by
Shirley M. Moore;
Rachel Schiffman;
Drenna Waldrop-Valverde;
Nancy S. Redeker;
Donna Jo McCloskey;
Miyong T. Kim;
Margaret M. Heitkemper;
Barbara J. Guthrie;
Susan G. Dorsey;
Sharron L. Docherty;
Debra Barton;
Donald E. Bailey;
Joan K. Austin;
Patricia Grady
Purpose: Common data elements (CDEs) are increasingly being used by researchers to promote data sharing across studies. The purposes of this article are to (a) describe the theoretical, conceptual, and definition issues in the development of a set of C DEs for research addressing self-management of chronic conditions; (b) propose an initial set of CDEs and their measures to advance the science of self-management; and (c) recommend implications for future research and dissemination. Design and Methods: Between July 2014 and December 2015 the directors of the National Institute of Nursing Research (NINR)-funded P20 and P30 centers of excellence and NINR staff met in a series of telephone calls and a face-to-face NINR-sponsored meeting to select a set of recommended CDEs to be used in self-management research. A list of potential CDEs was developed from examination of common constructs in current self-management frameworks, as well as identification of variables frequently used in studies conducted in the centers of excellence. Findings: The recommended CDEs include measures of three self-management processes: activation, self-regulation, and self-efficacy for managing chronic conditions, and one measure of a self-management outcome, global health. Conclusions: The self-management of chronic conditions, which encompasses a considerable number of processes, behaviors, and outcomes across a broad range of chronic conditions, presents several challenges in the identification of a parsimonious set of CDEs. This initial list of recommended CDEs for use in self-management research is provisional in that it is expected that over time it will be refined. Comment and recommended revisions are sought from the research and practice communities. Clinical Relevance: The use of CDEs can facilitate generalizability of research findings across diverse population and interventions.
by
Julie A. Womack;
Terrence E. Murphy;
Harini Bathulapalli;
Kathleen M. Akgun;
Cynthia Gibert;
Ken M. Kunisaki;
David Rimland;
Maria Rodriguez-Barradas;
H. Klar Yaggi;
Amy C. Justice;
Nancy S. Redeker
by
Gayle G. Page;
Elizabeth Corwin;
Susan G. Dorsey;
Nancy S. Redeker;
Donna Jo McCloskey;
Joan K. Austin;
Barbara J. Guthrie;
Shirley M. Moore;
Debra Barton;
Miyong T. Kim;
Sharron L. Docherty;
Drenna Waldrop-Valverde;
Donald E. Bailey,Jr.;
Rachel F. Schiffman;
Angela Starkweather;
Teresa M. Ward;
Suzanne Bakken;
Kathleen T. Hickey;
Cynthia L. Renn;
Patricia Grady
Purpose: Biomarkers as common data elements (CDEs) are important for the characterization of biobehavioral symptoms given that once a biologic moderator or mediator is identified, biologically based strategies can be investigated for treatment efforts. Just as a symptom inventory reflects a symptom experience, a biomarker is an indicator of the symptom, though not the symptom per se. The purposes of this position paper are to (a) identify a “minimum set” of biomarkers for consideration as CDEs in symptom and self-management science, specifically biochemical biomarkers; (b) evaluate the benefits and limitations of such a limited array of biomarkers with implications for symptom science; (c) propose a strategy for the collection of the endorsed minimum set of biologic samples to be employed as CDEs for symptom science; and (d) conceptualize this minimum set of biomarkers consistent with National Institute of Nursing Research (NINR) symptoms of fatigue, depression, cognition, pain, and sleep disturbance.
Design and Methods: From May 2016 through January 2017, a working group consisting of a subset of the Directors of the NINR Centers of Excellence funded by P20 or P30 mechanisms and NINR staff met bimonthly via telephone to develop this position paper suggesting the addition of biomarkers as CDEs. The full group of Directors reviewed drafts, provided critiques and suggestions, recommended the minimum set of biomarkers, and approved the completed document. Best practices for selecting, identifying, and using biological CDEs as well as challenges to the use of biological CDEs for symptom and self-management science are described. Current platforms for sample outcome sharing are presented. Finally, biological CDEs for symptom and self-management science are proposed along with implications for future research and use of CDEs in these areas.
Findings: The recommended minimum set of biomarker CDEs include pro- and anti-inflammatory cytokines, a hypothalamic-pituitary-adrenal axis marker, cortisol, the neuropeptide brain-derived neurotrophic factor, and DNA polymorphisms. Conclusions: It is anticipated that this minimum set of biomarker CDEs will be refined as knowledge regarding biologic mechanisms underlying symptom and self-management science further develop. The incorporation of biological CDEs may provide insights into mechanisms of symptoms, effectiveness of proposed interventions, and applicability of chosen theoretical frameworks. Similarly, as for the previously suggested NINR CDEs for behavioral symptoms and self-management of chronic conditions, biological CDEs offer the potential for collaborative efforts that will strengthen symptom and self-management science.
Clinical Relevance: The use of biomarker CDEs in biobehavioral symptoms research will facilitate the reproducibility and generalizability of research findings and benefit symptom and self-management science.
by
Nancy S. Redeker;
Ruth Anderson;
Suzanne Bakken;
Elizabeth Corwin;
Sharron Docherty;
Susan G. Dorsey;
Margaret Heitkemper;
Donna Jo McCloskey;
Shirley Moore;
Carol Pullen;
Bruce Rapkin;
Rachel Schiffman;
Drenna Waldrop-Valverde;
Patricia Grady
Background: Use of common data elements (CDEs), conceptually defined as variables that are operationalized and measured in identical ways across studies, enables comparison of data across studies in ways that would otherwise be impossible. Although healthcare researchers are increasingly using CDEs, there has been little systematic use of CDEs for symptom science. CDEs are especially important in symptom science because people experience common symptoms across a broad range of health and developmental states, and symptom management interventions may have common outcomes across populations.
Purposes: The purposes of this article are to (a) recommend best practices for the use of CDEs for symptom science within and across centers; (b) evaluate the benefits and challenges associated with the use of CDEs for symptom science; (c) propose CDEs to be used in symptom science to serve as the basis for this emerging science; and (d) suggest implications and recommendations for future research and dissemination of CDEs for symptom science.
Design: The National Institute of Nursing Research (NINR)-supported P20 and P30 Center directors applied published best practices, expert advice, and the literature to identify CDEs to be used across the centers to measure pain, sleep, fatigue, and affective and cognitive symptoms.
Findings: We generated a minimum set of CDEs to measure symptoms.
Conclusions: The CDEs identified through this process will be used across the NINR Centers and will facilitate comparison of symptoms across studies. We expect that additional symptom CDEs will be added and the list will be refined in future work.
Clinical Relevance: Symptoms are an important focus of nursing care. Use of CDEs will facilitate research that will lead to better ways to assist people to manage their symptoms.
Precision health can provide an avenue to bridge and integrate ways of knowing for research and practice. Nurse scientists have a long-standing interest in using multiple sources of information to address research questions of significance to the profession and discipline of nursing, which can lead to much needed contributions to precision health care. In this paper, nursing scientists discuss emerging research methods including omics, electronic sensors, and geospatial data, and mixed methods that further develop nursing science and contribute to precision health initiatives. The authors provide exemplars of the types of knowledge and ways of knowing that, using these and other advanced data and analytic strategies, may advance precision health within the context of nursing science.