Thought Leadership

The Case for Multimodal Data Capture: Deeper Insights and Increased Prediction Accuracy

In July 2021, Evidation participated in an IEEE EMBS workshop that explored how machine learning can convert data into measures for both disease detection and overall quality of life. The key takeaways are presented in this article.

Publications

Estimating the Burden of Influenza-like Illness on Daily Activity at the Population Scale Using Commercial Wearable Sensors

Aziz Mezlini, Allison Shapiro, Eric J. Daza, Eamon Caddigan, Ernesto Ramirez, Tim Althoff, Luca Foschini

Evidation research reveals that most of the burden of influenza-like illness is invisible to health care and public health reporting systems.

Press Releases & Announcements

NIH and Evidation Health Enter Phase II of Project to Study Early Warning Signs of COVID-19

Evidation and NIH announce they've entered Phase II of a study to identify early warning signs of COVID-19 using data from commercial wearable sensors.

Press Releases & Announcements

Evidation Health and BARDA Partner on Early Warning System for COVID-19

Evidation announces a new effort, funded by BARDA, to detect COVID-19 symptoms via an early warning algorithm.

Press Releases & Announcements

NYC Health and Evidation Collaborate with the Icahn School of Medicine at Mount Sinai to Launch Study on COVID-19’s Impact on Mental Health

Evidation and NYC Health announce a nationwide study that aims to understand the mental health impact of the COVID-19 pandemic.

Thought Leadership

Rapid Identification of High-Risk Participants in a COVID-19 Research Study: How Evidation Enables Precision Recruitment at Scale

Evidation shares a recent method development paper published as a preprint on MedRxiv demonstrating precision recruitment in the context of COVID-19.

Publications

Characterizing COVID-19 and Influenza Illnesses in the Real World via Person-Generated Health Data

Shapiro A, Marinsek N, Clay I, Bradshaw B, Ramirez E, Min J, Trister A, Wang Y, Althoff A, and Foschini L

Evidation research reveals that COVID-19 symptoms tend to peak later and last longer than flu symptoms.

Publications

Dear Watch, Should I Get a COVID-19 Test? Designing Deployable Machine Learning for Wearables

Goldenberg A, Nestor B, Hunter J, Kainkaryam R, Drysdale E, Inglis J, Shapiro A, Nagaraj S, Ghassemi M, and Foschini L

Evidation research reveals that using a wearable device can reduce the burden of symptom surveys by 35%.

Publications

An Alternative to the ‘Light Touch’ Digital Health Remote Study: The Stress and Recovery in Frontline COVID-19 Healthcare Workers Study

Goodday M.S, Karlin E, Alfarano A, Brooks A, Chapman C, Desille R, Karlin D.R, Emami H, Woods N, Boch A, Foschini L, Wildman M, Cormack F, Taptiklis N, Pratap A, Ghassemi M, Goldenberg A, Nagaraj S, Walsh E, and Friend S

Evidation research found evidence for feasibility and acceptability of a participant-centric digital health study approach that involved building trust with participants and providing support through regular phone check-ins.

Publications

Precision Recruitment for High-Risk Participants in a COVID-19 Research Study

Mezlini A, Caddigan E, Shapiro A, Ramirez E, Kondow-McConaghy H, Yang J, DeMarco K, Naraghi-Arani P, Foschini L

Evidation describes an approach for reducing recruiting time and resources in a COVID-19 study by targeting recruitment to high-risk individuals.

Thought Leadership

Rapid Home Tests Are Good, but Digital Health Technology Is Even Better at Fighting COVID-19

Jennifer M. Radin, Giorgio Quer, Luca Foschini

Technology companies and device manufacturers should coordinate with public health officials to introduce a new era of precision public health.