{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T07:25:53Z","timestamp":1790061953571,"version":"4.0.1"},"reference-count":34,"publisher":"Human Kinetics","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,1]]},"abstract":"<jats:p><jats:italic><jats:bold>Background<\/jats:bold>:<\/jats:italic> Training load is typically described in terms of internal and external load. Investigating the coupling of internal and external training load is relevant to many sports. Here, continuous kernel-density estimation (KDE) may be a valuable tool to capture and visualize this coupling. <jats:italic><jats:bold>Aim<\/jats:bold>:<\/jats:italic> Using training load data in speed skating, we evaluated how well bivariate KDE plots describe the coupling of internal and external load and differentiate between specific training sessions, compared to training impulse scores or intensity distribution into training zones. <jats:italic><jats:bold>Methods<\/jats:bold>:<\/jats:italic> On-ice training sessions of 18 young (sub)elite speed skaters were monitored for velocity and heart rate during 2 consecutive seasons. Training session types were obtained from the coach\u2019s training scheme, including endurance, interval, tempo, and sprint sessions. Differences in training load between session types were assessed using Kruskal\u2013Wallis or Kolmogorov\u2013Smirnov tests for training impulse and KDE scores, respectively. <jats:italic><jats:bold>Results<\/jats:bold>:<\/jats:italic> Training impulse scores were not different between training session types, except for extensive endurance sessions. However, all training session types differed when comparing KDEs for heart rate and velocity (both <jats:italic>P<\/jats:italic>\u2009&lt;\u2009.001). In addition, 2D KDE plots of heart rate and velocity provide detailed insights into the (subtle differences in) coupling of internal and external training load that could not be obtained by 2D plots using training zones. <jats:italic><jats:bold>Conclusion<\/jats:bold>:<\/jats:italic> 2D KDE plots provide a valuable tool to visualize and inform coaches on the (subtle differences in) coupling of internal and external training load for training sessions. This will help coaches design better training schemes aiming at desired training adaptations.<\/jats:p>","DOI":"10.1123\/ijspp.2022-0493","type":"journal-article","created":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T16:55:03Z","timestamp":1682009703000},"page":"634-642","source":"Crossref","is-referenced-by-count":10,"title":["Capturing the Complex Relationship Between Internal and External Training Load: A Data-Driven Approach"],"prefix":"10.1123","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8296-828X","authenticated-orcid":true,"given":"Stephan","family":"van der Zwaard","sequence":"first","affiliation":[{"name":"Leiden Institute of Advanced Computer Science, Leiden University, Amsterdam, the Netherlands"},{"name":"Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6130-4671","authenticated-orcid":true,"given":"Ruby T.A.","family":"Otter","sequence":"additional","affiliation":[{"name":"School of Sports Studies, Hanze University of Applied Sciences, Groningen, the Netherlands"},{"name":"Department of Biomedical Sciences of Cells & Systems, Section of Anatomy & Medical Physiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4709-6172","authenticated-orcid":true,"given":"Matthias","family":"Kempe","sequence":"additional","affiliation":[{"name":"Department of Human Movement Sciences, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0335-5099","authenticated-orcid":true,"given":"Arno","family":"Knobbe","sequence":"additional","affiliation":[{"name":"Leiden Institute of Advanced Computer Science, Leiden University, Amsterdam, the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0988-9630","authenticated-orcid":true,"given":"Inge K.","family":"Stoter","sequence":"additional","affiliation":[{"name":"Innovation Lab Thialf, Heerenveen, the Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"100","reference":[{"issue":"suppl 2","key":"r1","series-title":"Sports Med","first-page":"139","article-title":"Monitoring training load to understand fatigue in athletes","volume":"44","author":"Halson SL","year":"2014","unstructured":"Halson SL. 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