Evidence map›Paper›PMID 31484503›Full record

ArticleBMC geriatrics2019

Association between tea consumption and depressive symptom among Chinese older adults.

Ke Shen, Bin Zhang, Qiushi Feng

Abstract read
In one paragraph

Article in BMC geriatrics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing 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

30 citing papers in PubMed.

  1. Article
  2. Dietary habits and successful aging in Chinese older adults: a cross-sectional analysis of the CLHLS.Archives of public health = Archives belges de sante publique · 2025
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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

3 authors.

Ke ShenSchool of Social Development and Public Policy, Fudan University, Shanghai, China.
Bin ZhangSchool of Social Development and Public Policy, Fudan University, Shanghai, China.
Qiushi FengDepartment of Sociology & Centre for Family and Population Research (CFPR), National University of Singapore, AS1 #04-30, 11 Arts Link, Singapore, 117570, Singapore. socfq@nus.edu.sg.ORCID 0000-0002-3572-4442

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDespite accumulating evidence on the protective effect of tea consumption against depression, studies specifically focusing on the elderly population are yet limited. This paper examined the association between the frequency and duration of tea drinking and depressive symptoms of older adults by gender and age groups, based on a nationally representative sample in China.

methodThe study employed the panel data from 2005, 2008/2009, 2011/2012 and 2014 waves of Chinese Longitudinal Healthy Longevity Survey (CLHLS). We used the frequency and consistency of tea drinking behaviors to identify four types of tea consumption amongst Chinese seniors. Depressive symptoms were assessed by a five-item scale. Linear mixed effects models were applied.

resultsWe found that consistent and frequent tea-drinking was associated with significantly less depressive symptoms, and such impact was partially mediated by socioeconomic status, health behavior, physical health, cognitive function, and social engagement. However, the association was only significant for males and the oldest-old, rather than females and younger elders.

conclusionsConsistent and frequent tea-drinking may effectively reduce the risk of depressive symptoms for the Chinese elderly. The promotion of the traditional lifestyle of tea drinking could be a cost-effective way towards healthy aging for China.

Indexed as

Life StyleTeaAgedAged, 80 and overChinaCognitionDepressionFemaleHealthy AgingHumansLongitudinal StudiesMaleMiddle AgedRandom AllocationSocial ClassTeaChineseDepressive symptomElderlyTea

Identifiers

PMID31484503
PMCPMC6724308

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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.