This paper is in the following e-collection/theme issue:

Early Results from COVID-19 Studies (290) JMIR Theme Issue: COVID-19 Special Issue (2414) Digital Biomarkers and Digital Phenotyping (182) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1494) Infectious Diseases (non-STD/STI) (1529) Outbreak and Pandemic Preparedness and Management (1705) Hygiene and Infection Prevention (305)

Published on in Vol 5, No 5 (2021): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23461, first published .
Digital Phenotypes for Understanding Individuals' Compliance With COVID-19 Policies and Personalized Nudges: Longitudinal Observational Study

Digital Phenotypes for Understanding Individuals' Compliance With COVID-19 Policies and Personalized Nudges: Longitudinal Observational Study

Digital Phenotypes for Understanding Individuals' Compliance With COVID-19 Policies and Personalized Nudges: Longitudinal Observational Study

Authors of this article:

Ahmed Ibrahim1 Author Orcid Image ;   Heng Zhang1 Author Orcid Image ;   Sarah Clinch1 Author Orcid Image ;   Ellen Poliakoff1 Author Orcid Image ;   Bijan Parsia1 Author Orcid Image ;   Simon Harper1 Author Orcid Image
Article Authors Cited by (2) Tweetations (2) Metrics

Ahmed Ibrahim   1 * , MSc ;   Heng Zhang   1 * , MSc ;   Sarah Clinch   1 , PhD ;   Ellen Poliakoff   1 , PhD ;   Bijan Parsia   1 , PhD ;   Simon Harper   1 , PhD

1 Department of Computer Science, The University of Manchester, Manchester, United Kingdom

*these authors contributed equally

Corresponding Author:

  • Heng Zhang, MSc
  • Department of Computer Science
  • The University of Manchester
  • LF7, Kilburn Building
  • Kilburn Buidling
  • Manchester, M13 9PL
  • United Kingdom
  • Phone: 44 7427630668
  • Email: heng.zhang@manchester.ac.uk