Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2019 Sep 3;12(1):557.
doi: 10.1186/s13104-019-4585-5.

A research proposal testing a new model of ambulation activity among long-term care residents with dementia/cognitive impairment: the study protocol of a prospective longitudinal natural history study

Mary Elizabeth Bowen  1   2 , Meredeth A Rowe  3 , Ming Ji  3 , Pamela Cacchione  4
Affiliations

A research proposal testing a new model of ambulation activity among long-term care residents with dementia/cognitive impairment: the study protocol of a prospective longitudinal natural history study

Mary Elizabeth Bowen et al. BMC Res Notes. .

Abstract

Background: Excessive and patterned ambulation is associated with falls, urinary tract infections, co-occurring delirium and other acute events among long-term care residents with cognitive impairment/dementia. This study will test a predictive longitudinal data model that may lead to the preservation of function of this vulnerable population.

Methods/design: This is a single group, longitudinal study with natural observations. Data from a real-time locating system (RTLS) will be used to objectively and continuously measure ambulation activity for up to 2 years. These data will be combined with longitudinal acute event and functional status data to capture patterns of change in health status over time. Theory-driven multilevel models will be used to test the trajectories of falls and other acute conditions as a function of the ambulation activity and demographic, functional status, gait quality and balance ability including potential mediation and/or moderation effects. Data-driven machine learning algorithms will be applied to run screening of the high dimensional RTLS data together with other variables to discover new and robust predictors of acute events.

Discussion: The findings from this study will lead to the early identification of older adults at risk for falls and the onset of acute medical conditions and interventions for individualized care.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Proposed relationships between intra-individual changes in ambulation activity and acute changes in physical health
See this image and copyright information in PMC

References

    1. Centers for Disease Control and Prevention . Falls in nursing homes. Atlanta: Centers for Disease Control and Prevention; 2012.
    1. Levinson DR. Adverse events in skilled nursing facilities: national incidence among medicare beneficiaries. Washington: U.S. Department of Health and Human Services; 2014.
    1. Morse JM, Morse RM, Tylko SJ. Development of a scale to identify the fall-prone patient. Can J Aging. 1989;8:366–367. doi: 10.1017/S0714980800008576. - DOI
    1. Rubenstein LZ, Josephson KR, Robbins AS. Falls in the nursing home. Ann Intern Med. 1994;121(6):442–451. doi: 10.7326/0003-4819-121-6-199409150-00009. - DOI - PubMed
    1. Dever Fitzgerald T, et al. The impact of fall risk assessment on nurse fears, patient falls, and functional ability in long-term care. Disabil Rehabil. 2016;38(11):1041–1052. doi: 10.3109/09638288.2015.1085102. - DOI - PubMed