Technology-driven diabetes management: a review of digital therapeutics, mobile apps, and clinical evidence

Bidisha Bhattacharya et al.

Informatics for Health and Social Care2026https://doi.org/10.1080/17538157.2025.2609747article
ABDC C
Weight
0.50

What the paper says

Digital health technologies are revolutionizing the treatment of diabetes by providing creative ways to enhance patient outcomes. In this study, the role of digital medicines in the treatment of diabetes is examined with special attention paid to wearable technology, mobile applications, and clinical data that backs up their usage. Software-driven interventions designed to prevent, manage, or treat diabetes using specialized data-driven methods are referred to as digital therapies. The article examines many smartphone applications that support insulin management, nutritional tracking, blood glucose monitoring, and lifestyle changes. It examines important clinical trials that demonstrate how successfully these tools reduce HbA1c levels, enhance glycemic control, and promote long-term treatment regimen adherence. New advancements in AI such as personalized AI algorithms, integration of continuous glucose monitors with mobile apps, remote patient monitoring, telemedicine, behavioral nudges, machine learning, and data analytics that enhance the personalization of diabetic care are also the subject of the evaluation. Digital treatments have the potential to revolutionize diabetes care by providing more accessible, patient-centered, and effective care.

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https://doi.org/https://doi.org/10.1080/17538157.2025.2609747

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@article{bidisha2026,
  title        = {{Technology-driven diabetes management: a review of digital therapeutics, mobile apps, and clinical evidence}},
  author       = {Bidisha Bhattacharya et al.},
  journal      = {Informatics for Health and Social Care},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1080/17538157.2025.2609747},
}

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Technology-driven diabetes management: a review of digital therapeutics, mobile apps, and clinical evidence

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.