Clinical trial NCT04989452: Cough Sound Recordings to Understand COVID-19-like Symptom Progression to Understand COVID-19-like Symptom Progression
Official title: A Pilot Study for the Collection of Vocalized, Individual Digital Cough Sound Recordings to Understand COVID-19-like Symptom Progression
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Covid19
Lead sponsor: ResApp Health Limited
Sponsor class: INDUSTRY
First posted: 2021-08-04
Enrollment: 100
Sex: ALL
Minimum age (years): 21
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: A list of COVID-19 positive and negative participants, and their consent to inform them of this study (and possible future studies) will be created through the administration of Study 1. Using this list of potential study participants we seek to enroll as many participants identified through Study 1 as possible (up to 1,500 individuals). Participants will be aged 21 years and older and will not be excluded based on gender, demographic group, or geographic location within the US.

Brief summary: Decentralized clinical study designed to collect further cough sounds, self-reported symptoms, and medical treatment questionnaires from participants enrolled on the COVID-Cough Study ("Study 1").

The aim of this further data collection study ("Study 2") is to:

1. develop an understanding of changes in cough sounds associated with COVID-19 and how they alter during the disease;
2. develop an understanding of other causes of COVID-19-like symptoms and their associated cough sound patterns; and
3. gain a broader understanding of the clinical outcomes of individuals who present for COVID-19 testing.

Detailed description: This study is designed to be administered in a decentralized manner to collect self-reported COVID-19 symptoms, cough sample recordings and descriptions of recent medical treatment to investigate the following:

1. Whether patterns unique to COVID-19 disease can be detected in cough sounds collected over time;
2. The incidence of COVID-19 and other lung diseases within individuals who test negative for COVID-19;
3. Whether existing machine learning algorithms can detect the presence of disease in the lower respiratory tract in participants; and
4. Whether distinct cough sound patterns in COVID-19 disease are associated with the requirement of medical treatment for COVID-19 disease. Through the administration of Study 1, a list of potential participants will be created for whom the result of a recent rt-qPCR COVID-19 test is known. Participants have been consented to allow for outreach regarding further studies. Cough samples and answers to medical questions have been collected from these individuals at the time of testing, and the aim of Study 2 is to gather further cough samples and questionnaire answers over a period of 25 days.

Eligibility criteria: Inclusion Criteria:

* be aged 21 years and older;
* be a resident of the USA;
* have a personal (i.e., not shared) iPhone 6s or newer running iOS 14 or later;
* be able to read and understand English;
* be able to provide informed consent;
* be willing to follow study procedures;
* be able to provide at least 5 coughs (voluntary and/or spontaneous);
* have enrolled in the COVID-Cough Study and successfully returned an at-home rt-qPCR test to the study partner laboratory

Exclusion Criteria:

Participant has one or more medical contraindication to voluntary cough, including the following:

* Severe respiratory distress;
* History of pneumothorax;
* Eye, chest, or abdominal surgery within 3 months of enrolling for the study; or
* Hemoptysis (coughing up of blood) within 1 month of enrolling for the study;

Primary outcomes:
measure: Collection of cough sound audio recordings made using a smartphone, self-reported respiratory symptoms, COVID-19 rt-qPCR result and medical treatment information on 3 occasions (day 1, day 10, day 25).
timeFrame: 6 months
measure: Sensitivity and specificity of using cough sound analysis on longitudinal cough sounds to detect COVID-19 as compared to rt-qPCR test.
timeFrame: 10 months
measure: Sensitivity and specificity of longitudinal cough sounds to detect whether study participants have required recent medical care for COVID-19 related illness.
timeFrame: 10 months
Secondary outcomes:
measure: Confusion matrix outputs of cough sound algorithm prediction of COVID-19 severity compared against COVID-19 severity measured by a combination of medical history survey and rt-qPCR test.
timeFrame: 10 months
Other outcomes:
Locations:
city: New York
country: United States
facility: Strategy Health LLC
state: New York
Study officials:
affiliation: Strategy Health
name: Shrawan Patel, MBBS,BSc
role: PRINCIPAL_INVESTIGATOR
References: