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

Journals

  1. Prinjha S, Ricci-Cabello I, Newhouse N, Farmer A. British South Asian Patients’ Perspectives on the Relevance and Acceptability of Mobile Health Text Messaging to Support Medication Adherence for Type 2 Diabetes: Qualitative Study. JMIR mHealth and uHealth 2020;8(4):e15789 View
  2. Røed M, Vik F, Hillesund E, Lippevelde W, Øverby N. Associations between parental food choice motives, health-promoting feeding practices, and infants’ fruit and vegetable intakes: the Food4toddlers study. Food & Nutrition Research 2020;64(0) View
  3. Sabbah N, Carles G, Demar M, Nacher M. Diabetes in French Guiana, adapting national standards of therapeutic education and care to the amazonian challenge. World Journal of Diabetes 2021;12(2):98 View
  4. Hansen A, Wangberg S, Årsand E. Lifestyle changes among people with type 2 diabetes are associated with participation in online groups and time since diagnosis. BMC Health Services Research 2021;21(1) View
  5. Xu R, Zhou L, Wong E, Wang D. The Association Between Patients' eHealth Literacy and Satisfaction With Shared Decision-making and Well-being: Multicenter Cross-sectional Study. Journal of Medical Internet Research 2021;23(9):e26721 View
  6. Gram I, Skeie G, Oyeyemi S, Borch K, Hopstock L, Løchen M. A Smartphone-Based Information Communication Technology Solution for Primary Modifiable Risk Factors for Noncommunicable Diseases: Pilot and Feasibility Study in Norway. JMIR Formative Research 2022;6(2):e33636 View
  7. Midthassel T, Hansen A, Aslam M. Are lifestyle changes from online information associated with discussing the information with a doctor? A cross -sectional study. PLOS ONE 2021;16(12):e0261471 View
  8. Jacob C, Sezgin E, Sanchez-Vazquez A, Ivory C. Sociotechnical Factors Affecting Patients’ Adoption of Mobile Health Tools: Systematic Literature Review and Narrative Synthesis. JMIR mHealth and uHealth 2022;10(5):e36284 View
  9. Mistry S, Shaw M, Raffan F, Johnson G, Perren K, Shoko S, Harris-Roxas B, Haigh F. Inequity in Access and Delivery of Virtual Care Interventions: A Scoping Review. International Journal of Environmental Research and Public Health 2022;19(15):9411 View
  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
  13. Yao R, Zhang W, Evans R, Cao G, Rui T, Shen L. Inequities in Health Care Services Caused by the Adoption of Digital Health Technologies: Scoping Review. Journal of Medical Internet Research 2022;24(3):e34144 View
  14. Hansen A, Johansen M, Mordaunt D. Personal continuity of GP care and outpatient specialist visits in people with type 2 diabetes: A cross-sectional survey. PLOS ONE 2022;17(10):e0276054 View
  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. Telemedicine use among patients with Rheumatoid Arthritis: identification of motivational determinants based on Data from a Nationwide Survey in Germany (Preprint). Journal of Medical Internet Research 2023 View