Wearable Assisted Viral Evidence (WAVE) Study A Decentralized, Prospective Study Exploring the Relationship Between Passively-collected Data From Wearable Activity Devices and Respiratory Viral Infections

Recruiting

• Conditions: Influenza, Human; Respiratory Syncytial Virus (RSV); COVID-19; Influenza A; Influenza B

• Registration Number: NCT06207929
• Lead Sponsor: Evidation Health

Brief Summary

The goal of this decentralized, observational study is to enroll and observe adults in the contingent United States during the 2023-2024 flu season. The main study objectives are to create a dataset of paired wearable data, self-reported symptoms, and respiratory viral infection (RVI) from PCR testing during the 2023-2024 flu season and to develop algorithm that is able to accurately classify asymptomatic and symptomatic RVI and understand the algorithm's performance metrics.

Detailed Description

Not available

Study Timeline

• Study First Submitted: 2023-12-20
• Study First Submitted QC: 2024-01-04
• Study First Posted: 2024-01-17
• Study Start: 2024-01-21
• Status Verified: 2024-02
• Last Update Submitted: 2024-02-06
• Last Update Posted: 2024-02-08
• Primary Completion: 2024-05
• Study Completion: 2024-05

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 10000

Inclusion Criteria

• Lives in the United States
• Speaks, reads, and understands English
• Currently owns and uses a consumer wearable device (Apple Watch, Garmin, or Fitbit) with necessary step and heart rate data at minimum or willing to wear a study-provided device and download the Fitbit app
• Willing to connect their wearable device to the Evidation platform and wear it daily for at least 10 hours for the duration of the study
• Owns a smartphone with Apple iOS 15 installed or higher OR Android version 9.0 installed or higher or willing to update
• Willing to respond to daily and weekly questionnaires for a 10-week period
• Willing to complete at-home nasal swab tests and return the nasal swab samples within 24 hours of being asked to complete it
• Meets data density requirements for wearable devices

Exclusion Criteria

• Self reported diagnosis of both flu and COVID by a healthcare professional or using an at-home test in the past 3 months
• Currently enrolled in another interventional study to prevent or treat COVID-19 or another flu-related program being conducted by Evidation (individuals currently participating in Evidation's FluSmart program will be told that their participation will be paused)
• Has a primary mailing address that is a P.O box, Army Post Office (APO), Fleet Post Office (FPO), or Diplomatic Post Office (DPO) address, or U.S. military base located overseas, or U.S. territories (Puerto Rico, U.S. Virgin Islands, Guam, Northern Mariana Island, or American Samoa)

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
The primary objectives are to develop a dataset of paired wearable data, self-reported symptoms, and confirmed respiratory viral infection and use the dataset to develop an algorithm to classify asymptomatic/symptomatic RVIs	Through study completion, approximately 10 months	This study will gather wearable device data, including heart rate, sleep, activity, and other data types from commercially available wearable activity trackers and smartwatches (e.g. Apple Watch, Fitbit, Garmin devices), as well as self-reported data related to the experience of symptoms associated with respiratory viral infections, and pair this data with the results from PCR tests of serial at-home nasal swabs for SARS-CoV-2, Influenza A, Influenza B, and respiratory syncytial virus (RSV). This data will be used to determine if these data types can be used to develop an algorithm for classifying asymptomatic and symptomatic RVI. Algorithm performance will be assessed across a variety of dimensions including ROC AUC, sensitivity, specificity, PPV, and NPV.

Secondary Outcome Measures

Name	Time	Method
The secondary objective of this observational study is to determine if algorithm performance differs across various demographic groups	Through study completion, approximately 10 months	We will test algorithm performance for various different groups of participants to better understand if the algorithm performs difference depending on participant demographics. For example, we will test for performance metrics across different subgroups related to gender, ethnicity, and age. For each subgroup, we will report on ROC AUC, sensitivity, specificity, PPV, and NPV. as appropriate.

Trial Locations

Locations (1)

Evidation Health; 🇺🇸 San Mateo, California, United States