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. 2021 Jan;12(1):1-9.
doi: 10.1055/s-0040-1719043. Epub 2021 Jan 6.

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

Mirza Mansoor Baig  1 , Hamid GholamHosseini  1 , Jairo Gutierrez  1 , Ehsan Ullah  1 , Maria Lindén  2
Affiliations

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

Mirza Mansoor Baig et al. Appl Clin Inform. 2021 Jan.

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.

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Conflict of interest statement

None declared.

Figures

Fig. 1
Fig. 1
Phase 1 observation of the multimethodological approach to Information Systems Research. LTC, long-term condition.
Fig. 2
Fig. 2
The key building blocks of the proposed model.
Fig. 3
Fig. 3
Framework of interpretation engine using multiple components: individualized monitoring, evidence-based reasoning, knowledge-base, and weighted parameters.
Fig. 4
Fig. 4
Top five contributing risk factors. HbA1c, hemoglobin A1c.
Fig. 5
Fig. 5
Hemoglobin A1c (HbA1c) trend for the selected participants from 2016 to 2019. Original study period 2016 to 2017 and the follow-up study period is 2018 to 2019.
See this image and copyright information in PMC

References

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    1. Baig M M, Gholamhosseini H, Afifi S, Mirza F.Current Challenges and Barriers to the Wider Adoption of Wearable Sensor Applications and Internet-of-Things in Health and Well-beingInternational Conference on Information Resources Management (CONF-IRM);2019Auckland, New Zealand
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    1. Baig M M, Afifi S, GholamHosseini H, Mirza F. Cham: Springer International Publishing; 2019. Managing long-term conditions: wearable sensors and IoT-based monitoring applications; pp. 1–5. - PubMed
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