To test the feasibility of implementing digitally enhanced psychotherapy and research in a community child and adolescent mental health center including the acceptability of the digital technology to patients, parents and therapists. To use passively collected physiological data and actively collected clinical and biochemical data from the patient and parents to detect and predict episodes of obsessive-compulsive disorder (OCD) -related episodes in children and accommodating behaviour in parents.
Background: Psychiatric and specifically mechanistic research have stagnated mainly due to the time, labour and bias inherent in human-based technologies that dominate the field. To advance translational and precision psychiatry, researchers within psychiatry must forge long-term collaborations with researchers and developers within technology. Objectives: To improve assessment and psychotherapy for youth obsessive-compulsive disorder (OCD) through developing an artificial intelligence tool to support patients, parents and therapists in cognitive behavioural therapy. To give an innovative push in the public sector hospitals and research through integration of wearable sensors and machine learning techniques. Methods: 10 patients (8-17 years) and one of their parents from a child and adolescent mental health center will be recruited as in the larger TECTO project. To examine whether the algorithms can distinguish between patients and typically developing children, 10 typically developing sex and age matched children and one of their parents or guardians will also be recruited from the catchment area. Passively sensed physiological indicators of stress are used as input to privacy preserving signal processing and machine learning algorithms, which predict OCD-episodes, clinical severity and family accommodation. Oxytocin, as a biomarker for family accommodation, is measured through saliva samples. Signal processing will be used to extract acoustic and physiological features of importance for therapeutic response. Expected results: Results from the proposed project will be used to develop artificial intelligence (AI) tools that support clinicians, patients and parents, which will be implemented and evaluated in a public-sector hospital. Technology-enhanced therapy can be used in a stepped care model, in which subclinical symptoms are first monitored using passive sensors and then AI interventions are offered, supported by a healthcare professional, and when outpatient care is needed, the AI tool can support patient engagement. The results of this project will also advance research in computational science and psychiatry by testing biomarkers of clinical relevance.
Study Type
OBSERVATIONAL
The E4 wristband will be worn by all groups for the duration of the study. It measures blood volume pulse, electrodermal activity, skin temperature, and movement. Patients will be asked to press the event tagging button when they feel stressed by OCD. Control will be asked to press the button when they feel anxious. Parents will be asked to press the button with they notice their child feels stressed by OCD or anxious.
Patients will receive treatment as usual at the child and adolescent mental health center. TAU can range from waitlist to one session of psychoeducation to group or individual psychotherapy to medication.
Child and Adolescent Mental Health Center - Capital Region of Denmark
Copenhagen, Denmark
Binary feasibility
Binary feasibility outcomes in terms of recruitment, retention, biosensor functionality, acceptability of the biosensor, adherence to wearing the biosensor, adverse reactions to the biosensor, and physiological, audio, and visual signals as markers of OCD distress, severity and family dynamics. "Success" indicates that the a priori feasibility criteria have been met; "revise" indicates that the criteria have not been met.
Time frame: Baseline to Week 8
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Enrollment
36
One ERP session will be offered in Week 0 and Week 8.