Machine Learning and Non-Invasive Monitoring Technologies for Training Load Management in Women's Volleyball: A Scoping Review.

ABSTRACT: Training load monitoring in women's volleyball is a challenge for optimizing performance and mitigating injury risk. Non-invasive monitoring technologies and machine learning (ML) can support decision-making, but the evidence remains heterogeneous. This scoping review mapped and integrated the evidence on training load management, fatigue, and performance in women's volleyball and identified gaps. The PRISMA Extension for Scoping Reviews (PRISMA-ScR) and the Joanna Briggs Institute (JBI) framework were followed. A systematic search was conducted in Scopus, Web of Science, and PubMed, covering January 2020 to September 2025. We included studies in female players at any competitive level, including mixed-sex studies meeting a minimum threshold of female participation, that evaluated external

SUBMITTER: Sanhueza Tapia HG 

PROVIDER: S-EPMC12944405 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature