Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Apr 15:7:1514971.
doi: 10.3389/fdgth.2025.1514971. eCollection 2025.

The Bridge2AI-voice application: initial feasibility study of voice data acquisition through mobile health

Elijah Moothedan  1 , Micah Boyer  2 , Stephanie Watts  2 , Yassmeen Abdel-Aty  2 , Satrajit Ghosh  3 , Anaïs Rameau  4 , Alexandros Sigaras  5 , Olivier Elemento  5 ; Bridge2AI-Voice Consortium; Yael Bensoussan  2
Collaborators, Affiliations

The Bridge2AI-voice application: initial feasibility study of voice data acquisition through mobile health

Elijah Moothedan et al. Front Digit Health. .

Abstract

Introduction: Bridge2AI-Voice, a collaborative multi-institutional consortium, aims to generate a large-scale, ethically sourced voice, speech, and cough database linked to health metadata in order to support AI-driven research. A novel smartphone application, the Bridge2AI-Voice app, was created to collect standardized recordings of acoustic tasks, validated patient questionnaires, and validated patient reported outcomes. Before broad data collection, a feasibility study was undertaken to assess the viability of the app in a clinical setting through task performance metrics and participant feedback.

Materials & methods: Participants were recruited from a tertiary academic voice center. Participants were instructed to complete a series of tasks through the application on an iPad. The Plan-Do-Study-Act model for quality improvement was implemented. Data collected included demographics and task metrics including time of completion, successful task/recording completion, and need for assistance. Participant feedback was measured by a qualitative interview adapted from the Mobile App Rating Scale.

Results: Forty-seven participants were enrolled (61% female, 92% reported primary language of English, mean age of 58.3 years). All owned smart devices, with 49% using mobile health apps. Overall task completion rate was 68%, with acoustic tasks successfully recorded in 41% of cases. Participants requested assistance in 41% of successfully completed tasks, with challenges mainly related to design and instruction understandability. Interview responses reflected favorable perception of voice-screening apps and their features.

Conclusion: Findings suggest that the Bridge2AI-Voice application is a promising tool for voice data acquisition in a clinical setting. However, development of improved User Interface/User Experience and broader, diverse feasibility studies are needed for a usable tool.Level of evidence: 3.

Keywords: artificial intelligence; biomarkers; mobile application; voice; voice biomarkers.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Bridge2AI-Voice app interface.
Figure 2
Figure 2
Bridge2AI-Voice app interface.
See this image and copyright information in PMC

References

    1. Robin J, Harrison JE, Kaufman LD, Rudzicz F, Simpson W, Yancheva M. Evaluation of speech-based digital biomarkers: review and recommendations. Digit Biomark. (2020) 4(3):99–108. 10.1159/000510820 - DOI - PMC - PubMed
    1. Fagherazzi G, Fischer A, Ismael M, Despotovic V. Voice for health: the use of vocal biomarkers from research to clinical practice. Digit Biomark. (2021) 5(1):78–88. 10.1159/000515346 - DOI - PMC - PubMed
    1. Babrak L, Menetski J, Rebhan M, Nisato G, Zinggeler M, Brasier N, et al. Traditional and digital biomarkers: two worlds apart? Digit Biomark. (2019) 3(2):92–102. 10.1159/000502000 - DOI - PMC - PubMed
    1. Bensoussan Y, Elemento O, Rameau A. Voice as an AI biomarker of health—introducing audiomics. JAMA Otolaryngol Head Neck Surg. Published Online February. (2024) 22:283–4. 10.1001/jamaoto.2023.4807 - DOI - PubMed
    1. Idrisoglu A, Dallora AL, Anderberg P, Berglund JS. Applied machine learning techniques to diagnose voice-affecting conditions and disorders: systematic literature review. J Med Internet Res. (2023) 25:e46105. 10.2196/46105 - DOI - PMC - PubMed

LinkOut - more resources