Evidence map›Paper›PMID 41289567›Full record

ArticleJMIR mental health2025

Associations Between Both Smartphone Addiction and Objectively Measured Smartphone Use and Sleep Quality and Duration Among University Students: Cross-Sectional Study.

Jian Yin, Xuanyi Tang, Zeshi Liu, Yangyang Gong, Hui Yang, Yanping Zhang

Abstract read
In one paragraph

Article in JMIR mental health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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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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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

6 authors.

Jian Yin *Department of Laboratory, Second Affiliated Hospital of Xi'an Jiaotong University, No. 157 West Five Road, Xi'an, 710004, China, +86-15929443510.ORCID 0009-0008-2121-7037
Xuanyi Tang *School of Public Health, Shandong University, Jinan, China.ORCID 0009-0005-7503-5683
Zeshi LiuDepartment of Laboratory, Second Affiliated Hospital of Xi'an Jiaotong University, No. 157 West Five Road, Xi'an, 710004, China, +86-15929443510.ORCID 0009-0001-2429-4440
Yangyang GongDepartment of Laboratory, Second Affiliated Hospital of Xi'an Jiaotong University, No. 157 West Five Road, Xi'an, 710004, China, +86-15929443510.ORCID 0009-0006-9179-4214
Hui YangDepartment of External Cooperation and Exchange, Shaanxi Provincial Health Commission, Xi'an, China.ORCID 0009-0000-9342-6729
Yanping ZhangDepartment of Laboratory, Second Affiliated Hospital of Xi'an Jiaotong University, No. 157 West Five Road, Xi'an, 710004, China, +86-15929443510.ORCID 0009-0000-8019-8966

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The impact of smartphone use on sleep remains intensely debated. Most existing studies have used self-reported smartphone use data. Moreover, few studies have simultaneously examined associations between both smartphone addiction and objectively measured smartphone use and sleep, and the dose-response relationship between smartphone use and risk of poor sleep has been consistently overlooked, requiring systematic and further research on this topic. Objective: This study aimed to examine the associations between smartphone addiction and objectively measured smartphone use and sleep quality and duration. Methods: This cross-sectional study enrolled 17,713 participants from a university in China. We assessed objective smartphone screen time and unlocks by collecting screenshots of use records and measured smartphone addiction using a validated questionnaire. Sleep quality and duration were estimated via the Pittsburgh Sleep Quality Index. Binary logistic regression, linear regression, and restricted cubic spline regression models were used for the analyses. Results: A total of 14.3% (2533/17,713) of the participants met the criterion for poor sleep, with a mean sleep duration of 507.1 (SD 103.2) minutes per night. Notably, university students with smartphone addiction exhibited 184% higher risk of poor sleep (odds ratio [OR] 2.84, 95% CI 2.59-3.11) and a 15.47-minute-shorter nighttime sleep duration (β=-15.47, 95% CI -18.53 to -12.42) compared to those without smartphone addiction. Regarding objectively measured smartphone use, participants with ≥63 hours per week of smartphone screen time had 22% higher odds of poor sleep (OR 1.22, 95% CI 1.08-1.37) and a 6.66-minute-shorter nighttime sleep duration (β=-6.66, 95% CI -10.19 to -3.13) compared to those with 0 to 21 hours of screen time per week, whereas those with approximately 21 to 42 hours per week of smartphone screen time had a 5.47-minute-longer nighttime sleep duration (β=5.47, 95% CI 1.28-9.65). Similarly, compared to those with 0 to 50 smartphone unlocks per week, participants with ≥400 smartphone unlocks per week showed 61% higher odds of poor sleep (OR 1.61, 95% CI 1.41-1.85) accompanied by a 4.09-minute-shorter nighttime sleep duration (β=-4.09, 95% CI -8.08 to -0.09), whereas those with approximately 50 to 150 smartphone unlocks per week had a 5.84-minute-longer sleep duration (β=5.84, 95% CI 2.32-9.36). An inverted U-shaped association between smartphone screen time and sleep duration was observed (P<.001 for nonlinearity). Conclusions: Smartphone addiction, excessive objectively measured smartphone screen time, and unlocks are positively associated with both sleep quality and duration. Restricted cubic spline analyses revealed different nuanced dose-response relationships, with an inverted U-shaped association observed between smartphone screen time and sleep duration.

Indexed as

Internet Addiction DisorderSleep QualitySmartphoneStudentsAdolescentAdultChinaCross-Sectional StudiesFemaleHumansMaleSleepSurveys and QuestionnairesUniversitiesYoung Adultsleep disorderssleep durationsmartphone addictionsmartphone screen timesmartphone unlocks

Identifiers

PMID41289567
PMCPMC12646561

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.