Evidence map›Paper›PMID 38786371›Full record

ArticleHealthcare (Basel, Switzerland)2024

Incidence and Factors Associated with Falls in Older People in a Long-Term Care Facility: A Prospective Study in Taiwan.

Hung-Chun Lee, Chia-Jung Hsieh, Jih-Shuin Jerng

Abstract read
In one paragraph

Article in Healthcare (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Hung-Chun LeeDepartment of Nursing, National Taiwan University Hospital, Taipei 100225, Taiwan.ORCID 0000-0001-8291-2799
Chia-Jung HsiehSchool of Nursing, College of Nursing, National Taipei University of Nursing and Health Sciences, Taipei 112303, Taiwan.ORCID 0000-0002-1062-8307
Jih-Shuin JerngDepartment of Internal Medicine, National Taiwan University Hospital, Taipei 100225, Taiwan.ORCID 0000-0002-5324-8523

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe effectiveness of applying a fall-risk assessment to prevent falls in residents of long-term care facilities has not been investigated.

methodsThis prospective study enrolled elderly residents in a long-term care facility in Taiwan. Caregivers were provided with a health-status assessment and fall-risk data to enhance their fall-prevention practices. A multivariate analysis was performed to identify the factors associated with falls.

resultsA total of 123 subjects, including 68 and 55 for general and nursing-care models, respectively, were assessed. Their health status and risk of falls were provided to the care units to enhance their fall-prevention practices. Subjects in the nursing-care model had more dementia and more prescribed medications, worse physiologic conditions, and higher fall risk. Of them, 28 (23%) had subsequent falls. A univariate analysis showed that those with and without falls were similar in demographic characteristics, prescribed medications, physiologic function, and fall risk. There was a tendency for more falls in the nursing-care model residents (accounting for 61% of those who fell;

conclusionResidents at the long-term care facility had a significant risk of falls despite knowledge of their fall risk and the implementation of preventive measures. In this context of being aware of the risk, gender, and psychiatric symptoms were significantly associated with falls. Caregivers at long-term care facilities should implement further measures to prevent falls based on behavioral and psychological symptoms.

Indexed as

fallinstitutional residentrisk factor

Identifiers

PMID38786371
PMCPMC11121478

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.