Clinical Decision Support for Early Detection of Prediabetes and Type 2 Diabetes Mellitus Using Wearable Technology

Baig, Mirza Mansoor; Mirza, Farhaan; GholamHosseini, Hamid; Gutiérrez, Jairo; Ullah, Ehsan

doi:10.1109/embc.2018.8513343

Cited by 10 publications

(9 citation statements)

References 10 publications

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“…4,36 In the 2-year follow-up, we found that the diabetic profile of participant 2 (►Table 3) was improved and in control (average HbA1c of 64 mmol/mol) when compared with the participant 1 (average HbA1c of 87.5 mmol/mol). 28 The main contributing factors were the visibility of the real-time activity data, motivation to use the smart shirt, access to real-time data via the smartphone app and cloud dashboard, timely feedback, alerts, and notifications. Data analysis was computed on-demand or as a monthly report to keep the participants motived and involved.…”

Section: Discussionmentioning

confidence: 99%

“…19,[39][40][41] Further improvements are required such as adding diet information, ethnicity, location, socioeconomic status, living status, and other social factors that could have a significant influence on the healthy and active lifestyle for managing T2DM and delaying the prediabetes. [2][3][4]27,28 Early Detection of Prediabetes and T2DM Baig et al 7…”

Section: Discussionmentioning

confidence: 99%

“…These areas include wearable (wireless body-worn sensors and devices), reliable and accurate, real-time transmission, low cost, low maintenance, and usability (user-friendly and timely processing of real-time data). [26][27][28][29] We used the Hexoskin smart vest [30][31][32][33] for collecting activity data and vital signs. It has integrated sensors for real-time collection of steps, activity as well as ECG, heart rate, and breaths per minute.…”

Section: Market Available Monitoring Devices and Sensorsmentioning

confidence: 99%

“…This research aimed to design and develop a clinical decision support system to detect prediabetes and diabetes. 28 The early detection model was based on the local and national guidelines for prediabetes and T2DM. The developed model was uniquely trained and supported with various data, including demographics, activity, and clinical for a deep understanding of all possible contributing factors.…”

Section: Market Available Monitoring Devices and Sensorsmentioning

confidence: 99%

“…The developed model was uniquely trained and supported with various data, including demographics, activity, and clinical for a deep understanding of all possible contributing factors. 28 The proposed study helps to determine the prediabetes risk factor but not to make an actual diagnosis.…”

Section: Market Available Monitoring Devices and Sensorsmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Market Available Monitoring Devices and Sensorsmentioning

confidence: 99%

Section: Market Available Monitoring Devices and Sensorsmentioning

confidence: 99%

Section: Market Available Monitoring Devices and Sensorsmentioning

confidence: 99%

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

et al. 2021

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Towards adequate prediction of prediabetes using spatiotemporal ECG and EEG feature analysis and weight-based multi-model approach

Igbe

Kandwal

et al. 2020

Knowledge-Based Systems

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Carbon Nanotube Microscale Fiber Grid as an Advanced Calibration System for Multispectral Optoacoustic Imaging

et al. 2022

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Optoacoustic (photoacoustic) imaging has gained tremendous attention in research and in clinical practice as a point-of-care system for noninvasive, fast, and safe tests. The first optoacoustic (OA) tomograph has recently passed the Food and Drug Administration (FDA) approval stage for clinical applications aimed at early breast cancer diagnostics. Furthermore, a broad application of OA imaging for Biomedical and Materials Science fields requires a proper tool to test the equipment and verify the quality of the measurements on a daily basis. In the present work, we propose fibers based on single-walled carbon nanotubes (SWCNTs) as a material for designing a stable and reliable calibration grid. The main advantage of the developed test system is the broad optical absorption of SWCNT-based fibers, ranging from visible to mid-infrared regions. Inspired by stringed instruments, we elaborate a grid to calibrate and verify spatial resolution in three projections and sensitivity of OA imaging systems. Thus, the real calibration grid parameters, such as fiber length and diameter, could be translated to the OA signal measurements. This proof-of-the-concept study evaluates the geometry of fibers, that is, the length/diameter and design of fibers, such as free-standing/twisted, and shows the fabrication procedure of the calibration grid prototype toward the successful validation of the OA imaging system, including raster-scanning optoacoustic mesoscopy (RSOM) at one wavelength and tomography at several wavelengths, which have grand prospects in preclinical and clinical practices. Besides, the more advanced geometry based on double-twisted fibers, or twistrons, applied here provided us with a chance to reach the lower resolution limit for RSOM because of the difference in diameter between the thin and thick parts in the morphology is verified by scanning electron microscopy.

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Clinical Decision Support for Early Detection of Prediabetes and Type 2 Diabetes Mellitus Using Wearable Technology

Cited by 10 publications

References 10 publications

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

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

Towards adequate prediction of prediabetes using spatiotemporal ECG and EEG feature analysis and weight-based multi-model approach

Carbon Nanotube Microscale Fiber Grid as an Advanced Calibration System for Multispectral Optoacoustic Imaging

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