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

Health Risk and Perception in Cognitive Reasoning (22) User-centred Design Case Studies (654) Artificial Intelligence (1781) Diabetes Self-Management (541) Participatory Design and Participatory Research (370) Artificial Intelligence in Diabetes Care and Prevention (42) Diabetes Education and Elearning for Patients (132) Focus Groups and Qualitative Research for Human Factors Research (1069) Design and Evaluation of Patient Education Materials (103)

Published on in Vol 9, No 1 (2022): Jan-Mar

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29973, first published .
User-Centered Design of A Novel Risk Prediction Behavior Change Tool Augmented With an Artificial Intelligence Engine (MyDiabetesIQ): A Sociotechnical Systems Approach

User-Centered Design of A Novel Risk Prediction Behavior Change Tool Augmented With an Artificial Intelligence Engine (MyDiabetesIQ): A Sociotechnical Systems Approach

User-Centered Design of A Novel Risk Prediction Behavior Change Tool Augmented With an Artificial Intelligence Engine (MyDiabetesIQ): A Sociotechnical Systems Approach

Authors of this article:

Cathy Shields1 Author Orcid Image ;   Scott G Cunningham1 Author Orcid Image ;   Deborah J Wake2 Author Orcid Image ;   Evridiki Fioratou3 Author Orcid Image ;   Doogie Brodie4 Author Orcid Image ;   Sam Philip5 Author Orcid Image ;   Nicholas T Conway6 Author Orcid Image
Article Authors Cited by (13) Tweetations (9) Metrics

Journals

  1. Gardner C, Wake D, Brodie D, Silverstein A, Young S, Cunningham S, Sainsbury C, Ilia M, Lucas A, Willis T, Halligan J. Evaluation of prototype risk prediction tools for clinicians and people living with type 2 diabetes in North West London using the think aloud method. DIGITAL HEALTH 2023;9 View
  2. Bul K, Holliday N, Bhuiyan M, Clark C, Allen J, Wark P. Usability and Preliminary Efficacy of an Artificial Intelligence–Driven Platform Supporting Dietary Management in Diabetes: Mixed Methods Study. JMIR Human Factors 2023;10:e43959 View
  3. Scanzera A, Beversluis C, Potharazu A, Bai P, Leifer A, Cole E, Du D, Musick H, Chan R. Planning an artificial intelligence diabetic retinopathy screening program: a human-centered design approach. Frontiers in Medicine 2023;10 View
  4. Ferreira A, Ferreira D, Barbosa B, Aline de Oliveira U, Aparecida Padua E, Oliveira Chiarini F, Baena de Moraes Lopes M, Premaor M. Neural network-based method to stratify people at risk for developing diabetic foot: A support system for health professionals. PLOS ONE 2023;18(7):e0288466 View
  5. Oladimeji K, Nyatela A, Gumede S, Dwarka D, Lalla-Edward S. Impact of Artificial Intelligence (AI) on Psychological and Mental Health Promotion: An Opinion Piece. New Voices in Psychology 2023 View
  6. Mackenzie S, Sainsbury C, Wake D. Diabetes and artificial intelligence beyond the closed loop: a review of the landscape, promise and challenges. Diabetologia 2024;67(2):223 View
  7. Bai P, Beversluis C, Song A, Alicea N, Eisenberg Y, Layden B, Scanzera A, Leifer A, Musick H, Chan R. Opportunities to Apply Human-centered Design in Health Care With Artificial Intelligence–based Screening for Diabetic Retinopathy. International Ophthalmology Clinics 2024;64(4):5 View
  8. Bienefeld N, Keller E, Grote G. AI Interventions to Alleviate Healthcare Shortages and Enhance Work Conditions in Critical Care: Qualitative Analysis. Journal of Medical Internet Research 2025;27:e50852 View
  9. Butler J, Doubleday A, Sattar U, Nies M, Jeppesen A, Wright M, Reese T, Kawamoto K, Fiol G, Madaras-Kelly K. “Be Really Careful about That”: Clinicians' Perceptions of an Intelligence Augmentation Tool for In-Hospital Deterioration Detection. Applied Clinical Informatics 2025;16(02):377 View
  10. Azadi A, García-Peñalvo F. Optimizing Clinical Decision Support System Functionality by Leveraging Specific Human-Computer Interaction Elements: Insights From a Systematic Review. JMIR Human Factors 2025;12:e69333 View
  11. Abareshi F, Zand F, Sharifian R, Choobineh A. The most used methods for evaluating health information technology systems usability. A scoping review. WORK: A Journal of Prevention, Assessment & Rehabilitation 2025 View
  12. Hagelsrum K, Andermo S, Svedberg T, von Rosen P, Johansson U, Hagströmer M, Rossen J. Users' perceptions of an mHealth service to support healthy lifestyle habits among adult individuals with type 2 diabetes in Sweden – A qualitative study. BMC Digital Health 2025;3(1) View
  13. Ortega A, Rooper I, Massion T, Azubuike C, Lipman L, Lakhtakia T, Camino M, Parsons L, Tack E, Alshurafa N, Kay M, Graham A. Co-designing prediction data visualizations for a digital binge eating intervention. Translational Behavioral Medicine 2025;15(1) View