Database Open Access

Treadmill Maximal Exercise Tests from the Exercise Physiology and Human Performance Lab of the University of Malaga

Denis Mongin Jeronimo García Romero Jose Ramon Alvero Cruz

Published: April 30, 2021. Version: 1.0.0


When using this resource, please cite: (show more options)
Mongin, D., García Romero, J., & Alvero Cruz, J. R. (2021). Treadmill Maximal Exercise Tests from the Exercise Physiology and Human Performance Lab of the University of Malaga (version 1.0.0). PhysioNet. https://doi.org/10.13026/de8x-qg95.

Please include the standard citation for PhysioNet: (show more options)
Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.

Abstract

The present database is an ensemble of the cardiorespiratory measurements acquired during 992 treadmill maximal graded exercise tests (GET) performed in the Exercise Physiology and Human Performance Lab of the University of Malaga. Heart rate, oxygen consumption, carbon dioxide generation, and pulmonary ventilation are measured on a breath-to-breath basis along with the treadmill speed during maximal effort tests. Participants are amateur and professional athletes are of ages ranging from 10 to 63 years old. The age, height, and weight of the participants are provided, as well as the temperature and humidity during the test.


Background

Cardiac and respiratory data measured during graded exercise tests are key measures to calculate several cardio-respiratory indices used in sport science [1] and medicine [2,3]. All the parts of the oxygen consumption and heart rate dynamics during a GET are of interest: the rate of increase at exercise onset [4], the slope during exercise [5,6], the maximal values [7,8], the dynamics changes at ventilatory thresholds [9,10], the nonlinear dynamics during effort [11–13], and finally the dynamics during recovery [14,15]. The study of each of these segments of the oxygen consumption or heart rate dynamics during effort has led to indices used to characterize and predict health, fitness, or performance, such as ventilatory thresholds, heart resting rate, rate of heart rate increase, deflection point of the performance curve, etc. But comparisons between existing calculation methods for these indices are lacking, and reproducibility of the calculation is sometimes hindered by the lack of available open-source code. The goal of this dataset is then two-fold: To facilitate the publication and diffusion of calculation methods and associated code to analyze cardiorespiratory measurements of maximal exercise tests; To encourage studies comparing different calculation methods of indices derived from the cardio-respiratory measurements acquired during effort tests, such as ventilatory thresholds or heart rate recovery.


Methods

The measurements were taken between 2008 and 2018. The athletes performed a maximal Graded Exercise Testing (GET) on a PowerJog J series treadmill connected to a CPX MedGraphics gas analyzer system (Medical Graphics, MN, USA) with breath-by-breath measurements of respiratory parameters -including oxygen consumption and pulmonary ventilation- and heart rate collected by a Mortara 12-lead ECG device.

The stress tests consisted of a continuous (ramping) or step-by-step incremental effort. Most of the exercise phases are preceded by a warmup period of walking at 5 km/h. When incremental, the step amplitudes range from 0.5 to 1 km/h. The participants were asked to go beyond exhaustion, and the test was considered maximal if the oxygen consumption was saturated. The effort was then ceased, and to avoid vasovagal syncope, the treadmill speed was set back to the initial 5 km/h speed, and the participant was asked to walk.

Exercise testing was voluntary, and before its initiation, written informed consent was obtained from the participants and the legal guardians of those under 18 years of age. All effort tests were performed under the supervision of a doctor in sport science, and their analysis was carried out according to the principles of the Declaration of Helsinki. The study protocols were approved by the Research Ethics Committee of the University of Málaga.


Data Description

The dataset contains two files:

  • subject-info.csv contains the participant info at the time of the test. The variable ID identifies a participant, whereas the variable ID_test identifies an exercise test. This file contains 992 lines, one for each test. The different variables are described in the table below, with their corresponding amount or median [Inter Quartile Range] value.

variable

value

ID_test 992
ID 857
Age (years) 27.10 [21.10, 36.32]
Weight (kg) 73.00 [66.00, 80.23]
Height (cm) 175.00 [170.00, 180.00]
Humidity (%) 47.00 [42.00, 54.00]
Temperature (°C) 22.90 [20.80, 24.40]
Sex = 1 (Female) (%) 149 (15.0)
  • test_measure.csv, contains all the cardiorespiratory measurements taken during each effort test. The data is in long format, so the file contains one line for each breath measurement for all of the 992 effort tests, resulting in the 575087 lines present in the file. The time of each measurement is identified by the variable time indicating the seconds elapsed since the effort test start, the exercise test is identified by the ID_test variable, and the variable ID indicates the participant. These effort tests contain a median [Inter Quartile Range] of 580 [484, 673] measures, for a median duration of 1093.00 [978.75, 1208.00] seconds. The variables in this file are:
    time Time since the measurement starts, in seconds
    Speed Speed of the treadmill, in km/h
    HR Heart rate, in beat per min
    VO2 Oxygen consumption, in mL/min
    VCO2 Carbon dioxide production, in mL/min
    RR Respiration rate, in respiration per minute
    VE Pulmonary ventilation, in L/min
    ID Participant identification
    ID_test Effort test identification

Note that VO2, VCO2, and VE measures are missing for 30 tests.


Usage Notes

ID_test, the variable identifying the GET, is named from the ID of the participant paired with the GET index. For example, ID_date = 245_3 is the third exercise test of participant ID = 245. An example of calculation in R [16] of simple heart rate recovery and cardiorespiratory index from these files is provided in an associated GitHub repository [17].


Acknowledgements

The authors would like to thank all past and present members of the School of Sports Medicine of University of Málaga and Research Group CTS-132. Junta de Andalucía (Exercise Physiology).


Conflicts of Interest

The authors have no conflicts of interest to declare.


References

  1. Medicine AC of S. ACSM’s Guidelines for Exercise Testing and Prescription. Edición: 9th edition. Philadelphia: Lippincott Williams and Wilkins; 2013.
  2. Cole CR, Blackstone EH, Pashkow FJ, Snader CE, Lauer MS. Heart-Rate Recovery Immediately after Exercise as a Predictor of Mortality. New England Journal of Medicine. 1999 Oct 28;341(18):1351–7.
  3. Ross Robert, Blair Steven N., Arena Ross, Church Timothy S., Després Jean-Pierre, Franklin Barry A., et al. Importance of Assessing Cardiorespiratory Fitness in Clinical Practice: A Case for Fitness as a Clinical Vital Sign: A Scientific Statement From the American Heart Association. Circulation. 2016 Dec 13;134(24):e653–99.
  4. Bellenger CR, Thomson RL, Howe PRC, Karavirta L, Buckley JD. Monitoring athletic training status using the maximal rate of heart rate increase. Journal of Science and Medicine in Sport. 2016 Jul 1;19(7):590–5.
  5. Bodner ME, Rhodes EC. A review of the concept of the heart rate deflection point. Sports Med. 2000 Jul;30(1):31–46.
  6. Swain DP, Leutholtz BC, King ME, Haas LA, Branch JD. Relationship between % heart rate reserve and % VO2 reserve in treadmill exercise. Med Sci Sports Exerc. 1998 Feb;30(2):318–21.
  7. Smith TP, McNaughton LR, Marshall KJ. Effects of 4-wk training using Vmax/Tmax on VO2max and performance in athletes. Med Sci Sports Exerc. 1999 Jun;31(6):892–6.
  8. Karvonen J, Vuorimaa T. Heart rate and exercise intensity during sports activities. Practical application. Sports Med. 1988 May;5(5):303–11.
  9. Binder RK, Wonisch M, Corra U, Cohen-Solal A, Vanhees L, Saner H, et al. Methodological approach to the first and second lactate threshold in incremental cardiopulmonary exercise testing. Eur J Cardiovasc Prev Rehabil. 2008 Dec;15(6):726–34.
  10. Conconi F, Ferrari M, Ziglio PG, Droghetti P, Codeca L. Determination of the anaerobic threshold by a noninvasive field test in runners. J Appl Physiol Respir Environ Exerc Physiol. 1982 Apr;52(4):869–73.
  11. Artiga Gonzalez A, Bertschinger R, Brosda F, Dahmen T, Thumm P, Saupe D. Kinetic analysis of oxygen dynamics under a variable work rate. Hum Mov Sci. 2019 Aug;66:645–58.
  12. Mongin D, Chabert C, Uribe Caparros A, Collado A, Hermand E, Hue O, et al. Validity of dynamical analysis to characterize heart rate and oxygen consumption during effort tests. Scientific Reports. 2020 Jul 24;10(1):12420.
  13. Mongin D, Chabert C, Caparros AU, Guzmán JFV, Hue O, Alvero-Cruz JR, et al. The complex relationship between effort and heart rate: a hint from dynamic analysis. Physiol Meas. 2020 Nov;41(10):105003.
  14. Borresen J, Lambert MI. Autonomic control of heart rate during and after exercise : measurements and implications for monitoring training status. Sports Med. 2008;38(8):633–46.
  15. Bellenger CR, Fuller JT, Thomson RL, Davison K, Robertson EY, Buckley JD. Monitoring Athletic Training Status Through Autonomic Heart Rate Regulation: A Systematic Review and Meta-Analysis. Sports Med. 2016 Oct;46(10):1461–86.
  16. R Core Team. R: A Language and Environment for Statistical Computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2019. Available from: https://www.R-project.org
  17. Denis Mongin / HRR_comparison [Internet]. GitLab. [cited 2021 Mar 19]. Available from: https://gitlab.com/dmongin/hrr_comparison

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