Identifying Patient Profiles for Developing Tailored Diabetes Self-Management Interventions: A Latent Class Cluster...

Area of Science:

• Behavioral Medicine
• Health Psychology
• Clinical Trial Analysis

Background:

• A one-size-fits-all approach to diabetes interventions may be less effective than tailored strategies.
• Understanding patient psychosocial needs is crucial for developing personalized interventions.

Purpose of the Study:

• To identify distinct patient profiles based on psychosocial characteristics.
• To inform the development of tailored interventions for individuals with diabetes.

Main Methods:

• Latent class cluster analysis of baseline psychosocial measures from the ENCOURAGE trial.
• Included measures: trust in physicians, perceived discrimination, patient-physician efficacy, social support, patient activation, and diabetes distress.
• Primary outcomes: hemoglobin A1c, BMI, blood pressure, LDL cholesterol, quality of life; Secondary outcomes: diabetes distress, patient engagement.

Main Results:

• Three participant classes were identified: Class 1 (high psychosocial support, low distress, good glycemic control), Class 2 (moderate psychosocial factors, higher A1c), and Class 3 (high distress, low support, similar A1c to Class 2).
• Intervention effects varied significantly across the three identified classes.
• Class 1: n=72, A1c=7.1±1.3%; Class 2: n=178, A1c=8.1±2.1%; Class 3: n=155, A1c=8.2±2.1%.

Conclusions:

• Distinct patient subpopulations responding differently to the ENCOURAGE intervention were identified.
• These profiles can serve as targets for tailored interventions.
• Future research should assess the efficiency and effectiveness of targeting scarce resources to these groups for improved population health.