Why the study?
Treatments for diabetes rarely incorporate individualized real-time data, and traditional methods to analyze longitudinal data from wearable sensors suffer from subjectivity, bias, and complexity.
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The paper proposes an AI-driven precision medicine framework to integrate data from wearable sensors and continuous glucose monitors for managing metabolic disorders.
The incidence of metabolic disorders is increasing at an alarming rate. A theoretically reversible collection of risk factors called metabolic syndrome precedes some of these conditions, such as diabetes. Additionally, treatments designed for diabetes do not often incorporate the individualized real-time lifestyle and physiological data. Monitoring glucose levels for diabetics and stopping prediabetics from progressing further can be aided by continuous glucose monitoring. The ubiquitous and unobtrusive nature of wrist-worn smart watches and continuous glucose monitors allow a longitudinal flow of information rich data. However, a comprehensive inspection or cascaded statistical tests must be performed to draw insights from this data. These methods suffer from subjectivity, observation bias and complexity. To overcome these, Artificial intelligence (AI) can be leveraged to draw these insights due to scalable utility of data. Alongside phenotyping glucose changes AI can also help reduce the costs associated with glucose level monitoring. This study proposes to develop an AI-driven precision medicine framework to incorporate data from wrist-worn sensors and glucose monitors to deliver insights.
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Ali et al. (2024) studied this question.
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