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 (WAVE)
Study Overview
Status
Status
Conditions
Conditions
Study Type
Study Type
Enrollment (Actual)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Ernesto Ramirez, PhD
- Phone Number: 415-650-4741
- Email: wavestudy@evidation.com
Study Contact Backup
- Name: Stephanie Salwen, PhD
- Email: wavestudy@evidation.com
Study Locations
-
-
California
-
San Mateo, California, United States, 94402
- Evidation Health
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
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 Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
|---|
|
Study Population
Adult participants (ages 18+) who reside in the contiguous United States
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
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
Time Frame: 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.
|
Through study completion, approximately 10 months
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
The secondary objective of this observational study is to determine if algorithm performance differs across various demographic groups
Time Frame: 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.
|
Through study completion, approximately 10 months
|
Collaborators and Investigators
Sponsor
Sponsor
Collaborators
Collaborators
Investigators
Investigators
- Principal Investigator: Ernesto Ramirez, PhD, Evidation Health
Publications and helpful links
General Publications
- 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.
- 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.
- 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.
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Actual)
Primary Completion
Study Completion (Actual)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Estimated)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- WAVE Study
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.