Frontiers in digital health · 2025
The Bridge2AI-voice application: initial feasibility study of voice data acquisition through mobile health.
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Moothedan Elijah, Boyer Micah, Watts Stephanie, Abdel-Aty Yassmeen, Ghosh Satrajit, Rameau Anaïs, Sigaras Alexandros, Elemento Olivier, Bridge2AI-Voice Consortium, Bensoussan Yael
Аннотация
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.
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.