Adult Attachment Style and Marital Satisfaction

ABSTRACT

Background: Personality traits are stable factors which can aid in understanding patient health behaviour.The five-factor model of personality can serve as a theoretical background for exploration andunderstanding of health behaviours. Extant studies report the implication of domains of Neuroticism andConscientiousness in diabetes self-care behaviours. However, no study exists that take in to account a specificset of self-care behaviours in a chronic illness population and connect these to possible personality profiles.This paper presents an analysis of clustering of personality domains with relevant specific diabetes self-carebehaviours, relevant demographic factors and blood glucose related outcomes in adult patients.Methods: Self-report questionnaire data were collected from a sample of 295 patients of diabetes whichincluded two types of diabetes (Type1= 52; Type 2 = 243). Tools included a measure of patientinformation schedule, 60-items of NEO-FFI personality inventory and Summary of Diabetes Self-careActivities (SDSCA). Following simple linear correlation, regression analysis, clustering process wasinitiated via Two-Step Cluster Analysis which identified two clusters as an optimal solution. Unsupervisedk-means segmentation helped deduced two significantly different clusters of patients.Results: Two clusters were found to be significantly different with respect to four domains of personalityExtraversion, Openness to Experience, Agreeableness and Conscientiousness, three critical self-carebehaviours - following a general diet, following recommended diet of fruits and vegetables and performingexercise behaviour and two of the physiologic measures of blood glucose control – Fasting Blood Sugar(FBS) and Post Prandial post meal measure (PP).Conclusion: The results show personality traits tend to cluster. This indicates that specific traits can begrouped together. In addition, specific self-care behaviours were associated with these trait combinations.Irrespective of individual factors such as age, duration of illness and gender the obtained patient profileswere distinct to one another. These findings have significant meaning for future diabetes related healthprograms. Findings can help in development of tailor-made intervention programs with use of suchknowledge of trait clustering.