Advancing Symptom Science Through Use of Common Data Elements

Affiliations

¹ Beatrice Renfield Term Professor of Nursing, Yale School of Nursing, New Haven, CT, USA.
² Professor and Associate Dean for Research, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA.
³ The Alumni Professor of Nursing and Professor of Biomedical Informatics, Columbia University, New York, NY, USA.
⁴ Associate Dean for Research and Professor, Emory University, Atlanta, GA, USA.
⁵ Associate Professor, Duke University, Durham, NC, USA.
⁶ Professor and Chair, Department of Pain and Translational Symptom Science, University of Maryland, Baltimore, MD, USA.
⁷ Professor and Department Chairperson, University of Washington, Seattle, WA, USA.
⁸ Lead Program Director, National Institute of Nursing Research, NIH, Bethesda, MD, USA.
⁹ Edward J. and Louise Mellen Professor of Nursing and Associate Dean for Research, Case Western Reserve University, Cleveland, OH, USA.
¹⁰ Professor, University of Nebraska Medical Center, Omaha, NB, USA.
¹¹ Professor of Epidemiology and Population Health, Albert Einstein College of Medicine, New York, NY, USA.
¹² Professor and Associate Dean for Research, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.
¹³ Associate Professor, Emory University, Atlanta, GA, USA.
¹⁴ Director, National Institute of Nursing Research, NIH, Bethesda, MD, USA.

PMID: 26250061
PMCID: PMC4618317
DOI: 10.1111/jnu.12155

Advancing Symptom Science Through Use of Common Data Elements

Nancy S Redeker et al. J Nurs Scholarsh. 2015 Sep.

. 2015 Sep;47(5):379-88.

doi: 10.1111/jnu.12155. Epub 2015 Aug 6.

Authors

Affiliations

¹ Beatrice Renfield Term Professor of Nursing, Yale School of Nursing, New Haven, CT, USA.
² Professor and Associate Dean for Research, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA.
³ The Alumni Professor of Nursing and Professor of Biomedical Informatics, Columbia University, New York, NY, USA.
⁴ Associate Dean for Research and Professor, Emory University, Atlanta, GA, USA.
⁵ Associate Professor, Duke University, Durham, NC, USA.
⁶ Professor and Chair, Department of Pain and Translational Symptom Science, University of Maryland, Baltimore, MD, USA.
⁷ Professor and Department Chairperson, University of Washington, Seattle, WA, USA.
⁸ Lead Program Director, National Institute of Nursing Research, NIH, Bethesda, MD, USA.
⁹ Edward J. and Louise Mellen Professor of Nursing and Associate Dean for Research, Case Western Reserve University, Cleveland, OH, USA.
¹⁰ Professor, University of Nebraska Medical Center, Omaha, NB, USA.
¹¹ Professor of Epidemiology and Population Health, Albert Einstein College of Medicine, New York, NY, USA.
¹² Professor and Associate Dean for Research, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.
¹³ Associate Professor, Emory University, Atlanta, GA, USA.
¹⁴ Director, National Institute of Nursing Research, NIH, Bethesda, MD, USA.

PMID: 26250061
PMCID: PMC4618317
DOI: 10.1111/jnu.12155

Abstract

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.

Keywords: Cognition; fatigue; mood; pain; sleep disturbance; symptom management.

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References

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Advancing Symptom Science Through Use of Common Data Elements

Affiliations

Advancing Symptom Science Through Use of Common Data Elements

Authors

Affiliations

Abstract

References

Publication types

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources

Other Literature Sources