Evidence map›Paper›PMID 36203678›Full record

ArticleFrontiers in public health2022

Mobile phone addiction and depressive symptoms among Chinese University students: The mediating role of sleep disturbances and the moderating role of gender.

Meng Liu, Chuntian Lu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 1 pooled it
14.2field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

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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

30 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.

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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

2 authors at 1 institution in 1 country.

Meng LiuDepartment of Sociology, School of Humanities and Social Sciences, Xi'an Jiaotong University, Xi'an, China.
Chuntian LuDepartment of Sociology, School of Humanities and Social Sciences, Xi'an Jiaotong University, Xi'an, China.
Xi'an Jiaotong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: With the continuous updating of mobile phone functions, the phenomenon of mobile phone addiction among University students is becoming more and more serious. It is important to identify the potential risk factors for mobile phone addiction. The aim of the study was to examine whether there is a relationship between mobile phone addiction and depression symptoms in University students, and to investigate whether sleep disturbances play a mediating role between mobile phone addiction and depression symptoms, as well as the moderating role of gender. Methods: A cross-sectional study, carried out between September to December 2021, recruited 973 students (478 males) from seven comprehensive universities in western China. The Mobile Phone Addiction Index (MPAI), the Patient Health Questionnaire-9 (PHQ9), and the Pittsburgh Sleep Quality Index (PSQI) were used to complete measures of mobile phone addiction, depressive symptoms, and sleep disturbances. For statistical analyses, descriptive statistics, correlation, regression, mediation and moderated mediation analyses were used. Furthermore, we tested the mediation model and moderated mediation model using the SPSS macro PROCESS. Results: In this study, it was found that there were positive correlations between mobile phone addiction and depressive symptoms among Chinese University students. Mediation analyses revealed that this relationship was partially mediated by sleep disturbances, but the mediating role was not moderated by gender. Conclusion: Sleep disturbances have a partial mediating role in the relationship between mobile phone addiction and depressive symptoms. Our results highlight the critical role of prevention and early identification of mobile phone addiction among University students, especially those with sleep disturbances.

Indexed as

DepressionSleep Wake DisordersChinaCross-Sectional StudiesHumansMaleSleepStudentsTechnology AddictionUniversitiesdepressive symptomsgendermobile phone addictionsleep disturbancesUniversity student

Identifiers

PMID36203678
PMCPMC9531624
OpenAlexW4297906865

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.