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Voice analyses using smartphone-based data in patients with bipolar disorder, unaffected relatives and healthy control individuals, and during different affective states

BACKGROUND: Voice features have been suggested as objective markers of bipolar disorder (BD). AIMS: To investigate whether voice features from naturalistic phone calls could discriminate between (1) BD, unaffected first-degree relatives (UR) and healthy control individuals (HC); (2) affective states...

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Autores principales: Faurholt-Jepsen, Maria, Rohani, Darius Adam, Busk, Jonas, Vinberg, Maj, Bardram, Jakob Eyvind, Kessing, Lars Vedel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8632566/
https://www.ncbi.nlm.nih.gov/pubmed/34850296
http://dx.doi.org/10.1186/s40345-021-00243-3
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author Faurholt-Jepsen, Maria
Rohani, Darius Adam
Busk, Jonas
Vinberg, Maj
Bardram, Jakob Eyvind
Kessing, Lars Vedel
author_facet Faurholt-Jepsen, Maria
Rohani, Darius Adam
Busk, Jonas
Vinberg, Maj
Bardram, Jakob Eyvind
Kessing, Lars Vedel
author_sort Faurholt-Jepsen, Maria
collection PubMed
description BACKGROUND: Voice features have been suggested as objective markers of bipolar disorder (BD). AIMS: To investigate whether voice features from naturalistic phone calls could discriminate between (1) BD, unaffected first-degree relatives (UR) and healthy control individuals (HC); (2) affective states within BD. METHODS: Voice features were collected daily during naturalistic phone calls for up to 972 days. A total of 121 patients with BD, 21 UR and 38 HC were included. A total of 107.033 voice data entries were collected [BD (n  = 78.733), UR (n  = 8004), and HC (n  =  20.296)]. Daily, patients evaluated symptoms using a smartphone-based system. Affective states were defined according to these evaluations. Data were analyzed using random forest machine learning algorithms. RESULTS: Compared to HC, BD was classified with a sensitivity of 0.79 (SD 0.11)/AUC  = 0.76 (SD 0.11) and UR with a sensitivity of 0.53 (SD 0.21)/AUC of 0.72 (SD 0.12). Within BD, compared to euthymia, mania was classified with a specificity of 0.75 (SD 0.16)/AUC  =  0.66 (SD 0.11). Compared to euthymia, depression was classified with a specificity of 0.70 (SD 0.16)/AUC  =  0.66 (SD 0.12). In all models the user dependent models outperformed the user independent models. Models combining increased mood, increased activity and insomnia compared to periods without performed best with a specificity of 0.78 (SD 0.16)/AUC  =  0.67 (SD 0.11). CONCLUSIONS: Voice features from naturalistic phone calls may represent a supplementary objective marker discriminating BD from HC and a state marker within BD.
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spelling pubmed-86325662021-12-01 Voice analyses using smartphone-based data in patients with bipolar disorder, unaffected relatives and healthy control individuals, and during different affective states Faurholt-Jepsen, Maria Rohani, Darius Adam Busk, Jonas Vinberg, Maj Bardram, Jakob Eyvind Kessing, Lars Vedel Int J Bipolar Disord Research BACKGROUND: Voice features have been suggested as objective markers of bipolar disorder (BD). AIMS: To investigate whether voice features from naturalistic phone calls could discriminate between (1) BD, unaffected first-degree relatives (UR) and healthy control individuals (HC); (2) affective states within BD. METHODS: Voice features were collected daily during naturalistic phone calls for up to 972 days. A total of 121 patients with BD, 21 UR and 38 HC were included. A total of 107.033 voice data entries were collected [BD (n  = 78.733), UR (n  = 8004), and HC (n  =  20.296)]. Daily, patients evaluated symptoms using a smartphone-based system. Affective states were defined according to these evaluations. Data were analyzed using random forest machine learning algorithms. RESULTS: Compared to HC, BD was classified with a sensitivity of 0.79 (SD 0.11)/AUC  = 0.76 (SD 0.11) and UR with a sensitivity of 0.53 (SD 0.21)/AUC of 0.72 (SD 0.12). Within BD, compared to euthymia, mania was classified with a specificity of 0.75 (SD 0.16)/AUC  =  0.66 (SD 0.11). Compared to euthymia, depression was classified with a specificity of 0.70 (SD 0.16)/AUC  =  0.66 (SD 0.12). In all models the user dependent models outperformed the user independent models. Models combining increased mood, increased activity and insomnia compared to periods without performed best with a specificity of 0.78 (SD 0.16)/AUC  =  0.67 (SD 0.11). CONCLUSIONS: Voice features from naturalistic phone calls may represent a supplementary objective marker discriminating BD from HC and a state marker within BD. Springer Berlin Heidelberg 2021-12-01 /pmc/articles/PMC8632566/ /pubmed/34850296 http://dx.doi.org/10.1186/s40345-021-00243-3 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research
Faurholt-Jepsen, Maria
Rohani, Darius Adam
Busk, Jonas
Vinberg, Maj
Bardram, Jakob Eyvind
Kessing, Lars Vedel
Voice analyses using smartphone-based data in patients with bipolar disorder, unaffected relatives and healthy control individuals, and during different affective states
title Voice analyses using smartphone-based data in patients with bipolar disorder, unaffected relatives and healthy control individuals, and during different affective states
title_full Voice analyses using smartphone-based data in patients with bipolar disorder, unaffected relatives and healthy control individuals, and during different affective states
title_fullStr Voice analyses using smartphone-based data in patients with bipolar disorder, unaffected relatives and healthy control individuals, and during different affective states
title_full_unstemmed Voice analyses using smartphone-based data in patients with bipolar disorder, unaffected relatives and healthy control individuals, and during different affective states
title_short Voice analyses using smartphone-based data in patients with bipolar disorder, unaffected relatives and healthy control individuals, and during different affective states
title_sort voice analyses using smartphone-based data in patients with bipolar disorder, unaffected relatives and healthy control individuals, and during different affective states
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8632566/
https://www.ncbi.nlm.nih.gov/pubmed/34850296
http://dx.doi.org/10.1186/s40345-021-00243-3
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