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

Abstract

Purpose

Interventions that are tailored to the specific psychosocial needs of people with diabetes may be more effective than a “one size fits all” approach. The purpose of this study is to identify patient profiles with distinct characteristics to inform the development of tailored interventions.

Methods

A latent class cluster analysis was conducted with data from the ENCOURAGE trial based on participant responses to 6 baseline psychosocial measures, including trust in physicians, perceived discrimination, perceived efficacy in patient–physician interactions, social support, patient activation, and diabetes distress. The trial’s primary outcomes were hemoglobin A1c, body mass index, systolic blood pressure, low-density lipoprotein cholesterol, and quality of life; secondary outcomes were diabetes distress and patient engagement.

Results

Three classes of participants were identified: Class 1 (n = 72) had high trust, activation, perceived efficacy and social support; low diabetes distress; and good glycemic control (7.1 ± 1.3%). Class 2 (n = 178) had moderate values in all measures with higher baseline A1c (8.1 ± 2.1%). Class 3 (n = 155) had high diabetes distress; low trust, patient engagement, and perceived efficacy; with similar baseline A1c (8.2 ± 2.1%) as Class 2. Intervention effects differed for these 3 classes.

Conclusion

Three distinct subpopulations, which exhibited different responses to the ENCOURAGE intervention, were identified based on baseline characteristics. These groups could be used as intervention targets. Future studies can determine whether these approaches can be used to target scarce resources efficiently and effectively in real-world settings to maximize the impact of interventions on population health, especially in impoverished communities.