Associations Between the Severity of Obsessive-Compulsive Disorder and Vocal Features in Children and Adolescents: Protocol for a Statistical and Machine Learning Analysis
BackgroundArtificial 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. ObjectiveWe designed a...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2022-10-01
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| Series: | JMIR Research Protocols |
| Online Access: | https://www.researchprotocols.org/2022/10/e39613 |
| _version_ | 1797734609129046016 |
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| author | Line Katrine Harder Clemmensen Nicole Nadine Lønfeldt Sneha Das Nicklas Leander Lund Valdemar Funch Uhre Anna-Rosa Cecilie Mora-Jensen Linea Pretzmann Camilla Funch Uhre Melanie Ritter Nicoline Løcke Jepsen Korsbjerg Julie Hagstrøm Christine Lykke Thoustrup Iben Thiemer Clemmesen Kersten Jessica Plessen Anne Katrine Pagsberg |
| author_facet | Line Katrine Harder Clemmensen Nicole Nadine Lønfeldt Sneha Das Nicklas Leander Lund Valdemar Funch Uhre Anna-Rosa Cecilie Mora-Jensen Linea Pretzmann Camilla Funch Uhre Melanie Ritter Nicoline Løcke Jepsen Korsbjerg Julie Hagstrøm Christine Lykke Thoustrup Iben Thiemer Clemmesen Kersten Jessica Plessen Anne Katrine Pagsberg |
| author_sort | Line Katrine Harder Clemmensen |
| collection | DOAJ |
| description |
BackgroundArtificial 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.
ObjectiveWe 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.
MethodsAudio 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.
ResultsSimulated results are presented. The actual results using real data will be presented in future publications.
ConclusionsA 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 |
| first_indexed | 2024-03-12T12:46:50Z |
| format | Article |
| id | doaj.art-d88c7047066542b2b55b3acd586f8679 |
| institution | Directory Open Access Journal |
| issn | 1929-0748 |
| language | English |
| last_indexed | 2024-03-12T12:46:50Z |
| publishDate | 2022-10-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Research Protocols |
| spelling | doaj.art-d88c7047066542b2b55b3acd586f86792023-08-28T23:20:35ZengJMIR PublicationsJMIR Research Protocols1929-07482022-10-011110e3961310.2196/39613Associations Between the Severity of Obsessive-Compulsive Disorder and Vocal Features in Children and Adolescents: Protocol for a Statistical and Machine Learning AnalysisLine Katrine Harder Clemmensenhttps://orcid.org/0000-0001-5527-5798Nicole Nadine Lønfeldthttps://orcid.org/0000-0003-3851-8027Sneha Dashttps://orcid.org/0000-0002-4017-1280Nicklas Leander Lundhttps://orcid.org/0000-0002-0492-429XValdemar Funch Uhrehttps://orcid.org/0000-0003-4342-4412Anna-Rosa Cecilie Mora-Jensenhttps://orcid.org/0000-0003-2139-4398Linea Pretzmannhttps://orcid.org/0000-0003-1133-9811Camilla Funch Uhrehttps://orcid.org/0000-0003-3738-9737Melanie Ritterhttps://orcid.org/0000-0003-2006-5680Nicoline Løcke Jepsen Korsbjerghttps://orcid.org/0000-0003-4842-5342Julie Hagstrømhttps://orcid.org/0000-0003-4337-307XChristine Lykke Thoustruphttps://orcid.org/0000-0003-1106-9073Iben Thiemer Clemmesenhttps://orcid.org/0000-0002-6286-5398Kersten Jessica Plessenhttps://orcid.org/0000-0001-5233-6145Anne Katrine Pagsberghttps://orcid.org/0000-0003-0818-8338 BackgroundArtificial 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. ObjectiveWe 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. MethodsAudio 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. ResultsSimulated results are presented. The actual results using real data will be presented in future publications. ConclusionsA 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)DERR1-10.2196/39613https://www.researchprotocols.org/2022/10/e39613 |
| spellingShingle | Line Katrine Harder Clemmensen Nicole Nadine Lønfeldt Sneha Das Nicklas Leander Lund Valdemar Funch Uhre Anna-Rosa Cecilie Mora-Jensen Linea Pretzmann Camilla Funch Uhre Melanie Ritter Nicoline Løcke Jepsen Korsbjerg Julie Hagstrøm Christine Lykke Thoustrup Iben Thiemer Clemmesen Kersten Jessica Plessen Anne Katrine Pagsberg Associations Between the Severity of Obsessive-Compulsive Disorder and Vocal Features in Children and Adolescents: Protocol for a Statistical and Machine Learning Analysis JMIR Research Protocols |
| title | Associations Between the Severity of Obsessive-Compulsive Disorder and Vocal Features in Children and Adolescents: Protocol for a Statistical and Machine Learning Analysis |
| title_full | Associations Between the Severity of Obsessive-Compulsive Disorder and Vocal Features in Children and Adolescents: Protocol for a Statistical and Machine Learning Analysis |
| title_fullStr | Associations Between the Severity of Obsessive-Compulsive Disorder and Vocal Features in Children and Adolescents: Protocol for a Statistical and Machine Learning Analysis |
| title_full_unstemmed | Associations Between the Severity of Obsessive-Compulsive Disorder and Vocal Features in Children and Adolescents: Protocol for a Statistical and Machine Learning Analysis |
| title_short | Associations Between the Severity of Obsessive-Compulsive Disorder and Vocal Features in Children and Adolescents: Protocol for a Statistical and Machine Learning Analysis |
| title_sort | associations between the severity of obsessive compulsive disorder and vocal features in children and adolescents protocol for a statistical and machine learning analysis |
| url | https://www.researchprotocols.org/2022/10/e39613 |
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