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. Thieme. All rights reserved.
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. Thieme. All rights reserved.
Authors: Alan J Garber; Martin J Abrahamson; Joshua I Barzilay; Lawrence Blonde; Zachary T Bloomgarden; Michael A Bush; Samuel Dagogo-Jack; Ralph A DeFronzo; Daniel Einhorn; Vivian A Fonseca; Jeffrey R Garber; W Timothy Garvey; George Grunberger; Yehuda Handelsman; Robert R Henry; Irl B Hirsch; Paul S Jellinger; Janet B McGill; Jeffrey I Mechanick; Paul D Rosenblit; Guillermo E Umpierrez Journal: Endocr Pract Date: 2016-01 Impact factor: 3.443
Authors: Manbinder S Sidhu; Laura Griffith; Kate Jolly; Paramjit Gill; Tom Marshall; Nicola K Gale Journal: Ethn Health Date: 2016-01-13 Impact factor: 2.772
Authors: Cara M Smith; Steven N Chillrud; Darby W Jack; Patrick Kinney; Qiang Yang; Aimee M Layton Journal: J Occup Environ Med Date: 2019-04 Impact factor: 2.162
Authors: Jena Daniels; Nick Haber; Catalin Voss; Jessey Schwartz; Serena Tamura; Azar Fazel; Aaron Kline; Peter Washington; Jennifer Phillips; Terry Winograd; Carl Feinstein; Dennis P Wall Journal: Appl Clin Inform Date: 2018-02-21 Impact factor: 2.342