Dr. Vorderstrasse is Associate Professor of Nursing with Tenure and Director of the Florence S. Downs PhD Program in Nursing Research and Theory. She received a B.S.N. from Mount Saint Mary College, a M.S.N. from Yale University School of Nursing, and a D.N.Sc. from Yale University School of Nursing.
Dr. Vorderstrasse’s research in the development and implementation of behavioral interventions for diabetes and cardiovascular disease (CVD) aims to expand preventive and self-management support for adults at risk for, or living with chronic diseases. Her contributions in chronic disease prevention have identified that genetic risk testing for chronic conditions may improve risk reduction in particular groups. She is also among the first to demonstrate that virtual environments are a feasible and effective way to provide self-management education and support to improve outcomes in diabetes and CVD. Her research has been supported with competitive funding from the Air Force Medical Sciences, NINR, NLM and NHLBI. As an expert in these areas, she has presented her work at the American Diabetes Association Scientific Sessions, American Heart Association Scientific Sessions, the International Society of Nurses in Genetics, and the American College of Preventive Medicine. She was an invited panelist for the first ANCC Advance Genetics Nursing certification portfolio. Through these presentations, consultations and research studies, she has been a thought leader for research, education and policy in nursing and the implementation of novel technologies, such as genomics and virtual environments.
Prior to joining the faculty at NYU, Dr. Vorderstrasse was Associate Professor of Nursing and Faculty Lead for Precision Health research at the Duke University School of Nursing.
Certificate NIH/NINR Summer Genetics InstituteDNSc, Yale University School of NursingMSN, Yale University School of NursingBSN, Mount Saint Mary College
Fellow, American Academy of NursingInternational Society of Nursing in GeneticsAmerican Heart Association
Analyzing Unstructured Communication in a Computer-Mediated Environment for Adults With Type 2 Diabetes:: A Research Protocol PMID: 28438726
Creating a sustainable collaborative consumer health application for chronic disease self-managementAbstractAs the prevalence of chronic diseases increase, there is a need for consumer-centric health informatics applications that assist individuals with disease self-management skills. However, due to the cost of development of these applications, there is also a need to build a disease agnostic architecture so that they could be reused for any chronic disease. This paper describes the architecture of a collaborative virtual environment (VE) platform, LIVE
Diabetes self-management quality improvement initiative for medically underserved patientsAbstractThe burden of diabetes is greater for minorities and medically underserved populations in the United States. An evidence-based provider-delivered diabetes self-management education intervention was implemented in a federally qualified health center for medically underserved adult patients with type 2 diabetes. The findings provide support for the efficacy of the intervention on improvement in self-management behaviors and glycemic control among underserved patients with diabetes, while not substantially changing provider visit time or workload.
Diabetes Self-management Training in a Virtual EnvironmentAbstractDiabetes self-management training (DSMT) improves diabetes health outcomes. However, low numbers of patients receive DSMT. Using virtual environments (VEs) for DSMT is an innovative approach to removing barriers for patients. The purpose of this paper is to describe the experience of health professionals and diabetes educators establishing and teaching DSMT in a VE, Diabetes LIVE
Genetic Basis of Positive and Negative Symptom Domains in SchizophreniaAbstractSchizophrenia is a highly heritable disorder, the genetic etiology of which has been well established. Yet despite significant advances in genetics research, the pathophysiological mechanisms of this disorder largely remain unknown. This gap has been attributed to the complexity of the polygenic disorder, which has a heterogeneous clinical profile. Examining the genetic basis of schizophrenia subphenotypes, such as those based on particular symptoms, is thus a useful strategy for decoding the underlying mechanisms. This review of literature examines the recent advances (from 2011) in genetic exploration of positive and negative symptoms in schizophrenia. We searched electronic databases PubMed, Web of Science, and Cumulative Index to Nursing and Allied Health Literature using key words schizophrenia, symptoms, positive symptoms, negative symptoms, cognition, genetics, genes, genetic predisposition, and genotype in various combinations. We identified 115 articles, which are included in the review. Evidence from these studies, most of which are genetic association studies, identifies shared and unique gene associations for the symptom domains. Genes associated with neurotransmitter systems and neuronal development/maintenance primarily constitute the shared associations. Needed are studies that examine the genetic basis of specific symptoms within the broader domains in addition to functional mechanisms. Such investigations are critical to developing precision treatment and care for individuals afflicted with schizophrenia.
Genetic correlates of insight in schizophreniaAbstractInsight in schizophrenia is clinically important as it is associated with several adverse outcomes. Genetic contributions to insight are unknown. We examined genetic contributions to insight by investigating if polygenic risk scores (PRS) and candidate regions were associated with insight. Method: Schizophrenia case-only analysis of the Clinical Antipsychotics Trials of Intervention Effectiveness trial. Schizophrenia PRS was constructed using Psychiatric Genomics Consortium (PGC) leave-one out GWAS as discovery data set. For candidate regions, we selected 105 schizophrenia-associated autosomal loci and 11 schizophrenia-related oligodendrocyte genes. We used regressions to examine PRS associations and set-based testing for candidate analysis. Results: We examined data from 730 subjects. Best-fit PRS at p-threshold of 1e-07 was associated with total insight (R 2 =0.005, P =0.05, empirical P =0.054) and treatment insight (R2 =0.005, P =0.048, empirical P =0.048). For models that controlled for neurocognition, PRS significantly predicted treatment insight but at higher p-thresholds (0.1 to 0.5) but did not survive correction.Patients with highest polygenic burden had 5.9 times increased risk for poor insight compared to patients with lowest burden. PRS explained 3.2% (P = 0.002, empirical P = 0.011) of variance in poor insight. Set-based analyses identified two variants associated with poor insight- rs320703, an intergenic variant (within-set P = 6e. -04, FDR P = 0.046) and rs1479165 in SOX2-OT (within-set P = 9e. -04, FDR P = 0.046). Conclusion: To the best of our knowledge, this is the first study examining genetic basis of insight. We provide evidence for genetic contributions to impaired insight. Relevance of findings and necessity for replication are discussed.
Impact of Genetic Testing and Family Health History Based Risk Counseling on Behavior Change and Cognitive Precursors for Type 2 DiabetesAbstractFamily health history (FHH) in the context of risk assessment has been shown to positively impact risk perception and behavior change. The added value of genetic risk testing is less certain. The aim of this study was to determine the impact of Type 2 Diabetes (T2D) FHH and genetic risk counseling on behavior and its cognitive precursors. Subjects were non-diabetic patients randomized to counseling that included FHH +/− T2D genetic testing. Measurements included weight, BMI, fasting glucose at baseline and 12 months and behavioral and cognitive precursor (T2D risk perception and control over disease development) surveys at baseline, 3, and 12 months. 391 subjects enrolled of which 312 completed the study. Behavioral and clinical outcomes did not differ across FHH or genetic risk but cognitive precursors did. Higher FHH risk was associated with a stronger perceived T2D risk (pKendall < 0.001) and with a perception of “serious” risk (pKendall < 0.001). Genetic risk did not influence risk perception, but was correlated with an increase in perception of “serious” risk for moderate (pKendall = 0.04) and average FHH risk subjects (pKendall = 0.01), though not for the high FHH risk group. Perceived control over T2D risk was high and not affected by FHH or genetic risk. FHH appears to have a strong impact on cognitive precursors of behavior change, suggesting it could be leveraged to enhance risk counseling, particularly when lifestyle change is desirable. Genetic risk was able to alter perceptions about the seriousness of T2D risk in those with moderate and average FHH risk, suggesting that FHH could be used to selectively identify individuals who may benefit from genetic risk testing.
Improving diabetes self-management support: Goal-setting across the continuum of careAbstractIN BRIEF Goal-setting has consistently been promoted as a strategy to support behavior change and diabetes self-care. Although goal-setting conversations occur most often in outpatient settings, clinicians across care settings need to better understand and communicate about the priorities, goals, and concerns of those with diabetes to develop collaborative, person-centered partnerships and to improve clinical outcomes. The electronic health record is a mechanism for improved communication and collaboration across the continuum of care. This article describes a quality improvement project that was intended to improve the person-centeredness of care for adults with diabetes by offering goal-setting and self-management support during and after hospitalization.
Unraveling interrelationships among psychopathology symptoms, cognitive domains and insight dimensions in chronic schizophreniaAbstractIntroduction: Insight in schizophrenia is long known to have a complex relationship with psychopathology symptoms and cognition. However, very few studies have examined models that explain these interrelationships. Methods: In a large sample derived from the NIMH Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) schizophrenia trial (N = 1391), we interrogated these interrelationships for potential causal pathways using structural equation modeling. Using the NIMH consensus model, latent variables were constructed for psychopathology symptom dimensions, including positive, negative, disorganized, excited and depressed from the Positive and Negative Syndrome Scale (PANSS) items. Neurocognitive variables were created from five predefined domains of working memory, verbal memory, reasoning, vigilance and processing speed. Illness insight and treatment insight were tested using latent variables constructed from the Illness and Treatment Attitude Questionnaire (ITAQ). Results: Disorganized symptoms had the strongest effect on insight. Illness insight mediated the relationship of positive, depressed, and disorganized symptoms with treatment insight. Neurocognition mediated the relationship between disorganized and treatment insight and depressed symptoms and treatment insight. There was no effect of negative symptoms on either illness insight or treatment insight. Taken together, our results indicate overlapping and unique relational paths for illness and treatment insight dimensions, which could suggest differences in causal mechanisms and potential interventions to improve insight.
Visualization of Multidimensional Data in Nursing ScienceAbstractNursing scientists have long been interested in complex, context-dependent questions addressing individual- and population-level challenges in health and illness. These critical questions require multilevel data (e.g., genetic, physiologic, biologic, behavioral, affective, and social). Advances in data-gathering methods have resulted in the collection of large sets of complex, multifaceted, and often non-comparable data. Scientific visualization is a powerful methodological tool for facilitating understanding of these multidimensional data sets. Our purpose is to demonstrate the utility of scientific visualization as a method for identifying associations, patterns, and trends in multidimensional data as exemplified in two studies. We describe a brief history of visual analysis, processes involved in scientific visualization, and opportunities and challenges in the use of visualization methods. Scientific visualization can play a crucial role in helping nurse scientists make sense of the structure and underlying patterns in their data to answer vital questions in the field.