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
