AUTHOR=Moothedan Elijah , Boyer Micah , Watts Stephanie , Abdel-Aty Yassmeen , Ghosh Satrajit , Rameau Anaïs , Sigaras Alexandros , Elemento Olivier ,  Bridge2AI-Voice Consortium , Bensoussan Yael , Bensoussan Yael , Elemento Olivier , Rameau Anais , Sigaras Alexandros , Ghosh Satrajit , Powell Maria , Ravitsky Vardit , Belisle-Pipon Jean Christophe , Dorr David , Payne Phillip , Johnson Alistair , Bahr Ruth , Bolser Donald , Rudzicz Frank , Lerner-Ellis Jordan , Jenkins Kathy , Awan Shaheen , Boyer Micah , Hersh William , Krussel Andrea , Bedrick Steven , Syed Toufeeq Ahmed , Toghranegar Jamie , Anibal James , Sutherland Duncan , Diaz-Ocampo Enrique , Silberhoz Elizabeth , Costello John , Gelbard Alexander , Vinson Kimberly , Neal Tempestt , Jayachandran Lochana , Ng Evan , Casalino Selina , Abdel-Aty Yassmeen , Hanna Karim , Zesiewicz Theresa , Moothedan Elijah , Evangelista Emily , Cruz Samantha Salvi , Zhao Robin , Ebraheem Mohamed , Newberry Karlee , De Santiago Iris , Eiseman Ellie , Rahman JM , Jo Stacy , Goldenberg Anna 

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

JOURNAL=Frontiers in Digital Health

VOLUME=Volume 7 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1514971

DOI=10.3389/fdgth.2025.1514971

ISSN=2673-253X

ABSTRACT=IntroductionBridge2AI-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 & methodsParticipants 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.ResultsForty-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.ConclusionFindings 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.