A Study on Research Trend in Sports Medicine Using Big Data Analysis: Based on Korean Peer-Reviewed Journals Published from 2014 to 2024

Purpose:This study aimed to comprehensively analyze the thematic trends and paradigm shifts in sports medicine research by applying keyword network analysis and text mining techniques to papers published in Korean academic journals over the past decade (2014)(2015)(2016)(2017)(2018)(2019)(2020)(2021)(2022)(2023)(2024).Through this, the study sought to identify the overall research flow in the field and provide foundational insights for setting future research directions in Korean sports medicine.Methods: Research articles related to sports medicine published between 2014 and 2024 were collected from the RISS (Research Information Sharing Service) using the keywords "sports medicine," "athletic training," and "sports rehabilitation."Data were processed and analyzed with the big data analysis program TextoM (The IMC Inc.).Text mining and term frequency-inverse document frequency (TF-IDF) analyses were performed, followed by semantic network and CONCOR (CONvergent of iterated CORrelation) analyses using Ucinet 6.0 (Analytic Technologies) and NetDraw to examine relational structures among keywords.Results: The most frequent keywords included "training" (2,133), "effect" (1,536), "research" (1,428), "athlete" (1,311), and "measurement" (889).In TF-IDF analysis, the highest values were observed for "disorder," "group," "athlete," "training," and "muscle strength."Six major research clusters were identified: (1) sports medicine/rehabilitation, (2) golf and taekwondo, (3) soccer, (4) program development, (5) psychology, and (6) weight training. Conclusion:The findings indicate a paradigm shift in Korean sports medicine research from treatment-and rehabilitation-centered studies toward prevention, performance enhancement, and multidisciplinary topics.These results provide a theoretical foundation for future integrative and specialized research designs in sports medicine.