ArticleJMIR human factors2022
User-Centered Design of A Novel Risk Prediction Behavior Change Tool Augmented With an Artificial Intelligence Engine (MyDiabetesIQ): A Sociotechnical Systems Approach.
Article in JMIR human factors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Optimizing Clinical Decision Support System Functionality by Leveraging Specific Human-Computer Interaction Elements: Insights From a Systematic Review.JMIR human factors · 2025Pooled it
- Article
- "Be Really Careful about That": Clinicians' Perceptions of an Intelligence Augmentation Tool for In-Hospital Deterioration Detection.Applied clinical informatics · 2025Article
- Co-designing prediction data visualizations for a digital binge eating intervention.Translational behavioral medicine · 2025Article
- AI Interventions to Alleviate Healthcare Shortages and Enhance Work Conditions in Critical Care: Qualitative Analysis.Journal of medical Internet research · 2025Article
- Opportunities to Apply Human-centered Design in Health Care With Artificial Intelligence-based Screening for Diabetic Retinopathy.International ophthalmology clinics · 2024Review
- Diabetes and artificial intelligence beyond the closed loop: a review of the landscape, promise and challenges.Diabetologia · 2024Review
- Usability and Preliminary Efficacy of an Artificial Intelligence-Driven Platform Supporting Dietary Management in Diabetes: Mixed Methods Study.JMIR human factors · 2023Article
- Planning an artificial intelligence diabetic retinopathy screening program: a human-centered design approach.Frontiers in medicine · 2023Article
- Neural network-based method to stratify people at risk for developing diabetic foot: A support system for health professionals.PloS one · 2023Article
- Article
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Authors and funding
7 authors at 4 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundDiabetes and its complications account for 10% of annual health care spending in the United Kingdom. Digital health care interventions (DHIs) can provide scalable care, fostering diabetes self-management and reducing the risk of complications. Tailorability (providing personalized interventions) and usability are key to DHI engagement/effectiveness. User-centered design of DHIs (aligning features to end users' needs) can generate more usable interventions, avoiding unintended consequences and improving user engagement.
objectiveMyDiabetesIQ (MDIQ) is an artificial intelligence engine intended to predict users' diabetes complications risk. It will underpin a user interface in which users will alter lifestyle parameters to see the impact on their future risks. MDIQ will link to an existing DHI, My Diabetes My Way (MDMW). We describe the user-centered design of the user interface of MDIQ as informed by human factors engineering.
methodsCurrent users of MDMW were invited to take part in focus groups to gather their insights about users being shown their likelihood of developing diabetes-related complications and any risks they perceived from using MDIQ. Findings from focus groups informed the development of a prototype MDIQ interface, which was then user-tested through the "think aloud" method, in which users speak aloud about their thoughts/impressions while performing prescribed tasks. Focus group and think aloud transcripts were analyzed thematically, using a combination of inductive and deductive analysis. For think aloud data, a sociotechnical model was used as a framework for thematic analysis.
resultsFocus group participants (n=8) felt that some users could become anxious when shown their future complications risks. They highlighted the importance of easy navigation, jargon avoidance, and the use of positive/encouraging language. User testing of the prototype site through think aloud sessions (n=7) highlighted several usability issues. Issues included confusing visual cues and confusion over whether user-updated information fed back to health care teams. Some issues could be compounded for users with limited digital skills. Results from the focus groups and think aloud workshops were used in the development of a live MDIQ platform.
conclusionsActing on the input of end users at each iterative stage of a digital tool's development can help to prioritize users throughout the design process, ensuring the alignment of DHI features with user needs. The use of the sociotechnical framework encouraged the consideration of interactions between different sociotechnical dimensions in finding solutions to issues, for example, avoiding the exclusion of users with limited digital skills. Based on user feedback, the tool could scaffold good goal setting, allowing users to balance their palatable future complications risk against acceptable lifestyle changes. Optimal control of diabetes relies heavily on self-management. Tools such as MDMW/ MDIQ can offer personalized support for self-management alongside access to users' electronic health records, potentially helping to delay or reduce long-term complications, thereby providing significant reductions in health care costs.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.