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Appl Clin Inform 2021; 12(01): 001-009
DOI: 10.1055/s-0040-1719043
Research Article

Early Detection of Prediabetes and T2DM Using Wearable Sensors and Internet-of-Things-Based Monitoring Applications

Mirza Mansoor Baig
1   School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand
,
Hamid GholamHosseini
1   School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand
,
Jairo Gutierrez
1   School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand
,
Ehsan Ullah
1   School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand
,
Maria Lindén
2   School of Innovation Design and Engineering, Mälardalen University, Västerås, Sweden
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Abstract

Background Prediabetes 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.

Objectives This research is focused on early detection of prediabetes and T2DM using wearable technology and Internet-of-Things-based monitoring applications.

Methods We 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.

Results The proposed model was tested and validated using Kappa analysis and achieved an overall agreement of 91%.

Conclusion We 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.

Keywords

wearable smart shirt - M-health application - type 2 diabetes mellitus - long-term conditions - prediabetes - clinical decision support - chronic conditions

Protection of Human and Animals Subjects

The study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects and was reviewed by the Auckland University of Technology Institutional Review Board.




Publication History

Received: 07 July 2020

Accepted: 25 September 2020

Article published online:
06 January 2021

© 2021. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

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