Wearable Sensor to Monitor COVID-19 Like Signs and Symptoms

NCT ID: NCT04393558

Last Updated: 2025-04-17

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

75 participants

Study Classification

OBSERVATIONAL

Study Start Date

2020-04-20

Study Completion Date

2020-10-26

Brief Summary

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1. Develop a wearable sensor package to gather data on COVID-19-like signs and symptoms such as elevated body temperature, respiratory parameters, heart rate ,cough and gait.
2. Create algorithms to monitor and track changes to COVID19-like signs and symptoms for developing a better care and isolation strategies for COVID-19 pandemic.

Detailed Description

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Aim 1:

Each enrolled participant will be asked to wear the sensor on a daily basis. Duration of the participation varies based on the symptom severity. With the currently available information, recovery times are ranging from 7 days to 56 days. The duration of the study participation can begin at the early detection to all the way until complete recovery or discharge. Participants may be asked to use the sensors anywhere from 7 days to 60 days. Duration of study will be based on the participant's self-reported symptoms or as appropriate determined by the PI. This will allow the research team to collect a comprehensive data set that can characterize both COVID-like and non-COVID-like signs and symptoms.

Aim 2:

Data collected from Aim#1 will aid in generating machine learning algorithms to characterize the signs and symptoms. Further algorithm development will be carried out to develop signs and symptoms progression and regression models for early warning or warning to prevent return to work of health-care staff or civilians

Wearable sensors are compact battery powered miniature electronic devices that are attached to a user's body to record physiological, biochemical and physical activity information. Different types of sensors can be used to monitor these digital biomarkers. Inertial measurement units (IMUs), including accelerometers, gyroscopes, magnetometers are typically used to measure physical activity, movement signatures. Miniature temperature, galvanic skin response (GSR), photoplethysmogram (PPG), oxygen saturation (SPO2) sensors are increasingly embedded in wearable devices for vital sign monitoring. Non-invasive monitoring is very ideal in the current pandemic situation. These sensors can be potentially deployed in large scale to monitor cases of suspected infection and patients recovering from COVID-19.

This project is planning to develop a sensor system that is capable of gathering data on COVID-19 like symptoms such as cough, body temperature, respiratory parameters. Machine algorithms will be developed to handle data analysis and derive useful clinical and monitor signs and symptoms in cases of suspected infection and individuals actively recovering from COVID-19 like symptoms

Conditions

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COVID-19 Healthy Control

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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COVID-19

Individuals experiencing COVID-19 like symptoms.

ADAM Sensor

Intervention Type DEVICE

ADAM sensor The data collected from this sensor contains a wide range of core and novel respiratory digital biomarkers as a home-based early identification system. The core measurements include: heart rate, heart rate variability, temperature, physical activity (including sleep quality) and respiratory rate. The novel respiratory digital biomarkers include: respiratory cadence (expiration / inspiration time), coughing, swallowing, throat clearing, and talk time.

Healthy Controls

Individuals without any known significant health problems

ADAM Sensor

Intervention Type DEVICE

ADAM sensor The data collected from this sensor contains a wide range of core and novel respiratory digital biomarkers as a home-based early identification system. The core measurements include: heart rate, heart rate variability, temperature, physical activity (including sleep quality) and respiratory rate. The novel respiratory digital biomarkers include: respiratory cadence (expiration / inspiration time), coughing, swallowing, throat clearing, and talk time.

Interventions

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ADAM Sensor

ADAM sensor The data collected from this sensor contains a wide range of core and novel respiratory digital biomarkers as a home-based early identification system. The core measurements include: heart rate, heart rate variability, temperature, physical activity (including sleep quality) and respiratory rate. The novel respiratory digital biomarkers include: respiratory cadence (expiration / inspiration time), coughing, swallowing, throat clearing, and talk time.

Intervention Type DEVICE

Eligibility Criteria

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Inclusion Criteria

* Ages between 18-95 years old
* Currently experiencing any COVID-like signs and symptoms such as fever, cough, shortness of breath, trouble breathing, persistent pain or pressure in the chest, confusion or inability to arouse, bluish lips or face.
* Individuals who are not experience any COVID like signs and symptoms (will be asked to be healthy control)
* Able and willing to give written consent and comply with study procedures.

Exclusion Criteria

* Inability to understand instructions and follow a three step command.
* The subject is pregnant, nursing or planning a pregnancy.
* Inability to provide written consent.
Minimum Eligible Age

18 Years

Maximum Eligible Age

95 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Shirley Ryan AbilityLab

OTHER

Sponsor Role lead

Responsible Party

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Arun Jayaraman, PT, PhD

Director Max Nader Laboratory

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Arun Jayaraman, PhD

Role: PRINCIPAL_INVESTIGATOR

Shirley Ryan AbilityLab

Locations

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Shirley Ryan AbilityLab

Chicago, Illinois, United States

Site Status

Countries

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United States

Other Identifiers

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STU00212522

Identifier Type: -

Identifier Source: org_study_id

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