Evidence map›Paper›PMID 40210993›Full record

ArticleNPJ digital medicine2025

Longitudinal voice monitoring in a decentralized Bring Your Own Device trial for respiratory illness detection.

Mar Santamaria, Yiorgos Christakis, Charmaine Demanuele, Yao Zhang, Pirinka Georgiev Tuttle, Fahimeh Mamashli, Jiawei Bai, Rogier Landman, Kara Chappie, Stefan Kell and 11 more

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04748445 (Acute Respiratory Illness Surveillance), which is not on this map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT04748445 completednot on this map

Acute Respiratory Illness Surveillance (ARIS) by Monitoring Voice and Illness Symptom Changes Using a Mobile Application in a Low-Interventional Decentralized Study.

TypeobservationalSponsorPfizerRan2021 to 2022Enrolled9,151ConditionsHealthyArmsSARS-CoV-2/Influenza/RSV RT-PCR
3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

Mar Santamaria *Pfizer Inc., Cambridge, MA, USA. Mar.Santamaria@pfizer.com.
Yiorgos Christakis *Pfizer Inc., Cambridge, MA, USA.
Charmaine DemanuelePfizer Inc., Cambridge, MA, USA.
Yao ZhangPfizer Inc., Cambridge, MA, USA.
Pirinka Georgiev TuttlePfizer Inc., Cambridge, MA, USA.
Fahimeh MamashliPfizer Inc., Cambridge, MA, USA.
Jiawei BaiPfizer Inc., Cambridge, MA, USA.
Rogier LandmanPfizer Inc., Cambridge, MA, USA.
Kara ChappiePfizer Inc., Cambridge, MA, USA.
Stefan KellPfizer Inc., Cambridge, MA, USA.
John G SamuelssonPfizer Inc., Cambridge, MA, USA.
Kisha TalbertPfizer Inc., Cambridge, MA, USA.
Leonardo SeoaneOchsner Health, New Orleans, LA, USA.
W Mark RobertsOchsner Health, New Orleans, LA, USA.
Edmond Kato KabagambeOchsner Health, New Orleans, LA, USA.
Joseph CapeloutoOchsner Health, New Orleans, LA, USA.
Paul WacnikPfizer Inc., Cambridge, MA, USA.
Jessica SeligPfizer Inc., Cambridge, MA, USA.
Lukas AdamowiczPfizer Inc., Cambridge, MA, USA.
Sheraz KhanPfizer Inc., Cambridge, MA, USA. Sheraz.Khan@pfizer.com.
Robert J MatherPfizer Inc., Cambridge, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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).

Identifiers

PMID40210993
PMCPMC11986159

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.