Clinical trial NCT06207929: 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
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
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Influenza, Human
COVID-19
Influenza A
Influenza B
Respiratory Syncytial Virus (RSV)
Keywords: Flu
Influenza
RSV
COVID
Wearable
Lead sponsor: Evidation Health
Sponsor class: INDUSTRY
Collaborators: name: Biomedical Advanced Research and Development Authority
First posted: 2024-01-17
Enrollment: 18157
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: OTHER
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Adult participants (ages 18+) who reside in the contiguous United States

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.


Eligibility criteria: 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)

Primary outcomes:
measure: 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
timeFrame: Through study completion, approximately 10 months
Secondary outcomes:
measure: The secondary objective of this observational study is to determine if algorithm performance differs across various demographic groups
timeFrame: Through study completion, approximately 10 months
Other outcomes:
Locations:
city: San Mateo
country: United States
facility: Evidation Health
state: California
Study officials:
affiliation: Evidation Health
name: Ernesto Ramirez, PhD
role: PRINCIPAL_INVESTIGATOR
References:
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.
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.
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.
citation: 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.
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.
citation: 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.
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.
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.
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.
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.