Identifying Weekly Physical and Physiological Profiles in Professional Basketball Using Heart Rate Variability and Training Load Clustering
< .001). Unsupervised clustering offers a practical and data-driven framework to summarizing weekly physiological-load states in elite, potentially improving individualized load-management strategies. Future studies should investigate the extent to which these clustered profiles are associated with performance outcomes, fatigue markers, and injury risk.
Authors
- Toni Caparrós (ORCID: https://orcid.org/0000-0002-5169-1935)
- Daniel Fernández (ORCID: https://orcid.org/0000-0003-0012-2094)
- Pierpaolo Sansone (ORCID: https://orcid.org/0000-0002-7355-3795)
- Víctor Olmos (ORCID: https://orcid.org/0000-0003-0132-6849)
- Martí Casals (ORCID: https://orcid.org/0000-0002-1775-8331)
- Marc Olmos-Peñarroja (ORCID: https://orcid.org/0009-0002-5469-9657)
Institutions
- Universitat de Vic - Universitat Central de Catalunya (ES)
- Universitat Autònoma de Barcelona (ES)
- Bamberg County Hospital (US)
- Pegaso University (IT)
- Institut Nacional d'Educació Física de Catalunya (ES)
- Universitat de Barcelona (ES)
- University of Bamberg (DE)
- Universitat Politècnica de Catalunya (ES)
Publication Details
- Journal
- Research Quarterly for Exercise and Sport
- Published
- 2026-04-17
- DOI
- https://doi.org/10.1080/02701367.2026.2653698
- Primary Topic
- Heart Rate Variability and Autonomic Control
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Royal Society Te Apārangi
- Centro de Investigación Biomédica en Red de Salud Mental
- European Regional Development Fund
- Marsden Fund
- Departament de Recerca i Universitats, Generalitat de Catalunya