养老院居民活动模式特征：TEAM-UP 试验结果。

Characteristics of Nursing Home Resident Movement Patterns: Results from the TEAM-UP Trial.

机构信息

Susan M. Kennerly, PhD, RN, CNE, WCC, FAAN, is Professor, College of Nursing, East Carolina University, Greenville, North Carolina, United States. Phoebe D. Sharkey, PhD, is Professor Emerita, Sellinger School of Business, Loyola University Maryland, Baltimore, Maryland. Susan D. Horn, PhD, is Adjunct Professor, School of Medicine, University of Utah, Salt Lake City. Tianyu Zheng, MS, is Biostatistician, Department of Population Health Sciences, University of Utah. Jenny Alderden, PhD, APRN, is Associate Professor, School of Nursing, Boise State University, Boise, Idaho. Valerie K. Sabol, PhD, ACNP, GNP, CNE, ANEF, FAANP, FAAN, is Professor, School of Nursing, Duke University, Durham, North Carolina. Meredeth Rowe, PhD, RN, FGSA, FAAN, is Professor, College of Nursing, University of South Florida Health, Tampa. Tracey L. Yap, PhD, RN, CNE, WCC, FGSA, FAAN, is Associate Professor, School of Nursing, Duke University.

出版信息

Adv Skin Wound Care. 2022 May 1;35(5):271-280. doi: 10.1097/01.ASW.0000822696.67886.67.

DOI:10.1097/01.ASW.0000822696.67886.67

PMID:35195085

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9012525/

Abstract

OBJECTIVE

To determine movement patterns of nursing home residents, specifically those with dementia or obesity, to improve repositioning approaches to pressure injury (PrI) prevention.

METHODS

A descriptive exploratory study was conducted using secondary data from the Turn Everyone And Move for Ulcer Prevention (TEAM-UP) clinical trial examining PrI prevention repositioning intervals. K-means cluster analysis used the average of each resident's multiple days' observations of four summary mean daily variables to create homogeneous movement pattern clusters. Growth mixture models examined movement pattern changes over time. Logistic regression analyses predicted resident and nursing home cluster group membership.

RESULTS

Three optimal clusters partitioned 913 residents into mutually exclusive groups with significantly different upright and lying patterns. The models indicated stable movement pattern trajectories across the 28-day intervention period. Cluster profiles were not differentiated by residents with dementia (n = 450) or obesity (n = 285) diagnosis; significant cluster differences were associated with age and Braden Scale total scores or risk categories. Within clusters 2 and 3, residents with dementia were older (P < .0001) and, in cluster 2, were also at greater PrI risk (P < .0001) compared with residents with obesity; neither group differed in cluster 1.

CONCLUSIONS

Study results determined three movement pattern clusters and advanced understanding of the effects of dementia and obesity on movement with the potential to improve repositioning protocols for more effective PrI prevention. Lying and upright position frequencies and durations provide foundational knowledge to support tailoring of PrI prevention interventions despite few significant differences in repositioning patterns for residents with dementia or obesity.

摘要

目的

确定养老院居民，尤其是患有痴呆症或肥胖症居民的活动模式，以改进预防压力性损伤（PrI）的翻身方法。

方法

采用 TEAM-UP 临床试验中预防 PrI 翻身间隔的二次数据，进行描述性探索性研究。K-均值聚类分析使用每位居民多天观察的四个摘要每日变量的平均值创建同质的活动模式聚类。增长混合模型研究了随时间的活动模式变化。逻辑回归分析预测居民和养老院集群组的成员身份。

结果

三个最优聚类将 913 名居民分为相互排斥的组，其直立和卧位模式存在显著差异。模型表明，在 28 天的干预期间，活动模式轨迹稳定。聚类特征与痴呆症（n = 450）或肥胖症（n = 285）诊断的居民没有差异；与年龄和 Braden 量表总分或风险类别相关的显著聚类差异。在聚类 2 和 3 内，患有痴呆症的居民年龄较大（P <.0001），并且在聚类 2 中，他们的 PrI 风险也更大（P <.0001），而肥胖症患者则没有差异；在聚类 1 中，两组均无差异。