Evidence map›Paper›PMID 40903086›Full record

Observational studyBMJ open2025

Prediction of treatment outcome in patients receiving internet-delivered cognitive behavioural therapy for depressive and anxiety symptoms: a machine learning analysis of data from a healthcare-embedded longitudinal study.

Noa Roemmel, Sanaa Bahmane, Heather D Hadjistavropoulos, Marcie Nugent, Roselind Lieb, Gunther Meinlschmidt

Registry-linked trialAbstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05758285 (Using Responsible Artificial Intelligence), which is not on this map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT05758285 completednot on this map

Using Responsible Artificial Intelligence (AI) to Predict Online Therapy Outcome and Engagement

TypeobservationalSponsorUniversity Hospital, Basel, SwitzerlandRan2023 to 2025Enrolled6,671ConditionsMental Health Care, Mental DisordersArmsAI-Based Prediction of Treatment Engagement and Outcomes
3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Noa RoemmelDepartment of Digital and Blended Psychosomatics and Psychotherapy, University Hospital Basel, Basel, Switzerland.ORCID http://orcid.org/0000-0001-7118-7720
Sanaa BahmaneDepartment of Digital and Blended Psychosomatics and Psychotherapy, University Hospital Basel, Basel, Switzerland.
Heather D HadjistavropoulosOnline Therapy Unit, Department of Psychology, University of Regina, Regina, Saskatchewan, Canada.
Marcie NugentOnline Therapy Unit, Department of Psychology, University of Regina, Regina, Saskatchewan, Canada.
Roselind LiebDivision of Clinical Psychology and Epidemiology, Faculty of Psychology, University of Basel, Basel, Switzerland.
Gunther MeinlschmidtDepartment of Digital and Blended Psychosomatics and Psychotherapy, University Hospital Basel, Basel, Switzerland gunther.meinlschmidt@unibas.ch.ORCID http://orcid.org/0000-0002-3488-193X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDigital therapeutics (DTx) show promise in bridging mental healthcare gaps. However, treatment selection often relies on availability and trial-and-error, prolonging suffering and increasing costs. Personalised prediction models could help identify individuals benefiting most from specific DTx.

objectiveThe aim of this secondary analysis was to establish a machine learning-based prediction model for positive treatment outcomes in patients with depressive or anxiety symptoms after 8 weeks of internet-delivered cognitive behavioural therapy (iCBT).

methodsWe analysed a large real-world dataset of patients from the online therapy unit iCBT programme in Saskatchewan, Canada (2013-2021). Clinically significant changes in depressive symptoms or anxiety were measured using the Patient Health Questionnaire-9 (PHQ-9) and the Generalised Anxiety Disorder-7 (GAD-7). We trained six prediction models using sociodemographic and mental health-related factors at baseline, compared model performances and calculated Shapley values for feature importance.

findingsData from 4175 patients using 34 features for prediction, identified by least absolute shrinkage and selection operator regression, showed the Gradient Boosted Model (gbm) and logistic regression (log) performed best, with balanced accuracies of 0.76, 95% CI (0.70 to 0.83) and 0.70, 95% CI (0.63 to 0.77). Shapley values indicated GAD-7 scores at baseline as the most important predictor of clinically significant improvement, along with mental health history and sociodemographic variables.

conclusionsThe gbm and log models achieved comparable accuracy in predicting clinically significant improvement after iCBT, supporting the use of simpler, interpretable methods in clinical practice. CLINICAL IMPLICATIONS: These findings could help improve mental health treatment selection, iCBT assignment, enhance effectiveness and optimise treatment for patients. TRIAL REGISTRATION NUMBER: NCT05758285.

Indexed as

AnxietyAnxiety DisordersCognitive Behavioral TherapyDepressionInternet-Based InterventionMachine LearningAdultAgedFemaleHumansInternetLongitudinal StudiesMaleMiddle AgedSaskatchewanTreatment OutcomeDepression & mood disordersMachine LearningMENTAL HEALTHPUBLIC HEALTH

Identifiers

PMID40903086
PMCPMC12410605

What OpenQuestion holds

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.