JMIR Publications

ABSTRACT

Background:

Diabetes and its complications account for 10% of annual UK healthcare spending. Digital healthcare interventions (DHIs) can provide scalable care, fostering diabetes self-management and reducing the risk of complications. Tailorability and usability are key to DHI engagement/effectiveness. User-centred design of DHIs, aligning features to end users’ needs, can generate more usable interventions, avoiding unintended consequences and improving user engagement.

Objective:

MyDiabetesIQ is an Artificial Intelligence engine, intended to provide users with tailored forecasts of their diabetes complications risk. It will underpin a user interface in which users will alter lifestyle parameters to see the impact this has on future risks,. MyDiabetesIQ will link to an existing DHI, My Diabetes My Way (MDMW). We describe user-centred design, informed by human factors engineering, of the user interface of MyDiabetesIQ.

Methods:

Current users of MDMW were invited to take part in focus groups to gather their insights about users being shown their complications risks, and any risks they perceived from using MyDiabetesIQ. Findings from focus groups informed the development of a prototype MyDiabetesIQ interface. The prototype was 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 analysed thematically (a combination of inductive and deductive analysis). For think aloud data, a sociotechnical model was used as a framework for thematic analysis.

Results:

Focus group participants (n=8) felt that some users could become anxious when shown their future complications risks. They highlighted the importance of easy navigation, avoidance of jargon, and 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 healthcare teams. Some issues could be compounded for users with limited digital skills. Results of focus groups and think aloud workshops are being used in the development of a live MyDiabetesIQ platform.

Conclusions:

Acting on the input of end users at each iterative stage of development can help to prioritise users throughout the design process, ensuring alignment of DHI features with their needs. Use of the sociotechnical framework encouraged 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. Good control of diabetes relies heavily on self-management. Tools such as MDMW/MyDiabetesIQ can offer personalised 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 healthcare costs.