Recent advances in computational tools and resources for the self-management of type 2 diabetes
Abstract
Background : While healthcare systems are investing resources on type 2 diabetes patients, self-management is becoming the new trend for these patients. Due to the pervasiveness of computing devices, a number of computerized systems are emerging to support the self-management of patients. Objective : The primary objective of this review is to identify and categorize the computational tools that exist for the self-management of type 2 diabetes, and to identify challenges that need to be addressed. Results : The tools have been categorized into web applications, mobile applications, games and ubiquitous diabetes management systems. We provide a detailed description of the salient features of each category along with a comparison of the various tools, listing their challenges and practical implications. A list of platforms that can be used to develop new tools for the self-management of type 2 diabetes, namely mobile applications development, sensor development, cloud computing, social media, and machine learning and predictive analysis platforms, are also provided. Discussions : This paper identifies a number of challenges in the existing categories of computational tools and consequently presents possible avenues for future research. Failure to address these issues will negatively impact on the adoption rate of the self-management tools and applications.
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AI-powered wearables and mobile apps reduce the cognitive burden on patients by automating much of day-to-day diabetes management.
“In addition to improving patient outcomes, these tools reduce the cognitive burden on patients by automating much of the day-to-day management of diabetes (42).”
From Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review (2025) · cites this paper as [42]
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Osonuga, A., Olawade, D. B., Gore, M., Rotifa, O. B., Egbon, E., Chinwah, V., & Boussios, S. (2025) · Annals of Translational Medicine · doi:10.21037/atm-25-62 · Cited by 4 · see citing works
- Authors
- Moonian, O., Jodheea-Jutton, A., Khedo, K. K., Baichoo, S., Nagowah, S. D., Nagowah, L., Mungloo-Dilmohamud, Z., & Cheerkoot-Jalim, S.
- Affiliations
- University of Mauritius (MU)
- Corresponding author
- Baichoo, Shakuntala
- ORCID
- Venue
- Informatics for Health and Social Care
- Publisher
- Informa UK Limited
- Published
- 2019-01-17
- Volume
- 45
- Issue
- 1
- Pages
- 77-95
- ISSN
- 1753-8157 (ISSN-L)
- 1753-8165
- OpenAlex
- W2909691179
- Cited by
- 15
- References
- 9
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 4 · 2023: 3 · 2022: 3 · 2021: 1 · 2020: 1
- FWCI
- 0.23
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Informatics for Health and Social Care (publishedVersion)
- PubMed (publishedVersion)
- 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 › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Keywords
- type 2 diabetes, self-management, mobile health apps, web application, ubiquitous computing, wearable sensors, cloud computing, social media, machine learning, predictive analytics, technology adoption
- MeSH terms
- Self-Management, Diabetes Mellitus, Type 2, Humans, Blood Glucose Self-Monitoring, Health Behavior, Telemedicine, Video Games, Internet, Social Media, Mobile Applications
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