Clinical Trials Logo

Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT05758285
Other study ID # 2022-02263; th23Meinlschmidt
Secondary ID
Status Recruiting
Phase
First received
Last updated
Start date March 1, 2023
Est. completion date February 2025

Study information

Verified date April 2024
Source University Hospital, Basel, Switzerland
Contact Gunther Meinlschmidt, Prof.
Phone +41 61 328 63 10
Email gunther.meinlschmidt@usb.ch
Is FDA regulated No
Health authority
Study type Observational

Clinical 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. 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.


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.


Recruitment information / eligibility

Status Recruiting
Enrollment 7000
Est. completion date February 2025
Est. primary completion date February 2025
Accepts healthy volunteers Accepts Healthy Volunteers
Gender All
Age group 18 Years and older
Eligibility 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

Study Design


Related Conditions & MeSH terms


Intervention

Other:
AI-Based Prediction of Treatment Engagement and Outcomes
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).

Locations

Country Name City State
Switzerland University Hospital Basel, Department of Psychosomatic Medicine Basel

Sponsors (1)

Lead Sponsor Collaborator
University Hospital, Basel, Switzerland

Country where clinical trial is conducted

Switzerland, 

Outcome

Type Measure Description Time frame Safety issue
Other Number of messages sent by client Patients' engagement with the digital psychotherapy intervention by assessing the number of patient messages week 1 until week 8
Other Number of messages received by client Patients' engagement with the digital psychotherapy intervention by assessing the number of therapist messages week 1 until week 8
Other Number of phone calls to physician notes Patients' engagement with the digital psychotherapy intervention by assessing the number of phone calls week 1 until week 8
Other Number of times client logged in Patients' engagement with the digital psychotherapy intervention by assessing the number of lessons accessed week 1 until week 8
Primary 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. week 1 until week 8
Primary 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. week 1 until week 8
See also
  Status Clinical Trial Phase
Completed NCT05321602 - Study to Evaluate the PK Profiles of LY03010 in Patients With Schizophrenia or Schizoaffective Disorder Phase 1
Completed NCT05080777 - Pilot Pragmatic Clinical Trial to Embed Tele-Savvy Into Health Care Systems N/A
Recruiting NCT06012149 - Braining: Implementation of Physical Exercise for Patients in Specialist Psychiatry N/A
Recruiting NCT03222375 - SQUED™ Series 28.1 Home-use and Treatment of Autowave Reverberator of Autism N/A
Active, not recruiting NCT02836080 - Integrated Collaborative Care Teams for Youth With Mental Health and/or Addiction Challenges (YouthCan IMPACT) N/A
Active, not recruiting NCT02907658 - Efficacy of Internet Use Disorder Prevention N/A
Completed NCT02710344 - Using Telehealth to Improve Psychiatric Symptom Management N/A
Enrolling by invitation NCT02487888 - A Study of the Impact of Genetic Testing on Clinical Decision Making and Patient Care N/A
Recruiting NCT02292056 - Medication Safety and Contraceptive Counseling for Reproductive Aged Women With Psychiatric Conditions N/A
Active, not recruiting NCT02761733 - The Effectiveness of a Decision-Support Tool for Adult Consumers With Mental Health Needs and Their Care Managers N/A
Completed NCT01947283 - Effectiveness of DECIDE in Patient-Provider Communication, Therapeutic Alliance & Care Continuation N/A
Completed NCT01633138 - Performance-based Reinforcement to Enhance Cognitive Remediation Therapy N/A
Completed NCT01690013 - Life Quality and Health in Patients With Klinefelter Syndrome N/A
Completed NCT01415323 - Agitation in the Acute Psychiatric Department
Completed NCT01656707 - Adaptive Treatment for Adolescent Cannabis Use Disorders N/A
Completed NCT01701765 - Outcomes and Discharge of Long-stay Psychiatric Patients N/A
Completed NCT00375167 - Efficacy of the Recovery Workbook as a Psychoeducational Tool for Facilitating Recovery N/A
Terminated NCT00757497 - Transcranial Direct Current Brain Stimulation to Treat Patients With Childhood-Onset Schizophrenia Phase 1
Terminated NCT03527550 - Cognitive Control Training for Urgency in a Naturalistic Clinical Setting N/A
Withdrawn NCT03518996 - Non-Invasive Brain Stimulation and Delirium N/A