Correlation between Cardiopulmonary Indices and Running Performance in a 14.5 km Endurance Running Event
Abstract
:1. Introduction
2. Materials and Methods
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Hulteen, R.M.; Smith, J.J.; Morgan, P.J.; Barnett, L.M.; Hallal, P.C.; Colyvas, K.; Lubans, D.R. Global participation in sport and leisure-time physical activities: A systematic review and meta-analysis. Prev. Med. 2017, 95, 14–25. [Google Scholar] [CrossRef] [PubMed]
- Borgers, J.; Breedveld, K.; Tiessen-Raaphorst, A.; Thibaut, E.; Vandermeerschen, H.; Vos, S.; Scheerder, J. A study on the frequency of participation and time spent on sport in different organisational settings. Eur. Sport Manag. Q. 2016, 16, 635–654. [Google Scholar] [CrossRef]
- Anthony, D.; Rüst, C.A.; Cribari, M.; Rosemann, T.; Lepers, R.; Knechtle, B. Differences in participation and performance trends in age group half and full marathoners. Chin. J. Physiol. 2014, 57, 209–219. [Google Scholar] [CrossRef] [PubMed]
- Knechtle, B.; Nikolaidis, P.T.; Zingg, M.A.; Rosemann, T.; Rüst, C.A. Half-marathoners are younger and slower than marathoners. SpringerPlus 2016, 5, 76. [Google Scholar] [CrossRef]
- Lima, M.G.; Malta, D.C.; Monteiro, C.N.; da Silva Sousa, N.F.; Stopa, S.R.; Medina, L.D.P.B.; de Azevedo Barros, M.B. Leisure-time physical activity and sports in the Brazilian population: A social disparity analysis. PLoS ONE 2019, 14, e0225940. [Google Scholar] [CrossRef] [PubMed]
- Stamatakis, E.; Chaudhury, M. Temporal trends in adults’ sports participation patterns in England between 1997 and 2006: The health survey for England. Br. J. Sports Med. 2008, 42, 601–608. [Google Scholar] [CrossRef]
- Eime, R.M.; Harvey, J.T.; Charity, M.J.; Casey, M.M.; Van Uffelen, J.G.Z.; Payne, W.R. The contribution of sport participation to overall health enhancing physical activity levels in Australia: A population-based study. BMC Public Health 2015, 15, 806. [Google Scholar] [CrossRef]
- Wisconsin Office of Outdoor Recreation. 2018 Participation Report: The Physical Activity Council’s Annual Study Tracking Sports, Fitness and Recreation Participation in the US. Available online: https://outdoorrecreation.wi.gov/Documents/Research%20Library%20Page%20files/US%20-%20Demographics%20%26%20Participation/Physical%20Activity%20Coucil%20Participation%20Report_2018.pdf (accessed on 15 June 2022).
- Pedisic, Z.; Shrestha, N.; Kovalchik, S.; Stamatakis, E.; Liangruenrom, N.; Grgic, J.; Titze, S.; Biddle, S.J.H.; Bauman, A.E.; Oja, P. Is running associated with a lower risk of all-cause, cardiovascular and cancer mortality, and is the more the better? A systematic review and meta-analysis. Br. J. Sports Med. 2019, 54, 898–905. [Google Scholar] [CrossRef]
- Lavie, C.J.; Lee, D.C.; Sui, X.; Arena, R.; O’Keefe, J.H.; Church, T.S.; Milani, R.V.; Blair, S.N. Effects of running on chronic diseases and cardiovascular and all-cause mortality. Mayo Clin. Proc. 2015, 90, 1541–1552. [Google Scholar] [CrossRef]
- Lee, D.C.; Brellenthin, A.G.; Thompson, P.D.; Sui, X.; Lee, I.M.; Lavie, C.J. Running as a key lifestyle medicine for longevity. Prog. Cardiovasc. Dis. 2017, 60, 45–55. [Google Scholar] [CrossRef] [Green Version]
- Wang, Y.; Lee, D.C.; Brellenthin, A.G.; Eijsvogels, T.M.; Sui, X.; Church, T.S.; Lavie, C.J.; Blair, S.N. Leisure-time running reduces the risk of incident type 2 diabetes. Am. J. Med. 2019, 132, 1225–1232. [Google Scholar] [CrossRef]
- Hespanhol, J.L.C.; Pillay, J.D.; van Mechelen, W.; Verhagen, E. Meta-analyses of the effects of habitual running on indices of health in physically inactive adults. Sport Med. 2015, 45, 1455–1468. [Google Scholar] [CrossRef] [PubMed]
- Lee, D.C.; Pate, R.R.; Lavie, C.J.; Sui, X.; Church, T.S.; Blair, S.N. Leisure-time running reduces all-cause and cardiovascular mortality risk. J. Am. Coll. Cardiol. 2014, 4, 472–481. [Google Scholar] [CrossRef]
- Videbæk, S.; Bueno, A.M.; Nielsen, R.O.; Rasmussen, S. Incidence of running-related injuries per 1000 h of running in different types of runners: A systematic review and meta-analysis. Sport Med. 2015, 45, 1017–1026. [Google Scholar] [CrossRef] [PubMed]
- Kluitenberg, B.; van Middelkoop, M.; Diercks, R.; van der Worp, H. What are the differences in injury proportions between different populations of runners? A systematic review and meta-analysis. Sport Med. 2015, 45, 1143–1161. [Google Scholar] [CrossRef] [PubMed]
- Gauffin, H.; Tillander, B.; Dahlström, Ö.; Lyth, J.; Raysmith, B.; Jacobsson, J.; Timpka, T. Maintaining motivation and health among recreational runners: Panel study of factors associated with self-rated performance outcomes at competitions. J. Sci. Med. Sport 2019, 22, 1319–1323. [Google Scholar] [CrossRef]
- León-Guereño, P.; Tapia-Serrano, M.A.; Sánchez-Miguel, P.A. The relationship of recreational runners’ motivation and resilience levels to the incidence of injury: A mediation model. PLoS ONE 2020, 15, e0231628. [Google Scholar] [CrossRef]
- Linton, L.; Valentin, S. Running with injury: A study of UK novice and recreational runners and factors associated with running related injury. J. Sci. Med. Sport 2018, 21, 1221–1225. [Google Scholar] [CrossRef]
- Keogh, A.; Smyth, B.; Caulfield, B.; Lawlor, A.; Berndsen, J.; Doherty, C. Prediction equations for marathon performance: A systematic review. Int. J. Sports Physiol. Perform. 2019, 14, 1159–1169. [Google Scholar] [CrossRef]
- Gómez-Molina, J.; Ogueta-Alday, A.; Camara, J.; Stickley, C.; Rodríguez-Marroyo, J.A.; García-López, J. Predictive variables of half-marathon performance for male runners. J. Sport Sci. Med. 2017, 16, 187–194. [Google Scholar]
- Boullosa, D.; Esteve-Lanao, J.; Casado, A.; Peyré-Tartaruga, L.A.; Gomes da Rosa, R.; Del Coso, J. Factors affecting training and physical performance in recreational endurance runners. Sports 2020, 8, 35. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Esteve-Lanao, J.; Del Rosso, S.; Larumbe-Zabala, E.; Cardona, C.; Alcocer-Gamboa, A.; Boullosa, D.A. Predicting recreational runners’ marathon performance time during their training preparation. J. Strength Cond. Res. 2021, 35, 3218–3224. [Google Scholar] [CrossRef] [PubMed]
- Salinero, J.J.; Soriano, M.L.; Lara, B.; Gallo-Salazar, C.; Areces, F.; Ruiz-Vicente, D.; Abián-Vicén, J.; González-Millán, C.; Del Coso, J. Predicting race time in male amateur marathon runners. J. Sports Med. Phys. Fit. 2017, 57, 1169–1177. [Google Scholar] [CrossRef] [PubMed]
- Winter, U.J.; Gitt, A.K.; Fritsch, J.; Berge, P.G.; Pothoff, G.; Hilger, H.H. Methodologic aspects of modern, computerized ergospirometry (CPX): Ramp program, constant workload test and CO2 rebreathing method. Z. Kardiol. 1994, 83, 13–26. [Google Scholar] [PubMed]
- Wasserman, K.; Hansen, J.E.; Sue, D.Y.; Stringer, W.W.; Whipp, B.J. Principles of Exercise Testing and Interpretation: Including Pathophysiology and Clinical Applications, 4th ed.; Lippincott Williams & Wilkins: Philadelphia, PA, USA, 2004. [Google Scholar]
- Scott, B.K.; Houmard, J.A. Peak running velocity is highly related to distance running performance. Int. J. Sports Med. 1994, 15, 504–507. [Google Scholar] [CrossRef] [PubMed]
- Bragada, J.A.; Santos, P.J.; Maia, J.A.; Colaço, P.J.; Lopes, V.P.; Barbosa, T.M. Longitudinal study in 3,000 m male runners: Relationship between performance and selected physiological parameters. J. Sport Sci. Med. 2010, 9, 439–444. [Google Scholar]
- Nicholson, R.M.; Sleivert, G.G. Indices of lactate threshold and their relationship with 10-km running velocity. Med. Sci. Sports Exerc. 2001, 33, 339–342. [Google Scholar] [CrossRef]
- Billat, L.V.; Koralsztein, J.P. Significance of the velocity at VO2max and time to exhaustion at this velocity. Sport Med. 1996, 22, 90–108. [Google Scholar] [CrossRef]
- Bassett, D.R.; Howley, E.T. Limiting factors for maximum oxygen uptake and determinants of endurance performance. Med. Sci. Sports Exerc. 2000, 32, 70–84. [Google Scholar] [CrossRef]
- Conley, D.L.; Krahenbuhl, G.S. Running economy and distance running performance of highly trained athletes. Med. Sci. Sports Exerc. 1980, 12, 357–360. [Google Scholar] [CrossRef]
- Daniels, J.; Daniels, N. Running economy of elite male and elite female runners. Med. Sci. Sports Exerc. 1992, 24, 483–489. [Google Scholar] [CrossRef] [PubMed]
- Bassett, D.R.; Howley, E.T. Maximal oxygen uptake: “Classical” versus “contemporary” viewpoints. Med. Sci. Sports Exerc. 1997, 29, 591–603. [Google Scholar] [CrossRef] [PubMed]
- Grant, S.; Craig, I.; Wilson, J.; Aitchison, T. The relationship between 3 km running performance and selected physiological variables. J. Sports Sci. 1997, 15, 403–410. [Google Scholar] [CrossRef] [PubMed]
- Borgen, N.T. Running performance, VO2max, and running economy: The widespread issue of endogenous selection bias. Sport Med. 2018, 48, 1049–1058. [Google Scholar] [CrossRef] [PubMed]
- O Sullivan, I.J.; Johnson, M.I.; Hind, K.; Breen, S.; Francis, P. Are changes in running economy associated with changes in performance in runners? A systematic review and meta-analysis. J. Sports Sci. 2019, 37, 1521–1533. [Google Scholar] [CrossRef] [PubMed]
- Alvero-Cruz, J.R.; Carnero, E.A.; García, M.A.G.; Alacid, F.; Correas-Gómez, L.; Rosemann, T.; Nikolaidis, P.T.; Knechtle, B. Predictive performance models in long-distance runners: A narrative review. Int. J. Environ. Res. Public Health 2020, 17, 8289. [Google Scholar] [CrossRef]
- Lee, L.L.; Arthur, A.; Avis, M. Using self-efficacy theory to develop interventions that help older people overcome psychological barriers to physical activity: A discussion paper. Int. J. Nurs. Stud. 2008, 45, 1690–1699. [Google Scholar] [CrossRef]
- André, N.; Agbangla, N.F. Are barriers the same whether I want to start or maintain exercise? A narrative review on healthy older adults. Int. J. Environ. Res. Public Health 2020, 17, 6247. [Google Scholar] [CrossRef]
Participants | Race Performance (min) | Average Race Speed (km/h) | Age (Years) | Height (cm) | Weight (kg) | BMI b (kg/m2) | Body Fat (%) | Muscle Mass (%) |
---|---|---|---|---|---|---|---|---|
1 | 57.93 | 15.02 | 37 | 189 | 83 | 23.24 | 19 | 38.3 |
2 | 59.38 | 14.65 | 37 | 180.5 | 76 | 23.33 | 21.6 | 37 |
3 | 64.18 | 13.56 | 48 | 181 | 77 | 23.50 | 18.7 | 37.7 |
4 | 66.7 | 13.04 | 51 | 169 | 68 | 23.81 | 18.1 | 38.4 |
5 | 67.83 | 12.83 | 52 | 178 | 74 | 23.36 | 18.6 | 37.4 |
6 | 71.23 | 12.21 | 38 | 171 | 73 | 24.96 | 23 | 37.1 |
7 | 71.42 | 12.18 | 34 | 176 | 72 | 23.24 | 17.2 | 40.7 |
8 | 71.85 | 12.11 | 39 | 184 | 92 | 27.17 | 28.1 | 33.4 |
9 | 74.38 | 11.70 | 32 | 156.5 | 57 | 23.27 | 32.9 | 28.4 |
10 | 78.13 | 11.14 | 39 | 173.5 | 78 | 25.91 | 26 | 35 |
11 | 79.83 | 10.90 | 39 | 169.5 | 78 | 27.15 | 25.7 | 35.8 |
12 | 81.23 | 10.71 | 66 | 173 | 79 | 26.40 | 20.7 | 35.4 |
13 | 86.63 | 10.04 | 33 | 177 | 66 | 21.70 | 20.3 | 35.5 |
14 | 86.65 | 10.04 | 40 | 168.5 | 68 | 23.95 | 30.8 | 30.5 |
15 | 89.03 | 9.77 | 34 | 171 | 60 | 20.52 | 22.6 | 33.6 |
Mean Value | 73.8 | 12.0 | 41.3 | 174.5 | 73.4 | 24.1 | 22.9 | 35.6 |
SD a | 9.7 | 1.6 | 9.2 | 7.8 | 8.8 | 2.0 | 4.8 | 3.2 |
Median | 71.85 | 12.11 | 39 | 173.5 | 74.0 | 23.50 | 21.6 | 35.8 |
Participants | Test Time (min) | VE b (L/min) | Running Speed at VT c Point (km/h) | Maximal Running Speed | VO2max d (L) | VO2max d (mL/min/kg) | Oxygen Pulse (mL/Beat) | Maximal Heart Rate (Beat/min) |
---|---|---|---|---|---|---|---|---|
1 | 11.17 | 123 | 15.4 | 16.1 | 4.24 | 51.1 | 24 | 170 |
2 | 12.28 | 144 | 17.1 | 17.4 | 3.96 | 52.1 | 22 | 177 |
3 | 10.08 | 103 | 11.6 | 15.1 | 3.21 | 41.7 | 22 | 160 |
4 | 11.75 | 132 | 16.6 | 16.6 | 3.8 | 55.9 | 21 | 181 |
5 | 9.97 | 146 | 13.6 | 14.9 | 3.3 | 44.6 | 23 | 141 |
6 | 10.15 | 118 | 15.1 | 15.1 | 3.21 | 44.0 | 25 | 169 |
7 | 11.17 | 123 | 16.1 | 16.1 | 3.76 | 52.2 | 22 | 175 |
8 | 11.32 | 135 | 15.6 | 16.5 | 4.08 | 44.3 | 29 | 163 |
9 | 10.10 | 95 | 14.4 | 15.1 | 2.66 | 46.7 | 14 | 185 |
10 | 11.25 | 143 | 15.1 | 16.1 | 3.45 | 44.2 | 24 | 171 |
11 | 9.63 | 131 | 14.6 | 14.6 | 3.48 | 44.6 | 20 | 176 |
12 | 8.97 | 116 | 13.4 | 13.9 | 3.46 | 43.8 | 21 | 171 |
13 | 8.82 | 108 | 13.1 | 13.9 | 3.14 | 47.6 | 17 | 182 |
14 | 9.10 | 113 | 13.6 | 14.1 | 3.01 | 44.3 | 17 | 178 |
15 | 8.03 | 91 | 12.6 | 13.1 | 2.18 | 36.3 | 12 | 179 |
Mean Value | 10.3 | 121.4 | 14.5 | 15.2 | 3.4 | 46.2 | 20.9 | 171.9 |
SD a | 1.2 | 17.4 | 1.5 | 1.2 | 0.5 | 4.9 | 4.4 | 11.0 |
Median | 10.10 | 123.00 | 14.60 | 15.10 | 3.45 | 44.59 | 22.00 | 175.00 |
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Tomovic, M.; Toliopoulos, A.; Koutlianos, N.; Dalkiranis, A.; Bubanj, S.; Deligiannis, A.; Kouidi, E. Correlation between Cardiopulmonary Indices and Running Performance in a 14.5 km Endurance Running Event. Int. J. Environ. Res. Public Health 2022, 19, 12289. https://doi.org/10.3390/ijerph191912289
Tomovic M, Toliopoulos A, Koutlianos N, Dalkiranis A, Bubanj S, Deligiannis A, Kouidi E. Correlation between Cardiopulmonary Indices and Running Performance in a 14.5 km Endurance Running Event. International Journal of Environmental Research and Public Health. 2022; 19(19):12289. https://doi.org/10.3390/ijerph191912289
Chicago/Turabian StyleTomovic, Milena, Alexandros Toliopoulos, Nikolaos Koutlianos, Anastasios Dalkiranis, Sasa Bubanj, Asterios Deligiannis, and Evangelia Kouidi. 2022. "Correlation between Cardiopulmonary Indices and Running Performance in a 14.5 km Endurance Running Event" International Journal of Environmental Research and Public Health 19, no. 19: 12289. https://doi.org/10.3390/ijerph191912289