# Publications & Abstracts

## Results

### REVEAL, a prospective, observational study  
**Authors:** Elizabeth A. Griffiths, Jae S. Min, Wei-Nchih Lee, Jeffrey C. Yu, Yogesh Patel, Karl-Johan Myren, David Dingli  
**Source:** BMC Part of Springer Nature  
  
Evidation and partners aimed to examine the impact of paroxysmal nocturnal hemoglobinuria (PNH) and its treatment on patients’ daily lives by combining patient-reported outcomes with wearable technology.

### Behavioral Engagement and Activation Model Study (BEAMS)  
**Authors:** John D Piette, Keni C S Lee, Hayden B Bosworth, Diana Isaacs, Christian J Cerrada, Raghu Kainkaryam, Jan Liska, Felix Lee, Adee Kennedy, David Kerr  
**Source:** Translational Behavioral Medicine  
  
Evidation and Sanofi partnered to look at Type 2 Diabetes patients with and without digital health technology (DHT) use to understand who might require more tailored support to adopt DHTs.

### Patient and physician perspectives on the use of a connected ecosystem for diabetes management: International cross-sectional observational study  
**Authors:** Elizabeth Benito-Garcia; Julio Vega; Eric J Daza; Wei-Nchih Lee; Adee Kennedy; Jean-Marc Chantelot  
**Source:** JMIR Formative Research  
  
Evidation and Sanofi partnered to understand which type 2 diabetes patients are more likely to participate in and benefit from connected ecosystem programs.

### Association of digital measures and self-reported fatigue  
**Source:** Frontiers in Digital Health  
  
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.

### Characterization of influenza-like Illness burden using commercial wearable sensor data and patient-reported outcomes: Mixed methods cohort study  
**Authors:** Victoria Hunter, Allison Shapiro, Devika Chawla, Faye Drawnel, Ernesto Ramirez, Elizabeth Phillips, Sara Tadesse-Bell, Luca Foschini, Vincent Ukachukwu  
**Source:** JMIR Publications  
  
Evidation research aimed to characterize the burden of influenza-like illness using commercial wearable sensor data and investigate the extent to which these data correlate with self-reported illness severity and duration.

### Machine learning COVID-19 detection from wearables  
**Authors:** Bret Nestor, Jaryd Hunter, Raghu Kainkaryam, Erik Drysdale, Jeffrey B Inglis, Allison Shapiro, Sujay Nagaraj, Marzyeh Ghassemi, Luca Foschini, Anna Goldenberg  
**Source:** The Lancet Digital Health  
  
Evidation research looks into which criteria must be met before a study can claim reasonable COVID-19 detection performance using data from wearable devices.

### Precision recruitment for high-risk participants in a COVID-19 cohort study  
**Authors:** Mezlini A, Caddigan E, Shapiro A, Ramirez E, Kondow-McConaghy H, Yang J, DeMarco K, Naraghi-Arani P, Foschini L  
**Source:** ScienceDirect  
  
Evidation describes an approach for reducing recruiting time and resources in a COVID-19 study by targeting recruitment to high-risk individuals.

### American Life in Realtime: A benchmark registry of health data for equitable precision health  
**Authors:** Ritika R. Chaturvedi, Marco Angrisani, Wendy M. Troxel, Tania Gutsche, Eva Ortega, Monika Jain, Adrien Boch & Arie Kapteyn  
**Source:** Nature Medicine  
  
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.

### Predictors of seeking care for influenza-like illness in a novel digital study  
**Authors:** 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  
**Source:** Open Forum Infectious Diseases  
  
Evidation conducted a study that used person-generated health data (PGHD) to identify factors associated with seeking care for ILI.

### COVID-19 real-world evidence primer  
**Authors:** Amy Cavet; Claire Cravero, MPH; Aaron Galaznik, MD; Bray Patrick-Lake, MFS; Aniketh Talwai  
**Source:** The Reagan-Udall Foundation for the FDA  
  
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.