School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand.
School of Innovation Design and Engineering, Mälardalen University, Västerås, Sweden.
Appl Clin Inform. 2021 Jan;12(1):1-9. doi: 10.1055/s-0040-1719043. Epub 2021 Jan 6.
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.
This research is focused on early detection of prediabetes and T2DM using wearable technology and Internet-of-Things-based monitoring applications.
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.
The proposed model was tested and validated using Kappa analysis and achieved an overall agreement of 91%.
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.
糖尿病前期和 2 型糖尿病（T2DM）是影响全球医疗保健服务的主要长期健康问题之一。少数有效的方法之一是通过健康积极的生活方式积极管理糖尿病。
本研究专注于使用可穿戴技术和基于物联网的监测应用程序早期检测糖尿病前期和 T2DM。
我们开发了一种基于自适应神经模糊推理的人工智能模型，通过个性化监测来检测糖尿病前期和 T2DM。该模型的关键影响因素包括心率、心率变异性、呼吸率、呼吸量和活动数据（步数、步频和卡路里）。使用先进的可穿戴式身体背心收集数据，并结合血糖、身高、体重、年龄和性别等手动记录。该模型与临床知识库一起分析数据。模糊规则用于通过现有干预措施、临床指南和方案建立基线值。
使用 Kappa 分析对提出的模型进行了测试和验证，总体一致性达到 91%。
我们还展示了原始模型预测结果的 2 年随访观察。此外，与传统/常规实践相比，使用 M 健康应用程序和可穿戴背心（智能衬衫）的参与者的糖尿病情况有所改善。
