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Predictors of Self-care among the Elderly with Diabetes Type 2: Using Social Cognitive Theory

Authors :
Mohsen Shati
Ahmad Naghibzadeh Tahami
Gholamrezan Yousefzadeh
Abedin Iranpour
Reza Fadayevatan
Vahidreza Borhaninejad
Source :
Diabetesmetabolic syndrome. 11(3)
Publication Year :
2016

Abstract

Introduction Diabetes is one of the most common chronic diseases among the elderly and is also a very serious health problem. Adopting theory-based self-care behaviors is an effective means in managing such diseases. This study aimed to determine the predictors of diabetes self-care in the elderly in Kerman based on a social cognitive theory. Material and methods In this cross-sectional study, 384 elderly diabetic patients who had referred to health screening centers in Kerman were chosen via cluster sampling. To collect information about self-care and its predictors, Toobert Glasgow's diabetes self-efficacy scale as well as a questionnaire was used which was based on social cognitive theory constructs. The validity and reliability of the questionnaire was confirmed. The data were analyzed using Pearson correlation and linear regression analysis in SPSS software 17. Findings Among the subjects, 67.37% (252) had poor self-care ability; 29.14% (109) had average ability, and 3.40% (13) enjoyed a proper level of self- care ability. There was a significant relationship between the constructs of the social cognitive theory (knowledge, self- efficacy, social support, outcome expectations, outcome expectancy and self-regulation) and the self-care score. Furthermore, the mentioned constructs could predict 0.47% of the variance of the self-care behaviors. Conclusion self-care behaviors in this study were poor. Therefore, it is necessary to develop an educational intervention based on cognitive theory constructs with the goal of properly managing diabetes in the elderly patients.

Details

ISSN :
18780334
Volume :
11
Issue :
3
Database :
OpenAIRE
Journal :
Diabetesmetabolic syndrome
Accession number :
edsair.doi.dedup.....641342b9f27882fd546d52d9bbf0ddc7