{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T09:05:09Z","timestamp":1779440709213,"version":"3.53.1"},"reference-count":77,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T00:00:00Z","timestamp":1779408000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>\n                    This study identified key physiological, biomechanical, and strength-related predictors of competitive performance in elite female race walkers and evaluated the effectiveness of classical and machine-learning models for individualized training optimization. Thirty nationally ranked female race walkers (25\u00a0\u00b1\u00a03 years) were assessed over four seasons (2021\u20132024). Laboratory and field tests included ergospirometry (VO\n                    <jats:sub>2<\/jats:sub>\n                    max, VCO2, VE (minute ventilation), respiratory exchange ratio (RER)), blood lactate (LA), heart rate (HR), gait kinematics (step length, speed), and lower-limb strength (1RM, maximal power). Temporal and seasonal dynamics were evaluated using Kruskal\u2013Wallis and g-Fisher tests. Predictive models included multiple regression, polynomial regression, multilayer perceptron (MLP), and radial basis function (RBF) networks, developed with correlation-vector analysis (R0, R1), collinearity diagnostics, and interaction terms. The most influential predictors were HR, step length, VO\n                    <jats:sub>2<\/jats:sub>\n                    max, and 1RM (R0 &gt; 0.70). RBF achieved the best predictive accuracy (\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    \u00a0=\u00a00.89; RMSE = 0.28), outperforming MLP (\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    \u00a0=\u00a00.87) and regression baselines (\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    \u00a0=\u00a00.61\u20130.81). Significant seasonal variation (\n                    <jats:italic>p<\/jats:italic>\n                    \u00a0&lt;\u00a00.001) underscored the value of time-dependent modeling. Conclusion: RBF neural networks offer superior performance for predicting race-walking outcomes; HR and step length are key real-time indicators, whereas VO\n                    <jats:sub>2<\/jats:sub>\n                    max and 1RM inform longer-term adaptation.\n                  <\/jats:p>","DOI":"10.7717\/peerj.21224","type":"journal-article","created":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T08:12:16Z","timestamp":1779437536000},"page":"e21224","source":"Crossref","is-referenced-by-count":0,"title":["Artificial neural network predictive models for optimizing the training process in race walking: a longitudinal observational study"],"prefix":"10.7717","volume":"14","author":[{"given":"Dariusz","family":"Skalski","sequence":"first","affiliation":[{"name":"Gda\u0144sk University of Physical Education and Sport, Gdansk, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Magdalena","family":"Pro\u0144czuk","sequence":"additional","affiliation":[{"name":"Gda\u0144sk University of Physical Education and Sport, Gdansk, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kinga","family":"\u0141osi\u0144ska","sequence":"additional","affiliation":[{"name":"Gda\u0144sk University of Physical Education and Sport, Gdansk, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Petr","family":"Stastny","sequence":"additional","affiliation":[{"name":"Department of Sport Games, Faculty of Physical Education and Sport, Charles University in Prague, Prague, Czech Republic"},{"name":"Institute of Sport Science, The Jerzy Kukuczka Academy of Physical Education, Katowice, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adam","family":"Maszczyk","sequence":"additional","affiliation":[{"name":"Gda\u0144sk University of Physical Education and Sport, Gdansk, Poland"},{"name":"Institute of Sport Science, The Jerzy Kukuczka Academy of Physical Education, Katowice, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adam","family":"Zajac","sequence":"additional","affiliation":[{"name":"Institute of Sport Science, The Jerzy Kukuczka Academy of Physical Education, Katowice, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4443","published-online":{"date-parts":[[2026,5,22]]},"reference":[{"issue":"1","key":"10.7717\/peerj.21224\/ref-1","first-page":"145","article-title":"Age-related, site-specific muscle loss in 1,507 Japanese men and women aged 20 to 95 years","volume":"10","author":"Abe","year":"2011","journal-title":"Journal of Sports Science & Medicine"},{"issue":"2","key":"10.7717\/peerj.21224\/ref-2","doi-asserted-by":"publisher","first-page":"93","DOI":"10.3390\/jfmk9020093","article-title":"Heart rate variability applications in strength and conditioning: a narrative 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