ArticleSensors (Basel, Switzerland)2015
Smartphone Application for the Analysis of Prosodic Features in Running Speech with a Focus on Bipolar Disorders: System Performance Evaluation and Case Study.
Article in Sensors (Basel, Switzerland), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Smartphone-Based Monitoring of Objective and Subjective Data in Affective Disorders: Where Are We and Where Are We Going? Systematic Review.Journal of medical Internet research · 2017Pooled it
- Trial
- A Clinical AI-Based System (MoodMon) for Affective Disorders: Algorithm Development and Validation.JMIR AI · 2026Article
- Acoustic and Natural Language Markers for Bipolar Disorder: A Pilot, mHealth Cross-Sectional Study.JMIR formative research · 2025Observational
- Development and Validation of a Machine Learning Method Using Vocal Biomarkers for Identifying Frailty in Community-Dwelling Older Adults: Cross-Sectional Study.JMIR medical informatics · 2025Article
- Voice analytics in the wild: Validity and predictive accuracy of common audio-recording devices.Behavior research methods · 2024Article
- Using voice biomarkers for frailty classification.GeroScience · 2024Article
- Process and Outcome Evaluations of Smartphone Apps for Bipolar Disorder: Scoping Review.Journal of medical Internet research · 2022Article
- Voice analyses using smartphone-based data in patients with bipolar disorder, unaffected relatives and healthy control individuals, and during different affective states.International journal of bipolar disorders · 2021Article
- Using Acoustic Speech Patterns From Smartphones to Investigate Mood Disorders: Scoping Review.JMIR mHealth and uHealth · 2021Article
- Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping Review.JMIR mental health · 2021Article
- Digital Communication Biomarkers of Mood and Diagnosis in Borderline Personality Disorder, Bipolar Disorder, and Healthy Control Populations.Frontiers in psychiatry · 2021Article
- Scoping Review of Healthcare Literature on Mobile, Wearable, and Textile Sensing Technology for Continuous Monitoring.Journal of healthcare informatics research · 2021Article
- Monitoring Sleep Changes via a Smartphone App in Bipolar Disorder: Practical Issues and Validation of a Potential Diagnostic Tool.Frontiers in psychiatry · 2021Article
- Voice and the new coronavirus.International journal of clinical practice · 2020Article
- [Ambulatory monitoring and digital phenotyping in the diagnostics and treatment of bipolar disorders].Der Nervenarzt · 2019Review
- Mobile Phone and Wearable Sensor-Based mHealth Approaches for Psychiatric Disorders and Symptoms: Systematic Review.JMIR mental health · 2019Review
- Smartphone-based objective monitoring in bipolar disorder: status and considerations.International journal of bipolar disorders · 2018Review
- Could Wearable and Mobile Technology Improve the Management of Essential Tremor?Frontiers in neurology · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bipolar disorder is one of the most common mood disorders characterized by large and invalidating mood swings. Several projects focus on the development of decision support systems that monitor and advise patients, as well as clinicians. Voice monitoring and speech signal analysis can be exploited to reach this goal. In this study, an Android application was designed for analyzing running speech using a smartphone device. The application can record audio samples and estimate speech fundamental frequency, F0, and its changes. F0-related features are estimated locally on the smartphone, with some advantages with respect to remote processing approaches in terms of privacy protection and reduced upload costs. The raw features can be sent to a central server and further processed. The quality of the audio recordings, algorithm reliability and performance of the overall system were evaluated in terms of voiced segment detection and features estimation. The results demonstrate that mean F0 from each voiced segment can be reliably estimated, thus describing prosodic features across the speech sample. Instead, features related to F0 variability within each voiced segment performed poorly. A case study performed on a bipolar patient is presented.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.