Lauretta Quinn, PhD, MS, BSN, RN, Funded Projects
Training in Biobehavioral Nursing Research (III)
Funding Source: National Institute of Nursing Research
Dates: 8/1/04– 5/31/09
Abstract: The overall objective of this training program is to prepare investigators with the understanding and skill to advance knowledge in the biological and biobehavioral basis of nursing care. Specific objectives relate to the development of pre- and postdoctoral fellows in substantive knowledge of biological and biobehavioral science, technical laboratory skills, research methodology, data analysis, and knowledge dissemination. The research interests of program faculty relate to (1) exercise and nutrition in health and disease, (2) neurobehavioral functioning in health and disease, and (3) cardiovascular functioning in health and disease. Highly qualified candidates will be chosen who are closely matched to expertise of program faculty. The training program includes coursework in specified areas of nursing, biological science, research methodology and statistics. Students will learn research techniques in the laboratories of faculty sponsors, and participate in weekly departmental research seminars and colloquia. Trainees will develop and implement research projects that may encompass studies at various levels of biological and biobehavioral functioning, such as intracellular signals and processes in animal models and integrated responses in the biological and biobehavioral domains in human subjects. This proposal requests funds for eight predoctoral and four postdoctoral trainees over a five-year period. The duration of training will be three years for each trainee. It is expected that this training program will prepare outstanding investigators who will advance knowledge of the biological and biobehavioral basis of nursing care.
Diabetes and Cardiovascular Risk Among Asian Indian Adolescents
Funding Source: University of Chicago Medical Centers
Dates: 9/22/09– 8/31/10
Abstract: Asians Indian immigrants (AI) have one of the highest rates of diabetes and heart disease and comprise one of the largest and fastest growing ethnic immigrant groups. Since the antecedents for diabetes and cardiovascular disease begin in childhood, we propose to study AI adolescents' risk factors related to obesity, diabetes, and heart disease. The purpose of this multidisciplinary, biobehavioral study is to explore predisposing biomarkers (metabolic, inflammatory), anthropometric factors (body composition); enabling (diet, physical activity, smoking); and reinforcing (social norms, social networks, peer and family modeling) risk factors for diabetes and heart disease in adolescent children of AI immigrants. Data will be collected by questionnaires, and objective measurements of blood and physical status. AI adolescents aged 13 to 19 years (n=60) and their parents (n=60) will be recruited from community and cultural centers in Chicago (N=60). Data analysis will be conducted using multivariable regression analysis for each set of factors in addition to a combined regression model assessing the influence of all factors on diabetes and heart disease risk. Our specific outcome variables are body composition (body mass index [BMI] percentile; body fat distribution [waist and hip circumference]); dyslipidemia (high density lipoproteins [HDL]; low density lipoproteins [LDL]; and triglycerides [TG]); blood pressure; insulin resistance (homeostasis model assessment [HOMA]); fasting blood glucose (FBG); inflammatory biomarkers (C-reactive protein [CRP] and adiponectin). The study will completed over the course of one year, and these data will support our evidence-based ecological framework and will inform the development of interventions to reduce risk targeted to adolescents of AI descent, as well as allow us to test feasibility for an NIH application responding to a PA on health disparities in NIDDK diseases (http://grants.nih.gov/grants/guide/pa-files/PA-06-183.html).
Multivariable Closed Loop Technologies for Physically Active Young Adults with Type 1 Diabetes
Funding Source: Illinois Institute of Technology
Dates: 9/30/09– 8/31/11
Abstract: Patients with type 1 diabetes would like to enjoy carefree and active lifestyles, conduct physical activities and exercise programs. A closed-loop insulin pump that does not necessitate manual inputs such as meal or physical activity information from the patient can accommodate these wishes. But the interpretation of sensor information and adaptation of the control system to significant metabolic variations is critical. This necessitates mathematical models that can represent the patient's state accurately as her/his metabolic state changes due to a wide spectrum of causes such as meals, physical activity, or stress. Detailed nonlinear models are not attractive for building the closed-loop control systems for miniaturized devices. They are difficult to adjust for each subject and for their metabolic variations over time and they consume significant computational resources. The alternative is simple recursive models that are updated at each sampling time to adapt to the current state of the subject. This project focuses on the development and clinical evaluation of: (1) Recursive patient-specific dynamic models using subcutaneous glucose measurements and physiological data that measure physical activity and stress to provide accurate predictions of blood glucose concentrations; (2) Early warning systems for hypoglycemia; and (3) Adaptive controllers based on these recursive models to manipulate the insulin infusion rate. Young adults in the 18-25 age group will be the focus of the study. Continuous glucose monitors (CGM) will provide glucose concentration information. Physiological signals from an armband body monitoring system will provide the metabolic/physiological information. The elimination of manual inputs entered by patients will reduce the inconveniences that they are experiencing on a daily basis and potential for human errors. Multiple-input (measured glucose concentration and metabolic/physiological information) single-output (predicted glucose concentration) models will be developed for the hypoglycemia warning and closed-loop control system. Generalized predictive controllers (GPC) and self-tuning regulators will be developed for regulating the blood glucose level by manipulating the insulin infusion rate. The performance of the modeling and control techniques will be evaluated by simulation studies using detailed compartmental models as in silico patients and clinical studies conducted at the General Clinical Research Center at Chicago Biomedicine. This project is a collaborative effort between Illinois Institute of Technology, University of Chicago Medical Center (now renamed Chicago Biomedicine), University of Illinois Chicago, and Iowa State University. The collaborative efforts of engineering, medicine and nursing combined with the "bench to bedside" design of the proposed study is consistent with the goals of translational research.
Improving Diabetes Self-Management in Low-Income Minority Population in Primary Care Centers
Funding Source: National Institute On Aging
Dates: 9/27/06– 7/31/11
Role on the Project : co-Investigator
Principal Investigator : Laurie Ruggiero
Abstract: Diabetes has been identified as an epidemic in the U. S. and is continuing to grow in prevalence, especially type 2 diabetes. In addition, the burden of diabetes, including prevalence and risk of complications, is greater for low-income individuals and minority groups, especially Latinos and African Americans. The Healthy People 2010 report notes that the burden of diabetes can be reduced through secondary and tertiary prevention and facilitating optimal self-management. Few controlled studies have focused on strategies to enhance diabetes self-management in minority or other underserved populations. One approach to increasing our reach and impact is to train nonprofessionals or paraprofessionals to work with multidisciplinary diabetes care teams to support optimal diabetes self-management with minimal added expense. The purpose of this study is to develop, implement, and evaluate the impact of an innovative intervention that combines diabetes self-management education, training, and support with aspects of case management delivered by Medical Assistant Coaches (MACs), to support optimal diabetes self-management (and secondary and tertiary prevention) in low-income minority populations with type 2 diabetes. This efficacy trial compares the Medical Assistant self-management coach (MAC) Intervention with "treatment as usual" (TAU). The target populations will form a total sample of 914 African Americans and Latinos receiving care at five Federally Qualified Health Centers serving low-income individuals in Chicago. The proposed study will use a prospective randomized two-group split-plot repeated measures design. Specifically, it will be a two (treatment groups: TAU, MAC) by four (time: baseline, 6-month, 12-month, 18- month) repeated measures design. The MAC Intervention will be individually tailored based on the Transtheoretical Model and culturally tailored, including Spanish translation where needed. The MAC intervention will be delivered monthly over a one-year period, including both face-to-face contacts during routine primary care visits and regular telephone coaching contacts. The primary outcome variable will be glycemic control measured by HbA1C values. Secondary outcomes include: psychosocial mediators, behavioral outcomes, and short- and longer term biomedical outcomes. If effective in improving glycemic control and/or other measures of diabetes self-management, this intervention has the potential to be easily implemented in other primary care clinics serving minority populations. Furthermore, this intervention has the potential to help contain costs while maximizing our reach and facilitating sustainability.