › Obsessive-Compulsive Spectrum Disorders
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Keywords
- pediatric obsessive-compulsive disorder, internet-based cognitive behavioral therapy, treatment outcome prediction, treatment response, machine learning, adolescents, predictors of treatment response, logistic regression comparison
- MeSH terms
- Machine Learning, Adolescent, Child, Female, Humans, Male, Obsessive-Compulsive Disorder, Cognitive Behavioral Therapy, Outcome Assessment, Health Care, Telemedicine, Internet
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