Evidence map›Paper›PMID 42482729›Full record

ArticleFrontiers in psychology2026

Exploring the interrelationships between athlete burnout, depression, and anxiety: a network analysis approach.

Yuantai Fu, Yong-Gwan Song, Renchun Liang, Jiaqi Wang, Junyan Zhao, Yuwen Shangguan, Taojing Zhuang

Abstract read
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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.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Yuantai Fu *Department of Marine Sports, Pukyong National University, Busan, Republic of Korea.
Yong-Gwan Song *Department of Marine Sports, Pukyong National University, Busan, Republic of Korea.
Renchun LiangCollege of Physical Education and Sports, Hainan Normal University, Haikou, Hainan, China.
Jiaqi WangCollege of Elementary Education, Hainan Normal University, Haikou, Hainan, China.
Junyan ZhaoDepartment of Marine Sports, Pukyong National University, Busan, Republic of Korea.
Yuwen ShangguanDepartment of Exercise Physiology, Kunsan National University, Gunsan-si, Jeollabuk-do, Republic of Korea.
Taojing ZhuangDepartment of Orthopaedics, Changzhou Maternal and Child Health Care Hospital, Changzhou Medical Center, Nanjing Medical University, Changzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Athlete burnout frequently co-occurs with depression and anxiety, yet prior studies relying on total scores obscure how individual symptoms interact across these constructs. Objective: To identify central and bridge symptoms linking athlete burnout, depression, and anxiety using network analysis. Methods: A total of 1,226 Chinese collegiate athletes (637 male, 589 female; M Results: GAD-2 (uncontrollable worry; EI = 1.07), PHQ-2 (depressed mood; EI = 1.22), and ABQ-E8 (physical exhaustion; EI = 1.11) were the most central nodes. PHQ-3 (sleep disturbance; bEI = 0.47) emerged as the strongest bridge across the three clusters. The strongest edges connected ABQ-E8-PHQ-4 (fatigue; r = 0.37) and ABQ-E2 (training fatigue)-PHQ-1 (anhedonia; r = 0.29). Conclusion: GAD-2, PHQ-2, and ABQ-E8 exhibited high centrality and represent key intervention targets for reducing the overall symptomatology of burnout-depression-anxiety comorbidity in athletes, and should therefore be prioritized; PHQ-3, as the strongest bridge symptom connecting the three clusters, represents a target whose treatment may interrupt the transdiagnostic symptom-transmission pathways that maintain the comorbidity. These clearly defined intervention targets also facilitate the development of a brief, multi-symptom screening tool tailored to athlete populations.

Indexed as

anxietyathlete burnoutdepressionmental healthnetwork analysis

Identifiers

PMID42482729
PMCPMC13384827

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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.