Evidence map›Paper›PMID 35877291›Full record

ArticleBehavioral sciences (Basel, Switzerland)2022

Associations between Mobile Internet Use and Self-Rated and Mental Health of the Chinese Population: Evidence from China Family Panel Studies 2020.

Haifeng Ding, Chengsu Zhang, Wan Xiong

Abstract read
In one paragraph

Article in Behavioral sciences (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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.

Haifeng DingSchool of Public Administration and Law, Hunan Agricultural University, Changsha 410100, China.ORCID 0000-0003-1568-9982
Chengsu ZhangSchool of Management, University of Sanya, Sanya 572000, China.
Wan XiongSchool of Business, Shanghai University of Finance and Economics, Shanghai 200434, China.

Funding

Projects of National Social Science Fund of China 20CSH011
6 · The paper itself

Abstract

With societal and technological developments, mobile Internet has become the most popular and widespread means to use the Internet in China. Thus, exploring the relationship between mobile Internet use and the self-rated health and mental health of the Chinese population is of great importance. This study empirically examined the impact of mobile Internet use on residents' health using data from the China Family Panel Studies 2020 and conducted a heterogeneity analysis. The results revealed a significant negative association between mobile Internet use and the self-rated health of the population, but a significant positive association was found relative to their mental health. The results of this analysis passed a robustness test. The results of the heterogeneity analysis showed that mobile Internet use had a more significant association with the health of residents with secondary school education and university education compared to those with primary school education or below and graduate education. Furthermore, this study addresses the endogeneity problem using the propensity-score matching model, which is shown to be better at eliminating sample selectivity bias. If endogeneity is not addressed, the negative association with mobile Internet use on residents' self-rated health will be underestimated and its positive association with their mental health will be overestimated. The Chinese government should issue guidelines on the duration of Internet use, strictly regulate exaggerated and harmful content on mobile network platforms, and strengthen people's online skills through training to improve their digital literacy, especially for rural populations.

Indexed as

CFPSChinamental healthmobile Internet useself-rated health

Identifiers

PMID35877291
PMCPMC9311927

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.