Linear and Nonlinear Prediction Models Show Comparable Precision for Maximal Mean Speed in a 4x1000 m Field Test

Abut, F., & Akay, M. F. (2015). Machine learning and statistical methods for the prediction of maximal oxygen uptake: recent advances. Medical Devices: Evidence and Research, 8, 369-379. doi: 10.2147/MDER.S57281Search in Google Scholar

Akay, M. F., Zayid, E. I. M., Aktürk, E., & George, J. D. (2011). Artificial neural networkbased model for predicting VO2max from a submaximal exercise test. Expert Systems with Applications, 38, 2007-2010. doi: 10.1016/j.eswa.2010.07.135Search in Google Scholar

ACSM. (2011). Quantity and quality of exercise for developing and maintaining cardiorespiratory, musculoskeletal, and neuromotor fitness in apparently healthy adults: guidance for prescribing exercise. Medicine & Science in Sports & Exercise, 43(7), 1334-1359. doi: 10.1249/MSS.0b013e318213fefbSearch in Google Scholar

Black, N. E., Vehrs, P. R., Fellingham, G. W., George, J. D., & Hager, R. (2016) Prediction of VO2max in Children and Adolescents Using Exercise Testing and Physical Activity Questionnaire Data. Research Quarterly for Exercise and Sport, 87(1), 89-100, doi: 10.1080/02701367.2015.1124969Search in Google Scholar

Bird, S. R., Theakston, S. C., Owen, A. & Nevill, A. M. (2003). Characteristics Associated With 10-km Running Performance Among a Group of Highly Trained Male Endurance Runners Age 21-63 Years. Journal of Aging and Physical Activity, 11, 333-350. doi: 10.1123/japa.11.3.333Search in Google Scholar

Breiman, L. (2001). Random forests. Machine learning, 45, 5-32. 10.1023/A:1010933404324Open DOISearch in Google Scholar

Darr, K. C, Bassett, D. R, Morgan, B. J., Thomas, D. P. (1988). Effects of age and training status on heart rate recovery after peak exercise. American Journal of Physiol, 254(2), H340-343.10.1152/ajpheart.1988.254.2.H3403344824Search in Google Scholar

Deuster, P., & Heled, Y. (2008). Testing for maximal aerobic power. In P. H. Seidenberg, & A. I. Beutler (Eds.), The sports medicine resource manual (pp. 520-528). Philadelphia: Elsevier. doi: 10.1016/B978-141603197-0.10069-2 Search in Google Scholar

Dimpka, U. (2009). Post-exercise heart rate recovery: an index of cardiovascular fitness. Journal of Exercise Physiology online, 12(1), 19-22.Search in Google Scholar

Edelmann-Nusser, J., Hohmann, A., & Henneberg, B. (2002). Modeling and Prediction of Competitive Performance in Swimming Upon Neural Networks. European Journal of Sport Science, 2(2) [electronic resource].10.1080/17461390200072201Search in Google Scholar

Günther, F., & Fritsch, S. (2010). neuralnet: Training of neural networks. The R Journal, 1(2), 30-38.10.32614/RJ-2010-006Search in Google Scholar

Haff, G. G., & Dumke, C. (2012). Laboratory manual for exercise physiology. Champain, IL: Human Kinetics. Search in Google Scholar

Held, T. (2000). Überprüfung der Ausdauerleistungsfähigkeit. Der 4x1000-m-Lauftest (mobilepraxis) [Assessing endurance performance. The 4x1000-m-Test]. mobile - Die Fachzeitschrift für Sport, 6, 5-9.Search in Google Scholar

Held, T., Steiner, R., Hübner, K., Tschopp, M., Peltola, K., & Marti, B. (2000). Selbst gewählte submaximale Laufgeschwindigkeiten als Prädiktoren des Dauerleistungsvermögens [Selft-selected submaximal running velocities as predictors of endurance capacity]. Schweizerische Zeitschrift für Sportmedizin undSearch in Google Scholar

Sporttraumatologie, 2(48), 64-69.Search in Google Scholar

Jäger, J. M., Kurz, J., & Müller, H. (2016). Predicting maximum speed in a 4x1000 m FieldTest based on estimated VO2max values from Shuttle Run Test and Queens College Step Test. In D. Saupe, T. Dahmen, C. Widmann, I. Baiker, M. Gratkowski, S. Wolf, & R. Bertschinger (Eds.), Proceedings dvs-Workshop Modelling in Endurance Sports (pp. 20-24). Konstanz: Universität Konstanz. URI http://nbnresolving.de/urn:nbn:de:bsz:352-0-371586Search in Google Scholar

Kattan, A., Abdullah, R., & Geem, Z. W. (2011). Artificial neural network training and software implementation techniques. New York: Nova Science.Search in Google Scholar

Léger, L. A., & Lambert, J. (1982). A maximal multistage 20-m shuttle run test to predict VO2max. European Journal of Applied Physiology, 49(1), 1-12.10.1007/BF004289587201922Open DOISearch in Google Scholar

Léger, L. A., Mercier, D., Gadoury, C., & Lambert, J. (1988). The multistage 20 metre shuttle run test for aerobic fitness. Journal of Sports Sciences, 6(2), 93-101. doi: 10.1080/02640418808729800Search in Google Scholar

Marshall, M. R., Coe, D. P., & Pivarnik, J. M. (2014). Development of a Prediction Model to Predict VO2peak in Adolescent Girls Using the Bruce Protocol to Exhaustion. Research Quarterly for Exercise and Sport, 85(2), 251-256, doi: 10.1080/02701367.2014.893053Search in Google Scholar

Mayorga-Vega, D., Aguilar-Soto, P., & Viciana, J. (2015). Criterion-Related Validity of the 20-M Shuttle Run Test for Estimating Cardiorespiratory Fitness: A Meta-Analysis. Journal of Sports Science and Medicine, 14(3), 536-547.Search in Google Scholar

Maszczyk, A., Rocznoik, R., Waskiewicz, Z., Czuba, M., Mikolajec, K., Zajac, A. & Stanula, A. (2012). Application of regression and neural models to predict competitive swimming performance. Perceptual and Motor Skills, 114(2), 610-626. doi: 10.2466/05.10.PMS.114.2.610-626Search in Google Scholar

Maszczyk, A., Zajac, A., & Ryguła, I. (2011). A neural network model approach to athlete selection. Sports Engineering, 13(2), 83-93. doi: 10.1007/s12283-010-0055-ySearch in Google Scholar

McArdle, W. D., Katch, F. I., Pechar, G. S., Jacobson, L., & Ruck, S. (1972). Reliability and interrelationships between maximal oxygen intake, physical work capacity and steptest scores in college women. Medicine & Science in Sports & Exercise, 4(4), 182-186. doi: 10.1249/00005768-197200440-00019Search in Google Scholar

McLaughlin, J. E., Howley, E. T., Bassett Jr., D. R., Thompson, D. L., & Fitzhugh, E. C. (2010). Test of the Classic Model for Predicting Endurance Running Performance. Medicine & Science in Sports & Exercise, 42(5), 991-997. doi: 10.1249/MSS.0b013e3181c0669dSearch in Google Scholar

Newsholme, E. A., Blomstrand, E., & Ekblom, B. (1992). Physical and mental fatigue: Metabolic mechanisms and importance of plasma amino acids. British medical bulletin, 48(3), 477-495.10.1093/oxfordjournals.bmb.a0725581360309Search in Google Scholar

O’Brian, R. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality & Quantity, 41, 673-690. doi: 10.1007/s11135-006-9018-6Search in Google Scholar

Schöllhorn, W. I., Jäger, J. M., & Janssen, D. (2008). Artificial neural network models of sports motion. In Y. Hong, & R. Bartlett, Handbook of Biomechanics and Human Movement Science (pp. 50-64). London: Routledge.Search in Google Scholar

Shave, R., & Franco, A. (2006). The physiology of endurance training. In G. P. Whyte, The physiology of training (pp. 61-84). Philadelphia, PA: Elsevier.Search in Google Scholar

Stickland, M. K., Petersen, S. R., & Bouffard, M. (2003). Prediction of maximal aerobic power from the 20-m multi-stage shuttle run test. Canadian Journal of Applied Physiology, 2(28), 272-282.10.1139/h03-02112825335Open DOISearch in Google Scholar

van Someren, K. A. (2006). The physiology of anaerobic endurance training. In G. P. Whyte, The physiology of training (pp. 85-115). Philadelphia, PA: Elsevier.Search in Google Scholar

Verikas, A., Gelzinis, A., & Bacauskiene, M. (2011). Mining data with random forests: A survey and results of new tests. Pattern recognition, 44, 330-349.10.1016/j.patcog.2010.08.011Open DOISearch in Google Scholar

R Core Team. (2016). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.Search in Google Scholar

Ramsbottom, R., Brewer, J., & Williams, C. (1988). A progressive shuttle run test to estimate maximal oxygen uptake. British Journal of Sports Medicine, 22(4), 141-144.10.1136/bjsm.22.4.14114787283228681Open DOISearch in Google Scholar

Takeshima, N., & Tanaka, K. (1995). Prediction of endurance running performance for middle-aged and older runners. British Journal of Sports Medicine, 29(1), 20-23. doi: 10.1136/bjsm.29.1.20Search in Google Scholar