Meta-analysis of exercise addiction prevalence across diverse athletic disciplines

OBJECTIVE: To examine the prevalence and inter-sport differences of exercise addiction through a systematic meta-analysis. METHODS: Following PRISMA guidelines, Chinese and English databases (CNKI, VIP, PubMed, and Embase) were systematically searched for studies reporting exercise addiction prevalence using validated assessment scales. Stata 17.0 was used to perform a meta-analysis of 30 eligible studies encompassing various sports, including running, fitness training, and triathlon. RESULTS: Significant differences were observed in addiction detection rates across sports disciplines. The pooled prevalence of exercise addiction was 12.26%, with a standardized mean score of 48.28 (95% CI: 44.46-52.10). Fitness and triathlon demonstrated significantly higher prevalence rates compared with other sports. The prevalence among young adults (18-30 years) was approximately 15% higher than that of middle-aged individuals. Gender differences were also observed: men showed a slightly higher risk in strength-based sports, whereas women were more likely to exhibit compulsive exercise behaviors in mind-body activities. CONCLUSION: Exercise addiction is relatively prevalent across diverse sports disciplines, with notable differences by sport type, age group, and gender. High-goal-oriented sports such as fitness training and triathlon appear particularly associated with elevated addiction risk.

Authors

Institutions

Publication Details

Journal
Acta Psychologica
Published
2026-05-18
DOI
https://doi.org/10.1016/j.actpsy.2026.107092
Primary Topic
Sport Psychology and Performance
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Meta-analysis of exercise addiction prevalence across diverse athletic disciplines

Kai Ma, Yongzi Zhu, Kun Wang
Acta Psychologica
Sport Psychology and Performance
article

Meta-analysis of exercise addiction prevalence across diverse athletic disciplines

Kai Ma, Yongzi Zhu, Kun Wang
article en

Abstract

OBJECTIVE: To examine the prevalence and inter-sport differences of exercise addiction through a systematic meta-analysis. METHODS: Following PRISMA guidelines, Chinese and English databases (CNKI, VIP, PubMed, and Embase) were systematically searched for studies reporting exercise addiction prevalence using validated assessment scales. Stata 17.0 was used to perform a meta-analysis of 30 eligible studies encompassing various sports, including running, fitness training, and triathlon. RESULTS: Significant differences were observed in addiction detection rates across sports disciplines. The pooled prevalence of exercise addiction was 12.26%, with a standardized mean score of 48.28 (95% CI: 44.46-52.10). Fitness and triathlon demonstrated significantly higher prevalence rates compared with other sports. The prevalence among young adults (18-30 years) was approximately 15% higher than that of middle-aged individuals. Gender differences were also observed: men showed a slightly higher risk in strength-based sports, whereas women were more likely to exhibit compulsive exercise behaviors in mind-body activities. CONCLUSION: Exercise addiction is relatively prevalent across diverse sports disciplines, with notable differences by sport type, age group, and gender. High-goal-oriented sports such as fitness training and triathlon appear particularly associated with elevated addiction risk.

Acta PsychologicaVol. 267
China University of Geosciences (Beijing) (CN)
Good health and well-being
Openalex Percentile: Top 3%
Sport Psychology and Performance
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.