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Diabetes: Management and Pharmacotherapy

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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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Predicting diabetes self-management education engagement: machine learning algorithms and models.

Xiangxiang Jiang¹, Gang Lv², Minghui Li³

¹Department of Clinical Pharmacy and Outcomes Sciences, University of South Carolina College of Pharmacy, Columbia, South Carolina, USA.

BMJ Open Diabetes Research & Care

|February 18, 2025

Summary

Diabetes self-management education (DSME) participation is low in older US adults. Machine learning identified key factors influencing DSME engagement and highlighted racial/ethnic disparities, suggesting a need for tailored approaches.

Keywords:

Diabetes Mellitus, Type 2 Health Education Health Promotion

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Area of Science:

Gerontology
Health Services Research
Data Science

Background:

Diabetes self-management education (DSME) is crucial for diabetes care, yet its utilization is limited in the US.
Understanding DSME participation in older adults is essential for improving health outcomes.

Purpose of the Study:

To investigate DSME participation rates among older US adults.
To identify comprehensive factors influencing DSME engagement using machine learning.
To explore racial/ethnic disparities in DSME participation.

Main Methods:

Utilized Medicare Current Beneficiary Survey data (2017-2019) for US Medicare beneficiaries with diabetes.
Employed the National Institute on Aging Health Disparities Research Framework for variable selection.
Applied five common machine learning models to analyze factors associated with DSME.

Main Results:

37.94% of eligible participants received DSME.
Machine learning models achieved >70% accuracy, with Random Forest reaching 85% accuracy.
Identified 74 key variables influencing DSME, revealing significant racial/ethnic disparities.

Conclusions:

Identified biological, behavioral, sociocultural, and environmental factors impacting DSME engagement.
Findings underscore the need for tailored DSME strategies to address racial/ethnic disparities.
Aligning DSME National Standards with identified factors can enhance guideline effectiveness and promote equitable care.