Building a Research Data Repository for Chronic Condition Self-Management Using Harmonized Data

Abstract

Background Building nursing research data repositories with the goal of comparing and synthesizing results across numerous studies and public sharing of data is still in early stages of development. Objectives We describe the process of using common data elements (CDEs) to build a data repository for research addressing self-management of chronic conditions. Issues in the development of CDEs, lessons learned in the creation of a combined data set across seven studies of different chronic condition populations, and recommendations for creating and sharing harmonized nursing research data sets are provided. Methods In 2014, at initiation of a National Institutes of Health-funded Centers of Excellence in Self-Management Research, our center investigators defined a set of CDEs for use in future center-funded pilot studies consisting of populations having different chronic conditions with the intent to combine the study data sets. Over the next 4 years, center investigators were provided with standardized codebooks and data collection protocols for applying the CDEs and data storage. Data from seven pilot studies were subsequently combined. Results Although each pilot study was small—with sample sizes ranging from 18 to 31 participants—our combined data set of 179 participants provides us with a sample size sufficient to conduct analyses that could not be done with the individual small samples alone. The research data repository addressing self-management of chronic conditions will soon be available for public sharing. Discussion Our experience demonstrates that, with careful, upfront planning and ongoing vigilant oversight, CDEs can be applied across studies consisting of different chronic condition populations to combine data sets to create research data repositories for public sharing.

A Center Experience with Common Data Elements in Chronic Illness Self-Management Research

“…Although numerous discussions were had about using CDEs as a center, several challenges were faced in the actual implementation and use of CDEs across the studies. This is similar to the experiences of others engaged in exploring the use of CDEs within funded centers of an institution (Cohen et al, 2015; Moore et al, 2020) and across multiple institutions (Corwin et al, 2017). Within the SMSC, these challenges were mainly due to timing, a lack of familiarity with CDEs, and generating initial interest among researchers.…”

“…Using a framework such as the IFSMT that incorporates family will strengthen the selection and development of CDEs for family research. As the scope of CDEs and repositories expand to include biomarkers (Page et al, 2018) and the depth of available data grows with shared use (Moore et al, 2020), tremendous opportunity exists to expand the science of self-management and related theoretical underpinnings. Although common concepts may be more easily identified than common measures, emerging and established centers alike have an opportunity to advance science in a specific area with the use of CDEs.…”

“…Cross-center dialogue with the P20-funded Center for Accelerating Precision Pain Self-Management at the University of Connecticut was particularly constructive for those interested in researching pain and for those involved in the research of understudied populations. In addition, such dialogue elicited opportunities to explore findings from measures used by multiple studies conducted within these centers, particularly context measures collected with the NINR demographic CDE and the recommended (Moore et al, 2020) Index of Self-Regulation measure (Fleury, 1998).…”

Neural Processing of Health Information and Hypertension Self-Management in African Americans

“…As part of a National Institutes of Health-funded center of excellence focused on the brainbehavior connections of self-management, a set of common data elements representing constructs frequently used in self-management frameworks to examine the processes, behaviors, and outcomes of chronic disease management was employed (Moore et al, 2020). The common data elements comprise both subjective and objective data and have been used by a few researchers (Jones et al, 2019;Moore et al, 2020;Still et al, 2021;Webel et al, 2019). Survey data for this study were collected directly in the REDCap (Research Electronic Database Capturing System; Harris et al, 2009Harris et al, , 2019, a secure web-based application that supports data collection via study iPads or computers.…”

Differences among physical activity actigraphy algorithms in three chronic illness populations

“…Each of the pilot studies used a two-group design to test a self-management intervention on a number of outcomes and used common measures. 18 Baseline data from 2015 to 2019 were used for the analyses reported herein.…”