{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["25(22)"],"submitter":["Abrunedo-Lombardero J"],"pubmed_abstract":["Understanding how training and match load influence autonomic recovery is essential for optimizing athlete monitoring. This proof-of-concept study aimed to examine the impact of training and match load on next-day heart rate variability (HRV) and to explain how different load measures influenced the internal response, using SHapley Additive Explanations (SHAP) to interpret machine learning models. Five semi-professional basketball players (23 ± 5 years; 191 ± 7 cm; 90 ± 11 kg) were monitored throughout a competitive season. HRV and load metrics were recorded daily. Differences in the natural logarithm of the root mean square of successive differences (LnRMSSD) across Non-Training, Training, and Match days were analyzed using linear mixed models. Additionally, a Gradient Boosting Machine mo"],"journal":["Sensors (Basel, Switzerland)"],"pagination":["6928"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12656197"],"repository":["biostudies-literature"],"pubmed_title":["An Explainable Machine Learning Approach to Explain the Effects of Training and Match Load on Ultra-Short-Term Heart Rate Variability in Semi-Professional Basketball Players."],"pmcid":["PMC12656197"],"pubmed_authors":["Abrunedo-Lombardero J","Padron-Cabo A","Velez-Serrano D","Iglesias-Soler E","Alvaro-Meca A"],"additional_accession":[]},"is_claimable":false,"name":"An Explainable Machine Learning Approach to Explain the Effects of Training and Match Load on Ultra-Short-Term Heart Rate Variability in Semi-Professional Basketball Players.","description":"Understanding how training and match load influence autonomic recovery is essential for optimizing athlete monitoring. This proof-of-concept study aimed to examine the impact of training and match load on next-day heart rate variability (HRV) and to explain how different load measures influenced the internal response, using SHapley Additive Explanations (SHAP) to interpret machine learning models. Five semi-professional basketball players (23 ± 5 years; 191 ± 7 cm; 90 ± 11 kg) were monitored throughout a competitive season. HRV and load metrics were recorded daily. Differences in the natural logarithm of the root mean square of successive differences (LnRMSSD) across Non-Training, Training, and Match days were analyzed using linear mixed models. Additionally, a Gradient Boosting Machine mo","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Nov","modification":"2026-05-19T03:27:11.788Z","creation":"2026-05-19T03:12:21.899Z"},"accession":"S-EPMC12656197","cross_references":{"pubmed":["41305136"],"doi":["10.3390/s25226928"]}}