Prevalence of eating disorders in aquatic athletes: a systematic review and meta-analysis
Abstract
Aquatic athletes may face unique pressures related to body aesthetics and weight management, potentially increasing their risk of eating disorders (EDs). This systematic review and meta-analysis aimed to estimate the prevalence of EDs in aquatic athletes and assess the quality of the available evidence. A systematic search was conducted in PubMed, Embase, and Web of Science for studies published in English that reported on the prevalence of EDs among aquatic athletes. After screening and eligibility assessments, eight studies met the inclusion criteria, comprising a total of 715 athletes from various countries, including Poland, Canada, Brazil, Norway, the United States, and the United Kingdom. Quality assessment was performed using an adapted Newcastle-Ottawa Scale (NOS), and a random-effects meta-analysis was conducted to estimate pooled prevalence. Sensitivity analysis and a Doi plot were utilized to evaluate the publication bias. The meta-analysis estimated a pooled prevalence of EDs in aquatic athletes at 27.56% (95% CI: 14.27–46.50%), with a heterogeneity (I²) of 76%, indicating substantial variability in study designs and participant characteristics. Sensitivity analysis confirmed the robustness of the findings, and the Doi plot indicated significant asymmetry (LFK index = -3.44), suggesting potential publication bias or variability across studies. This study revealed a high prevalence of EDs among aquatic athletes. Further research is required on the factors…
How strong is the evidence?
Evidence Hub reads this as a meta-analysis, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
Read the paper
Related studies
- The Matrisome: In Silico Definition and In Vivo Characterization by Proteomics of Normal and Tumor Extracellular Matrices
- The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities
- In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling
- In silico prediction of splice-altering single nucleotide variants in the human genome
- In silico clinical trials: how computer simulation will transform the biomedical industry
- In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.