MHealth apps for gestational diabetes mellitus that provide clinical decision support or artificial intelligence: A scoping review. Issue 1 (16th November 2021)

• Record Type: Journal Article
• Authors: Daley, Bridget J.; Ni'Man, Michael; Neves, Mariana R.; Bobby Huda, Mohammed S.; Marsh, William; Fenton, Norman E.; Hitman, Graham A.; McLachlan, Scott
• Is Part Of: Diabetic medicine. Volume 39:Issue 1(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-11-16
• Subjects: apps -- artificial intelligence -- clinical decision system support -- gestational diabetes -- mHealth -- scoping review; Diabetes -- Periodicals; 616.462
• DOI: 10.1111/dme.14735
• Languages: English
• ISSNs: 0742-3071

Abstract

Abstract: Aims: Gestational diabetes (GDM) is the most common metabolic disorder of pregnancy, requiring complex management and empowerment of those affected. Mobile health (mHealth) applications (apps) are proposed for streamlining healthcare service delivery, extending care relationships into the community, and empowering those affected by prolonged medical disorders to be equal collaborators in their healthcare. This review investigates mHealth apps intended for use with GDM; specifically those powered by artificial intelligence (AI) or providing decision support. Methods: A scoping review using the novel Survey Tool approach for collaborative literature Reviews (STaR) process was performed. Results: From 18 papers, 11 discrete GDM‐based mHealth apps were identified, but only 3 were reasonably mature with only one currently in use in a clinical setting. Two‐thirds of the apps provided condition‐relevant contextual user feedback that could aid in patient self care. However, although each app targeted one or more components of the GDM clinical pathway, no app addressed the entirety from diagnosis to postpartum. Conclusions: There are limited mHealth apps for GDM that incorporate AI or AI‐based decision support. Many exist only to record patient information like blood glucose readings or diet, provide generic patient education or advice, or to reduce adverse events by providing medication or appointment alerts. Significant barriers remain that continue to limit the adoption of mHealth apps in clinical care settings. Further research and development are needed to deliver intelligent holistic mHealth apps using AI that can truly reduce healthcare resource use and improve outcomes by enabling patient self care in the community.