Itinai.com light and shadow chase in a bright clinical trial 94e57646 2deb 4898 b35d 841dc91eb7a5 3
Itinai.com light and shadow chase in a bright clinical trial 94e57646 2deb 4898 b35d 841dc91eb7a5 3

Educational intervention on perceived stress among adults with type 2 diabetes and metabolic syndrome: a non-randomized clinical trial

Educational Intervention on Perceived Stress in Adults with Type 2 Diabetes and Metabolic Syndrome

Key Findings from the Clinical Trial

During the six-month non-randomized clinical trial, a nurse-led educational health-promoting program was conducted with 51 adults diagnosed with type 2 diabetes mellitus and metabolic syndrome. The intervention resulted in a significant decrease in perceived stress (p=0.028). Additionally, stressed participants in the intervention group experienced a significant decrease in blood glucose levels (p=0.001) and a significant increase in high-density lipoprotein-cholesterol (p=0.003) concentrations after the six-month intervention.

Practical Solutions and Value

Implementing nurse-led educational health-promoting programs can effectively decrease perceived stress and improve metabolic syndrome parameters among adults with type 2 diabetes mellitus. This approach offers a practical solution for healthcare units to enhance patient outcomes and well-being.

Utilizing AI in Clinical Practice

Clinical trials play a crucial role in developing safe and effective treatments. Our AI-driven platform, DocSym, consolidates ICD-11 standards, clinical protocols, and research into a single, easily accessible knowledge base for clinicians. This streamlines operations and supports scheduling, monitoring treatments, and telemedicine, ultimately improving patient care and expanding services digitally.

By leveraging AI, clinics can enhance their workflows, reduce paper routines, and improve patient outcomes. Learn more about how our platform can support your practice at aidevmd.com.

AI-Powered Health Tools

Interactive AI Tools to Help You Understand Your Health

Solutions for Smart Healthcare

Clinical Research