Digital health tools to promote diabetes education and management of cardiovascular risk factors among under-resourced populations

Diabetes is a major risk factor for the development of cardiovascular disease, the leading cause of death in the United States, further highlighting the need for improved diabetes management, particularly among individuals from ethnic minorities or low socioeconomic status

Predictors of Dropouts From a San Diego Diabetes Program: A Case Control Study

INTRODUCTION: The objective of this study was to determine the demographic, treatment, clinical, and behavioral factors associated with dropping out of a nurse-based, low-income, multiethnic San Diego diabetes program. METHODS: Data were collected during a 17-month period in 2000 and 2002 on patients with type 2 diabetes from Project Dulce, a disease management program in San Diego County designed to care for an underserved diabetic population. The study sample included 69 cases and 504 controls representing a racial/ethnic mix of 53% Hispanic, 7% black, 16% Asian, 22% white, and 2% other. Logistic regression was used to determine factors associated with patient dropout. RESULTS: Patients who had high initial clinical indicators including blood pressure and hemoglobin A1c and those who smoked currently or smoked in the past were more likely to drop out of the diabetes program. CONCLUSION: This study provides markers of patient dropout in a low-income, multiethnic, type 2 diabetic population. Reasons for dropout in this program can be investigated to prevent further cohort loss

Predictors of glycemic control among patients with Type 2 diabetes: A longitudinal study

BACKGROUND: Diabetes is the sixth leading cause of death and results in significant morbidity. The purpose of this study is to determine what demographic, health status, treatment, access/quality of care, and behavioral factors are associated with poor glycemic control in a Type 2 diabetic, low-income, minority, San Diego population. METHODS: Longitudinal observational data was collected on patients with Type 2 diabetes from Project Dulce, a program in San Diego County designed to care for an underserved diabetic population. The study sample included 573 patients with a racial/ethnic mix of 53% Hispanic, 7% black, 18% Asian, 20% white, and 2% other. We utilized mixed effects models to determine the factors associated with poor glycemic control using hemoglobin A1C (A1C) as the outcome of interest. A multi-step model building process was used resulting in a final parsimonious model with main effects and interaction terms. RESULTS: Patients had a mean age of 55 years, 69% were female, the mean duration of diabetes was 7.1 years, 31% were treated with insulin, and 57% were obese. American Diabetes Association (ADA) recommendations for blood pressure and total cholesterol were met by 71% and 68%, respectively. Results of the mixed effects model showed that patients who were uninsured, had diabetes for a longer period of time, used insulin or multiple oral agents, or had high cholesterol had higher A1C values over time indicating poorer glycemic control. The younger subjects also had poorer control. CONCLUSION: This study provides factors that predict glycemic control in a specific low-income, multiethnic, Type 2 diabetic population. With this information, subgroups with high risk of disease morbidity were identified. Barriers that prevent these patients from meeting their goals must be explored to improve health outcomes

A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings

BackgroundA composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data.MethodsWe assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation.ResultsThe analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals.ConclusionThe GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments

Tolerability, safety and adherence to treatment with insulin detemir injection in the treatment of type 2 diabetes

Athena Philis-TsimikasScripps Whittier Diabetes Institute, La Jolla, CA, USAAbstract: The progressive nature of type 2 diabetes poses challenges in the clinic: treatment must be continually reviewed and adjusted in response to the patient’s changing pathophysiology. Ultimately, insulin replacement therapy will be necessary as the physiological insulin response is compromised. The modern basal insulin analog insulin detemir has been the subject of several clinical trials and observational studies in type 2 diabetes. Compared with NPH insulin, insulin detemir offers an improved balance between achieving current glycemic targets with acceptable tolerability. Insulin detemir also has a unique weight-sparing effect which is associated with body mass index, and this may be a particular advantage to obese patients with type 2 diabetes. This review summarizes data from key clinical studies of insulin detemir, and also provides insights from observational studies.Keywords: type 2 diabetes mellitus, insulin detemir, modern analog, basal insuli