Evidence map›Paper›PMID 41510193›Full record

ArticleDepression and anxiety2026

A Longitudinal Network Analysis of Depressive Symptoms Among Older Adults: Findings From an 8-Year Prospective China National Survey.

Meng-Yi Chen, He-Li Sun, Yuan Feng, Qinge Zhang, Zhaohui Su, Teris Cheung, Matteo Malgaroli, Todd Jackson, Yu-Tao Xiang

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In one paragraph

Article in Depression and anxiety, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Meng-Yi ChenUnit of Psychiatry, Department of Public Health and Medicinal Administration, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao SAR, China, umac.mo.ORCID https://orcid.org/0000-0002-9971-0787
He-Li SunCollege of Acupuncture-Moxibustion and Tuina, International Institute for Innovation in Acupuncture, Beijing University of Chinese Medicine, Beijing, China, bucm.edu.cn.
Yuan FengBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China, ccmu.edu.cn.
Qinge ZhangBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China, ccmu.edu.cn.
Zhaohui SuSchool of Public Health, Southeast University, Nanjing, China, seu.edu.cn.ORCID https://orcid.org/0000-0003-2005-9504
Teris CheungSchool of Nursing, Hong Kong Polytechnic University, Hong Kong SAR, China, polyu.edu.hk.
Matteo MalgaroliDepartment of Psychiatry, NYU Grossman School of Medicine, New York, USA, med.nyu.edu.
Todd JacksonDepartment of Psychology, University of Macau, Macao SAR, China, umac.mo.
Yu-Tao XiangUnit of Psychiatry, Department of Public Health and Medicinal Administration, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao SAR, China, umac.mo.ORCID https://orcid.org/0000-0002-2906-0029

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Late-life depression (LLD) is a significant global public health challenge among older adults. Exploring central/influential symptoms with longitudinal study designs can enhance the efficacy of detection, early prevention, and interventions for LLD. This study aimed to identify key symptoms of LLD using a panel graphical vector autoregression (panel-GVAR) model based on longitudinal national survey data. Methods: Data from the China Health and Retirement Longitudinal Study (CHARLS) between 2013 and 2020, encompassing four waves, were utilized to construct a longitudinal depressive symptom network. Depressive symptoms were assessed using the 10-item Center for Epidemiological Studies Depression Scale (CESD-10). In expected influence (in-EI) and out expected influence (out-EI) were identified to characterize the interaction of symptoms within the temporal network, while expected influence (EI) was used to examine the interaction of symptoms in both the contemporaneous network and the between-subjects network. Results: A total of 1393 older adults were assessed. A persistently significant increase in the prevalence of depression was observed over time. In the temporal network, "restless sleep" (CESD7) and "could not get going" (CESD10) were the most influential symptom and most influenced symptom, respectively. In both the contemporaneous network and the between-subjects network, "felt depressed" (CESD3) emerged as the most central symptom within the community of depressive symptoms. Conclusions: Given the challenges associated with treating LLD and its adverse effects on daily life for older adults, timely interventions targeting identified key symptoms may help prevent and mitigate depression in this population.

Indexed as

DepressionAgedAged, 80 and overChinaFemaleHumansLongitudinal StudiesMaleMiddle AgedPrevalenceProspective Studiesdepressionlongitudinal studynetwork analysisolder adults

Identifiers

PMID41510193
PMCPMC12777696

What Socratic holds

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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 Socratic graph.