Clinical trial NCT05758285: Using Responsible Artificial Intelligence (AI) to Predict Online Therapy Outcome and Engagement
Official title: Using Responsible Artificial Intelligence (AI) to Predict Online Therapy Outcome and Engagement
Study type: OBSERVATIONAL
Overall status: ACTIVE_NOT_RECRUITING
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
AI-based prediction models
Online Therapy Unit
Anxiety symptoms
Depressive symptoms
Wellbeing Course
Lead sponsor: University Hospital, Basel, Switzerland
Sponsor class: OTHER
First posted: 2023-03-07
Enrollment: 6671
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
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.

Brief 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. 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.

Eligibility criteria: Inclusion Criteria:

* 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.

Exclusion Criteria:

* Data will only be excluded in case of errors in data collection

Primary outcomes:
measure: Change in Patient Health Questionnaire 9-item (PHQ9) (percent change)
timeFrame: week 1 until week 8
measure: Change in General Anxiety Disorder-7 Questionnaire (GAD7) (percent change)
timeFrame: week 1 until week 8
Secondary outcomes:
Other outcomes:
measure: Number of messages sent by client
timeFrame: week 1 until week 8
measure: Number of messages received by client
timeFrame: week 1 until week 8
measure: Number of phone calls to physician notes
timeFrame: week 1 until week 8
measure: Number of times client logged in
timeFrame: week 1 until week 8
Locations:
city: Basel
country: Switzerland
facility: University Hospital Basel, Department of Psychosomatic Medicine
Study officials:
affiliation: University Hospital Basel, Department of Psychosomatic Medicine
name: Gunther Meinlschmidt, Prof.
role: PRINCIPAL_INVESTIGATOR
References: