Resources

Evidation and Duke Big Ideas Lab partner to boost participation and increase equity in digital health research

Evidation and Duke Big Ideas Lab announce a partnership with the goal of increasing diversity and representation in digital health studies

Predictors of seeking care for influenza-like illness in a novel digital study

Evidation conducted a study that used person-generated health data (PGHD) to identify factors associated with seeking care for ILI.

Devika Chawla, Alejandra Benitez, Hao Xu, Victoria Whitehill, Sara Tadesse-Bell, Allison Shapiro, Ernesto Ramirez, Kelly Scherer, Luca Foschini, Faye Drawnel, Barry Clinch, Marco Prunotto, Vincent Ukachukwu

Evidation launches FluSmart, a direct-to-person digital flu monitoring program, to better understand flu in everyday life

Evidation announces the launch of FluSmart, a direct-to-person digital flu monitoring program hosted on the Evidation platform.

Individuals diagnosed with breast cancer or in remission self-report QoL

Person-generated health data (PGHD) shows promise in detecting cognitive impairment & informing therapies.

COVID-19 real-world evidence primer

Evidation contributed to this online resource consisting of seven chapters that cover types of RWD, methods in RWE generation, examples of RWE studies, and more.

Amy Cavet; Claire Cravero, MPH; Aaron Galaznik, MD; Bray Patrick-Lake, MFS; Aniketh Talwai

The case for multimodal data capture: Deeper insights and increased prediction accuracy

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.

What possibly affects nighttime heart rate? Conclusions from n-of-1 observational data

Evidation's Lead Biostatistician, Eric J. Daza, worked with the Quality of Life Technology Lab to conduct an n-of-1 study using the MoTR method to evaluate the suggested effects of daily stressors on nighttime heart rate, sleep time, and physical activity in an individualized way.

Igor Matias, Eric J. Daza, Katarzyna Wac

Model-Twin Randomization (MoTR): A Monte Carlo method for estimating the within-individual average treatment effect using wearable sensors

In this academic preprint, the model twin randomization (MoTR; "motor") method for analyzing possible causes and effects an individual's intensive longitudinal data is introduced.

Eric J. Daza, Logan Schneider

How can we optimise health technology assessment and reimbursement decisions to accelerate access to new cardiovascular medicines?

This paper builds on discussions among clinical trialists, industry representatives, regulators, and patients, which took place at the 17th Global Cardiovascular Clinical Trialists (CVCT) Forum in December 2020.

Martin R. Cowie, Biykem Bozkurt, Javed Butler, Andrew Briggs, Maria Kubin, Adrian Jonas, Amanda I. Adler, Bray Patrick-Lake, Faiez Zannad

Boston University, Evidation publish study on voice as a digital marker in detection of Alzheimer’s disease

Evidation researchers, alongside researchers at Boston University School of Medicine, have announced the publication of study results that examine the use of voice recordings and transcripts as potential diagnostic tools for Alzheimer's Disease.