[1. Abraham A. (2005) Handbook of Measuring System Design: Artificial Neural Networks. Edited by P. H. Sydenham and Richard Thorn. Chichester, England: Wiley.10.1002/0471497398.mm421]Search in Google Scholar
[2. Akalan C., Robergs R., Kravitz L. (2008) Prediction of VO2max from an Individualized Submaximal Cycle Ergometer Protocol. J. Exerc. Physiol. Online, 11(2): 1–17.]Search in Google Scholar
[3. Akay F., Abut F. (2015) Machine Learning and Statistical Methods for the Prediction of Maximal Oxygen Uptake: Recent Advances. Medical Devices: Evidence and Research. DOI: 10.2147/MDER.S57281.10.2147/MDER.S57281455629826346869]Search in Google Scholar
[4. Akay F., Inan C., Bradshaw I.D., George J.D. (2009) Support Vector Regression and Multilayer Feed Forward Neural Networks for Non-Exercise Prediction of VO2max. Expert Syst. Appl., 36(6): 10112–10119. DOI: 10.1016/j.eswa.2009.01.009.10.1016/j.eswa.2009.01.009]Search in Google Scholar
[5. Al-Mallah, Mouaz H., Elshawi R., Ahmed A.M., Qureshi W.T., Brawner C.A., Blaha M.J., Ahmed H.M., Ehrman J.K., Keteyian S.J., Sakr S. (2017) Using Machine Learning to Define the Association between Cardiorespiratory Fitness and All-Cause Mortality (from the Henry Ford Exercise Testing Project). Am. J. Card., 120(11): 2078–2084. DOI: 10.1016/j.amjcard.2017.08.029.10.1016/j.amjcard.2017.08.02928951020]Search in Google Scholar
[6. American College of Sports Medicine, and Pescatello L.S. (2014) ACSM’s Guidelines for Exercise Testing and Prescription. Philadelphia: Wolters Kluwer Health/Lippincott Williams & Wilkins.]Search in Google Scholar
[7. Astrand I. (1967) Aerobic Work Capacity: Its Relation to Age, Sex and Other Factors. Circulation Res., 211–217.]Search in Google Scholar
[8. Basset D. Howley E. (2000) Limiting Factors for Maximum Oxygen Uptake and Determinants of Endurance Performance. Med. Sci. Sports Exerc., 32 (1): 70–84.10.1097/00005768-200001000-0001210647532]Search in Google Scholar
[9. Beltrame T., Amelard R., Wong A., Hughson R.L. (2017) Prediction of Oxygen Uptake Dynamics by Machine Learning Analysis of Wearable Sensors during Activities of Daily Living. Sci. Rep., 7 (April): 45738. DOI: 10.1038/srep45738.10.1038/srep45738538111828378815]Search in Google Scholar
[10. Beltrame T., Amelard R., Villar R., Shafiee M.J., WongA., Hughson R.L. (2016) Estimating Oxygen Uptake and Energy Expenditure during Treadmill Walking by Neural Network Analysis of Easy-to-Obtain Inputs. J. Appl. Physiol., 121(5): 1226–1233. DOI: 10.1152/japplphysiol.00600.2016.10.1152/japplphysiol.00600.201627687561]Search in Google Scholar
[11. Beltrame T., Amelard R., Wong A., Hughson R.L. (2018) Extracting Aerobic System Dynamics during Unsupervised Activities of Daily Living Using Wearable Sensor Machine Learning Models. J. Appl. Physiol., 124(2): 473–481. DOI: 10.1152/japplphysiol.00299.2017.10.1152/japplphysiol.00299.2017586736728596271]Search in Google Scholar
[12. Blair S.N., Kampert J.B., Kohl H.W., Barlow C.E., Macera C.A., Paffenbarger R.S., Gibbons L.W. (1996) Influences of Cardiorespiratory Fitness and Other Precursors on Cardiovascular Disease and All-Cause Mortality in Men and Women. JAMA, 276(3): 205–210.10.1001/jama.1996.03540030039029]Search in Google Scholar
[13. Capostagno B., Lambert M.I., Lamberts R.P. (2016) A Systematic Review of Submaximal Cycle Tests to Predict, Monitor, and Optimize Cycling Performance. Int. J. Sports Physiol. Perf., 11(6): 707–714. DOI: 10.1123/ijspp.2016-0174.10.1123/ijspp.2016-017427701968]Search in Google Scholar
[14. Chilibeck P.D., Paterson D.H., Petrella R.J., Cunningham D.A. (1996) The Influence of Age and Cardiorespiratory Fitness on Kinetics of Oxygen Uptake. Can. J. Appl. Physiol., 21(3): 185–196.10.1139/h96-015]Search in Google Scholar
[15. Crouter S.E., Clowers K.G., Bassett D. (2006) A Novel Method for Using Accelerometer Data to Predict Energy Expenditure. J. Appl. Physiol., 100(4): 1324–1331. DOI: 10.1152/japplphysiol.00818.2005.10.1152/japplphysiol.00818.2005]Search in Google Scholar
[16. Ekelund L.G., Haskell W.L., Johnson J.L., Whaley F.S., Criqui M.H., Sheps D.S., (1988) Physical Fitness as a Predictor of Cardiovascular Mortality in Asymptomatic North American Men. N. Engl. J. Med., 319(21): 1379–1384. DOI: 10.1056/NEJM198811243192104.10.1056/NEJM198811243192104]Search in Google Scholar
[17. Erikssen G., Liestøl K., Bjørnholt J., Thaulow E., Sandvik L., Erikssen J. (1998) Changes in Physical Fitness and Changes in Mortality. Lancet, 352(9130): 759–762.10.1016/S0140-6736(98)02268-5]Search in Google Scholar
[18. Faul F., Erdfelder E, Lang A.G., Buchner A. (2007) G*Power 3: AFlexible Statistical Power Analysis Program for the Social, Behavioral, and Biomedical Sciences. Behav. Res. Methods, 39(2): 175–191.10.3758/BF0319314617695343]Search in Google Scholar
[19. García-Massó X., Serra-Añó P., García-Raffi L., Sánchez-Pérez E., Giner-Pascual M., González L.M. (2014) Neural Network for Estimating Energy Expenditure in Paraplegics from Heart Rate. Int. J. Sports Med., 35(12): 1037–1043. DOI: 10.1055/s-0034-1368722.10.1055/s-0034-136872224886923]Search in Google Scholar
[20. Gavin H.P. (2017) The Levenberg-Marquardt Method for Nonlinear Least Squares Curve-Fitting Problems. Department of Civil and Environmental Engineering, Duke University.]Search in Google Scholar
[21. Guazzi M., Adams V., Conraads V., Halle M., Mezzani A., Vanhees L., Arena R., Fletcher G.F., Forman D.E., Kitzman D.W., Lavie C.J., Myers J. (2012) Clinical Recommendations for Cardiopulmonary Exercise Testing Data Assessment in Specific Patient Populations. Circulation, 126 (18): 2261–2274. DOI: 10.1161/CIR.0b013e31826fb946.10.1161/CIR.0b013e31826fb946477732522952317]Search in Google Scholar
[22. Gulati M., Black H.R., Shaw L.J., Arnsdorf M.F., Bairey Merz C.N., Lauer M.S., Marwick T.H., Pandey D.K., Wicklund R.H., Thisted R.A. (2005) The Prognostic Value of aNomogram for Exercise Capacity in Women. N. Engl. J. Med., 353(5): 468–75. DOI: 10.1056/NEJMoa044154.10.1056/NEJMoa04415416079370]Search in Google Scholar
[23. Hills A.P., Byrne N.M., Ramage A.J. (1998) Submaximal Markers of Exercise Intensity. J. Sports Sci., 16(sup1): 71–76. DOI: 10.1080/026404198366696.10.1080/02640419836669622587719]Search in Google Scholar
[24. Howley E., Bassett D., Welch H. (1995) Criteria for Maximal Oxygen Uptake: AReview and Commentary. Med. Sci. Sports Exerc., 27(9): 1292–1301.10.1249/00005768-199509000-00009]Search in Google Scholar
[25. Jamnick N.A., By S., Pettitt C.D., Pettitt R.W. (2016) Comparison of the YMCA and aCustom Submaximal Exercise Test for Determining VO2max. Med. Sci. Sports Exerc., 48(2): 254–259. DOI: 10.1249/MSS.0000000000000763.10.1249/MSS.000000000000076326339726]Search in Google Scholar
[26. Katch V., Weltman A, Sady S., Freedson P. (1978) Validity of the Relative Percent Concept for Equating Training Intensity. Eur. J. Appl. Physiol. Occup. Physiol., 39(4): 219–227.10.1007/BF00421445]Search in Google Scholar
[27. Kemps H.M.C., Schep G., Hoogsteen J., Thijssen E.J.M., De Vries W.R., Zonderland M.L., Doevendans P. (2009) Oxygen Uptake Kinetics in Chronic Heart Failure: Clinical and Physiological Aspects. Neth. Heart J., 17(6): 238–244.10.1007/BF03086254]Search in Google Scholar
[28. Lin C.W., Yang Y.T.C., Wang J.S., Yang Y.C. (2012) AWearable Sensor Module with aNeural-Network-Based Activity Classification Algorithm for Daily Energy Expenditure Estimation. IEEE Trans. Inf. Tech. Biomed., 16(5): 991–998. DOI: 10.1109/TITB.2012.2206602.10.1109/TITB.2012.2206602]Search in Google Scholar
[29. Liu Y., Starzyk J.A., Zhu Z. (2007) Optimizing Number of Hidden Neurons in Neural Networks. Artif. Intell. Appl., 138–143.]Search in Google Scholar
[30. Mann B.P., Khasawneh F.A., Fales R. (2011) Using Information to Generate Derivative Coordinates from Noisy Time Series. Commun. Nonlinear Sci. Numer. Simul., 16(8): 2999–3004. DOI: 10.1016/j.cnsns.2010.11.011.10.1016/j.cnsns.2010.11.011]Search in Google Scholar
[31. Mazzoleni M.J., Battaglini C.L., Martin K.J., Coffman E.M., Ekaidat J.A., Wood W.A., Mann B.P. (2017) A Dynamical Systems Approach for the Submaximal Prediction of Maximum Heart Rate and Maximal Oxygen Uptake. Sports Eng., 21(1): 31–41. DOI: 10.1007/s12283-017-0242-1.10.1007/s12283-017-0242-1]Search in Google Scholar
[32. Mazzoleni M.J., Battaglini C.L., Martin K.J., Coffman E.M., Wood W.A., Mann B.P. (2016) Modeling and Predicting Heart Rate Dynamics across a Broad Range of Transient Exercise Intensities during Cycling. Sports Eng., 19(2): 117–127. DOI: 10.1007/s12283-015-0193-3.10.1007/s12283-015-0193-3]Search in Google Scholar
[33. Morris M., Lamb K., Cotterrell D., Buckley J. (2009) Predicting Maximal Oxygen Uptake via a Perceptually Regulated Exercise Test (PRET) J. Exerc. Sci. Fit., 7 (2): 122–128.10.1016/S1728-869X(09)60015-0]Search in Google Scholar
[34. Myers J., Prakash M., Froelicher V., Do D., Partington S., Atwood J.A. (2002) Exercise Capacity and Mortality among Men Referred for Exercise Testing. N. Engl. J. Med., 346 (11): 793–801. DOI: 10.1056/NEJMoa011858.10.1056/NEJMoa01185811893790]Search in Google Scholar
[35. Nevill A.M., Cooke C. B. (2017) The Dangers of Estimating VO2max Using Linear, Nonexercise Prediction Models. Med. Sci. Sports Exerc., 49(5): 1036-1042. DOI: 10.1249/MSS.0000000000001178.10.1249/MSS.000000000000117827922463]Search in Google Scholar
[36. Plasqui G. Westerterp K.R. (2005) Accelerometry and Heart Rate as aMeasure of Physical Fitness: Proof of Concept: Med. Sci. Sports Exerc., 37(5): 872–76. DOI: 10.1249/01.MSS.0000161805.61893.C0.10.1249/01.MSS.0000161805.61893.C015870644]Search in Google Scholar
[37. Poole D.C., Jones A.M. (2012) Oxygen Uptake Kinetics. In Compr. Physiol., edited by Ronald Terjung. Hoboken, NJ, USA: John Wiley & Sons, Inc. DOI: wiley.com/10.1002/cphy.c100072.]Search in Google Scholar
[38. Robergs R.A., Landwehr R. (2002) The Surprising History of the ‘HRmax= 220-Age’ equation. J. Exerc. Physiol. Online, 5(2): 1–10.]Search in Google Scholar
[39. Ross R., Blair S.N., Arena R., Church R.S., Després J.P., Franklin B.A., Haskell W.L. (2016) Importance of Assessing Cardiorespiratory Fitness in Clinical Practice: ACase for Fitness as aClinical Vital Sign: AScientific Statement from the American Heart Association. Circulation, CIR–0000000000000461.10.1161/CIR.000000000000046127881567]Search in Google Scholar
[40. Rothney M.P., Neumann M., Béziat A., Chen K.Y. (2007) An Artificial Neural Network Model of Energy Expenditure Using Nonintegrated Acceleration Signals. J. Appl. Physiol., 103(4): 1419–1427. DOI: 10.1152/japplphysiol.00429.2007.10.1152/japplphysiol.00429.200717641221]Search in Google Scholar
[41. Ruch N., Joss F., Jimmy G., Melzer K., Hänggi J., Mäder U. (2013) Neural Network versus Activity-Specific Prediction Equations for Energy Expenditure Estimation in Children. J. Appl. Physiol., 115(9): 1229–1236. DOI: 10.1152/japplphysiol.01443.2012.10.1152/japplphysiol.01443.201223990244]Search in Google Scholar
[42. Sandvik L., Erikssen J., Thaulow E., Erikssen G., Mundal R., Rodahl K. (1993) Physical Fitness as a Predictor of Mortality among Healthy, Middle-Aged Norwegian Men. N. Engl. J. Med., 328(8): 533–537. DOI: 10.1056/NEJM199302253280803.10.1056/NEJM1993022532808038426620]Search in Google Scholar
[43. Snell P.G., Stray-Gundersen J., Levine B.D., Hawkins M.N., Raven P.B. (2007) Maximal Oxygen Uptake as aParametric Measure of Cardiorespiratory Capacity. Med. Sci. Sports Exerc., 39(1): 103–107. DOI: 10.1249/01.mss.0000241641.75101.64.10.1249/01.mss.0000241641.75101.6417095937]Search in Google Scholar
[44. Soares de Araújo C.G. Duarte C.V. (2015) Maximal Heart Rate in Young Adults: AFixed 188bpm Outperforms Values Predicted by aClassical Age-Based Equation. Int. J. Cardiol., 184: 609–610. DOI: 10.1016/j.ijcard.2015.02.043.10.1016/j.ijcard.2015.02.04325769008]Search in Google Scholar
[45. Staudenmayer J., Pober D., Crouter S., Bassett D., Freedson P. (2009) An Artificial Neural Network to Estimate Physical Activity Energy Expenditure and Identify Physical Activity Type from an Accelerometer. J. Appl. Physiol., 107(4): 1300–1307. DOI: 10.1152/japplphysiol.00465.2009.10.1152/japplphysiol.00465.2009276383519644028]Search in Google Scholar
[46. Stirling J., Zakynthinaki M., Saltin B. (2005) A Model of Oxygen Uptake Kinetics in Response to Exercise: Including aMeans of Calculating Oxygen Demand/Deficit/Debt. Bull. Math. Biol., 67(5): 989–1015. DOI: 10.1016/j.bulm.2004.12.005.10.1016/j.bulm.2004.12.00515998492]Search in Google Scholar
[47. Stirling J.R., Zakynthinaki M.S., Billat V. (2008) Modeling and Analysis of the Effect of Training on VO2 Kinetics and Anaerobic Capacity. Bull. Math. Bio., 70(5): 1348–1370. DOI: 10.1007/s11538-008-9302-9.10.1007/s11538-008-9302-918306003]Search in Google Scholar
[48. Stringer W., Hansen J., Wasserman K. (1997) Cardiac Output Estimated Noninvasively from Oxygen Uptake during Exercise. J. Appl. Physiol., 82(3): 908-912. DOI: 10.1152/jappl.1997.82.3.908.10.1152/jappl.1997.82.3.9089074981]Search in Google Scholar
[49. Swain D.P., Abernathy K.S., Smith C.S., Lee S.J., Bunn S.A. (1994) Target Heart Rates for the Development of Cardiorespiratory Fitness. Med. Sci. Sports. Exerc., 26(1): 112–116.10.1249/00005768-199401000-00019]Search in Google Scholar
[50. Wright S.P., Hall Brown T.S., Collier S.R., Sandberg K. (2017) How Consumer Physical Activity Monitors Could Transform Human Physiology Research. Am. J. Physiol. Regul. Integr. Comp. Physiol., 312(3): R358–367. DOI: 10.1152/ajpregu.00349.2016.10.1152/ajpregu.00349.2016540199728052867]Search in Google Scholar
[51. Yamaji K., Miyashita M., Shepharo R.J. (1978) Relationship between Heart Rate and Relative Oxygen Intake in Male Subjects Aged 10 to 27 Years. J. Hum. Ergol., 7 (1): 29–39.]Search in Google Scholar
[52. Yardley M., Havik O.E., Grov I., Relbo A., Gullestad L., Nytrøen K. (2016) Peak Oxygen Uptake and Self-Reported Physical Health Are Strong Predictors of Long-Term Survival after Heart Transplantation. Clin. Transplant., 30(2): 161–169. DOI: 10.1111/ctr.12672.10.1111/ctr.1267226589579]Search in Google Scholar
[53. Żołądź J.A., Duda K., Majerczak J. (1998) Oxygen Uptake Does Not Increase Linearly at High Power Outputs during Incremental Exercise Test in Humans. Eur. J. Appl. Physiol. Occup. Physiol., 77(5): 445–451.10.1007/s0042100503589562296]Search in Google Scholar