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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
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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. |
format | Online Article Text |
id | pubmed-8632566 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
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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