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BACKGROUND Physiological signals such as heart rate and electrodermal activity can provide insight into an individual’s mental state, which is invaluable information for mental health care. Using recordings of physiological signals from wearable devices in-the-wild can facilitate objective monitoring of symptom severity and evaluation of treatment progress. OBJECTIVE We designed a study to test the feasibility of predicting obsessive-compulsive disorder (OCD) events from physiological signals recorded using wrist-worn devices in-the-wild. Here, we present an analysis plan for the study to document our a-priori hypotheses and increase the robustness of the findings of our planned study. METHODS Time-domain and frequency-domain features are extracted from the heart rate, skin conductivity, and skin temperature signals within sliding windows. The features are used to predict the distress event logged by participants during data collection. We will test the prediction models within participants across time and across multiple participants. Model selection and estimation using two-layer cross-validation is outlined for both scenarios. RESULTS Physiological signals from 18 participants (nine diagnosed with OCD and nine controls) between the ages of 8 and 17 years are collected using wrist-worn devices during waking hours for up to eight weeks. For the nine participants with an OCD diagnosis and the severity of the diagnosis, number of, and type of symptoms has been graded using the CY-BOCS. CONCLUSIONS A major strength of the planned study is the investigation of predictions of OCD events in-the-wild. A major challenge of the study is the inherent noise of in-the-wild data and the lack of contextual knowledge associated with the recorded signals. This pre-registered analysis plan may help reduce interpretation bias of the upcoming results. CLINICALTRIAL ClinicalTrials.gov: NCT05064527, registered October 1, 2021.

Background Artificial intelligence tools have the potential to objectively identify youth in need of mental health care. Speech signals have shown promise as a source for predicting various psychiatric conditions and transdiagnostic symptoms. Objective We designed a study testing the association between obsessive-compulsive disorder (OCD) diagnosis and symptom severity on vocal features in children and adolescents. Here, we present an analysis plan and statistical report for the study to document our a priori hypotheses and increase the robustness of the findings of our planned study. Methods Audio recordings of clinical interviews of 47 children and adolescents with OCD and 17 children and adolescents without a psychiatric diagnosis will be analyzed. Youths were between 8 and 17 years old. We will test the effect of OCD diagnosis on computationally derived scores of vocal activation using ANOVA. To test the effect of OCD severity classifications on the same computationally derived vocal scores, we will perform a logistic regression. Finally, we will attempt to create an improved indicator of OCD severity by refining the model with more relevant labels. Models will be adjusted for age and gender. Model validation strategies are outlined. Results Simulated results are presented. The actual results using real data will be presented in future publications. Conclusions A major strength of this study is that we will include age and gender in our models to increase classification accuracy. A major challenge is the suboptimal quality of the audio recordings, which are representative of in-the-wild data and a large body of recordings collected during other clinical trials. This preregistered analysis plan and statistical report will increase the validity of the interpretations of the upcoming results. International Registered Report Identifier (IRRID) DERR1-10.2196/39613