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Diabetes: Management and Pharmacotherapy01:15

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The therapy for diabetes aims to alleviate hyperglycemia-related symptoms, prevent acute metabolic decompensation, and reduce chronic end-organ complications. Glycemic control is evaluated through short-term (self-monitoring, continuous glucose monitoring) and long-term (A1c, fructosamine) metrics, enabling near real-time tracking of blood glucose levels and reflecting glycemic control over specific time frames.
Insulin remains the cornerstone of treatment for most patients with type 1 and many...
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Diabetes Mellitus: Overview and Type I Subtype01:22

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Diabetes mellitus is a chronic metabolic disorder characterized by high blood glucose levels due to inadequate insulin production, insulin resistance, or both. The condition affects millions worldwide and can significantly impact their health and quality of life.
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Type 2 diabetes, characterized by insulin resistance, arises when the insulin receptors on cells lose responsiveness to insulin, diminishing the cell's capacity to take up glucose, resulting in elevated blood glucose levels. To receive a diagnosis of Type 2 diabetes, a series of blood glucose tests are necessary to assess whether the blood glucose falls within normal parameters. If the result is out of the normal range, a patient may be diagnosed as prediabetic or diabetic, depending on the...
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For most patients, experiencing several weeks of polyuria, polydipsia, fatigue, and significant weight loss may indicate the presence of diabetes. Furthermore, adults displaying the phenotypic appearance of type 2 diabetes (particularly those who are obese and not initially insulin-requiring), may have islet cell autoantibodies, suggesting autoimmune-mediated β cell destruction and a diagnosis of latent autoimmune diabetes of adults (LADA). The categorization of glucose homeostasis is...
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Carbohydrates are polymers composed of molecules containing atoms of carbon, hydrogen and oxygen. One gram of carbohydrate can provide four kilo-calories of energy, which makes it the most efficient instant energy source.
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Diabetes mellitus is a chronic metabolic disorder characterized by hyperglycemia. The four categories of diabetes are type 1 diabetes, type 2 diabetes, other specific types of diabetes, and gestational diabetes.
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Machine Learning and Smart Devices for Diabetes Management: Systematic Review.

Mohammed Amine Makroum1, Mehdi Adda1, Abdenour Bouzouane2

  • 1Département de Mathématiques, Informatique et Génie, Université du Québec à Rimouski (UQAR), 300 Allée des Ursulines, Rimouski, QC G5L 3A1, Canada.

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PubMed
Summary

Smart devices and AI enhance diabetes self-management. Wearable technology improves blood sugar control, predicts dangerous events, and boosts patients' quality of life.

Keywords:
artificial intelligencediabetesdigital healthglucose monitoringmachine learningwearables

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Area of Science:

  • Biomedical Engineering
  • Health Informatics
  • Chronic Disease Management

Background:

  • Smart devices are increasingly used for diabetes management, aiming to improve blood glucose stability and prevent hypo/hyperglycemia.
  • The primary goal of diabetes self-management is to enhance patients' lifestyle and overall quality of life.
  • Technological advancements offer new avenues for supporting individuals with diabetes.

Purpose of the Study:

  • To systematically review the literature on smart devices for diabetes monitoring and management.
  • To identify how technology, particularly artificial intelligence, is applied in diabetes care.
  • To assess the impact of these technologies on patient outcomes and quality of life.

Main Methods:

  • Systematic review of 89 studies published between 2011 and 2021.
  • Searches conducted in databases like Scopus, Google Scholar, and PubMed.
  • Keywords included "Diabetes", "Technology", "Self-management", and "Artificial Intelligence".

Main Results:

  • Most studies focused on solving key diabetes management challenges, including blood glucose prediction and risk event detection.
  • Wearable devices were frequently integrated with artificial intelligence (AI) techniques.
  • Research indicates significant scientific interest in wearable devices for chronic condition management.

Conclusions:

  • Wearable devices show great promise in healthcare, especially for managing diabetes and preventing complications.
  • These technologies aid in improving diabetes illness management.
  • The use of smart devices has demonstrably enhanced the quality of life for individuals with diabetes.