Related Experiment Video
Updated: Oct 2, 2025
Exergaming in Older People Living with HIV Improves Balance, Mobility and Ameliorates Some Aspects of Frailty
Published on: October 6, 2016
Characteristics of Nursing Home Resident Movement Patterns: Results from the TEAM-UP Trial
Susan M Kennerly1, Phoebe D Sharkey, Susan D Horn
1Susan 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.
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.
