Evidence map›Paper›PMID 40021702›Full record

ArticleScientific reports2025

Bidirectional association between ADL disability and depressive symptoms among older adults: longitudinal evidence from CHARLS.

Wenjing Yan, Lina Wang, Chao Li, Yihan Meng, Qi Guo, Hongjuan Li

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

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3 · Its place in the literature

Who cites it

20 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Wenjing YanSchool of Physical Education, Shanxi Normal University, Taiyuan, Shanxi, China.
Lina WangBeijing College of Sports, Beijing, China.
Chao LiSchool of Physical Education, Qingdao University, Guangzhou, Guangdong, China.
Yihan MengSchool of Sport Science and Key Laboratory of the Ministry of Education of Exercise and Physical Fitness, Beijing Sport University, Beijing, China.
Qi GuoDepartment of Prenatal Diagnosis Center,Women and Children's Hospital of Chongqing Medical University, Department of Prenatal Diagnosis Center,Chongqing Health Center for Women and Children, Chongqing, China. guoqi@hospital.cqmu.edu.cn.
Hongjuan LiSchool of Sport Science and Key Laboratory of the Ministry of Education of Exercise and Physical Fitness, Beijing Sport University, Beijing, China. janerobin@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study investigates the bidirectional relationship between Activities of Daily Living (ADL) disability and depressive symptoms (DS) among middle-aged and older adults in China, utilizing data from the China Health and Retirement Longitudinal Study (CHARLS) from 2015 to 2018. A total of 8994 participants were analyzed to assess the impact of ADL on the risk of depressive symptoms, while 9673 participants were included to examine the influence of depressive symptoms on the risk of ADL disability. Cox proportional hazards regression models were employed to evaluate these relationships, adjusting for sociodemographic, lifestyle, and health-related covariates. Results revealed that ADL disability significantly increased the risk of depressive symptoms (HR = 1.090, 95% CI 1.058-1.123), and depressive symptoms were associated with a higher risk of ADL disability (HR = 1.033, 95% CI 1.025-1.042). Subgroup analyses demonstrated that factors such as education level, social activity, and disability significantly modified the relationship between ADL and depressive symptoms, while age, marital status, and pain influenced the association between depressive symptoms and ADL. Sensitivity analyses confirmed the robustness of these findings. This study highlights the complex interplay between ADL and depressive symptoms, emphasizing the need for targeted interventions to mitigate the risk of depression and improve functional independence in aging populations.

Indexed as

Activities of Daily LivingDepressionPersons with DisabilitiesAgedAged, 80 and overChinaFemaleHumansLongitudinal StudiesMaleMiddle AgedRisk FactorsActivities of daily livingCohort studyDepressive symptoms

Identifiers

PMID40021702
PMCPMC11871208

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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.