Thesis (Ph. D.)--University of Canterbury, 2008. / Typescript (photocopy). Includes bibliographical references (leaves 263-313). Also available via the World Wide Web.

Aim: To test the interactive effects of the constructs of Modified Social Learning Theory (MSLT) in relation to predicting health behaviour in Type 2 Diabetes. Methods: The study is mixed methods and employs an exploratory sequential design. Qualitative Phase: (N=12) Semi-structured interviews with adults with insulin-treated type 2 diabetes, explored how beliefs and values influence self-management behaviour. Interim Phase: Thematic analysis allowed development of an adapted Health Value Measure. Quantitative phase: (N=107) Valid questionnaires measured Health Value, Health Locus of Control (HLC) and Self-efficacy (SE). Health Value was measured pre and post diagnosis in order to compare any changes with time. Anxiety and depression was controlled for using the Hospital Anxiety and Depression(HAD) scale. Five subscales measured diabetes outcome behaviour: general diet, specific diet, exercise, blood sugar and foot care. Hierarchical Multiple Regression(HMR) analyses consisted of four blocks, including three two-way interaction terms and one three-way interaction term to test the interactive effects of the three-predictor variables on outcome behaviours. ANOVA’s were conducted in an effort to add support to HMR results. Results: The interviews suggest that people may hold terminal (beliefs about desired end states)/instrumental health values (beliefs about desired modes of action) pre-diagnosis but these are mainly instrumental post-diagnosis in order to meet their new needs and maintain quality of life. The qualitative data also drew attention to the way in which LOC and SE beliefs impact on behaviour. Additionally, differing dimensions of various emergent themes highlight the demands Type 2 diabetes places on a person and how this influences beliefs and values. Interim phase results resulted in the new items being removed from the adapted health value measure prior to the quantitative data analysis, as item 5 was deemed problematic. Sensitivity analysis was carried out to increase the robustness of the quantitative findings due to removing 29 cases with missing data from Dataset 1. Dataset 1 includes 78 complete cases and Dataset 2 contains 107 cases, 29 of which had missing values and were replaced using regression imputation. HMR analyses produced significant results that support MSLT when the three-way interaction variable was added to block 4. ANOVA results produced minimum support for MSLT. Conclusion: Support for MSLT has been found and can be used to inform interventions to change self-management behaviours of patients with poor diabetes control. Change in health value orientation post-diagnosis purports further investigation, as it is supported by qualitative results but not quantitative.

The aim of the current study was to examine the associations among youth diabetes self-efficacy, family conflict, disease care and glycemic control via a comprehensive path model. Data were from a baseline assessment of a longitudinal RCT of 257 adolescent/parent dyads (adolescents aged 11–14). Each member of the dyad separately completed the Self-efficacy for Diabetes Self-Management Scale, Family Environment Conflict subscale, Diabetes Family Conflict Scale, Diabetes Behavior Rating Scale, and 24-hr Diabetes Interview Blood Glucose Frequency subscale. Additionally, a biological marker of glycemic control, or HbA1c, and relevant demographic variables were collected. A mediation model found higher youth diabetes self-efficacy mediated the link between lower family conflict and better disease care (β = -.08, p <.01) to glycemic control (β = .05, p <.05.). Further, the relation of higher self-efficacy to better glycemic control was mediated by better disease care (β = -.06, p <.05). Higher youth diabetes self-efficacy (β = -.16, p <.05), lower family conflict (β = .19, p <.001), and better disease care (β = -.17, p <.01) each were directly related to better glycemic control. The overall model with relevant demographic factors fit the data well [χ² (2) = .50, p = .78, CFI = 1.00, RMSEA= .00] and accounted for 13% of the variance in self-efficacy, 32% of the variance for disease care, and 25% of the variance in glycemic control. Interventions that target better youth diabetes self-efficacy and lower family conflict concurrently may promote better disease care and glycemic control.

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