Evidence map›Paper›PMID 36161144›Full record

ArticleInnovation in aging2022

Older Adults' Biobehavioral Fall Risks Were Affected by the COVID-19 Pandemic: Lessons Learned for Future Fall Prevention Research to Incorporate Multilevel Perspectives.

Hiroko Kiyoshi-Teo, Shigeko Seiko Izumi, Sydnee Stoyles, Siobhan K McMahon

Abstract read
In one paragraph

Article in Innovation in aging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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  7. Nursing Science Interventions in Aging.Innovation in aging · 2022
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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

4 authors.

Hiroko Kiyoshi-TeoSchool of Nursing, Oregon Health and Science University, Portland, Oregon, USA.ORCID https://orcid.org/0000-0001-5884-5741
Shigeko Seiko IzumiSchool of Nursing, Oregon Health and Science University, Portland, Oregon, USA.ORCID https://orcid.org/0000-0002-9488-9458
Sydnee StoylesSchool of Nursing, Oregon Health and Science University, Portland, Oregon, USA.
Siobhan K McMahonSchool of Nursing, University of Minnesota, Minneapolis, Minnesota, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Objectives: Examining the impact of coronavirus disease 2019 (COVID-19) pandemic on fall risks may provide insight into how multilevel factors as described in National Institute of Nursing Research's (NINR's) draft strategic plan can guide future fall prevention research. This article describes the affect of COVID-19 on fall risks from the perspective of older adults who live in assisted living facilities (ALFs), and explores the needs and approaches to implement fall prevention interventions at individual, social, community, and policy levels. Research Design and Methods: Exploratory survey study. Participants from a fall prevention study at 2 ALFs in Oregon were invited to the study. Survey questions asked about COVID experience, and changes in fall risks and day-to-day activities in Spring 2020. Quantitative responses were analyzed using descriptive statistics and Cohen's Results: Thirteen participants (age: Discussion and Implications: These individual-level perspectives suggest that older adults were at increased risk for falling. Results exemplify the influence of broader-level factors (e.g., social, community, and policy) on individual biobehavioral factors (e.g., fall risks and health behaviors), and illustrate the value of examining multilevel factors in future fall prevention research.

Indexed as

BiobehavioralHealth behaviorsInjury preventionPandemicSocioecological framework

Identifiers

PMID36161144
PMCPMC9495495

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.