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Identification of self‐management patterns in pediatric type 1 diabetes using cluster analysis
Author(s) -
Rohan Jennifer M,
Delamater Alan,
Pendley Jennifer Shroff,
Dolan Lawrence,
Reeves Grafton,
Drotar Dennis
Publication year - 2011
Publication title -
pediatric diabetes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.678
H-Index - 75
eISSN - 1399-5448
pISSN - 1399-543X
DOI - 10.1111/j.1399-5448.2010.00752.x
Subject(s) - glycemic , medicine , cluster (spacecraft) , type 1 diabetes , self management , type 2 diabetes , diabetes mellitus , psychological intervention , diabetes management , endocrinology , psychiatry , machine learning , computer science , programming language
Rohan JM, Delamater A, Pendley JS, Dolan L, Reeves G, Drotar D. Identification of self‐management patterns in pediatric type 1 diabetes using cluster analysis. Objectives: This study identified three distinct patterns of self‐management groups for a sample of 239 youth (9–11 years) with type 1 diabetes and their maternal and paternal caregivers, and assessed their relationship to glycemic control (HbA1c). Methods: Youth and their maternal and paternal caregivers were administered the diabetes self‐management profile (DSMP) to assess self‐management. Glycemic control was based on hemoglobin A1c. Results: Two‐step cluster analysis identified three different self‐management groups based on youth, maternal, and paternal reports. Analysis of variance indicated that the pattern of less optimal diabetes self‐management was associated with worse glycemic control. Conclusion: Our results objectively describe differences in patterns of self‐management in youth with type 1 diabetes, that relate to glycemic control. Interventions based on these specific patterns of self‐management may improve diabetes management and enhance glycemic control in children and adolescents with type 1 diabetes.