Does self-efficacy in diabetes management differ by type of diabetes and gender? Results from network analysis

Charting a path to health: The empowering influence of self-efficacy for the self-management of type 1 diabetes in children and adolescents

Type 1 diabetes (T1D) in children and adolescents requires a lifelong commitment to disease control, which involves insulin treatment and constant blood glucose monitoring. Framed by Albert Bandura's self-efficacy theory, we focused on analysing the impact of domain-specific self-efficacy for T1D control in children and adolescents and its relationship with different indicators of glycaemic control over time. The study included 205 participants (56.1% male), including 51.7% children and 48.3% adolescents aged 6-18 (M = 13.27, SD = 3.66) years in four longitudinal phases (6 months between phases). The results revealed that higher self-efficacy predicted better health outcomes, with more time spent actively monitoring glucose and more time in the target range. The positive effect of self-efficacy was observed to be maintained over time. This study underscores the importance of taking into account the developmental timing in the onset of T1D. There was a significant relationship between self-efficacy and glucose indicators in adolescents. Although their glucose indicators were worse, self-efficacy became more relevant as they moved from parental management to the self-management of T1D. Implications of the results show the positive effect of self-efficacy on health per glucose indicators, thus suggesting interventions that promote self-efficacy in this population.

Network analysis to examine sex differences linked to emotional well-being in cardiovascular disease

Psychosocial dimensions are essential to guarantee an optimal approach to improve emotional well-being in patients with cardiovascular disease (CVD). There is evidence of sex differences regarding these dimensions. Thus, the connections between them are crucial to implement personalized therapies. Network model analyses were conducted on data from 593 CVD patients. The models included scores from the Hospital Anxiety and Depression Scale (HADS), positive (PA) and negative affect (NA), positivity (PS), satisfaction of life (SLS), social support (SS), self-efficacy on emotion regulation (RESE), cardiac self-efficacy (CSE) and the Short Form-12 Health Survey. The main sex differences were found in: PA-PS (p = 0.03), SS-RESE (p = 0.04), for which the positive associations are stronger for men than for women and PA-RESE (p < 0.01) for which the positive association is stronger for women than for men. These nuances should be considered to implement tailored and integrative therapies for each CVD patient.

Discriminating Low to High Adherent Type 2 Patients with Diabetes by Glycosylated Hemoglobin A1c, Eating Self-Efficacy and Other Psychosocial Determinants: Difference Between Patient and Physician Adherence Models

OBJECTIVE

Develop individual discriminant models using clinical and psychosocial variables for physicians and patients with diabetes based on their perceptions of patient adherence.

METHODS

This was a cross-sectional research design utilizing a discriminant analysis approach. Type 2 patients on treatment for diabetes for at least 2 years prior to research were selected. Clinical data were obtained from patient records, and psychosocial variables were collected by survey instruments filled out by patients. A final sample of 200 patients was recruited.

RESULTS

We found a positive correlation between patient and physician assessment of patient adherence behaviors. Greater adherence efforts were associated with lower HbA1c. Better quality of the patient-physician relationship was linked to better patient adherence. Increased HbA1c, longer therapy duration and higher BMI described low patient adherence for physicians. Lower HbA1c, female gender and fewer difficulties in marital adjustment characterized high adherence for patients. Dietary self-efficacy as well as emotional and social isolation discriminated mid-level adherers in both models.

CONCLUSION

This research confirmed that patients and physicians perceived and judged patients' adherence behaviors differently. Physicians and patients associated different clinical and psychological factors with low and high adherence. Further research is recommended to clarify how the quality of the physician-patient as well as the patient-spouse relationship affect dietary efficacy and patient adherence. A randomized, controlled clinical trial approach is recommended to establish the effectiveness of interventions aiming to improve dietary self-efficacy on adherence outcomes.

Network approach in health behavior research: how can we explore new questions?

Background: The network approach has recently been introduced to clinical psychology and provides a powerful framework for analyzing variables in a system. Since then, its applications have rapidly spread to various fields of social sciences. Unlike in the case of clinical psychology, the peculiarities of the phenomena under study in social sciences have not received sufficient attention. In this paper, along with practical illustrations, we discuss what a system of psychological variables represents and what the interrelationships between the variables mean in the context of health behavior research. Additionally, we explore the structural analysis of the system which has not been the focus of the recent applications of network analysis in health psychology. Discussion: In this paper, we illustrate two approaches of incorporating observable behavioral variables in a system and strategies for investigating structural components of the system. We illustrate these two approaches with an analysis of cross-sectional data on adolescents' beliefs and behavior with respect to registering their choice regarding organ donation in the Netherlands. Furthermore, with this paper, we wish to facilitate a larger discussion on conceptualizing networks of psychological variables, which will guide the analysis and the interpretation of node level interactions as well as network level structures.