Evidence map›Paper›PMID 36424995›Full record

ArticleFrontiers in psychiatry2022

Depression symptoms and quality of life in empty-nest elderly among Chengdu: A cross-sectional study.

Lanying He, Jian Wang, Feng Wang, Lili Zhang, Yinglin Liu, Fan Xu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 2 pooled it
1.1field-weighted citation impact, top 22% 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

7 citing papers in PubMed, 2 syntheses or guidelines pooled it, 7 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. Article
  7. 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

6 authors at 2 institutions in 1 country.

Lanying HeDepartment of Neurology, The Second People's Hospital of Chengdu, Chengdu, China.
Jian WangDepartment of Neurology, The Second People's Hospital of Chengdu, Chengdu, China.
Feng WangDepartment of Neurology, The Second People's Hospital of Chengdu, Chengdu, China.
Lili ZhangDepartment of Neurology, The Second People's Hospital of Chengdu, Chengdu, China.
Yinglin LiuDepartment of Neurology, The Second People's Hospital of Chengdu, Chengdu, China.
Fan XuDepartment of Public Health, Chengdu Medical College, Chengdu, China.
Chengdu Second People's Hospital · CNChengdu Medical College · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To estimate the prevalence of depression symptoms and quality of life (QoL) and examined the influence of factors in the empty nest elderly. Methods: This was a cross-sectional study, which was conducted from February 2022 to May 2022. We recruited a convenience sample of no empty-nest elderly and empty-nest elderly (≥60 years) living in Chengdu. QoL was assessed using WHOQOL-BREF, Geriatric Depression Scale (GDS-15) was used to assess depression symptoms. Multivariable logistic regression was used to analyze data between independent variables with depression symptoms. Results: Two thousand twenty-six participants were included in this study, 39.0% (660/1,082) experienced depression symptoms among empty-nest elderly. Age (aOR, 1.02; 95% CI, 1.00-1.04; Conclusions: Depression symptoms are common mental health problems among empty-nest elderly. We found that age, chronic disease ≥2 and physical activity were important factors that have an impact on depressive symptoms. Empty-nest elderly would have lowered QoL score.

Indexed as

depressionempty-nest elderlymental healthquality of lifeWHOQOL-BREF

Identifiers

PMID36424995
PMCPMC9679215
OpenAlexW4308594744

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.