Advancing Symptom Science Through Use of Common Data Elements

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 Journal of Nursing Scholarship, 47:5, ©2015 Sigma Theta Tau International.

Biological Basis for the Clustering of Symptoms

“…The use of common data elements (CDEs) makes certain that a construct is consistently measured across studies both conceptually and operationally. 38 It is also recommended that symptoms should be consistently measured across disciplines. 39 …”

Network Structure of Perinatal Depressive Symptoms in Latinas: Relationship to Stress and Reproductive Biomarkers

“…While the network approach to mood symptoms has received recognition in recent years, primarily in psychology and most recently in psychiatry (Fried et al, ), it has not to our knowledge reached nursing research. Better understanding and management of adverse health symptoms, including psychological distress, is recognized as a priority for nursing studies (Henly, ; Redeker et al, ), and a focus on depressive symptoms has been recognized as one of the core dimensions by the National Institute of Nursing Research through their symptom science agenda (Lee, Meek, & Grady, ). In this paper, we aim to advance the field of symptom‐focused studies in nursing research via a conceptual and empirical introduction of symptoms network analysis.…”

Biomarkers as Common Data Elements for Symptom and Self‐Management Science

“…Culture (Moser et al, 2014;Park & Johantgen, 2016), language, reading level, aging, sex, and developmental level (Schaakxs et al, 2017), among other factors, influence how symptoms and self-management are reported and measured (Redeker et al, 2015). Factors such as aging, race, sex, and gender may also influence biomarkers, genes, and gene expression.…”