Accepted for/Published in: JMIR Human Factors
Date Submitted: Jun 25, 2021
Open Peer Review Period: Jun 25, 2021 - Aug 20, 2021
Date Accepted: Oct 15, 2021
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Online structured education for type 2 diabetes: An interdisciplinary research approach
ABSTRACT
Background:
Digital health research encompasses methods from Human-Computer Interaction, and health research.
Objective:
The aim of this paper is to describe how these methods were combined to develop HeLP-Diabetes: Starting Out, an online structured education programme for people newly diagnosed with type 2 diabetes.
Methods:
The development process consisted of three phases: (i) initial design for effectiveness; (ii) optimisation for usability and (iii) “in the wild” testing in the NHS with people newly diagnosed with type 2 diabetes, and further revisions. We took an iterative user-centred approach and followed steps from the HCI design lifecycle and the Medical Research Council guidance on developing and evaluating complex interventions.
Results:
The usability testing highlighted at an early stage where changes needed to be made to the language and layout of the programme. The “in the wild” testing provided data on uptake and barriers to use. The study suggested low uptake and completion of the programme, but those who used it seemed to benefit. The qualitative findings suggested that barriers to use included an expectation that the programme would take too long. This informed refinements to the programme.
Conclusions:
The use of interdisciplinary methods resulted in an iterative development process, and refinements to the programme that were based on user needs and data on uptake. The final intervention was more suitable for a definitive evaluation than the initial version. The description of our approach informs other digital health researchers about how to make interventions more sensitive to user needs.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.