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

COVID-19 Clinical Trial

— WAVE  

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06207929
• Other study ID #: WAVE Study
• Status: Recruiting
• Start date: January 21, 2024
• Est. completion date: May 2024

Study information

• Verified date: February 2024
• Source: Evidation Health
• Contact: Ernesto Ramirez, PhD
• Phone: 415-650-4741
• Email: wavestudy[@]evidation.com
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 10000
• Est. completion date: May 2024
• Est. primary completion date: May 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

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)

Related Conditions & MeSH terms

• COVID-19
• Influenza A
• Influenza B
• Influenza, Human
• Respiratory Syncytial Virus (RSV)
• Virus Diseases

Locations

Country	Name	City	State
United States	Evidation Health	San Mateo	California

Sponsors (2)

Lead Sponsor	Collaborator
Evidation Health	Biomedical Advanced Research and Development Authority

Country where clinical trial is conducted

United States, 

References & Publications (10)

Hunter V, Shapiro A, Chawla D, Drawnel F, Ramirez E, Phillips E, Tadesse-Bell S, Foschini L, Ukachukwu V. Characterization of Influenza-Like Illness Burden Using Commercial Wearable Sensor Data and Patient-Reported Outcomes: Mixed Methods Cohort Study. J Med Internet Res. 2023 Mar 23;25:e41050. doi: 10.2196/41050. — View Citation

Mayer C, Tyler J, Fang Y, Flora C, Frank E, Tewari M, Choi SW, Sen S, Forger DB. Consumer-grade wearables identify changes in multiple physiological systems during COVID-19 disease progression. Cell Rep Med. 2022 Apr 19;3(4):100601. doi: 10.1016/j.xcrm.2022.100601. eCollection 2022 Apr 19. — View Citation

Merrill MA, Safranchik E, Kolbeinsson A, et al. Homekit2020: A Benchmark for Time Series Classification on a Large Mobile Sensing Dataset with Laboratory Tested Ground Truth of Influenza Infections. Conference on Health, Inference, and Learning PMLR 209:207-228. 2023 Jun.

Mezlini A, Shapiro A, Daza EJ, Caddigan E, Ramirez E, Althoff T, Foschini L. Estimating the Burden of Influenza-like Illness on Daily Activity at the Population Scale Using Commercial Wearable Sensors. JAMA Netw Open. 2022 May 2;5(5):e2211958. doi: 10.1001/jamanetworkopen.2022.11958. — View Citation

Nestor B, Hunter J, Kainkaryam R, Drysdale E, Inglis JB, Shapiro A, Nagaraj S, Ghassemi M, Foschini L, Goldenberg A. Machine learning COVID-19 detection from wearables. Lancet Digit Health. 2023 Apr;5(4):e182-e184. doi: 10.1016/S2589-7500(23)00045-6. No abstract available. — View Citation

Shandhi MMH, Cho PJ, Roghanizad AR, Singh K, Wang W, Enache OM, Stern A, Sbahi R, Tatar B, Fiscus S, Khoo QX, Kuo Y, Lu X, Hsieh J, Kalodzitsa A, Bahmani A, Alavi A, Ray U, Snyder MP, Ginsburg GS, Pasquale DK, Woods CW, Shaw RJ, Dunn JP. A method for intelligent allocation of diagnostic testing by leveraging data from commercial wearable devices: a case study on COVID-19. NPJ Digit Med. 2022 Sep 1;5(1):130. doi: 10.1038/s41746-022-00672-z. — View Citation

Shapiro A, Marinsek N, Clay I, Bradshaw B, Ramirez E, Min J, Trister A, Wang Y, Althoff T, Foschini L. Characterizing COVID-19 and Influenza Illnesses in the Real World via Person-Generated Health Data. Patterns (N Y). 2020 Dec 13;2(1):100188. doi: 10.1016/j.patter.2020.100188. eCollection 2021 Jan 8. — View Citation

Temple DS, Hegarty-Craver M, Furberg RD, Preble EA, Bergstrom E, Gardener Z, Dayananda P, Taylor L, Lemm NM, Papargyris L, McClain MT, Nicholson BP, Bowie A, Miggs M, Petzold E, Woods CW, Chiu C, Gilchrist KH. Wearable Sensor-Based Detection of Influenza in Presymptomatic and Asymptomatic Individuals. J Infect Dis. 2023 Apr 12;227(7):864-872. doi: 10.1093/infdis/jiac262. — View Citation

Tokars JI, Olsen SJ, Reed C. Seasonal Incidence of Symptomatic Influenza in the United States. Clin Infect Dis. 2018 May 2;66(10):1511-1518. doi: 10.1093/cid/cix1060. — View Citation

Wiemken TL, Khan F, Puzniak L, Yang W, Simmering J, Polgreen P, Nguyen JL, Jodar L, McLaughlin JM. Seasonal trends in COVID-19 cases, hospitalizations, and mortality in the United States and Europe. Sci Rep. 2023 Mar 8;13(1):3886. doi: 10.1038/s41598-023-31057-1. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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	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.	Through study completion, approximately 10 months
Secondary	The secondary objective of this observational study is to determine if algorithm performance differs across various demographic groups	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.	Through study completion, approximately 10 months

External Links

•   2022-2023 U.S. Flu Season: Preliminary In-Season Burden Estimates. Centers for Disease Control and Prevention
•   COVID Data Tracker. Centers for Disease Control and Prevention
•   Key Facts About Influenza (Flu). Centers for Disease Control and Prevention
•   Past Seasons Estimated Influenza Disease Burden. Centers for Disease Control and Prevention.
•   RESP-NET Interactive Dashboard. Centers for Disease Control and Prevention