Database Open Access

Cardiorespiratory measurement from graded cycloergometer exercise testing

Clovis Chabert Denis Mongin Eric Hermand Aurélie Collado Olivier Hue

Published: June 30, 2022. Version: 1.0.0

When using this resource, please cite: (show more options)
Chabert, C., Mongin, D., Hermand, E., Collado, A., & Hue, O. (2022). Cardiorespiratory measurement from graded cycloergometer exercise testing (version 1.0.0). PhysioNet.

Additionally, please cite the original publication:

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 1.0.1). PhysioNet.

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.


The present database is an ensemble of cardiorespiratory measurements acquired during 18 cycloergometer maximal graded exercise tests performed in the "Adaptation, Climat Tropical, Exercice et Santé" (ACTES) laboratory of the French West Indies University. Data correspond to heart R-wave to R-wave intervals, Oxygen consumption, and mechanical power output on a beat-to-beat basis recorded all along the graded exercise test sessions. Participants are 18 teenagers athletes (15.2±2 years) from the Regional Physical and Sports Education Centre (CREPS) of French West Indies (Guadeloupe, France), belonging to a regional division of sprint kayak and triathlon or a national division of fencing. Clinical data of the participants (i.e. age, Weight, Height, Power at ventilatory threshold 1 and 2, and sport discipline) are provided in a separate file.


Graded Exercise Testing (GET) is a key tool to evaluate the global fitness of athletes in a sport context [1], or patients in a medical context [2,3], by analysis of the cardiac and respiratory parameters. Indeed, several indexes of fitness from GET have been discovered, based on ventilatory thresholds, heart resting rate, rate of the heart rate increase, or deflection point of the performance curve [1].

Although extensively studied, the evolution of Heart Rate (HR) during physical exercise is still an active subject of research [10–12]. HR is mainly driven by the balance of the Autonomic Nervous System (ANS). During exercise, the sympathetic activity increase in parallel to the decrease of the parasympathetic activity [2] sharply change the heart R-wave to R-wave (RR) intervals profile, which mainly leads to variability decrease in term of time and frequency domain [3–9]. The aim of this dataset is twofold:

  1. share the dataset used to test the ability of an improved and extended approach of the HRV decay time to predict cardiorespiratory fitness.
  2. facilitate new studies working on HRV variations during GET of young athletes.


The database used in this work consists of records in 2017-18 of 18 young athletes (10 males and 8 females; 15.2 ± 2 year-old) of the CREPS located in the French West Indies (Guadeloupe, France), belonging to a national division of fencing, or a regional division of sprint kayak and triathlon. The subjects performed GET on a SRM Indoor Trainer electronic cycloergometer (Schoberer Rad Meßtechnik, Jülich, Germany) associated to a Metalyzer 3B gas analyzer system (CORTEX Biophysik GmbH, Leipzig, Germany). The RR series were derived from Electro Cardiogramm (ECG) recordings (Cardio 110BT, Customed, Ottobrunn, Germany, with 12 derivations). All along the test session, the cardiorespiratory parameters were recorded beat-to-beat with a time resolution of the ECG at 1 ms.

GET protocol corresponds to warm-up period of 3 min at 50 watts, followed by an incremental increase of power (15 Watts/min) until exhaustion. At exercise cessation, athletes stayed sat on the cycloergometer to record the cardiorespiratory parameters during their recovery. The Wasserman method [13] was used to calculate the ventilatory thresholds 1 (VT1) and 2 (VT2) from the respiratory data. Maximum aerobic power corresponds to the mechanical power of the last achieved step of the GET. HRmax and VO2max are the maximum values of the HR and VO2 averaged over 5 breaths from data measured with the Metalyzer 3B gas analyzer system. For each GET, temperature, humidity, and light were controlled to standardize conditions of tests between the athletes. The participants were also instructed to not take alcohol, caffeine, and to not practice intense physical exercise 24 hours before GET.

The tests were performed off-competition at the end of the season, under the supervision of a doctor in sport medicine that performed a complete medical screening before the athletes starts cycling. A medical questionnaire (cf. suppl. file: "Medical_screening_questionnaire.pdf"), and a written informed consent from the participants and the legal guardians was obtained prior to the study. The study was approved by the CREPS Committee of Guadeloupe (Ministry of Youth and Sports) and the CREPS Ethics Committee and performed according to the Declaration of Helsinki.

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, the variable P_vt1 and P_vt2 correspond to the power output in watts measured, respectively, at the ventilatory threshold 1 and 2. This file contains 18 lines, one for each subject. The different variables are described in the table below, with their corresponding amount or median [Inter Quartile Range] value.





    Age (years)

    15.0 [14.0, 16.8]

    Weight (kg)

    62.9 [54.3, 76.5]

    Height (cm)

    174.0 [165.0, 182.0]

    P_vt1 (W)

    105.5 [79.3, 143.0]

    P_vt2 (W)

    169.0 [141.3, 246.8]










  • test_measure.csv, contains the cardiorespiratory measurements taken during each effort test. The data (in long format) contains one line for each beat measurement of all the 18 effort tests, corresponding to a table with 52063 lines. The time of each measurement is identified by the variable time indicating the seconds elapsed centred on the start point of the effort test. The exercise test is identified by the ID variable that indicates the participant. These effort tests contain a median [Inter Quartile Range] of 2749 [2449, 3555] measures, for a median duration of 1223.7 [1081, 1560] seconds. The oxygen consumption, initially measured breath by breath, has been projected to the beat-by-beat time series. The variables in this file are:




    Participant identifier


    Time since the Graded effort test starts, in seconds


    Heart Rate R-wave to R-Wave interval, in milliseconds


    Oxygen consumption, in mL/min


    Mechanical power of the cycloergormeter, in watts

Usage Notes

This dataset has been used to carry out a research study on heart rate variability during exercise [14]. R code used to conduct the study is available in a public repository, allowing the analysis to be reproduced [15]. The dataset may support other studies on heart rate variability, especially in athletes during graded exercise testing.

Users of the dataset should be aware that the subjects included in this study are: i) athletes who focus on a range of sport disciplines (kayak, triathlon, and fencing); and they are ii) exposed to tropical climate due to the study location (French West Indies).

Release Notes

v1.0.0: First public release.


The study was approved by the ethics committee of the Training and Research inSports Science Unit in Guadeloupe (Ministry of Higher Education and Research). All athletes completed a medical screening questionnaire (cf. "Medical_screening_questionnaire.pdf") and gave written informed consent prior to the study, which was conducted according to the Declaration of Helsinki.

Conflicts of Interest

The authors have no conflicts of interest to declare.


  1. Mongin, D.; Chabert, C.; Courvoisier, D.S.; García-Romero, J.; Alvero-Cruz, J.R. Heart Rate Recovery to Assess Fitness: Comparison of Different Calculation Methods in a Large Cross-Sectional Study. Res Sports Med 2021, 1–14, doi:10.1080/15438627.2021.1954513.
  2. Mongin, D.; Chabert, C.; Caparros, A.U.; Guzmán, J.F.V.; Hue, O.; Alvero-Cruz, J.R.; Courvoisier, D.S. The Complex Relationship between Effort and Heart Rate: A Hint from Dynamic Analysis. Physiol. Meas. 2020, 41, 105003, doi:10.1088/1361-6579/abbb6e.
  3. Sandercock, G.R.H.; Brodie, D.A. The Use of Heart Rate Variability Measures to Assess Autonomic Control during Exercise. Scand J Med Sci Sports 2006, 16, 302–313, doi:10.1111/j.1600-0838.2006.00556.x.
  4. Tulppo, M.P.; Mäkikallio, T.H.; Takala, T.E.; Seppänen, T.; Huikuri, H.V. Quantitative Beat-to-Beat Analysis of Heart Rate Dynamics during Exercise. Am J Physiol 1996, 271, H244-252, doi:10.1152/ajpheart.1996.271.1.H244.
  5. Lewis, M.J.; Kingsley, M.; Short, A.L.; Simpson, K. Rate of Reduction of Heart Rate Variability during Exercise as an Index of Physical Work Capacity. Scand J Med Sci Sports 2007, 17, 696–702, doi:10.1111/j.1600-0838.2006.00616.x.
  6. Cottin, F.; Médigue, C.; Leprêtre, P.-M.; Papelier, Y.; Koralsztein, J.-P.; Billat, V. Heart Rate Variability during Exercise Performed below and above Ventilatory Threshold. Med Sci Sports Exerc 2004, 36, 594–600, doi:10.1249/01.mss.0000121982.14718.2a.
  7. Arai, Y.; Saul, J.P.; Albrecht, P.; Hartley, L.H.; Lilly, L.S.; Cohen, R.J.; Colucci, W.S. Modulation of Cardiac Autonomic Activity during and Immediately after Exercise. Am J Physiol 1989, 256, H132-141, doi:10.1152/ajpheart.1989.256.1.H132.
  8. Boettger, S.; Puta, C.; Yeragani, V.K.; Donath, L.; Müller, H.-J.; Gabriel, H.H.W.; Bär, K.-J. Heart Rate Variability, QT Variability, and Electrodermal Activity during Exercise. Med Sci Sports Exerc 2010, 42, 443–448, doi:10.1249/MSS.0b013e3181b64db1.
  9. Karapetian, G.K.; Engels, H.J.; Gretebeck, K.A.; Gretebeck, R.J. Effect of Caffeine on LT, VT and HRVT. Int J Sports Med 2012, 33, 507–513, doi:10.1055/s-0032-1301904.
  10. Mongin, D.; Chabert, C.; Uribe Caparros, A.; Collado, A.; Hermand, E.; Hue, O.; Alvero Cruz, J.R.; Courvoisier, D.S. Validity of Dynamical Analysis to Characterize Heart Rate and Oxygen Consumption during Effort Tests. Sci Rep 2020, 10, 12420, doi:10.1038/s41598-020-69218-1.
  11. Gronwald, T.; Hoos, O.; Ludyga, S.; Hottenrott, K. Non-Linear Dynamics of Heart Rate Variability during Incremental Cycling Exercise. Research in Sports Medicine 2019, 27, 88–98, doi:10.1080/15438627.2018.1502182.
  12. Perrone, M.A.; Volterrani, M.; Manzi, V.; Barchiesi, F.; Iellamo, F. Heart Rate Variability Modifications in Response to Different Types of Exercise Training in Athletes. J Sports Med Phys Fitness 2021, 61, 1411–1415, doi:10.23736/s0022-4707.21.12480-6.
  13. Wasserman, K.; Whipp, B.J.; Koyl, S.N.; Beaver, W.L. Anaerobic Threshold and Respiratory Gas Exchange during Exercise. J Appl Physiol 1973, 35, 236–243, doi:10.1152/jappl.1973.35.2.236
  14. Mongin, D.; Chabert, C.; Extremera, M.G.; Hue, O.; Courvoisier, D.S.; Carpena, P.; Galvan, P.A.B. Decrease of Heart Rate Variability during Exercise: An Index of Cardiorespiratory Fitness 2021, 2021.09.23.21263943.
  15. GitLab repository containing code for the paper on Decrease of heart rate variability during exercise: an index of cardiorespiratory fitness.


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