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mHealth apps for gestational diabetes mellitus that provide clinical decision support or artificial intelligence: A scoping review

Daley, BJ, Ni’Man, M, Neves, MR, Bobby Huda, MS, Marsh, W, Fenton, NE, Hitman, GA and McLachlan, S (2022) mHealth apps for gestational diabetes mellitus that provide clinical decision support or artificial intelligence: A scoping review. Diabetic Medicine, 39 (1). pp. 1-16. ISSN 0742-3071

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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.

Item Type: Article
Uncontrolled Keywords: 1103 Clinical Sciences, 1117 Public Health and Health Services, 1701 Psychology
Subjects: R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
T Technology > T Technology (General)
Divisions: Law
Publisher: Wiley
Date Deposited: 04 Feb 2022 11:51
Last Modified: 04 Feb 2022 11:54
DOI or ID number: 10.1111/dme.14735
URI: https://researchonline.ljmu.ac.uk/id/eprint/16232
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