Evidence map›Paper›PMID 42427381›Full record

ArticleFrontiers in psychology2026

Artificial intelligence addiction among university students in China: risk stratification, consequences, and exercise-based intervention.

Gengdan Hu, Yinan Zhou, Chuyan Zhang, Yipeng Sha

Abstract read
In one paragraph

Article in Frontiers in psychology, 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

4 authors.

Gengdan HuClinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China.
Yinan ZhouDepartment of Psychology, School of Humanities, Tongji University, Shanghai, China.
Chuyan ZhangSchool of Ocean and Earth Science, Tongji University, Shanghai, China.
Yipeng ShaDepartment of Physical Education (International College of Football), Tongji University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the deep integration of artificial intelligence into daily life, AI-addiction-like tendencies have emerged as a potential form of behavioral dependency among university students. Focusing on high-frequency AI users on campus, this study examined risk stratification of problematic AI use, real-life impacts, and the preliminary pre-post improvements following exercise-based interventions. A cross-sectional survey assessed AI usage patterns and functional impacts, identifying a substantial proportion of students with at least mild risk of problematic AI use. Problematic AI use was associated with reduced academic performance, social engagement, physical health, personal interests, and emotional well-being. A six-month structured exercise intervention delivered to at-risk student was associated with reductions in problematic AI use risk and improvements in daily functioning. These findings provide preliminary support for conceptualizing AI addiction as a multidimensional behavioral dependency potentially distinguishable from traditional Internet addiction and suggest that structured exercise may hold potential as a non-pharmacological approach for mitigating maladaptive AI use in university settings.

Indexed as

artificial intelligence addictionbehavioral addictionmental healthphysical exercise interventionreal-life consequencesuniversity students

Identifiers

PMID42427381
PMCPMC13346038

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.