A Nudge-Inspired AI-Driven Health Platform for Self-Management of Diabetes
Open accessAbstract
Diabetes mellitus is a serious chronic disease that affects the blood sugar levels in individuals, with current predictions estimating that nearly 578 million people will be affected by diabetes by 2030. Patients with type II diabetes usually follow a self-management regime as directed by a clinician to help regulate their blood glucose levels. Today, various technology solutions exist to support self-management; however, these solutions tend to be independently built, with little to no research or clinical grounding, which has resulted in poor uptake. In this paper, we propose, develop, and implement a nudge-inspired artificial intelligence (AI)-driven health platform for self-management of diabetes. The proposed platform has been co-designed with patients and clinicians, using the adapted 4-cycle design science research methodology (A4C-DSRM) model. The platform includes (a) a cross-platform mobile application for patients that incorporates a macronutrient detection algorithm for meal recognition and nudge-inspired meal logger, and (b) a web-based application for the clinician to support the self-management regime of patients. Further, the platform incorporates behavioral intervention techniques stemming from nudge theory that aim to support and encourage a sustained change in patient lifestyle. Application of the platform has been demonstrated through an illustrative case study via two exemplars. Further, a technical evaluation is conducted to understand the performance of the MDA to meet the personalization requirements of patients with type II diabetes.
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Using artificial intelligence to perform tasks currently handled by NPs, such as sending personalized feedback, reminders to submit BP records, nudging patients to take medications, and scheduling appointments for elevated BP, can improve patient adherence and compliance in remote monitoring.
“For example, using artificial intelligence (AI) to perform tasks currently handled by NPs,29 such as sending personalized feedback, reminders to submit BP records, nudging patients to take medications, and scheduling appointments for elevated BP, can improve patient adherence and compliance in remote monitoring.30”
From Program cost and return on investment analysis of remote patient monitoring for hypertension management in the cardiology department of a large healthcare system (2026) · cites this paper as [29]
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Cited by
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Zhang, D. S., Millet, L., Bellows, B. K., Lee, S., & Mann, D. (2026) · Journal of Telemedicine and Telecare · doi:10.1177/1357633x251403059
“For example, using arti fi cial intelligence (AI) to perform tasks currently handled by NPs, 29 such as sending personalized feedback, reminders to submit BP records, nudging patients to take medications, and scheduling appointments for elevated BP, can improve patient adherence and compliance in…”
Influential
- Authors
- Joachim, S., Forkan, A. R. M., Jayaraman, P. P., Morshed, A., & Wickramasinghe, N.
- Affiliations
- Swinburne University of Technology (AU)
- Central Queensland University (AU)
- Corresponding author
- Joachim, Shane
- ORCID
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2022-06-19
- Volume
- 22
- Issue
- 12
- Pages
- 4620
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC9227220
- OpenAlex
- W4283159605
- Cited by
- 49
- References
- 53
- Citations by year
- 2026: 11 · 2025: 23 · 2024: 7 · 2023: 7 · 2022: 1
- FWCI
- 10.91
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Health Sciences › Health Professions › Health Information Management › Innovation in Digital Healthcare Systems
- Keywords
- type 2 diabetes, diabetes self-management, nudge theory, artificial intelligence, mobile health apps, behavioral interventions, blood glucose regulation, personalized nutrition
- MeSH terms
- Self-Management, Algorithms, Artificial Intelligence, Diabetes Mellitus, Type 2, Humans, Mobile Applications
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