ArticleInternational journal of bipolar disorders2021
Voice analyses using smartphone-based data in patients with bipolar disorder, unaffected relatives and healthy control individuals, and during different affective states.
Article in International journal of bipolar disorders, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Digital Markers for Passive Remote Monitoring of Bipolar Disorder: Systematic Review.JMIR mental health · 2026Pooled it
- Speech and Language Markers of Bipolar Disorder: Challenges and Opportunities.Bipolar disorders · 2026Review
- A scoping review of the use of artificial intelligence as a psychological assessment tool.Translational psychiatry · 2026Article
- IDMBD: Intelligent Diagnostic Modelling of Bipolar Disorder at its early onset.Scientific reports · 2026Article
- Screening for depression risk via smartphone narratives with fully fine-tuned WavLM.Scientific reports · 2026Article
- Speech analysis for differentiating bipolar disorder and major depressive disorder during euthymic states.Annals of general psychiatry · 2026Article
- The effect of smartphone-based monitoring and treatment including clinical feedback in progressed bipolar disorder: the SmartBipolar trial randomised controlled parallel-group trial.International journal of bipolar disorders · 2026Article
- Investigating behavioral inertia in passively sensed smartphone parameters to differentiate affective episodes in patients with bipolar disorder.Neuroscience applied · 2026Article
- Digital tools for assessing bipolar disorder: A scoping review of the current landscape.Neuroscience applied · 2026Article
- Mood states recognition based on Mandarin speech and deep learning in patients with bipolar disorder.BMC psychiatry · 2025Article
- Acoustic and Natural Language Markers for Bipolar Disorder: A Pilot, mHealth Cross-Sectional Study.JMIR formative research · 2025Observational
- Disease Prediction Using Machine Learning on Smartphone-Based Eye, Skin, and Voice Data: Scoping Review.JMIR AI · 2025Review
- Digital phenotyping in bipolar disorder: Using longitudinal Fitbit data and personalized machine learning to predict mood symptomatology.Acta psychiatrica Scandinavica · 2025Article
- A Machine Learning-Based Case-Control Study on Suicide Risk Identification: Integrating Acoustic and Linguistic Features Under Stress Conditions.Depression and anxiety · 2025Article
- The Role of Selected Speech Signal Characteristics in Discriminating Unipolar and Bipolar Disorders.Sensors (Basel, Switzerland) · 2024Article
- Voice as a Biomarker of Pediatric Health: A Scoping Review.Children (Basel, Switzerland) · 2024Article
- Article
- Exploring the ability of vocal biomarkers in distinguishing depression from bipolar disorder, schizophrenia, and healthy controls.Frontiers in psychiatry · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundVoice features have been suggested as objective markers of bipolar disorder (BD).
aimsTo 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.
methodsVoice 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.
resultsCompared 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).
conclusionsVoice 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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