Author(s): WU, C., HUANG, H.1, ZHOU, L.2, HUANG, Z.3, Institution: XIAMEN UNIVERSITY, Country: CHINA, Abstract-ID: 1238

INTRODUCTION:
Effective training improves athletic performance by applying appropriate loads within a suitable range to stimulate adaptation through the cycle of loading and recovery, while inappropriate stimuli may lead to non-functional overreaching or overtraining syndrome and harm athletes’ health. To address the critical issue of determining and monitoring optimal training load, this study conducts a bibliometric analysis (1968–2024) to map the knowledge structure, thematic evolution, collaboration patterns, and emerging hotspots in training load monitoring research.
METHODS:
This study followed PRISMA guidelines and searched the Web of Science Core Collection, Scopus, and PubMed (inception–2024) using terms related to training load and monitoring. A total of 5,781 records were identified; after removing 810 duplicates and screening by two independent reviewers based on predefined inclusion and exclusion criteria, 4,192 eligible articles and reviews were included for bibliometric analysis using CiteSpace 6.4.R1.
RESULTS:
The earliest study on training load monitoring appeared in 1968, with slow growth from 2005–2013 and rapid expansion thereafter, peaking in 2021 (n = 537; about five times 2013). Research themes evolved from basic physiological studies (1970s–1980s), to endurance and overtraining (1990s–early 2000s), to a multidimensional framework integrating physiology, psychology, and performance (2005–2015), then to sports injury (2015–2020), and most recently to machine learning–driven intelligent monitoring (post-2021).Authorship analysis revealed several interconnected collaboration clusters centered on leading scholars, while Australia, the USA, and the UK were the main contributing countries (>600 publications each). The International Journal of Sports Physiology and Performance ranked first in output and citations, whereas Sports Medicine showed a high-impact, low-volume publication pattern.
CONCLUSION:
Training load monitoring research has undergone rapid expansion over the past decade, accompanied by notable thematic diversification and strengthening international collaboration. The field is trending toward integrated, multidimensional monitoring approaches and increasingly data-driven analytical strategies. Future research may benefit from (i) harmonizing monitoring metrics across contexts, (ii) linking load measures with recovery- and psychology-related indicators, and (iii) advancing predictive modeling (including AI-driven approaches) to better support practical decision-making in training prescription and risk management.
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