{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T22:12:30Z","timestamp":1766787150349,"version":"3.41.2"},"reference-count":22,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T00:00:00Z","timestamp":1744675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"Introduction<\/jats:title>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.<\/jats:p><\/jats:sec>Materials &amp; methods<\/jats:title>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.<\/jats:p><\/jats:sec>Results<\/jats:title>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.<\/jats:p><\/jats:sec>Conclusion<\/jats:title>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.<\/jats:p>Level of evidence<\/jats:bold>: 3.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fdgth.2025.1514971","type":"journal-article","created":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T06:32:51Z","timestamp":1744698771000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["The Bridge2AI-voice application: initial feasibility study of voice data acquisition through mobile health"],"prefix":"10.3389","volume":"7","author":[{"given":"Elijah","family":"Moothedan","sequence":"first","affiliation":[]},{"given":"Micah","family":"Boyer","sequence":"additional","affiliation":[]},{"given":"Stephanie","family":"Watts","sequence":"additional","affiliation":[]},{"given":"Yassmeen","family":"Abdel-Aty","sequence":"additional","affiliation":[]},{"given":"Satrajit","family":"Ghosh","sequence":"additional","affiliation":[]},{"given":"Ana\u00efs","family":"Rameau","sequence":"additional","affiliation":[]},{"given":"Alexandros","family":"Sigaras","sequence":"additional","affiliation":[]},{"given":"Olivier","family":"Elemento","sequence":"additional","affiliation":[]},{"name":"Bridge2AI-Voice Consortium","sequence":"additional","affiliation":[]},{"given":"Yael","family":"Bensoussan","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2025,4,15]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1159\/000510820","article-title":"Evaluation of speech-based digital biomarkers: review and recommendations","volume":"4","author":"Robin","year":"2020","journal-title":"Digit Biomark"},{"key":"B2","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1159\/000515346","article-title":"Voice for health: the use of vocal biomarkers from research to clinical practice","volume":"5","author":"Fagherazzi","year":"2021","journal-title":"Digit Biomark"},{"key":"B3","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1159\/000502000","article-title":"Traditional and digital biomarkers: two worlds apart?","volume":"3","author":"Babrak","year":"2019","journal-title":"Digit Biomark"},{"key":"B4","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1001\/jamaoto.2023.4807","article-title":"Voice as an AI biomarker of health\u2014introducing audiomics","volume":"22","author":"Bensoussan","year":"2024","journal-title":"JAMA Otolaryngol Head Neck Surg"},{"key":"B5","doi-asserted-by":"publisher","first-page":"e46105","DOI":"10.2196\/46105","article-title":"Applied machine learning techniques to diagnose voice-affecting conditions and disorders: systematic literature review","volume":"25","author":"Idrisoglu","year":"2023","journal-title":"J Med Internet Res"},{"key":"B6","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1002\/lary.29886","article-title":"Applications of artificial intelligence to office laryngoscopy: a scoping review","volume":"132","author":"Yao","year":"2022","journal-title":"Laryngoscope"},{"key":"B7","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1159\/000541456","article-title":"The imperative of voice data collection in clinical trials","volume":"8","author":"Fagherazzi","year":"2024","journal-title":"Digit Biomark"},{"key":"B8","doi-asserted-by":"publisher","first-page":"1333","DOI":"10.1002\/lary.31052","article-title":"Current practices in voice data collection and limitations to voice AI research: a national survey","volume":"134","author":"Evangelista","year":"2024","journal-title":"Laryngoscope"},{"year":"","key":"B9","article-title":"Bridge2AI. Precision Public Health: Voice as a Biomarker of Health \u2013 Building an ethically sourced, bioacoustics database to understand disease like never before"},{"key":"B10","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1016\/j.amepre.2009.02.002","article-title":"How we design feasibility studies","volume":"36","author":"Bowen","year":"2009","journal-title":"Am J Prev Med"},{"key":"B11","doi-asserted-by":"publisher","first-page":"1445","DOI":"10.21437\/Interspeech.2024-1926","article-title":"Developing multi-disorder voice protocols: a team science approach involving clinical expertise, bioethics, standards, and DEI","volume":"2024","author":"Bensoussan","year":"2024","journal-title":"Proc. Interspeech"},{"year":"","key":"B12","article-title":"Mobile Application Rating Scale (MARS)"},{"key":"B13","doi-asserted-by":"publisher","first-page":"00283","DOI":"10.1016\/j.jvoice.2024.08.029","article-title":"Evidence-based recommendations for tablet recordings from the Bridge2AI-voice acoustic experiments","author":"Awan","year":"2024","journal-title":"J Voice"},{"year":"","key":"B14","article-title":"How to improve: Model for improvement. Institute for Healthcare Improvement"},{"article-title":"Respondent fatigue. Encyclopedia of. Survey Research Methods","year":"","author":"Ben-Nun","key":"B15"},{"key":"B16","doi-asserted-by":"publisher","first-page":"e301","DOI":"10.2196\/jmir.6342","article-title":"Methods for evaluating respondent attrition in web-based surveys","volume":"18","author":"Hochheimer","year":"2016","journal-title":"J Med Internet Res"},{"key":"B17","first-page":"51","article-title":"Derivation of new readability formulas (automated readability index, fog count and flesch Reading ease formula) for navy enlisted personnel","author":"Kincaid","year":"1975","journal-title":"Naval Tech Train Comm Millington TN Res Branch"},{"key":"B18","doi-asserted-by":"publisher","first-page":"103362","DOI":"10.1016\/j.jbi.2019.103362","article-title":"Investigating voice as a biomarker: deep phenotyping methods for early detection of Parkinson\u2019s disease","volume":"104","author":"Tracy","year":"2020","journal-title":"J Biomed Inform"},{"key":"B19","doi-asserted-by":"publisher","first-page":"4713043","DOI":"10.1016\/j.artmed.2024.102953","article-title":"COPDVD: automated classification of chronic obstructive pulmonary disease on a new developed and evaluated voice dataset","volume":"156","author":"Idrisoglu","year":"2024","journal-title":"Artif Intell Med"},{"key":"B20","doi-asserted-by":"publisher","first-page":"109782","DOI":"10.1016\/j.diabres.2022.109782","article-title":"Scoping review of the relationship between diabetes and voice quality","volume":"185","author":"Saghiri","year":"2022","journal-title":"Diabetes Res Clin Pract"},{"key":"B21","doi-asserted-by":"publisher","first-page":"500","DOI":"10.3390\/bioengineering10040500","article-title":"A review of voice-based pain detection in adults using artificial intelligence","volume":"10","author":"Borna","year":"2023","journal-title":"Bioengineering"},{"key":"B22","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1177\/01945998221110839","article-title":"Artificial intelligence and laryngeal cancer: from screening to prognosis: a state of the art review","volume":"168","author":"Bensoussan","year":"2023","journal-title":"Otolaryngol Head Neck Surg"}],"container-title":["Frontiers in Digital Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2025.1514971\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T06:32:52Z","timestamp":1744698772000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2025.1514971\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,15]]},"references-count":22,"alternative-id":["10.3389\/fdgth.2025.1514971"],"URL":"https:\/\/doi.org\/10.3389\/fdgth.2025.1514971","relation":{},"ISSN":["2673-253X"],"issn-type":[{"type":"electronic","value":"2673-253X"}],"subject":[],"published":{"date-parts":[[2025,4,15]]},"article-number":"1514971"}}