Publications

Association of digital measures and self-reported fatigue

Evidation contributed to research that looked at whether or not multimodal digital data can be used to quantify fatigue in both healthy individuals and individuals with chronic inflammatory rheumatic disease.

Machine learning COVID-19 detection from wearables

Evidation research looks into which criteria must be met before a study can claim reasonable COVID-19 detection performance using data from wearable devices.

Bret Nestor, Jaryd Hunter, Raghu Kainkaryam, Erik Drysdale, Jeffrey B Inglis, Allison Shapiro, Sujay Nagaraj, Marzyeh Ghassemi, Luca Foschini, Anna Goldenberg

Precision recruitment for high-risk participants in a COVID-19 cohort study

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

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

American Life in Realtime: A benchmark registry of health data for equitable precision health

ALiR, funded by a grant from the NIH, is a first-of-its-kind, publicly available benchmark registry and research infrastructure for person-generated health data collected from smartphones and wearables, made possible by Evidation.

Ritika R. Chaturvedi, Marco Angrisani, Wendy M. Troxel, Tania Gutsche, Eva Ortega, Monika Jain, Adrien Boch & Arie Kapteyn

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

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

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