Family-based cognitive behavioural therapy versus family-based relaxation therapy for obsessive-compulsive disorder in children and adolescents (the TECTO trial): a statistical analysis plan for the randomised clinical trial

Background Obsessive compulsive disorder (OCD) in youth is characterized by behaviors, emotions, physiological reactions, and family interaction patterns. An essential component of therapy involves increasing awareness of the links among thoughts, emotions, behaviors, bodily sensations, and family interactions. An automatic assessment tool using physiological signals from a wearable biosensor may enable continuous symptom monitoring inside and outside of the clinic and support cognitive behavioral therapy for OCD. Objective The primary aim of this study is to evaluate the feasibility and acceptability of using a wearable biosensor to monitor OCD symptoms. The secondary aim is to explore the feasibility of developing clinical and research tools that can detect and predict OCD-relevant internal states and interpersonal processes with the use of speech and behavioral signals. Methods Eligibility criteria for the study include children and adolescents between 8 and 17 years of age diagnosed with OCD, controls with no psychiatric diagnoses, and one parent of the participating youths. Youths and parents wear biosensors on their wrists that measure pulse, electrodermal activity, skin temperature, and acceleration. Patients and their parents mark OCD episodes, while control youths and their parents mark youth fear episodes. Continuous, in-the-wild data collection will last for 8 weeks. Controlled experiments designed to link physiological, speech, behavioral, and biochemical signals to mental states are performed at baseline and after 8 weeks. Interpersonal interactions in the experiments are filmed and coded for behavior. The films are also processed with computer vision and for speech signals. Participants complete clinical interviews and questionnaires at baseline, and at weeks 4, 7, and 8. Feasibility criteria were set for recruitment, retention, biosensor functionality and acceptability, adherence to wearing the biosensor, and safety related to the biosensor. As a first step in learning the associations between signals and OCD-related parameters, we will use paired t tests and mixed effects models with repeated measures to assess associations between oxytocin, individual biosignal features, and outcomes such as stress-rest and case-control comparisons. Results The first participant was enrolled on December 3, 2021, and recruitment closed on December 31, 2022. Nine patient dyads and nine control dyads were recruited. Sixteen participating dyads completed follow-up assessments. Conclusions The results of this study will provide preliminary evidence for the extent to which a wearable biosensor that collects physiological signals can be used to monitor OCD severity and events in youths. If we find the study to be feasible, further studies will be conducted to integrate biosensor signals output into machine learning algorithms that can provide patients, parents, and therapists with actionable insights into OCD symptoms and treatment progress. Future definitive studies will be tasked with testing the accuracy of machine learning models to detect and predict OCD episodes and classify clinical severity. Trial Registration ClinicalTrials.gov NCT05064527; https://clinicaltrials.gov/ct2/show/NCT05064527 International Registered Report Identifier (IRRID) DERR1-10.2196/45123

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A Wearable Artificial Intelligence Feedback Tool (Wrist Angel) for Treatment and Research of Obsessive Compulsive Disorder: Protocol for a Nonrandomized Pilot Study (Preprint)

Lønfeldt¹,

Clemmensen²,

Pagsberg³

2022

Preprint

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BACKGROUND Obsessive compulsive disorder (OCD) in youth is characterized by behaviors, emotions, physiological reactions, and family interaction patterns. Machine learning (ML) models are available that can use behavioral, physiological, and speech signals that reflect autonomic nervous system activation to differentiate individuals with and without psychiatric symptoms and classify clinical severity with relatively high levels of accuracy. An outstanding question is whether ML models are accurate enough to be used as reliable, objective assessment tools in clinical practice. Furthermore, is it plausible that an automatic assessment tool using physiological signals from a wearable biosensor would enable continuous symptom monitoring inside and outside of the clinic. However, in-the-wild signal processing is still a challenge. Thus, we aim to develop ML models that can detect and predict OCD episodes and objectively classify clinical severity in and outside of the clinic. OBJECTIVE Thus, we aim to develop ML models that can detect and predict OCD episodes and objectively classify clinical severity in and outside of the clinic. METHODS In a pilot study, 10 children and adolescents with OCD and one of their parents and 10 children and adolescents with no psychiatric diagnoses and one of their parents wear biosensors on their wrist that measures pulse, electrodermal activity, skin temperature, and acceleration. Patients and their parents mark OCD episodes and controls mark fear episodes. Continuous, in-the-wild, data collection lasts eight weeks. Controlled experiments designed to link physiological signals to mental states are conducted at baseline and after eight weeks. Interpersonal interactions in the experiments are filmed and coded for behavior. The films are also processed with computer vision and for speech signals. Participants complete clinical interviews and questionnaires at baseline, Week 4, 7, and 8. RESULTS The purpose of the Wrist Angel pilot study is to test the feasibility of implementing psychotherapy and research enhanced with artificial intelligence in a public child and adolescent mental health center. If we find the study feasible in terms of recruitment, retention, biosensor functionality and acceptability, adherence to wearing the biosensor, safety related to the biosensor, and using physiological, audio, and visual signals as markers of OCD further studies will be conducted to integrate biosensor, audio and visual sensor output into ML algorithms that can provide patients, parents and therapists with actionable insights into their OCD symptoms and treatment progress. CONCLUSIONS Future definitive studies will be tasked with testing the accuracy of ML models to detect and predict OCD episodes and classify clinical severity. CLINICALTRIAL ClinicalTrials.gov: NCT05064527, registered October 1, 2021.

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Family-based cognitive behavioural therapy versus family-based relaxation therapy for obsessive-compulsive disorder in children and adolescents (the TECTO trial): a statistical analysis plan for the randomised clinical trial

Cited by 3 publications

References 45 publications

Review: Adverse event monitoring and reporting in studies of pediatric psychosocial interventions: a systematic review

Review: Adverse event monitoring and reporting in studies of pediatric psychosocial interventions: a systematic review

A Wearable Artificial Intelligence Feedback Tool (Wrist Angel) for Treatment and Research of Obsessive Compulsive Disorder: Protocol for a Nonrandomized Pilot Study

A Wearable Artificial Intelligence Feedback Tool (Wrist Angel) for Treatment and Research of Obsessive Compulsive Disorder: Protocol for a Nonrandomized Pilot Study (Preprint)

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