Longitudinal voice monitoring in a decentralized Bring Your Own Device trial for respiratory illness detection
Abstract The Acute Respiratory Illness Surveillance (AcRIS) Study was a low-interventional trial that examined voice changes with respiratory illnesses. This longitudinal trial was the first of its kind, conducted in a fully decentralized manner via a Bring Your Own Device mobile application. The ap...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01584-4 |
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| author | Mar Santamaria Yiorgos Christakis Charmaine Demanuele Yao Zhang Pirinka Georgiev Tuttle Fahimeh Mamashli Jiawei Bai Rogier Landman Kara Chappie Stefan Kell John G. Samuelsson Kisha Talbert Leonardo Seoane W. Mark Roberts Edmond Kato Kabagambe Joseph Capelouto Paul Wacnik Jessica Selig Lukas Adamowicz Sheraz Khan Robert J. Mather |
| author_facet | Mar Santamaria Yiorgos Christakis Charmaine Demanuele Yao Zhang Pirinka Georgiev Tuttle Fahimeh Mamashli Jiawei Bai Rogier Landman Kara Chappie Stefan Kell John G. Samuelsson Kisha Talbert Leonardo Seoane W. Mark Roberts Edmond Kato Kabagambe Joseph Capelouto Paul Wacnik Jessica Selig Lukas Adamowicz Sheraz Khan Robert J. Mather |
| author_sort | Mar Santamaria |
| collection | DOAJ |
| description | Abstract The Acute Respiratory Illness Surveillance (AcRIS) Study was a low-interventional trial that examined voice changes with respiratory illnesses. This longitudinal trial was the first of its kind, conducted in a fully decentralized manner via a Bring Your Own Device mobile application. The app enabled social-media-based recruitment, remote consent, at-home sample collection, and daily remote voice and symptom capture in real-world settings. From April 2021 to April 2022, the trial enrolled 9151 participants, followed for up to eight weeks. Despite mild symptoms experienced by reverse transcription polymerase chain reaction (RT-PCR) positive participants, two machine learning algorithms developed to screen respiratory illnesses reached the pre-specified success criteria. Algorithm testing on independent cohorts demonstrated that the algorithm’s sensitivity increased as symptoms increased, while specificity remained consistent. Study findings suggest voice features can identify individuals with viral respiratory illnesses and provide valuable insights into fully decentralized clinical trials design, operation, and adoption (study registered at ClinicalTrials.gov (NCT04748445) on 5 February 2021). |
| format | Article |
| id | doaj-art-5ca8c32e030b47c495287d65aa99b814 |
| institution | OA Journals |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-5ca8c32e030b47c495287d65aa99b8142025-08-20T02:28:07ZengNature Portfolionpj Digital Medicine2398-63522025-04-018111210.1038/s41746-025-01584-4Longitudinal voice monitoring in a decentralized Bring Your Own Device trial for respiratory illness detectionMar Santamaria0Yiorgos Christakis1Charmaine Demanuele2Yao Zhang3Pirinka Georgiev Tuttle4Fahimeh Mamashli5Jiawei Bai6Rogier Landman7Kara Chappie8Stefan Kell9John G. Samuelsson10Kisha Talbert11Leonardo Seoane12W. Mark Roberts13Edmond Kato Kabagambe14Joseph Capelouto15Paul Wacnik16Jessica Selig17Lukas Adamowicz18Sheraz Khan19Robert J. Mather20Pfizer Inc.Pfizer Inc.Pfizer Inc.Pfizer Inc.Pfizer Inc.Pfizer Inc.Pfizer Inc.Pfizer Inc.Pfizer Inc.Pfizer Inc.Pfizer Inc.Pfizer Inc.Ochsner HealthOchsner HealthOchsner HealthOchsner HealthPfizer Inc.Pfizer Inc.Pfizer Inc.Pfizer Inc.Pfizer Inc.Abstract The Acute Respiratory Illness Surveillance (AcRIS) Study was a low-interventional trial that examined voice changes with respiratory illnesses. This longitudinal trial was the first of its kind, conducted in a fully decentralized manner via a Bring Your Own Device mobile application. The app enabled social-media-based recruitment, remote consent, at-home sample collection, and daily remote voice and symptom capture in real-world settings. From April 2021 to April 2022, the trial enrolled 9151 participants, followed for up to eight weeks. Despite mild symptoms experienced by reverse transcription polymerase chain reaction (RT-PCR) positive participants, two machine learning algorithms developed to screen respiratory illnesses reached the pre-specified success criteria. Algorithm testing on independent cohorts demonstrated that the algorithm’s sensitivity increased as symptoms increased, while specificity remained consistent. Study findings suggest voice features can identify individuals with viral respiratory illnesses and provide valuable insights into fully decentralized clinical trials design, operation, and adoption (study registered at ClinicalTrials.gov (NCT04748445) on 5 February 2021).https://doi.org/10.1038/s41746-025-01584-4 |
| spellingShingle | Mar Santamaria Yiorgos Christakis Charmaine Demanuele Yao Zhang Pirinka Georgiev Tuttle Fahimeh Mamashli Jiawei Bai Rogier Landman Kara Chappie Stefan Kell John G. Samuelsson Kisha Talbert Leonardo Seoane W. Mark Roberts Edmond Kato Kabagambe Joseph Capelouto Paul Wacnik Jessica Selig Lukas Adamowicz Sheraz Khan Robert J. Mather Longitudinal voice monitoring in a decentralized Bring Your Own Device trial for respiratory illness detection npj Digital Medicine |
| title | Longitudinal voice monitoring in a decentralized Bring Your Own Device trial for respiratory illness detection |
| title_full | Longitudinal voice monitoring in a decentralized Bring Your Own Device trial for respiratory illness detection |
| title_fullStr | Longitudinal voice monitoring in a decentralized Bring Your Own Device trial for respiratory illness detection |
| title_full_unstemmed | Longitudinal voice monitoring in a decentralized Bring Your Own Device trial for respiratory illness detection |
| title_short | Longitudinal voice monitoring in a decentralized Bring Your Own Device trial for respiratory illness detection |
| title_sort | longitudinal voice monitoring in a decentralized bring your own device trial for respiratory illness detection |
| url | https://doi.org/10.1038/s41746-025-01584-4 |
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