Evidence map›Paper›PMID 41867980›Full record

ArticleFrontiers in psychology2026

Academic burnout in the TikTok era: a battle between pleasure and concentration.

Yanning Chen, Jimin Hu, Li Cheng

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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Yanning ChenSchool of Languages and Cultures, Hunan Institute of Technology, Hengyang, Hunan, China.
Jimin HuDepartment of Foreign Languages, Ganzhou Teachers College, Ganzhou, Jiangxi, China.
Li ChengSchool of Languages and Cultures, Hunan Institute of Technology, Hengyang, Hunan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The negative connection between short-video excessive usage habits and students' learning is becoming increasingly prominent in the field of education. With the excessive use of short videos, a phenomenon known as the "TikTok brain" has emerged. However, the research on the "TikTok brain" variable and its understanding are still at the initial exploration stage. This study tries to propose seven research hypotheses and build a corresponding theoretical model based on the self-determination theory, attempting to explore the relationship and transmission path between the TikTok brain, declined attention, and learning burnout. Research methods: students are both the main users of short videos and the high-risk group for learning burnout. Therefore, this study collected 500 valid questionnaires to verify the above research hypotheses. Among them, there are 243 male students (48.6%) and 257 female students (51.4%). Results: (1) There is a significant positive correlation between the TikTok brain and declined attention; (2) The TikTok brain is also significantly positively correlated with learning burnout; (3) Declined attention plays an effective mediating role between the TikTok brain and learning burnout. This study concludes that the immediate satisfaction cognitive model formed by short-video excessive usage will further intensify students' learning burnout through consuming their attention resources. This also provides an insight into educational practice. In the educational scenario, paying attention to guiding and intervening in students' digital usage habits to help them cultivate and maintain a sustainable learning state is necessary.

Indexed as

declined concentrationEnglish-learning burnoutshort videosthe self-determination theoryTikTok brain

Identifiers

PMID41867980
PMCPMC12999888

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.