Mental Health CareNCT05758285
Using Responsible Artificial Intelligence (AI) to Predict Online Therapy Outcome and Engagement
An observational study of Mental Health Care and Mental Disorders, run by University Hospital, Basel, Switzerland at 1 site in Switzerland.
Trial summary
This study is to develop AI-based models for the personalised prediction of treatment engagement and treatment outcomes in patients engaging in digital psychotherapy. A large, real-world dataset of patients in a digital psychotherapy program will be used to train AI algorithms. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Mental Health Care, Mental Disorders
- What is being tested?
- AI-Based Prediction of Treatment Engagement and Outcomes
- How many people
- 6,671 participants
- Where
- 1 siteSwitzerland
- Study dates
- 1 Mar 2023to 3 Sep 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Participants at the publicly funded, internet-delivered, cognitive behaviour therapy (iCBT) program in Saskatchewan, Canada. The program provides symptom screenings and an eight-week transdiagnostic iCBT program called the Wellbeing Course. In Saskatchewan, this transdiagnostic iCBT program has been integrated into the public mental health care, for example by assigning clinicians in community mental health clinics to the Online Therapy Unit and by encouraging therapists to direct patients to this service. Data was collected as part of research trials in the Online Therapy Unit from 2013 to 2021.
Who may be able to join
- Participants that were screened as eligible to take part in a Wellbeing Course trial offered at the Online Therapy Unit between Nov 4 2013 and Dec 21 2021.
- Participants that consented to the use of their data to evaluate and improve iCBT services.
- Accessed Lesson 1 of the course content and completed baseline questionnaires.
Who cannot join
- Data will only be excluded in case of errors in data collection
Trial locations and contacts
1 site in 1 country.
Switzerland1 site
University Hospital Basel, Department of Psychosomatic Medicine
Basel, 4031
About this study
This study is to develop AI-based models for the personalised prediction of treatment engagement and treatment outcomes in patients engaging in digital psychotherapy. A large, real-world dataset of patients in a digital psychotherapy program will be used to train AI algorithms. Responsible AI algorithms will be developed by describing, accounting for, and mitigating bias due to severity of mental disturbances in AI-based models, in addition to considering bias due to other sensitive attributes, such as gender, ethnicity, and socio-demographic status.
Read the detailed description
Mental disorders contribute greatly to the global disease burden, but many people do not have access to mental health care. This treatment gap is partly due to structural (e.g., availability) and attitude-related (e.g. fear of stigma) barriers in health care seeking. Digital therapeutics (DTx) in the form of digital mental health interventions or digital psychotherapy may be the solution to this problem. The integration of Information and Communication Technology (ICT) and mental health care has the potential to increase the efficiency of care delivery and enables personalisation of treatments. Artificial Intelligence (AI)-based analysis of large datasets from digital psychotherapy programs may allow developing and validating personalised prediction models. The prediction of individual engagement and the early identification of untoward engagement patterns may improve personalisation of DTx, which could help reduce nonadherence and improve treatment outcome. The personalised prediction of DTx outcomes and engagement patterns may be achieved by implementing AI-based approaches, such as Machine Learning prediction models. Personalised prediction models may lead to a better understanding of who profits most from what kind of DTx in a real-world setting. Taken together, personalisation of DTx treatment outcomes and engagement may i) improve decision making processes in patient-clinician dyads, ii) improve efficiency of digital psychotherapy, iii) reduce suffering of patients, and iv) reduce direct and indirect cost related to mental health care. There is a need to account for potential discrimination due to mental health in AI-based predictions models. Unbiased and non- discriminating AI is often referred to as responsible AI. Accounting for bias in AI-based prediction models based on a specific dataset is especially important in mental health care to prevent acceleration of health discrimination.
This study is to develop AI-based models for the personalised prediction of treatment engagement and treatment outcomes in patients engaging in digital psychotherapy. A large, real-world dataset of patients in a digital psychotherapy program will be used to train AI algorithms. Responsible AI algorithms will be developed by describing, accounting for, and mitigating bias due to severity of mental disturbances in AI-based models, in addition to considering bias due to other sensitive attributes, such as gender, ethnicity, and socio-demographic status. The aim of the proposed project is to estimate AI-based prediction models of treatment engagement and outcomes based on data from the Online Therapy Unit by Prof. Heather Hadjistavropoulos from the University of Regina, Canada. The Online Therapy Unit dataset contains a large amount of data on DTx from people with mental disorders (collected as part of research trials in the Online Therapy Unit from 2013 to 2021) and is derived from the publicly funded, internet-delivered, cognitive behaviour therapy (iCBT) program in Saskatchewan, Canada. In sum, the Online Therapy Unit dataset is highly suitable as a training and test dataset for AI-based prediction models, as it comprises a large number of participants, longitudinal data retrieved from the real world opposed to a clinical trial, and a rich set of predictive features.
- Conditions
- Mental Health Care, Mental Disorders
- Being tested
- AI-Based Prediction of Treatment Engagement and Outcomes
- Keywords
- Artificial Intelligence (AI), Digital Therapeutics (DTx), Internet-delivered, Cognitive Behaviour Therapy (iCBT), Information and Communication Technology (ICT), Explainable Artificial Intelligence (XAI), Digital psychotherapy, Personalised prediction models, Bias, AI-based prediction models, Online Therapy Unit, Anxiety symptoms, Depressive symptoms, Wellbeing Course
- Medical subject headings
- Mental Disorders, Anxiety Disorders, Depression
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-Based Prediction of Treatment Engagement and OutcomesOther
AI-based algorithms and prediction models of treatment engagement and outcomes based on data from the Online Therapy Unit by Prof. Heather Hadjistavropoulos will be trained to predict symptom improvement of patients from pre- to post-digital psychotherapy intervention and to predict patients' engagement with the digital psychotherapy intervention and to predict patient drop out probability. For prediction model estimation, state of the art AI-based algorithms, such as XGBoost, is used . XGBoost is a machine learning method developed by refining previously established decision-tree-based methodologies. Data is split into training and testing sets (e.g., 80/20 split).
What is being measured
The primary outcome is the main question the study is designed to answer.
Change in Patient Health Questionnaire 9-item (PHQ9) (percent change)
Change in PHQ9 (percent change) to evaluate symptom improvement vs. no symptom improvement pre- to post-digital psychotherapy intervention. Patient Health Questionnaire (PHQ-9): Total = /27 ; Depression Severity: 0-4 none, 5-9 mild, 10-14 moderate, 15-19 moderately severe, 20-27 severe.
Measured: week 1 until week 8
Change in General Anxiety Disorder-7 Questionnaire (GAD7) (percent change)
Change in General Anxiety Disorder-7 Questionnaire (GAD7) to evaluate symptom improvement vs. no symptom improvement pre- to post-digital psychotherapy intervention. Score 0-4: Minimal Anxiety · Score 5-9: Mild Anxiety · Score 10-14: Moderate Anxiety · Score greater than 15: Severe Anxiety.
Measured: week 1 until week 8
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Mar 2023
- Study start1 Mar 2023
- Primary completion6 Dec 2024
- Study completion3 Sep 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Mental Health CareStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 6,671 participants (actual)
Sponsor and investigators
- Lead sponsor
- University Hospital, Basel, Switzerland
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Gunther Meinlschmidt, Prof., Principal investigator, University Hospital Basel, Department of Psychosomatic Medicine
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05758285
- Sponsor's protocol ID
- 2022-02263; th23Meinlschmidt
- Status last verified
- Nov 2025
- First submitted
- 24 Feb 2023
- Last updated
- 18 Nov 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Roemmel N, Bahmane S, Hadjistavropoulos HD, Nugent M, Lieb R, Meinlschmidt G. 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. BMJ Open. 2025 Sep 3;15(9):e097651. doi: 10.1136/bmjopen-2024-097651.
PubMed 40903086
Study at a glance
From the registry record, last updated by the sponsor on 18 Nov 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Using Responsible Artificial Intelligence (AI) to Predict Online Therapy Outcome and Engagement
- ClinicalTrials.gov ID
- NCT05758285
- Acronym / sponsor ID
- 2022-02263; th23Meinlschmidt
- Conditions
- Mental Health Care, Mental Disorders
- Keywords
- Artificial Intelligence (AI), Digital Therapeutics (DTx), Internet-delivered, Cognitive Behaviour Therapy (iCBT), Information and Communication Technology (ICT), Explainable Artificial Intelligence (XAI), Digital psychotherapy, Personalised prediction models, Bias
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 6,671 participants (actual)
- Lead sponsor
- University Hospital, Basel, SwitzerlandUniversity, hospital or other
- Study start
- 1 Mar 2023 (actual)
- Primary completion
- 6 Dec 2024 (actual)
- Study completion
- 3 Sep 2025 (actual)
- First posted
- 7 Mar 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This study is to develop AI-based models for the personalised prediction of treatment engagement and treatment outcomes in patients engaging in digital psychotherapy. A large, real-world dataset of patients in a digital psychotherapy program will be used to train AI algorithms. Responsible AI algorithms will be developed by describing, accounting for, and mitigating bias due to severity of mental disturbances in AI-based models, in addition to considering bias due to other sensitive attributes, such as gender, ethnicity, and socio-demographic status.
Detailed description
Mental disorders contribute greatly to the global disease burden, but many people do not have access to mental health care. This treatment gap is partly due to structural (e.g., availability) and attitude-related (e.g. fear of stigma) barriers in health care seeking. Digital therapeutics (DTx) in the form of digital mental health interventions or digital psychotherapy may be the solution to this problem. The integration of Information and Communication Technology (ICT) and mental health care has the potential to increase the efficiency of care delivery and enables personalisation of treatments. Artificial Intelligence (AI)-based analysis of large datasets from digital psychotherapy programs may allow developing and validating personalised prediction models. The prediction of individual engagement and the early identification of untoward engagement patterns may improve personalisation of DTx, which could help reduce nonadherence and improve treatment outcome. The personalised prediction of DTx outcomes and engagement patterns may be achieved by implementing AI-based approaches, such as Machine Learning prediction models. Personalised prediction models may lead to a better understanding of who profits most from what kind of DTx in a real-world setting. Taken together, personalisation of DTx treatment outcomes and engagement may i) improve decision making processes in patient-clinician dyads, ii) improve efficiency of digital psychotherapy, iii) reduce suffering of patients, and iv) reduce direct and indirect cost related to mental health care. There is a need to account for potential discrimination due to mental health in AI-based predictions models. Unbiased and non- discriminating AI is often referred to as responsible AI. Accounting for bias in AI-based prediction models based on a specific dataset is especially important in mental health care to prevent acceleration of health discrimination.
This study is to develop AI-based models for the personalised prediction of treatment engagement and treatment outcomes in patients engaging in digital psychotherapy. A large, real-world dataset of patients in a digital psychotherapy program will be used to train AI algorithms. Responsible AI algorithms will be developed by describing, accounting for, and mitigating bias due to severity of mental disturbances in AI-based models, in addition to considering bias due to other sensitive attributes, such as gender, ethnicity, and socio-demographic status. The aim of the proposed project is to estimate AI-based prediction models of treatment engagement and outcomes based on data from the Online Therapy Unit by Prof. Heather Hadjistavropoulos from the University of Regina, Canada. The Online Therapy Unit dataset contains a large amount of data on DTx from people with mental disorders (collected as part of research trials in the Online Therapy Unit from 2013 to 2021) and is derived from the publicly funded, internet-delivered, cognitive behaviour therapy (iCBT) program in Saskatchewan, Canada. In sum, the Online Therapy Unit dataset is highly suitable as a training and test dataset for AI-based prediction models, as it comprises a large number of participants, longitudinal data retrieved from the real world opposed to a clinical trial, and a rich set of predictive features.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Participants at the publicly funded, internet-delivered, cognitive behaviour therapy (iCBT) program in Saskatchewan, Canada. The program provides symptom screenings and an eight-week transdiagnostic iCBT program called the Wellbeing Course. In Saskatchewan, this transdiagnostic iCBT program has been integrated into the public mental health care, for example by assigning clinicians in community mental health clinics to the Online Therapy Unit and by encouraging therapists to direct patients to this service. Data was collected as part of research trials in the Online Therapy Unit from 2013 to 2021.
Key inclusion criteria (3 listed)
- Participants that were screened as eligible to take part in a Wellbeing Course trial offered at the Online Therapy Unit between Nov 4 2013 and Dec 21 2021.
- Participants that consented to the use of their data to evaluate and improve iCBT services.
- Accessed Lesson 1 of the course content and completed baseline questionnaires.
Key exclusion criteria (1 listed)
- Data will only be excluded in case of errors in data collection
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-Based Prediction of Treatment Engagement and OutcomesAI-based algorithms and prediction models of treatment engagement and outcomes based on data from the Online Therapy Unit by Prof. Heather Hadjistavropoulos will be trained to predict symptom improvement of patients from pre- to post-digital psychotherapy intervention and to predict patients' engagement with the digital psychotherapy intervention and to predict patient drop out probability. For prediction model estimation, state of the art AI-based algorithms, such as XGBoost, is used . XGBoost is a machine learning method developed by refining previously established decision-tree-based methodologies. Data is split into training and testing sets (e.g., 80/20 split). | Other | – |
Primary outcome
Change in Patient Health Questionnaire 9-item (PHQ9) (percent change)
Time frame: week 1 until week 8
Change in PHQ9 (percent change) to evaluate symptom improvement vs. no symptom improvement pre- to post-digital psychotherapy intervention. Patient Health Questionnaire (PHQ-9): Total = /27 ; Depression Severity: 0-4 none, 5-9 mild, 10-14 moderate, 15-19 moderately severe, 20-27 severe.
Change in General Anxiety Disorder-7 Questionnaire (GAD7) (percent change)
Time frame: week 1 until week 8
Change in General Anxiety Disorder-7 Questionnaire (GAD7) to evaluate symptom improvement vs. no symptom improvement pre- to post-digital psychotherapy intervention. Score 0-4: Minimal Anxiety · Score 5-9: Mild Anxiety · Score 10-14: Moderate Anxiety · Score greater than 15: Severe Anxiety.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 18 Nov 2025.
- Results publication: PubMed 40903086Roemmel N, Bahmane S, Hadjistavropoulos HD, Nugent M, Lieb R, Meinlschmidt G. 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. BMJ Open. 2025 Sep 3;15(9):e097651. doi: 10.1136/bmjopen-2024-097651.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Gunther Meinlschmidt, Prof. principal investigator
University Hospital Basel, Department of Psychosomatic Medicine
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Mental Health Care and Mental Disorders, run by University Hospital, Basel, Switzerland at 1 site in Switzerland.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 18 Nov 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Switzerland. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Change in Patient Health Questionnaire 9-item (PHQ9) (percent change) (time frame: week 1 until week 8). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05758285, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 18 Nov 2025.