ArticleApplied clinical informatics2021
Early Detection of Prediabetes and T2DM Using Wearable Sensors and Internet-of-Things-Based Monitoring Applications.
Article in Applied clinical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it, 22 citations in OpenAlex.
- Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis.Frontiers in digital health · 2025Pooled it
- Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis.Yonsei medical journal · 2022Pooled it
- Integration of artificial intelligence and wearable technology in the management of diabetes and prediabetes.NPJ digital medicine · 2025Article
- Digital health technologies/interventions in smart ward development for elderly patients with diabetes: A perspective from China and beyond.World journal of diabetes · 2025Review
- A Review of Patient Bed Sensors for Monitoring of Vital Signs.Sensors (Basel, Switzerland) · 2024Review
- Diabetes management in the era of artificial intelligence.Archives of medical sciences. Atherosclerotic diseases · 2024Article
- Data Science Implementation Trends in Nursing Practice: A Review of the 2021 Literature.Applied clinical informatics · 2023Review
- Continuous Glucose Monitoring and Other Wearable Devices to Assess Hypoglycemia among Older Adult Outpatients with Diabetes Mellitus.Applied clinical informatics · 2023Article
- Principles for Real-World Implementation of Bedside Predictive Analytics Monitoring.Applied clinical informatics · 2021Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPrediabetes and type 2 diabetes mellitus (T2DM) are one of the major long-term health conditions affecting global healthcare delivery. One of the few effective approaches is to actively manage diabetes via a healthy and active lifestyle.
objectivesThis research is focused on early detection of prediabetes and T2DM using wearable technology and Internet-of-Things-based monitoring applications.
methodsWe developed an artificial intelligence model based on adaptive neuro-fuzzy inference to detect prediabetes and T2DM via individualized monitoring. The key contributing factors to the proposed model include heart rate, heart rate variability, breathing rate, breathing volume, and activity data (steps, cadence, and calories). The data was collected using an advanced wearable body vest and combined with manual recordings of blood glucose, height, weight, age, and sex. The model analyzed the data alongside a clinical knowledgebase. Fuzzy rules were used to establish baseline values via existing interventions, clinical guidelines, and protocols.
resultsThe proposed model was tested and validated using Kappa analysis and achieved an overall agreement of 91%.
conclusionWe also present a 2-year follow-up observation from the prediction results of the original model. Moreover, the diabetic profile of a participant using M-health applications and a wearable vest (smart shirt) improved when compared to the traditional/routine practice.
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