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

Demographics of Users, Social & Digital Divide (739) Diabetes (187) Apps, Mobile, Wearables for Diabetes (298)

Published on in Vol 21, No 5 (2019): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13615, first published .
Inequalities in the Use of eHealth Between Socioeconomic Groups Among Patients With Type 1 and Type 2 Diabetes: Cross-Sectional Study

Inequalities in the Use of eHealth Between Socioeconomic Groups Among Patients With Type 1 and Type 2 Diabetes: Cross-Sectional Study

Inequalities in the Use of eHealth Between Socioeconomic Groups Among Patients With Type 1 and Type 2 Diabetes: Cross-Sectional Study

Authors of this article:

Anne Helen Hansen1, 2 Author Orcid Image ;   Meghan Bradway3, 4 Author Orcid Image ;   Jan Broz5 Author Orcid Image ;   Tor Claudi6 Author Orcid Image ;   Øystein Henriksen7 Author Orcid Image ;   Silje C Wangberg8 Author Orcid Image ;   Eirik Årsand3, 9 Author Orcid Image
Article Authors Cited by (21) Tweetations (26) Metrics

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  10. Dos Santos T, Rodrigues T, Puñales M, Arrais R, Kopacek C. Newest Diabetes-Related Technologies for Pediatric Type 1 Diabetes and Its Impact on Routine Care: a Narrative Synthesis of the Literature. Current Pediatrics Reports 2021;9(4):142 View
  11. He W, Cao L, Liu R, Wu Y, Zhang W. Factors associated with internet use and health information technology use among older people with multi-morbidity in the United States: findings from the National Health Interview Survey 2018. BMC Geriatrics 2022;22(1) View
  12. Muehlensiepen F, Petit P, Knitza J, Welcker M, Vuillerme N. Factors Associated With Telemedicine Use Among Patients With Rheumatic and Musculoskeletal Disease: Secondary Analysis of Data From a German Nationwide Survey. Journal of Medical Internet Research 2023;25:e40912 View
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  15. Wang S, Yeh H, Stein A, Miller E. Use of Health Information Technology by Adults With Diabetes in the United States: Cross-sectional Analysis of National Health Interview Survey Data (2016-2018). JMIR Diabetes 2022;7(1):e27220 View
  16. Jankowiak M, Rój J, Wolniak R. The eHealth usage during COVID-19 pandemic 2020 year–Case of Poland. PLOS ONE 2023;18(9):e0290502 View
  17. Muehlensiepen F, Petit P, Knitza J, Welcker M, Vuillerme N. Identification of Motivational Determinants for Telemedicine Use Among Patients With Rheumatoid Arthritis in Germany: Secondary Analysis of Data From a Nationwide Cross-Sectional Survey Study. Journal of Medical Internet Research 2024;26:e47733 View
  18. Standaar L, van Tuyl L, Suijkerbuijk A, Brabers A, Friele R. Differences in e-health access, use, and perceived benefit between different socio-economic groups in the Dutch context: a secondary cross-sectional study (Preprint). JMIR Formative Research 2023 View
  19. Rinn R, Whittal A, Kremeti E, Lippke S. The social class of orthopedic rehabilitation patients: Are there differences in subjective health, return to work motivation, and participation in aftercare interventions?. Social Science & Medicine 2024;356:117152 View
  20. Grosman-Rimon L, Wegier P. With advancement in health technology comes great responsibility – Ethical and safety considerations for using digital health technology: A narrative review. Medicine 2024;103(33):e39136 View
  21. Hörhammer I, Suvanto J, Kinnunen M, Kujala S. Usefulness of self-guided digital services among mental health patients: The role of health confidence and sociodemographic characteristics. International Journal of Medical Informatics 2025;194:105693 View