Evidence map›Paper›PMID 42487777›Full record

ArticleFrontiers in public health2026

How reading behavior shapes physical and mental health: evidence from China.

Jiajia Li, Hanjing Zhang

Abstract read
In one paragraph

Article in Frontiers in public health, 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
–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

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

2 authors.

Jiajia LiDepartment of Economics and Management, Shanxi University, Taiyuan, China.
Hanjing ZhangDepartment of Economics and Management, Shanxi University, Taiyuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Against the backdrop of the deepening development of nationwide reading and the continuous advancement of the Healthy China strategy, reading is not only an important direction for expanding public cultural services but also a crucial support for building a Healthy China. This paper utilizes data from the Chinese General Social Survey and employs ordered Probit regression models to systematically examine the effects and underlying mechanisms of reading behavior regarding residents' physical and mental health. Findings indicate that reading behavior is significantly positively correlated with residents' physical and mental health, with heterogeneity observed across urban-rural, regional, and age dimensions. Specifically, the health benefits are significantly greater for rural residents and those in central and western regions compared to urban residents and those in eastern regions. Furthermore, reading exerts a significant positive effect on the health of middle-aged and older adults, while exhibiting a suppressive effect among the young population. Further mechanism analysis reveals that reading improves residents' physical and mental health through three pathways: enhancing self-cognition, improving subjective social status, and reducing health risk perception bias. Therefore, governments should continuously optimize public cultural services and implement differentiated reading promotion strategies targeting different regions and age groups, so as to maximize the health benefits of reading and provide strong support for improving residents' health and advancing the Healthy China strategy.

Indexed as

Health StatusMental HealthReadingAdolescentAdultAgedChinaFemaleHumansMaleMiddle AgedRural PopulationUrban PopulationYoung AdultHealthy China strategyheterogeneitymechanismsphysical and mental healthreading behavior

Identifiers

PMID42487777
PMCPMC13388146

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.