TY - JOUR

T1 - Associations Between the Severity of Obsessive-Compulsive Disorder and Vocal Features in Children and Adolescents

T2 - Protocol for a Statistical and Machine Learning Analysis

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

N1 - Publisher Copyright: © 2022 Line Katrine Harder Clemmensen.

PY - 2022

Y1 - 2022

N2 - 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.

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.

KW - adolescents

KW - AI

KW - artificial intelligence

KW - care

KW - children

KW - clinical trial

KW - data

KW - machine learning

KW - mental health

KW - obsessive-compulsive disorder

KW - OCD

KW - results

KW - speech

KW - speech signals

KW - teens

KW - tool

KW - validity

KW - vocal features

UR - http://www.scopus.com/inward/record.url?scp=85142382055&partnerID=8YFLogxK

U2 - 10.2196/39613

DO - 10.2196/39613

M3 - Journal article

C2 - 36306153

AN - SCOPUS:85142382055

VL - 11

JO - J M I R Research Protocols

JF - J M I R Research Protocols

SN - 1929-0748

IS - 10

M1 - e39613

ER -