What is the ability of the cardiovascular?

Cardiorespiratory fitness is defined as a component of physiologic fitness that relates to the ability of the circulatory and respiratory systems to supply oxygen during sustained physical activity.

From: The Sports Medicine Resource Manual, 2008

Reviewed by Dan Brennan, MD on November 27, 2021

Cardiovascular endurance is a measure of how well you can do exercises that involve your whole body at moderate to high intensity for an extended time. Improving your cardiovascular endurance can make it easier for you to carry out your daily tasks. It can also lessen your risk of diseases such as diabetes, heart disease, and stroke. 

You can raise your level of cardiovascular endurance by doing exercises that increase your heart and breathing rates, or aerobic exercise. According to many experts, aerobic exercise is the most important part of physical fitness. To achieve cardiovascular endurance, you should exercise aerobically 30 minutes per day, 3 to 7 days per week.

When you do aerobic exercise, your body responds in the following ways:

  • Your heart pumps more efficiently.
  • Your lungs work better.
  • Your blood volume and delivery system are improved.
  • Your resting heart rate is lowered.
  • Your heart pumps out more blood.
  • Your muscles get stronger.
  • Your ligaments, tendons, and bones get stronger.
  • Your body is more able to use fat as an energy source. 

As you increase your cardiovascular endurance through aerobic exercise, you'll get stronger and fitter. You'll also reap the following benefits: 

Lowered risk of disease. Aerobic exercise reduces your risk of developing many diseases, including: 

Better strength and stamina. Your heart and lungs will get stronger as you exercise. You'll also gain bone and muscle fitness. You may feel tired when you first start exercising, but you'll develop stamina over time.

A more active immune system. You're less likely to catch viral illnesses such as colds and flu if you're a regular exerciser. Your immune system is activated by aerobic exercise. 

Managed weight. Aerobic exercise, together with a healthy diet, can help you lose weight and keep it off.  

Stronger bones. Weight-bearing aerobic exercise, such as walking, can help reduce your risk of developing osteoporosis. 

Better mood. Aerobic exercise may help you relieve tension and anxiety. It may also help you relax and sleep better. For some people, exercise is as effective as antidepressants at lessening depression.  

Staying independent longer. Exercising makes you stronger and can help you stay mobile longer. It can also lower your risk of falls and injuries. Fitness will improve the quality of your life as you age.   

Fewer unhealthy behaviors. Time spent exercising is better than that spent smoking, drinking alcohol, or gambling. Exercise may also help regulate overeating. 

Almost everyone can benefit from physical exercise. However, not every exercise is right for everyone. Talk to your doctor about the best type of exercise for you. 

Start simple. If you're new to exercise, you may benefit from as little as 15 minutes of exercise. Work your way up to 30 minutes per day at least 3 days per week. Doing this should result in a measurable improvement in your cardiovascular endurance in eight to 12 weeks.  

Pick something you enjoy. Aerobic exercise is any nonstop activity that uses your large muscles and makes your heart and lungs work harder. You can pick one you enjoy or rotate through many different ones. Some examples include:

Don't overdo it. Doing the same type of exercise more than 5 days per week puts you at a higher risk for injuries. If you want to work out more than 5 days per week, change it up with exercises that use different muscle groups. Do some low- and then some high-impact activities to avoid too much stress on your joints and muscles.

Gradually work up. You should aim to push yourself slightly more than your normal movement level. Bump up your speed or distance no more than 10% to 20% each week. You should feel challenged, but not completely exhausted. For every 10 minutes you exercise, add 1 or 2 minutes weekly. 

Warm up, cool down, and stretch. Start by working at a low level for 5 to 10 minutes to warm up. Then gradually build up how hard you work until you reach your limit.

After you finish working at full intensity, slow down for 5 to 10 minutes before you stop. Stretch at this point, since your muscles will be warmed up.  

© 2021 WebMD, LLC. All rights reserved. View privacy policy and trust info

1. World Health Organization Top Ten Causes of Death. 24 May 2018. [(accessed on 19 May 2019)]; Available online: https://www.who.int/en/news-room/fact-sheets/detail/the-top-10-causes-of-death

2. Clausen J.S., Marott J.L., Holtermann A., Gyntelberg F., Jensen M.T. Midlife Cardiorespiratory Fitness and the Long-Term Risk of Mortality 46 Years of Follow-Up. J. Am. Coll. Cardiol. 2018;72:987–995. doi: 10.1016/j.jacc.2018.06.045. [PubMed] [CrossRef] [Google Scholar]

3. Melissade C.S.V., Leonessa B., Alice E.L., Guilherme V., Adriana C.A.G. Effect of physical exercise on the cardiorespiratory fitness of men-A systematic review and meta-analysis. Natl. Libr. Med. 2018;115:23–30. [PubMed] [Google Scholar]

4. Aertssen W., Bonney E., Ferguson G., Smits-Engelsman B. Subtyping children with developmental coordination disorder based on physical fitness outcomes. Hum. Mov. Sci. 2018;60:87–97. doi: 10.1016/j.humov.2018.05.012. [PubMed] [CrossRef] [Google Scholar]

5. Ricardo B.S., Stephan G.A.S., João P.N.F., Amanda S.M., Vanessa F.S.P., Lila M.O., Ronaldo V.T.S., Danielle A.C. Interdisciplinary therapy improves cardiorespiratory fitness and inflammatory markers in obese adult women. Obes. Med. 2016;2:1–7. [Google Scholar]

6. Chiu C.H. Application of Back-propagation Neural Network to Categorization of Physical Fitness Levels of Taiwanese Females. J. Med. Biol. Eng. 2010;31:31–35. doi: 10.5405/jmbe.695. [CrossRef] [Google Scholar]

7. Kiryu T., Sasaki I., Shibai K., Tanaka K. Providing appropriate exercise levels for the elderly. IEEE Eng. Med. Biol. Mag. 2001;20:116–124. doi: 10.1109/51.982283. [PubMed] [CrossRef] [Google Scholar]

8. So W.Y., Choi D.H. Differences in Physical Fitness and Cardiovascular Function Depend on BMI in Korean Men. J. Sports Sci. Med. 2010;9:239–244. [PMC free article] [PubMed] [Google Scholar]

9. Nawarycz T., Pytel K., Ostrowska-Nawarycz L. Evaluation of Health-Related Fitness Using Fuzzy Inference Elements; Proceedings of the International Conference on Artificial Intelligence and Soft Computing (ICAISC 2012); Zakopane, Poland. 29 April–3 May 2019; pp. 301–309. [Google Scholar]

10. ElSamahy E., Genedy A., Abbass M.A., Gaddallah M. A computer-based system for safe physical fitness evaluation; Proceedings of the 4th International Conference on Biomedical Engineering and Informatics (BMEI 2011); Shanghai, China. 15–17 October 2011; pp. 1443–1447. [Google Scholar]

11. Yu R., Yau F., Ho S.C., Woo J. Associations of cardiorespiratory fitness, physical activity, and obesity with metabolic syndrome in Hong Kong Chinese midlife women. BMC Public Health. 2013;13:614–623. doi: 10.1186/1471-2458-13-614. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

12. Pate R.R., Wang C.-Y., Dowda M., Farrell S.W., O’Neill J.R. Cardiorespiratory fitness levels among US youth 12 to 19 years of age. Arch. Pediatr. Adolesc. Med. 2006;160:1005–1012. doi: 10.1001/archpedi.160.10.1005. [PubMed] [CrossRef] [Google Scholar]

13. Deak G., Miron R., Astilean A., Domuta C. Fuzzy based method for assessing the training level of nonathletes and athletes; Proceedings of the International Conference on Automation Quality and Testing Robotics; Cluj-Napoca, Romania. 28–30 May 2010; pp. 1–5. [Google Scholar]

14. Patrascu A., Patrascu M., Hantiu I. Nonlinear fuzzy control of human heart rate during aerobic endurance training with respect to significant model variations; Proceedings of the 18th International Conference System Theory, Control and Computing (ICSTCC); Sinaia, Romania. 17–19 October 2014; pp. 311–316. [Google Scholar]

15. Cheng T.M., Savkin A.V., Celler B.G., Su S.W., Wang L. Nonlinear Modelling and Control of Human Heart Rate Response During Exercise With Various Work Load Intensities. IEEE Trans. Biomed. Eng. 2008;55:2499–2508. doi: 10.1109/TBME.2008.2001131. [PubMed] [CrossRef] [Google Scholar]

16. Su S.W., Chen W., Liu D., Fang Y., Kuang W., Yu X., Guo T., Celler B.G., Nguyen H.T. Dynamic modelling of heart rate response under different exercise intensity. Open Med. Inform. J. 2010;4:81–85. doi: 10.2174/1874431101004010081. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

17. Brouha L., Graybiel A., Heath C.W. The Step Test: A Simple Method for Measuring Physical Fitness for Hard Muscular Work in Adult Man. Res. Q. Am. Assoc. Health Phys. Educ. Recreat. 1943;14:31–37. [Google Scholar]

18. Brouha L., Fradd N.W., Savage B.M. Studies in Physical Efficiency of College Students. Res. Q. Am. Assoc. Health Phys. Educ. Recreat. 1944;15:211–224. doi: 10.1080/10671188.1944.10624822. [CrossRef] [Google Scholar]

19. Lee H.T., Roh H.L., Kim Y.S. Cardiorespiratory endurance evaluation using heart rate analysis during ski simulator exercise and the Harvard step test in elementary school students. J. Phys. Ther. Sci. 2016;28:641–645. doi: 10.1589/jpts.28.641. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

20. Lo K.-Y., Wu M.-C., Tung S.-C., Hsieh C.C., Yao H.-H., Ho C.-C. Association of School Environment and After-School Physical Activity with Health-Related Physical Fitness among Junior High School Students in Taiwan. Int. J. Environ. Res. Public Health. 2017;14:83. doi: 10.3390/ijerph14010083. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

21. Geoffrey D., Isabelle T., George A.M. Resting heart rate: A physiological predicator of lie detection ability. Physiol. Behav. 2018;186:10–15. [PubMed] [Google Scholar]

22. Bellenger C.R., Thomson R.L., Howe P.R., Karavirta L., Buckley J.D. Monitoring athletic training status using the maximal rate of heart rate increase. J. Sci. Med. Sport. 2016;19:590–595. doi: 10.1016/j.jsams.2015.07.006. [PubMed] [CrossRef] [Google Scholar]

23. Aladin A.I., Whelton S.P., Mallah M.H., Blaha M.J., Keteyian S.J., Juraschek S.P., Rubin J., Brawner C.A., Michos E.D. Relation of resting heart rate to risk for all-cause mortality by gender after considering exercise capacity (the Henry Ford exercise testing project) Am. J. Cardiol. 2014;114:1701–1706. doi: 10.1016/j.amjcard.2014.08.042. [PubMed] [CrossRef] [Google Scholar]

24. Dimopoulos S., Manetos C., Panagopoulou N., Karatzanos L., Nanas S. The Prognostic Role of Heart Rate Recovery after Exercise in Health and Disease. Austin J. Cardiovasc. Dis. Atheroscler. 2015;2:1014. [Google Scholar]

25. Koutlianos N., Dimitros E., Metaxas T., Deligiannis A.S., Kouidi E. Indirect estimation of VO2max in athletes by ACSM’s equation: Valid or not? Hippokratia. 2013;17:136–140. [PMC free article] [PubMed] [Google Scholar]

26. Smolander J., Juuti T., Kinnunen M.L., Laine K., Louhevaara V., Männikkö K., Rusko H. A new heart rate variability-based method for the estimation of oxygen consumption without individual laboratory calibration: Application example on postal workers. Appl. Ergon. 2008;39:325–331. doi: 10.1016/j.apergo.2007.09.001. [PubMed] [CrossRef] [Google Scholar]

27. Scribbans T.D., Vecsey S., Hankinson P.B., Foster W.S., Gurd B.J. The Effect of Training Intensity on VO2max in Young Healthy Adults: A Meta-Regression and Meta-Analysis. Int. J. Exerc. Sci. 2016;9:230–247. [PMC free article] [PubMed] [Google Scholar]

28. León-Ariza H.H., Botero-Rosas D.A., Zea-Robles A.C. Heart rate variability and body composition as VO2max determinants. Rev. Bras. Med. Esporte. 2017;23:317–321. doi: 10.1590/1517-869220172304152157. [CrossRef] [Google Scholar]

29. Cooper K.H. A means of assessing maximal oxygen intake. J. Am. Med. Assoc. 1968;203:135–138. doi: 10.1001/jama.1968.03140030033008. [PubMed] [CrossRef] [Google Scholar]

30. Saalasti S. Ph.D Thesis. University of Jyväskylän; Jyväskylän, Finland: 2003. Neural Networks for Heart Rate Time Series Analysis. [Google Scholar]

31. Haff G.G., Triplett N.T. Essential of Strength Training and Conditioning. 4th ed. National Strength and Conditioning Association (U.S.); Colorado Springs, CO, USA: Human Kinetics; Champaign, IL, USA: 2016. [Google Scholar]

32. Sports Administration of Ministry of Education of Taiwan Physical Fitness Guide: Cardiorespiratory Endurance. [(accessed on 26 May 2019)]; Available online: https://www.fitness.org.tw/direct02.php

33. Kent L., O’Neill B., Davidson G., Nevill A., Elborn J.S., Bradley J.M. Validity and reliability of cardiorespiratory measurements recorded by the LifeShirt during exercise tests. Respir. Physiol. Neurobiol. 2009;1167:162–167. doi: 10.1016/j.resp.2009.03.013. [PubMed] [CrossRef] [Google Scholar]

34. Cheung C.C., Krahn A.D., Andrade J.G. The emerging role of wearable technologies in detection of arrhythmia. Can. J. Cardiol. 2018;34:1083–1087. doi: 10.1016/j.cjca.2018.05.003. [PubMed] [CrossRef] [Google Scholar]

35. Pevnick J.M., Birkeland K., Zimmer R., Elad Y., Kedan I. Wearable technology for cardiology: An update and framework for the future. Trends Cardiovasc. Med. 2018;28:144–150. doi: 10.1016/j.tcm.2017.08.003. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

36. Ponce P., Gutierrez A.M., Rodriguez J. New Applications of Artificial Intelligence. Intech; London, UK: 2016. [Google Scholar]

37. Gegov A. Complexity Management in Fuzzy Systems. Springer; Berlin, Germany: 2007. [Google Scholar]

38. Zadeh L.A. Fuzzy Sets. Inf. Control. 1965;8:338–353. doi: 10.1016/S0019-9958(65)90241-X. [CrossRef] [Google Scholar]

39. Zadeh L.A. Outline of a New Approach to the Analysis of Complex Systems and Decision Processes. IEEE Trans. Syst. Man Cybern. 1973;SMC-3:28–44. doi: 10.1109/TSMC.1973.5408575. [CrossRef] [Google Scholar]

40. Cheng J.C., Su T.J., Cao Y.M., Pan C.Y. The Temperature Prediction System of Automobile Engine Based on Fuzzy Algorithm; Proceedings of the 2017 International Conference on Applied System Innovation (ICASI); Sapporo, Japan. 13–17 May 2017; pp. 1201–1204. [Google Scholar]

41. Sadollah A. Introductory Chapter: Which Membership Function is Appropriate in Fuzzy System? IntechOpen; London, UK: 2018. [Google Scholar]

42. Foundations of Fuzzy Logic—MATLAB & Simulink. [(accessed on 29 June 2019)]; Available online: https://www.mathworks.com/help/fuzzy/foundations-of-fuzzy-logic.html

43. Hartono H., Simanihuruk T. Optimization Model of Fuzzy Rule Based Expert System Using Max-Min Composition and Schema Mapping Translation. Int. Ser. Interdiscip. Sci. Technol. 2017;2:31–35. doi: 10.23960/ins.v2i1.30. [CrossRef] [Google Scholar]

44. Magdalena L. Fuzzy Rule-Based Systems. In: Kacprzyk J., Pedrycz W., editors. Springer Handbook of Computational Intelligence. Springer; Berlin/Heidelberg, Germany: 2015. [Google Scholar]

45. Virant J. Design Considerations of Time in Fuzzy Systems. Kluwer Academic Publishers; Dordrecht, The Netherland: 2000. [Google Scholar]

46. Arduino Arduino Nano. [(accessed on 26 May 2019)]; Available online: https://www.arduino.cc/en/Main/ArduinoBoardNano

47. Maharjan P., Toyabur R.M., Park J.Y. A human locomotion inspired hybrid nanogenerator for wrist-wearable electronic device and sensor applications. Nano Energy. 2018;46:383–395. doi: 10.1016/j.nanoen.2018.02.033. [CrossRef] [Google Scholar]

48. Mi Fit. [(accessed on 26 May 2019)]; Available online: https://play.google.com/store/apps/details?id=com.xiaomi.hm.health&hl=En

49. Google Fit. [(accessed on 26 May 2019)]; Available online: https://play.google.com/store/apps/details?id=com.google.android.apps.fitness&hl=En

50. Nike Run Club. [(accessed on 26 May 2019)]; Available online: https://play.google.com/store/apps/details?id=com.nike.plusgps&hl=En

51. Run on Earth. [(accessed on 26 May 2019)]; Available online: https://play.google.com/store/apps/details?id=com.pafers.runonearth3&hl=En

52. Cheng J.C., Su T.J., Lin H.Y., Wei C.W. The fatigue analysis for early warning system based on fuzzy algorithm; Proceedings of the 2017 International Conference on Applied System Innovation (ICASI); Sapporo, Japan. 13–17 May 2017; pp. 1661–1664. [Google Scholar]