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Type II Diabetes Mellitus III: Clinical Manifestations and Diagnosis

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Type 2 diabetes mellitus develops gradually and is often asymptomatic in early stages.Clinical ManifestationsWhen symptoms appear, they include fatigue, blurred vision, pruritus, delayed wound healing, and recurrent infections, particularly candidal infections. Peripheral neuropathy may present as numbness or tingling in the extremities. Classic hyperglycemia symptoms—polyuria, polydipsia, and polyphagia—are less common. Most patients are overweight and frequently have associated hypertension...

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A Protocol for Constructing a Rat Wound Model of Type 1 Diabetes

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Published on: February 17, 2023

Mobile phone-based pattern recognition and data analysis for patients with type 1 diabetes.

Stein Olav Skrøvseth¹, Eirik Årsand, Fred Godtliebsen

¹Norwegian Centre for Integrated Care and Telemedicine, University Hospital of North Norway, Tromsø, Norway.

Diabetes Technology & Therapeutics

|October 6, 2012

Summary

Mobile apps help type 1 diabetes patients manage blood glucose by collecting self-gathered data. Analysis reveals patterns, offering data-driven insights for improved disease management.

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Area of Science:

Digital Health
Diabetes Management
Mobile Health Interventions

Background:

Type 1 diabetes patients generate substantial data via electronic self-help tools.
Mobile phones offer potential for analyzing personal health data.
Exploring self-gathered data's role in improving blood glucose management is crucial.

Purpose of the Study:

To investigate how self-gathered data, collected via mobile applications, can enhance blood glucose management for individuals with type 1 diabetes.

Main Methods:

Thirty patients with type 1 diabetes used a mobile app for 3-6 months, recording blood glucose, insulin, diet, activity, and symptoms.
Data analysis focused on module usage and developing data processing/visualization tools.
Utilized period finding and scale-space trends for pattern identification.

Main Results:

Eighteen patients ('adopters') provided complete data for over 80 days.
Common reasons for withdrawal included phone usability issues.
Pattern recognition for predicting glucose levels showed poor performance, but trend analysis identified significant patterns for most adopters.

Conclusions:

Minimally intrusive mobile applications empower type 1 diabetes users to record data.
This data can offer valuable, data-driven insights for disease self-management.
Mobile health tools show potential for personalized diabetes care.