Published on in Vol 10 , No 4 (2022) :April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29510, first published .
Demographic Imbalances Resulting From the Bring-Your-Own-Device Study Design

Demographic Imbalances Resulting From the Bring-Your-Own-Device Study Design

Demographic Imbalances Resulting From the Bring-Your-Own-Device Study Design

Journals

  1. Chaturvedi R, Angrisani M, Troxel W, Gutsche T, Ortega E, Jain M, Boch A, Kapteyn A. American Life in Realtime: a benchmark registry of health data for equitable precision health. Nature Medicine 2023;29(2):283 View
  2. Shandhi M, Cho P, Roghanizad A, Singh K, Wang W, Enache O, Stern A, Sbahi R, Tatar B, Fiscus S, Khoo Q, Kuo Y, Lu X, Hsieh J, Kalodzitsa A, Bahmani A, Alavi A, Ray U, Snyder M, Ginsburg G, Pasquale D, Woods C, Shaw R, Dunn J. A method for intelligent allocation of diagnostic testing by leveraging data from commercial wearable devices: a case study on COVID-19. npj Digital Medicine 2022;5(1) View
  3. Li S, Halabi R, Selvarajan R, Woerner M, Fillipo I, Banerjee S, Mosser B, Jain F, Areán P, Pratap A. Recruitment and Retention in Remote Research: Learnings From a Large, Decentralized Real-world Study. JMIR Formative Research 2022;6(11):e40765 View
  4. Goergen C, Tweardy M, Steinhubl S, Wegerich S, Singh K, Mieloszyk R, Dunn J. Detection and Monitoring of Viral Infections via Wearable Devices and Biometric Data. Annual Review of Biomedical Engineering 2022;24(1):1 View
  5. Zinzuwadia A, Singh J. Wearable devices—addressing bias and inequity. The Lancet Digital Health 2022;4(12):e856 View