Evidence map›Paper›PMID 34649103›Full record

ArticleGeriatric nursing (New York, N.Y.)

Relationships of leisure social support and flow with loneliness among nursing home residents during the COVID-19 pandemic: An age-based moderating model.

Liang-Chih Chang, John Dattilo, Pei-Chun Hsieh, Fei-Hsin Huang

Open access · greenAbstract read
In one paragraph

Article in Geriatric nursing (New York, N.Y.). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.4field-weighted citation impact, top 15% of its field
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

3 citing papers in PubMed, 13 citations in OpenAlex.

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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 at 4 institutions in 3 countries.

Liang-Chih ChangDepartment of Living Sciences, National Open University, New Taipei City, Taiwan.
John DattiloDepartment of Recreation, Park, and Tourism Management, The Pennsylvania State University, PA, USA.
Pei-Chun HsiehDepartment of Recreation and Leisure Studies, Brock University, ON, Canada.
Fei-Hsin HuangDepartment of Tourism and Leisure, Lunghwa University of Science and Technology, Taoyuan City, Taiwan. Electronic address: feihsin@hotmail.com.
Brock University · CALunghwa University of Science and Technology · TWNational Open University · TWPennsylvania State University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Identifying predictors of loneliness is important to develop interventions that help older adults residing in nursing homes reduce their loneliness, particularly during the COVID-19 pandemic. Therefore, we examined whether leisure social support and flow (also identified as optimal experience) were predictive of loneliness, and whether age moderated the relationship between flow and loneliness. In total, 235 nursing home residents, aged 65 years or older, participated in our study. We conducted in-person surveys to measure their age, leisure social support, flow, and loneliness as well as used multiple linear regression analysis to analyze data. Results indicated that high levels of leisure social support and flow predicted low levels of loneliness. However, age decreased the negative relationship between flow and loneliness. We discuss implications of these results in terms of reducing loneliness, without depending highly on the presence of others, during times of social isolation associated with responses to the COVID-19 pandemic.

Indexed as

COVID-19LonelinessAgedHumansLeisure ActivitiesNursing HomesPandemicsSARS-CoV-2Social SupportFlowLeisure social supportLonelinessNursing homesOlder adults

Identifiers

PMID34649103
PMCPMC8506142
OpenAlexW3206423989

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.