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Identification of diabetes self-management profiles in adults: A cluster analysis using selected self-reported outcomes

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  • Ketia Alexandre
  • Fanny Vallet
  • Isabelle Peytremann-Bridevaux
  • Olivier Desrichard

Abstract

The present study describes adult diabetes self-management (DSM) profiles using self-reported outcomes associated with the engagement in diabetes care activities and psychological adjustment to the disease. We used self-reported data from a community-based cohort of adults with diabetes (N = 316) and conducted a cluster analysis of selected self-reported DSM outcomes (i.e., DSM behaviors, self-efficacy and perceived empowerment, diabetes distress and quality of life). We tested whether clusters differed according to sociodemographic, clinical, and care delivery processes variables. Cluster analysis revealed four distinct DSM profiles that combined high/low levels of engagement in diabetes care activities and good/poor psychological adjustment to the disease. The profiles were differently associated with the variables of perceived financial insecurity, taking insulin treatment, having depression, and the congruence of the care received with the Chronic Care Model. The results could help health professionals gain a better understanding of the different realities facing people living with diabetes, identify patients at risk of poor outcomes related to their DSM, and lead to the development of profile-specific DSM interventions.

Suggested Citation

  • Ketia Alexandre & Fanny Vallet & Isabelle Peytremann-Bridevaux & Olivier Desrichard, 2021. "Identification of diabetes self-management profiles in adults: A cluster analysis using selected self-reported outcomes," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-17, January.
    • Handle: RePEc:plo:pone00:0245721
    DOI: 10.1371/journal.pone.0245721
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