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

Héctor Gabriel Sanhueza Tapia1,2, Frano Giakoni-Ramírez2, Josivaldo de Souza-Lima2,3

  • 1Department of Physical Activity and Sport, University of Murcia, 30100 Murcia, Spain.

Sports (Basel, Switzerland)
|February 26, 2026
PubMed
Summary

Monitoring training load in women

Keywords:
artificial intelligenceneuromuscular fatiguenon-invasive monitoringtraining load managementwomen’s volleyball

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Area of Science:

  • Sports Science and Medicine
  • Performance Analysis
  • Injury Prevention

Background:

  • Optimizing training load and preventing injuries in women's volleyball presents significant challenges.
  • Existing evidence on using non-invasive technologies and machine learning (ML) for training load management is inconsistent.
  • A comprehensive understanding of current research is needed to guide future interventions.

Purpose of the Study:

  • To systematically map and synthesize evidence on training load management, fatigue, and performance in women's volleyball.
  • To identify current monitoring methods, their application, and limitations.
  • To highlight research gaps, particularly concerning women-specific factors.

Main Methods:

  • A scoping review following PRISMA-ScR and JBI guidelines.
  • Systematic literature search across Scopus, Web of Science, and PubMed (Jan 2020 - Sep 2025).
  • Inclusion of 53 studies evaluating load, fatigue, and performance in female volleyball players, using narrative/thematic synthesis.

Main Results:

  • Prevalent monitoring tools included inertial measurement units (IMUs), force platforms, heart rate (HR) and heart rate variability (HRV), wellness questionnaires, and global/local positioning systems (GPSs/LPSs).
  • High-intensity external load indicators were more sensitive to fatigue than accumulated volume.
  • Machine learning applications were limited, facing challenges in external validation and interpretability; women-specific moderators like the menstrual cycle were underrepresented.

Conclusions:

  • Current research predominantly uses established monitoring technologies, with a focus on external load intensity.
  • Machine learning shows potential but requires further development and validation for practical application in women's volleyball.
  • Future research should address women-specific physiological factors to enhance personalized training load management.