A Wearable AI Feedback Tool for Pediatric OCD
Condition: Obsessive-Compulsive Disorder
MeSH terms: Obsessive-Compulsive Disorder
Study type: Observational

Summary: 
      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.
    

Detailed description: 
      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.
    

Intervention name(s): wearable biosensor; Treatment as usual (TAU); Exposure and response prevention (ERP)
Intervention type(s): Device; Behavioral; Behavioral
Intervention description(s): 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.; One ERP session will be offered in Week 0 and Week 8.
Other intervention name(s): E4 wristband
Observational model: Case-Control
Time perspective: Prospective
Sampling method: Non-Probability Sample
Study population: 
        Patients: seeking treatment at a child and adolescent mental health center. Controls:
        living in the catchment area of a the same child and adolescent mental health center.
      
Biospecimen description: 
      Saliva samples will be collected from patients and healthy controls and one of their
      caregivers at baseline (Week 0) and Week 8 to measure oxytocin concentration.
    
Keywords: Signal processing; Oxytocin; Family Accommodation; Biosensor; Mobile app