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 -