TY  - JOUR
AU  - Clemmensen, Line Katrine Harder
AU  - Lønfeldt, Nicole Nadine
AU  - Das, Sneha
AU  - Lund, Nicklas Leander
AU  - Uhre, Valdemar Funch
AU  - Mora-Jensen, Anna-Rosa Cecilie
AU  - Pretzmann, Linea
AU  - Uhre, Camilla Funch
AU  - Ritter, Melanie
AU  - Korsbjerg, Nicoline Løcke Jepsen
AU  - Hagstrøm, Julie
AU  - Thoustrup, Christine Lykke
AU  - Clemmesen, Iben Thiemer
AU  - Plessen, Kersten Jessica
AU  - Pagsberg, Anne Katrine
PY  - 2022
DA  - 2022/10/28
TI  - Associations Between the Severity of Obsessive-Compulsive Disorder and Vocal Features in Children and Adolescents: Protocol for a Statistical and Machine Learning Analysis
JO  - JMIR Res Protoc
SP  - e39613
VL  - 11
IS  - 10
KW  - machine learning
KW  - obsessive-compulsive disorder
KW  - vocal features
KW  - speech signals
KW  - children
KW  - teens
KW  - adolescents
KW  - OCD
KW  - AI
KW  - artificial intelligence
KW  - tool
KW  - mental health
KW  - care
KW  - speech
KW  - data
KW  - clinical trial
KW  - validity
KW  - results
AB  - 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 
SN  - 1929-0748
UR  - https://www.researchprotocols.org/2022/10/e39613
UR  - https://doi.org/10.2196/39613
UR  - http://www.ncbi.nlm.nih.gov/pubmed/36306153
DO  - 10.2196/39613
ID  - info:doi/10.2196/39613
ER  -