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

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.

Principles for Real-World Implementation of Bedside Predictive Analytics Monitoring

“…2 Many predictive analytics tools can provide a snapshot of the patient's clinical state by integrating multiple pieces of information. 3,4 Moreover, some potentially catastrophic illnesses have prodromal signatures that can be detected by algorithms analyzing continuous cardiorespiratory monitoring. [5][6][7] This approach, called predictive analytics monitoring, does not rely on clinician-initiated data and has a true predictive quality 8 in that a risk estimate based on these measures might rise in a patient with no overt signs or symptoms of illness.For the bedside clinician, predictive analytics monitoring presents a new kind of information and a new paradigm of care.…”

Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis

“…After a full text review, 102 studies in total were included in the qualitative review. 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 …”

“…There were few studies on the screening and detection of diseases such as sleep apnea, 76 77 78 79 80 81 82 83 84 85 86 peripheral vascular diseases, 87 88 89 90 91 92 diabetes, 93 94 95 96 97 hyper/hypotensive disease, 98 99 100 101 102 valvular disease, 103 104 105 106 heart failure, 107 108 109 myocardial infarction, 110 cardiac arrest, 111 and other conditions, including cardiac amyloidosis and anemia ( Table 2 ). 112 113 114 115 …”

Continuous Glucose Monitoring and Other Wearable Devices to Assess Hypoglycemia among Older Adult Outpatients with Diabetes Mellitus

“…49 Body vests have been investigated and might actually address certain barriers, especially in disabled patients. 50 Currently, however, a collection of more usable devices is needed to enhance success in detecting, preventing, and treating HG.…”