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| Name | Class |
|---|---|
| Biomedical Advanced Research and Development Authority | FED |
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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Study Population | Adult participants (ages 18+) who reside in the contiguous United States |
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| 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 | 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 |
| Measure | Description | Time Frame |
|---|---|---|
| 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. |
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Inclusion Criteria:
Exclusion Criteria:
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Adult participants (ages 18+) who reside in the contiguous United States
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| Name | Affiliation | Role |
|---|---|---|
| Ernesto Ramirez, PhD | Evidation Health | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Evidation Health | San Mateo | California | 94402 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 36890264 | Background | 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. | |
| 29206909 | Background | 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. |
| Label | URL |
|---|---|
| COVID Data Tracker. Centers for Disease Control and Prevention | View source |
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We have provisions in our study protocol and consent to share Coded Study Data with approved external research partners. The sharing process is not yet finalized.
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| ICF | No | No | Yes | Informed Consent Form | Dec 21, 2023 | Jan 16, 2024 |
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All valid specimens will be tested using RT-PCR via the Abbott Alinity m Resp-4-Plex assay.
A subset of individuals selected by the study team will be asked to provide saliva samples after enrolling in the study using the Spectrum MAXSwab Saliva Collection Device. Saliva samples may be shipped to another lab for processing and may be stored indefinitely. Valid saliva samples may, or may not, be tested for IgA and IgG at a to be determined lab.
| Through study completion, approximately 10 months |
| 35759279 | Background | 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. |
| 35552722 | Background | 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. |
| 33506230 | Background | 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. |
| 36951890 | Background | 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. |
| Background | 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. |
| 35480626 | Background | 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. |
| 36963907 | Background | 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. |
| 36050372 | Background | 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. |
| RESP-NET Interactive Dashboard. Centers for Disease Control and Prevention | View source |
| Key Facts About Influenza (Flu). Centers for Disease Control and Prevention | View source |
| ICF_000.pdf |
| ID | Term |
|---|---|
| D007251 | Influenza, Human |
| D000086382 | COVID-19 |
| ID | Term |
|---|---|
| D012141 | Respiratory Tract Infections |
| D007239 | Infections |
| D009976 | Orthomyxoviridae Infections |
| D012327 | RNA Virus Infections |
| D014777 | Virus Diseases |
| D012140 | Respiratory Tract Diseases |
| D011024 | Pneumonia, Viral |
| D011014 | Pneumonia |
| D018352 | Coronavirus Infections |
| D003333 | Coronaviridae Infections |
| D030341 | Nidovirales Infections |
| D008171 | Lung Diseases |
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