The purpose of the AcRIS study is to obtain data to characterize the relationship between symptoms and voice features for (reverse transcription polymerase chain reaction (RT-PCR) confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), influenza virus, or Respiratory Syncytial Virus (RSV) positive participants with acute viral respiratory illness. This data will be used as the basis to build voice and symptom algorithm(s) for detection and monitoring of these illnesses. This would benefit vaccine development across several key disease areas, including SARS-CoV-2, influenza virus and RSV. The study also models concepts of more efficient "flexible" clinical trials involving not only voice capture, but also web-based participant recruitment, enhanced participant engagement, and remote sample collection that could make future clinical studies more efficient. The clinical data obtained in this observational study could provide the documentation of the technology's performance needed to enable its deployment in future interventional studies.
Age range
18 Years
Sex
ALL
See this in plain English?
AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Bring these to your next appointment. They're a starting point for a shared conversation — not a sign you qualify or a recommendation to enrol.
Generated to help you prepare — always confirm anything about your own eligibility and care with the study team and your doctor.
The trial coordinator is the person who runs the study day to day. These cover the practical side — logistics, costs, and what taking part would actually mean for your life. The study team confirms whether you meet the criteria; these are questions to ask, not a sign you qualify.
A starting point for the conversation — always confirm anything about your own eligibility, costs, and care with the study team and your doctor.
Change From Baseline in Self-Reported Symptom Scores From Well-to-Sick State Through Day 56
Timeframe: Baseline up to Day 56
Change From Baseline in Voice Features (AHH_Max Phonation Time, EE_Jitter Local Absolute, MM_Jitter Local Absolute) Values From Well-to-Sick State Through Day 56
Timeframe: Baseline up to Day 56
Change From Baseline in Voice Feature (Cepstral Peak Prominence, Harmonicity, MFCC Mean, MFCC Std, SNR, Shimmer Local dB, Spectral Flatness, Third Octave Band, and VLHR) Values From Well-to-Sick State Through Day 56
Timeframe: Baseline up to Day 56
Change From Baseline in Voice Feature (Coefficient of Variation, Mel Frequency Cepstral Coefficients (MFCC) 1st Order Delta, MFCC 2nd Order Delta) Values From Well-to-Sick State Through Day 56
Timeframe: Baseline up to Day 56
Change From Baseline in Voice Features (EE_Entropy, MM_Entropy) Values From Well-to-Sick State Through Day 56
Timeframe: Baseline up to Day 56
Change From Baseline in Voice Features (Formant and Formant Bandwidth) Values From Well-to-Sick State Through Day 56
Timeframe: Baseline up to Day 56
Change From Baseline in Voice Features (EE_Voiced Frames, MM_Voiced Frames) Values From Well-to-Sick State Through Day 56
Timeframe: Baseline up to Day 56
Change From Baseline in Voice Features (EE_Jitter Local, MM_Jitter Local) Values From Well-to-Sick State Through Day 56
Timeframe: Baseline up to Day 56
Change From Baseline in Voice Features (EE_Shimmer Local, MM_Shimmer Local) Values From Well-to-Sick State Through Day 56
Timeframe: Baseline up to Day 56
Change From Baseline in Voice Features (READ_Speaking Rate) Values From Well-to-Sick State Through Day 56
Timeframe: Baseline up to Day 56