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
NCT ID: NCT06207929
Last Updated: 2024-09-05
Study Results
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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COMPLETED
18157 participants
OBSERVATIONAL
2024-01-21
2024-08-07
Brief Summary
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Detailed Description
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Conditions
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Study Design
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OTHER
PROSPECTIVE
Study Groups
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Study Population
Adult participants (ages 18+) who reside in the contiguous United States
No interventions assigned to this group
Eligibility Criteria
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Inclusion Criteria
* 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
* 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)
18 Years
ALL
Yes
Sponsors
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Biomedical Advanced Research and Development Authority
FED
Evidation Health
INDUSTRY
Responsible Party
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Principal Investigators
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Ernesto Ramirez, PhD
Role: PRINCIPAL_INVESTIGATOR
Evidation Health
Locations
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Evidation Health
San Mateo, California, United States
Countries
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References
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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Provided Documents
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Document Type: Informed Consent Form
Related Links
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COVID Data Tracker. Centers for Disease Control and Prevention
RESP-NET Interactive Dashboard. Centers for Disease Control and Prevention
Other Identifiers
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WAVE Study
Identifier Type: -
Identifier Source: org_study_id
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