Background As 7.8% of the U.S. population is affected by diabetes, health care providers are tasked with providing resources to assist patients toward self-management. Psychosocial issues have an effect on diabetes self-care. Diabetes-related distress is associated with self-management and lower A1C. This cross-sectional study seeks to understand how demographic factors, psychological orientations, support, and diabetes management behaviors predict diabetes-related distress. Methods This study uses data from 267 adults with Type 2 diabetes. The Diabetes Distress Scale (DDS) is a 17-item scale measuring diabetes-related distress including emotional distress, physician related distress, regimen distress, and interpersonal distress. Results Hierarchical regression was conducted in four stages. The final model explains 48% of the variance in DDS. Significant factors related to lower DDS were older age, lower BMI, higher self-efficacy, higher levels of health care provider support, and a healthy diet. Discussion Findings of this study help health care providers know where to focus to reduce diabetes-related distress. Health care provider support is significant in reducing DDS. Diabetes education may want to include strategies that increase self-efficacy and assist people with diabetes to obtain a healthy weight through a more healthful diet.
Recent epidemiological data have shown that more than half of all new cases of type 1 diabetes occur in adults. Key genetic, immune, and metabolic differences exist between adult-and childhood-onset type 1 diabetes, many of which are not well understood. A substantial risk of misclassification of diabetes type can result. Notably, some adults with type 1 diabetes may not require insulin at diagnosis, their clinical disease can masquerade as type 2 diabetes, and the consequent misclassification may result in inappropriate treatment. In response to this important issue, JDRF convened a workshop of international experts in November 2019. Here, we summarize the current understanding and unanswered questions in the field based on those discussions, highlighting epidemiology and immunogenetic and metabolic characteristics of adult-onset type 1 diabetes as well as disease-associated comorbidities and psychosocial challenges. In adultonset, as compared with childhood-onset, type 1 diabetes, HLA-associated risk is lower, with more protective genotypes and lower genetic risk scores; multiple diabetes-associated autoantibodies are decreased, though GADA remains dominant. Before diagnosis, those with autoantibodies progress more slowly, and at diagnosis, serum C-peptide is higher in adults than children, with ketoacidosis being less frequent. Tools to distinguish types of diabetes are discussed, including body phenotype, clinical course, family history, autoantibodies, comorbidities, and C-peptide. By providing this perspective, we aim to improve the management of adults presenting with type 1 diabetes.Clinically, it has been relatively easy to distinguish the acute, potentially lethal, childhood-onset diabetes from the less aggressive condition that affects adults. However, experience has taught us that not all children with diabetes are insulin dependent and not all adults are non-insulin dependent. Immune, genetic, and metabolic analysis of these two, apparently distinct, forms of diabetes revealed inconsistencies, such that insulin-dependent and immune-mediated diabetes was redefined as type 1 diabetes, while most other forms were relabeled as type 2 diabetes. Recent data suggest a further shift in our thinking, with the recognition that more than half of all new cases of type 1 diabetes occur in adults. However, many adults may not require insulin at diagnosis of type 1 diabetes and have a more gradual onset of hyperglycemia, often leading to misclassification and inappropriate care. Indeed, misdiagnosis occurs in nearly 40% of adults with new type 1 diabetes, with the risk of error increasing with age (1,2). To consider this important issue, JDRF convened a workshop of international experts in November 2019 in New York, NY. In this Perspective, based on that workshop, we outline the evidence for
Objective: The purpose of this study was to validate the 10-item DSSI as a brief measure of social support for use in diverse adult populations. Methods: EFA was performed on 2010 Arizona Health Survey (AHS) data (n = 8215). Confirmatory Factor Analysis (CFA) then confirmed the factors structure by gender, ethnicity, and age, as well as for the total population. DSSI-10 and subscales were compared with variables related to social support. Results: CFI confirms this structure exhibits a good model fit. Low self-reported health status and low self-reported quality of life were related to lower DSSI scores. Living alone was significantly negatively related to the DSSI-10. Conclusions: Researchers may confidently use DSSI-10 to measure social support for diverse adult populations. This instrument can be used in epidemiological studies to increase understanding of mental and physical health in relationship to social supports in the general population.
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