Precision health: Advancing symptom and self-management science
| dc.contributor.author | Hickey, K.T. | |
| dc.contributor.author | Bakken, S. | |
| dc.contributor.author | Byrne, M.W. | |
| dc.date.accessioned | 2019-03-29T14:47:39Z | |
| dc.date.available | 2019-03-29T14:47:39Z | |
| dc.date.issued | 2019 | |
| dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061727076&doi=10.1016%2fj.outlook.2019.01.003&partnerID=40&md5=200f5919861abeafcb568c27fbf0c403 | |
| dc.identifier.uri | http://hdl.handle.net/10713/8719 | |
| dc.description.abstract | Background: Precision health considers individual lifestyle, genetics, behaviors, and environment context and facilitates interventions aimed at helping individuals achieve well-being and optimal health. Purpose: To present the Nursing Science Precision Health (NSPH) Model and describe the integration of precision health concepts within the domains of symptom and self-management science as reflected in the National Institute of Nursing Research P30 Centers of Excellence and P20 Exploratory Centers. Methods: Center members developed the NSPH Model and the manuscript based on presentations and discussions at the annual NINR Center Directors Meeting and in follow-up telephone meetings. Discussion: The NSPH Model comprises four precision components (measurement; characterization of phenotype including lifestyle and environment; characterization of genotype and other biomarkers; and intervention target discovery, design, and delivery) that are underpinned by an information and data science infrastructure. Conclusion: Nurse scientist leadership is necessary to realize the vision of precision health as reflected in the NSPH Model. © 2019 The Authors | en_US |
| dc.description.sponsorship | We thank Dr. Patricia Flatley Brennan, Director, National Library of Medicine, National Institutes of Health , for her presentation at the 2017 Center Director meeting which informed the information and data science infrastructure component of this paper. The preparation of the manuscript was supported by: Center for Pain Genomics ( P30NR014129 ), Center for Adaptive Leadership in Symptom Science ( P30NR014139 ), SMART Center II: Brain Behavior Connections in Self-Management Science (P30NR015326), Center to Advance Chronic Pain Research ( P30NR016579 ), Center for Innovation in Sleep Self-Management ( P30NR016585 ), Precision in Symptom Self-Management ( PriSSM ) Center ( P30NR016587 ), Yale Center for Sleep Disturbance in Acute and Chronic Conditions ( P20NR014126 ), UManage Center : UMass Center for Building the Science of Symptom Self-Management ( P20NR016599 ), Center for Accelerating Precision Pain Self-Management ( P20NR016605 ), and The Symptoms Self-Management Center ( P20NR016575 ) and Northeastern Center for Technology in Support of Self-Management and Health ( P20NR015320 ). | en_US |
| dc.description.uri | https://dx.doi.org/10.1016/j.outlook.2019.01.003 | en_US |
| dc.language.iso | en_US | en_US |
| dc.publisher | Elsevier | en_US |
| dc.relation.ispartof | Nursing Outlook | |
| dc.subject | air self-management | en_US |
| dc.subject | major nursing | en_US |
| dc.subject | precision health | en_US |
| dc.subject | symptom science | en_US |
| dc.subject.mesh | Data Science | en_US |
| dc.subject.mesh | Informatics | en_US |
| dc.title | Precision health: Advancing symptom and self-management science | en_US |
| dc.type | Article | en_US |
| dc.identifier.doi | 10.1016/j.outlook.2019.01.003 |
Files in this item
Name:
Publisher version