Validity of detrended fluctuation analysis of heart rate variability to determine intensity thresholds in elite cyclists
Background: The evaluation of performance in endurance athletes and the subsequent individualisation of training is based on the determination of individual physiological thresholds during incremental tests. Gas exchange or blood lactate analysis are usually implemented for this purpose, but these m...
- Autores:
-
Mateo March, Manuel
MOYA-RAMÓN, MANUEL
Javaloyes, Alejandro
Sánchez Muñoz, Cristóbal
Clemente-Suárez, Vicente Javier
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2022
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/9167
- Acceso en línea:
- https://hdl.handle.net/11323/9167
https://doi.org/10.1080/17461391.2022.2047228
https://repositorio.cuc.edu.co/
- Palabra clave:
- Thresholds
Heart rate variability
Elite cyclists
Nonlinear analysis
- Rights
- embargoedAccess
- License
- © 2022 European College of Sport Science
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| dc.title.eng.fl_str_mv |
Validity of detrended fluctuation analysis of heart rate variability to determine intensity thresholds in elite cyclists |
| title |
Validity of detrended fluctuation analysis of heart rate variability to determine intensity thresholds in elite cyclists |
| spellingShingle |
Validity of detrended fluctuation analysis of heart rate variability to determine intensity thresholds in elite cyclists Thresholds Heart rate variability Elite cyclists Nonlinear analysis |
| title_short |
Validity of detrended fluctuation analysis of heart rate variability to determine intensity thresholds in elite cyclists |
| title_full |
Validity of detrended fluctuation analysis of heart rate variability to determine intensity thresholds in elite cyclists |
| title_fullStr |
Validity of detrended fluctuation analysis of heart rate variability to determine intensity thresholds in elite cyclists |
| title_full_unstemmed |
Validity of detrended fluctuation analysis of heart rate variability to determine intensity thresholds in elite cyclists |
| title_sort |
Validity of detrended fluctuation analysis of heart rate variability to determine intensity thresholds in elite cyclists |
| dc.creator.fl_str_mv |
Mateo March, Manuel MOYA-RAMÓN, MANUEL Javaloyes, Alejandro Sánchez Muñoz, Cristóbal Clemente-Suárez, Vicente Javier |
| dc.contributor.author.spa.fl_str_mv |
Mateo March, Manuel MOYA-RAMÓN, MANUEL Javaloyes, Alejandro Sánchez Muñoz, Cristóbal Clemente-Suárez, Vicente Javier |
| dc.subject.proposal.eng.fl_str_mv |
Thresholds Heart rate variability Elite cyclists Nonlinear analysis |
| topic |
Thresholds Heart rate variability Elite cyclists Nonlinear analysis |
| description |
Background: The evaluation of performance in endurance athletes and the subsequent individualisation of training is based on the determination of individual physiological thresholds during incremental tests. Gas exchange or blood lactate analysis are usually implemented for this purpose, but these methodologies are expensive and invasive. The short-term scaling exponent alpha 1 of detrended Fluctuation Analysis (DFA-α1) of the Heart Rate Variability (HRV) has been proposed as a non-invasive methodology to detect intensity thresholds. Purpose: The aim of this study is to analyse the validity of DFA-α1 HRV analysis to determine the individual training thresholds in elite cyclists and to compare them against the lactate thresholds. Methodology: 38 male elite cyclists performed a graded exercise test to determine their individual thresholds. HRV and blood lactate were monitored during the test. The first (LT1 and DFA-α1-0.75, for lactate and HRV, respectively) and second (LT2 and DFA-α1-0.5, for lactate and HRV, respectively) training intensity thresholds were calculated. Then, these points were matched to their respective power output (PO) and heart rate (HR). Results: There were no significant differences (p > 0.05) between the DFA-α1-0.75 and LT1 with significant positive correlations in PO (r = 0.85) and HR (r = 0.66). The DFA-α1-0.5 was different against LT2 in PO (p = 0.04) and HR (p = 0.02), but it showed significant positive correlation in PO (r = 0.93) and HR (r = 0.71). Conclusions: The DFA1-a-0.75 can be used to estimate LT1 non-invasively in elite cyclists. Further research should explore the validity of DFA-α1-0.5. |
| publishDate |
2022 |
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2022-05-16T13:36:07Z |
| dc.date.available.none.fl_str_mv |
2022-05-16T13:36:07Z 2023-03-27 |
| dc.date.issued.none.fl_str_mv |
2022-03-27 |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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http://purl.org/coar/resource_type/c_6501 |
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Text |
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info:eu-repo/semantics/article |
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info:eu-repo/semantics/acceptedVersion |
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1746-1391 |
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https://hdl.handle.net/11323/9167 |
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https://doi.org/10.1080/17461391.2022.2047228 |
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10.1080/17461391.2022.2047228 |
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1536-7290 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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1746-1391 10.1080/17461391.2022.2047228 1536-7290 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
| url |
https://hdl.handle.net/11323/9167 https://doi.org/10.1080/17461391.2022.2047228 https://repositorio.cuc.edu.co/ |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.ispartofjournal.spa.fl_str_mv |
European Journal of Sport Science |
| dc.relation.references.spa.fl_str_mv |
Aguilera, J. F. T., Elias, V. F., & Clemente-Suárez, V. J. (2021). Autonomic and cortical response of soldiers in different combat scenarios. BMJ Military Health, 167, 172–176. Alcantara, J., Plaza-Florido, A., Amaro-Gahete, F. J., Acosta, F. M., Migueles, J. H., Molina-Garcia, P., … Martinez-Tellez, B. (2020). Impact of Using Different Levels of Threshold-Based Artefact Correction on the Quantification of Heart Rate Variability in Three Independent Human Cohorts. Journal of Clinical Medicine, 9. Baldari, C., Bonavolontà, V., Emerenziani, G. P., Gallotta, M. C., Silva, A. J., & Guidetti, L. (2009). Accuracy, reliability, linearity of Accutrend and Lactate Pro versus EBIO plus analyzer. European Journal of Applied Physiology, 107, 105–111. Burnley, M., & Jones, A. M. (2007). Oxygen uptake kinetics as a determinant of sports performance. European Journal of Sport Science, 7, 63–79. Caldwell, A. (2021). SimplyAgree jamovi module: Flexible and Reliable Agreement and Reliability Analyses. Casadei, B., Cochrane, S., Johnsoton, J., Conway, J., & Sleight, P. (1995). Pitfalls in the interpretation of spectral analysis of the heart rate variability during exercise in humans. Acta Physiologica Scandinavica, 153, 125–131. Casado, A., Hanley, B., Santos-Concejero, J., & Ruiz-Pérez, L. M. (2021). World-Class Long-Distance Running Performances Are Best Predicted by Volume of Easy Runs and Deliberate Practice of Short-Interval and Tempo Runs. Journal of Strength and Conditioning Research, 35, 2525–2531. Chen, Z., Ivanov, P. C., Hu, K., & Stanley, H. E. (2002). Effect of nonstationarities on detrended fluctuation analysis. Physical Review E, 65, 041107. Cisternas, N. S. (2019). ACSM Guidelines for Exercise Testing and Prescription 10th, https://www.academia.edu/36843773/ACSM_Guidelines_for_Exercise_Testing_and_Prescription_10th (accessed 2 October 2019). Clemente-Suárez, V. J. (2018). Periodized training achieves better autonomic modulation and aerobic performance than non-periodized training. Journal of Sports Medicine and Physical Fitness, 58, 1559–1564. Clemente-Suárez, V. J., Fernandes, R. J., Arroyo-Toledo, J. J., Figueiredo, P., González-Ravé, J. M., & Vilas-Boas, J. P. (2015). Autonomic adaptation after traditional and reverse swimming training periodizations. Acta Physiologica Hungarica, 102, 105–113. Cohen, J., & Maydeu-Olivares, A. (1992). A Power Primer. Psychological Bulletin, 112(1), 155–159. Echeverrıa, J. C., Woolfson, M. S., Crowe, J. A., Hayes-Gill, B. R., Croaker, G. D. H., & Vyas, H. (2003). Interpretation of heart rate variability via detrended fluctuation analysis and alphabeta filter. Chaos, 13, 467–475. Gronwald, T., Berk, S., Altini, M., Mourot, L., Hoos, O., & Rogers, B. (2021). Real-Time Estimation of Aerobic Threshold and Exercise Intensity Distribution Using Fractal Correlation Properties of Heart Rate Variability: A Single-Case Field Application in a Former Olympic Triathlete. Front Sport Act Living, 0, 148. Gronwald, T., & Hoos, O. (2020). Correlation properties of heart rate variability during endurance exercise: A systematic review. Annals of Noninvasive Electrocardiology, 25, e12697. Gronwald, T., Hoos, O., & Hottenrott, K. (2019). Effects of a Short-Term Cycling Interval Session and Active Recovery on Non-Linear Dynamics of Cardiac Autonomic Activity in Endurance Trained Cyclists. Journal of Clinical Medicine, 8, 194. . Hautala, A. J., Mäkikallio, T. H., Seppänen, T., Huikuri, H. V., & Tulppo, M. P. (2003). Short-term correlation properties of R-R interval dynamics at different exercise intensity levels. Clinical Physiology and Functional Imaging, 23, 215–223. Heart rate variability. (1996). Standards of measurement, physiological interpretation, and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. European Heart Journal, 17, 354–381. Hopkins, W., Marshall, S., Batterham, A., & Hanin, J. (2009). Progressive statistics for studies in sports medicine and exercise science. Medicine & Science in Sports & Exercise, 41, 3. Ivanov, P. C., Nunes Amaral, L. A., Goldberger, A. L., Havlin, S., Rosenblum, M. G., Stanley, H. E., & Struzik, Z. R. (2001). From 1/f noise to multifractal cascades in heartbeat dynamics. Chaos, 11, 641–652. Jamnick, N. A., Botella, J., Pyne, D. B., & Bishop, D. J. (2018). Manipulating graded exercise test variables affects the validity of the lactate threshold and [… formula …]. PLoS One, 13. Karemaker, J. M., & Lie, K. I. (2000). Heart rate variability: a telltale of health or disease. European Heart Journal, 21, 435–437. Lexell, J. E., & Downham, D. Y. (2005). How to assess the reliability of measurements in rehabilitation. American Journal of Physical Medicine & Rehabilitation, 84(9), 719–723. . Lillo-Bevia, J. R., & Pallarés, J. G. (2018). Validity and Reliability of the Cycleops Hammer Cycle Ergometer. International Journal of Sports Physiology and Performance, 13, 853–859. Lindsay, F. H., Hawley, J. A., Myburgh, K. H., Schomer, H. H., Noakes, T. D., & Dennis, S. C. (1996). Improved athletic performance in highly trained cyclists after interval training. Medicine & Science in Sports & Exercise, 28, 1427–1434. Lipponen, J. A., & Tarvainen, M. P. (2019). A robust algorithm for heart rate variability time series artefact correction using novel beat classification. Journal of medical engineering & technology, 43, 173–181. Lucía, A., Hoyos, J., Santalla, A., Earnest, C., & Chicharro, J. L. (2003). Tour de France versus Vuelta a Espana: which is harder? Medicine & Science in Sports & Exercise, 35, 872–878. Malpas, S. C. (2002). Neural influences on cardiovascular variability: possibilities and pitfalls. American Journal of Physiology-Heart and Circulatory Physiology, 282, 6–20. . McKay, A. K., Stellingwerff, T., Smith, E. S., Martin, D. T., Mujika, I., Goosey-Tolfrey, V. L., … Burke, L. M. (2022). Defining Training and Performance Caliber: A Participant Classification Framework. International Journal of Sports Physiology and Performance, 17, 317–331. Mendoza-Castejón, D., & Clemente-Suárez, V. J. (2020). Autonomic Profile, Physical Activity, Body Mass Index and Academic Performance of School Students. Sustainability, 12, 6718. Milanović, Z., Sporiš, G., & Weston, M. (2015). Effectiveness of High-Intensity Interval Training (HIT) and Continuous Endurance Training for VO2max Improvements: A Systematic Review and Meta-Analysis of Controlled Trials. Sports Medicine, 45, 1469–1481. Naranjo-Orellana, J., Nieto-Jimenez, C., & Ruso-Alvarez, J. F. (2020). Non-linear heart rate dynamics during and after three controlled exercise intensities in healthy men. Physiology International, 107, 501–512. https://www.frontiersin.org/articles/ Niskanen, J. P., Tarvainen, M. P., Ranta-Aho, P. O., & Karjalainen, P. A. (2004). Software for advanced HRV analysis. Computer Methods and Programs in Biomedicine, 76(1), 73–81. . Noeman, S. A., Hamooda, H. E., & Baalash, A. A. (2011). Noninvasive method to estimate anaerobic threshold in individuals with type 2 diabetes. Diabetology & Metabolic Syndrome, 3. Pallarés, J. G., Morán-Navarro, R., Ortega, J. F., Fernández-Elías, V. E., & Mora-Rodriguez, R. (2016). Validity and Reliability of Ventilatory and Blood Lactate Thresholds in Well-Trained Cyclists. PLoS One, 11, e0163389. Peltola, M. A. (2012). Role of editing of R-R intervals in the analysis of heart rate variability. Frontiers in Physiology, 3. . Peng, C. K., Havlin, S., Stanley, H. E., & Goldberger, A. L. (1995). Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos, 5, 82–87. Perrotta, A. S., Jeklin, A. T., Hives, B. A., Meanwell, L. E., & Warburton, D. E. (2017). Validity of the Elite HRV Smartphone Application for Examining Heart Rate Variability in a Field-Based Setting. Journal of Strength and Conditioning Research, 31, 2296–2302. Platisa, M. M., & Gal, V. (2008). Correlation properties of heartbeat dynamics. European Biophysics Journal, 37, 1247–1252. Poole, D. C., Rossiter, H. B., Brooks, G. A., & Gladden, L. B. (2021). The anaerobic threshold: 50+ years of controversy. The Journal of Physiology, 599, 737–767. Rogers, B., Giles, D., Draper, N., Hoos, O., & Gronwald, T. (2021a). A New Detection Method Defining the Aerobic Threshold for Endurance Exercise and Training Prescription Based on Fractal Correlation Properties of Heart Rate Variability. Frontiers in Physiology, 0, 1806. Rogers, B., Giles, D., Draper, N., Mourot, L., & Gronwald, T. (2021b). Detection of the Anaerobic Threshold in Endurance Sports: Validation of a New Method Using Correlation Properties of Heart Rate Variability. Journal of Functional Morphology and Kinesiology, 6, 38. Rogers, B., Mourot, L., & Gronwald, T. (2021c). Ventilatory Threshold Identification In A Cardiac Disease Population Based On Fractal Correlation Properties Of HRV. Medicine & Science in Sports & Exercise, 53, 438–438. Samuel, M., Lindsay, B., & Muniz-Pumares, D. (2021). Biological and methodological factors affecting [Formula: see text] response variability to endurance training and the influence of exercise intensity prescription. Experimental Physiology, 106, 1410–1424. Sánchez-Conde, P., & Clemente-Suárez, V. J. (2021). Autonomic Stress Response of Nurse Students in an Objective Structured Clinical Examination (OSCE). Sustainability, 13, 5803. San-Millán, I., & Brooks, G. A. (2018). Assessment of Metabolic Flexibility by Means of Measuring Blood Lactate, Fat, and Carbohydrate Oxidation Responses to Exercise in Professional Endurance Athletes and Less-Fit Individuals. Sports Medicine, 48, 467–479. Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin, 86(2), 420–428. . Smith, G. (2015). Simple Regression. Essent Stat Regression, Econom, 219–259. Tarvainen, M. P., Niskanen, J. P., Lipponen, J. A., Ranta-Aho, P. O., & Karjalainen, P. A. (2014). Kubios HRV - Heart rate variability analysis software. Computer Methods and Programs in Biomedicine. Tulppo, M. P., Makikallio, T. H., Takala, T. E., Seppanen, T. H. H. V., & Huikuri, H. V. (1996). Quantitative beat-to-beat analysis of heart rate dynamics during exercise. American Journal of Physiology-Heart and Circulatory Physiology, 113(11), 210–220. . Wyatt, F. B., Donaldson, A., & Brown, E. (2013). The overtraining syndrome: A meta-analytic review. Journal of Exercise Physiology Online, 16(2), 12–23. https://journals.plos.org/plosone/article?id= |
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Mateo March, ManuelMOYA-RAMÓN, MANUELJavaloyes, AlejandroSánchez Muñoz, CristóbalClemente-Suárez, Vicente Javier2022-05-16T13:36:07Z2023-03-272022-05-16T13:36:07Z2022-03-271746-1391https://hdl.handle.net/11323/9167https://doi.org/10.1080/17461391.2022.204722810.1080/17461391.2022.20472281536-7290Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Background: The evaluation of performance in endurance athletes and the subsequent individualisation of training is based on the determination of individual physiological thresholds during incremental tests. Gas exchange or blood lactate analysis are usually implemented for this purpose, but these methodologies are expensive and invasive. The short-term scaling exponent alpha 1 of detrended Fluctuation Analysis (DFA-α1) of the Heart Rate Variability (HRV) has been proposed as a non-invasive methodology to detect intensity thresholds. Purpose: The aim of this study is to analyse the validity of DFA-α1 HRV analysis to determine the individual training thresholds in elite cyclists and to compare them against the lactate thresholds. Methodology: 38 male elite cyclists performed a graded exercise test to determine their individual thresholds. HRV and blood lactate were monitored during the test. The first (LT1 and DFA-α1-0.75, for lactate and HRV, respectively) and second (LT2 and DFA-α1-0.5, for lactate and HRV, respectively) training intensity thresholds were calculated. Then, these points were matched to their respective power output (PO) and heart rate (HR). Results: There were no significant differences (p > 0.05) between the DFA-α1-0.75 and LT1 with significant positive correlations in PO (r = 0.85) and HR (r = 0.66). The DFA-α1-0.5 was different against LT2 in PO (p = 0.04) and HR (p = 0.02), but it showed significant positive correlation in PO (r = 0.93) and HR (r = 0.71). Conclusions: The DFA1-a-0.75 can be used to estimate LT1 non-invasively in elite cyclists. Further research should explore the validity of DFA-α1-0.5.9 páginasapplication/pdfengTaylor and Francis Ltd.United Kingdom© 2022 European College of Sport ScienceAtribución-NoComercial 4.0 Internacional (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/embargoedAccesshttp://purl.org/coar/access_right/c_f1cfValidity of detrended fluctuation analysis of heart rate variability to determine intensity thresholds in elite cyclistsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionhttps://www.tandfonline.com/doi/full/10.1080/17461391.2022.2047228European Journal of Sport ScienceAguilera, J. F. T., Elias, V. F., & Clemente-Suárez, V. J. (2021). Autonomic and cortical response of soldiers in different combat scenarios. BMJ Military Health, 167, 172–176.Alcantara, J., Plaza-Florido, A., Amaro-Gahete, F. J., Acosta, F. M., Migueles, J. H., Molina-Garcia, P., … Martinez-Tellez, B. (2020). Impact of Using Different Levels of Threshold-Based Artefact Correction on the Quantification of Heart Rate Variability in Three Independent Human Cohorts. Journal of Clinical Medicine, 9.Baldari, C., Bonavolontà, V., Emerenziani, G. P., Gallotta, M. C., Silva, A. J., & Guidetti, L. (2009). Accuracy, reliability, linearity of Accutrend and Lactate Pro versus EBIO plus analyzer. European Journal of Applied Physiology, 107, 105–111.Burnley, M., & Jones, A. M. (2007). Oxygen uptake kinetics as a determinant of sports performance. European Journal of Sport Science, 7, 63–79.Caldwell, A. (2021). SimplyAgree jamovi module: Flexible and Reliable Agreement and Reliability Analyses.Casadei, B., Cochrane, S., Johnsoton, J., Conway, J., & Sleight, P. (1995). Pitfalls in the interpretation of spectral analysis of the heart rate variability during exercise in humans. Acta Physiologica Scandinavica, 153, 125–131.Casado, A., Hanley, B., Santos-Concejero, J., & Ruiz-Pérez, L. M. (2021). World-Class Long-Distance Running Performances Are Best Predicted by Volume of Easy Runs and Deliberate Practice of Short-Interval and Tempo Runs. Journal of Strength and Conditioning Research, 35, 2525–2531.Chen, Z., Ivanov, P. C., Hu, K., & Stanley, H. E. (2002). Effect of nonstationarities on detrended fluctuation analysis. Physical Review E, 65, 041107.Cisternas, N. S. (2019). ACSM Guidelines for Exercise Testing and Prescription 10th, https://www.academia.edu/36843773/ACSM_Guidelines_for_Exercise_Testing_and_Prescription_10th (accessed 2 October 2019).Clemente-Suárez, V. J. (2018). Periodized training achieves better autonomic modulation and aerobic performance than non-periodized training. Journal of Sports Medicine and Physical Fitness, 58, 1559–1564.Clemente-Suárez, V. J., Fernandes, R. J., Arroyo-Toledo, J. J., Figueiredo, P., González-Ravé, J. M., & Vilas-Boas, J. P. (2015). Autonomic adaptation after traditional and reverse swimming training periodizations. Acta Physiologica Hungarica, 102, 105–113.Cohen, J., & Maydeu-Olivares, A. (1992). A Power Primer. Psychological Bulletin, 112(1), 155–159.Echeverrıa, J. C., Woolfson, M. S., Crowe, J. A., Hayes-Gill, B. R., Croaker, G. D. H., & Vyas, H. (2003). Interpretation of heart rate variability via detrended fluctuation analysis and alphabeta filter. Chaos, 13, 467–475.Gronwald, T., Berk, S., Altini, M., Mourot, L., Hoos, O., & Rogers, B. (2021). Real-Time Estimation of Aerobic Threshold and Exercise Intensity Distribution Using Fractal Correlation Properties of Heart Rate Variability: A Single-Case Field Application in a Former Olympic Triathlete. Front Sport Act Living, 0, 148.Gronwald, T., & Hoos, O. (2020). Correlation properties of heart rate variability during endurance exercise: A systematic review. Annals of Noninvasive Electrocardiology, 25, e12697.Gronwald, T., Hoos, O., & Hottenrott, K. (2019). Effects of a Short-Term Cycling Interval Session and Active Recovery on Non-Linear Dynamics of Cardiac Autonomic Activity in Endurance Trained Cyclists. Journal of Clinical Medicine, 8, 194. .Hautala, A. J., Mäkikallio, T. H., Seppänen, T., Huikuri, H. V., & Tulppo, M. P. (2003). Short-term correlation properties of R-R interval dynamics at different exercise intensity levels. Clinical Physiology and Functional Imaging, 23, 215–223.Heart rate variability. (1996). Standards of measurement, physiological interpretation, and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. European Heart Journal, 17, 354–381.Hopkins, W., Marshall, S., Batterham, A., & Hanin, J. (2009). 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Journal of Exercise Physiology Online, 16(2), 12–23. https://journals.plos.org/plosone/article?id=91ThresholdsHeart rate variabilityElite cyclistsNonlinear analysisPublicationORIGINALValidity of detrended fluctuation analysis of heart rate variability to determine intensity thresholds in elite cyclists.pdfValidity of detrended fluctuation analysis of heart rate variability to determine intensity thresholds in elite cyclists.pdfapplication/pdf2346354https://repositorio.cuc.edu.co/bitstreams/0ecc1e37-6076-48a4-899c-b4f30a5f62c2/download88a494f3072b4094699e051fcc722c31MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/a88ea491-dfb3-4bbe-bd73-2a8842628f70/downloade30e9215131d99561d40d6b0abbe9badMD52TEXTValidity of detrended fluctuation analysis of heart rate variability to determine intensity thresholds in elite cyclists.pdf.txtValidity of detrended fluctuation analysis of heart rate variability to determine intensity thresholds in elite 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