PMID- 33406540
OWN - NLM
STAT- MEDLINE
DCOM- 20211116
LR  - 20220107
IS  - 1869-0327 (Electronic)
IS  - 1869-0327 (Linking)
VI  - 12
IP  - 1
DP  - 2021 Jan
TI  - Early Detection of Prediabetes and T2DM Using Wearable Sensors and 
      Internet-of-Things-Based Monitoring Applications.
PG  - 1-9
LID - 10.1055/s-0040-1719043 [doi]
AB  - 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.
CI  - Thieme. All rights reserved.
FAU - Baig, Mirza Mansoor
AU  - Baig MM
AD  - School of Engineering, Computer and Mathematical Sciences, Auckland University of 
      Technology, Auckland, New Zealand.
FAU - GholamHosseini, Hamid
AU  - GholamHosseini H
AD  - School of Engineering, Computer and Mathematical Sciences, Auckland University of 
      Technology, Auckland, New Zealand.
FAU - Gutierrez, Jairo
AU  - Gutierrez J
AD  - School of Engineering, Computer and Mathematical Sciences, Auckland University of 
      Technology, Auckland, New Zealand.
FAU - Ullah, Ehsan
AU  - Ullah E
AD  - School of Engineering, Computer and Mathematical Sciences, Auckland University of 
      Technology, Auckland, New Zealand.
FAU - Linden, Maria
AU  - Linden M
AD  - School of Innovation Design and Engineering, Malardalen University, Vasteras, 
      Sweden.
LA  - eng
PT  - Journal Article
DEP - 20210106
PL  - Germany
TA  - Appl Clin Inform
JT  - Applied clinical informatics
JID - 101537732
SB  - IM
MH  - Artificial Intelligence
MH  - *Diabetes Mellitus, Type 2/diagnosis
MH  - Humans
MH  - Internet
MH  - *Prediabetic State/diagnosis
MH  - *Wearable Electronic Devices
PMC - PMC7787711
COIS- None declared.
EDAT- 2021/01/07 06:00
MHDA- 2021/11/17 06:00
PMCR- 2022/01/06
CRDT- 2021/01/06 20:05
PHST- 2021/01/06 20:05 [entrez]
PHST- 2021/01/07 06:00 [pubmed]
PHST- 2021/11/17 06:00 [medline]
PHST- 2022/01/06 00:00 [pmc-release]
AID - 200124ra [pii]
AID - 10.1055/s-0040-1719043 [doi]
PST - ppublish
SO  - Appl Clin Inform. 2021 Jan;12(1):1-9. doi: 10.1055/s-0040-1719043. Epub 2021 Jan 
      6.