Determination of the best recovery based on muscles synergy patterns and lactic acid

Armin Hakak Moghaddam Torbati, Leila Abbasnezhad, Ehsan Tahami

Abstract

The determination of the best recovery after an anaerobic exercise is an important challenge for professional athletes. This study compared and analyzed three methods that are often used in professional teams included: 1.Cold water pool 2. Use the massager 3. Running with 40 to 50 percent of heart rate. Methods: In this work recovery the 15 minutes recovery is done immediately after doing exercise. The impact of a particular method of recovery is quantified via lactic acid in the blood after the recovery and the synergy patterns of muscle activity. In each method, Biceps femoris, rectus femoris, tibialis anterior, lateral gastrocnemius muscles were analyzed. Results showed that there were synergy patterns in two running and ice methods, because maximum errors between basis vectors in all of the subjects were 0.13 and 0.18 respectively and Standard deviation of maximum MSE errors for all subjects is 6 (MSE[1] index), whereas in massage recovery synergy has not been recognized because minimum error between basis vectors in all of the subjects was 4.29 and Standard deviation of maximum MSE error for all subjects is 4. Running has the best result in evacuating lactic acid. However, result in the ice method is similar to running.

[1] Mean square error


Keywords

Recovery; Synergies; Monark cycle ergometer; The Hals Algorithm; Lactic acid; Electromyogram signal

References

Bishop, A., Jones, E., Woods, A. (2008). Recovery from Training: A Brief Review. J Strength Cond Res.; 22(3), 1015-24. https://doi.org/10.1519/JSC.0b013e31816eb518

Cichocki, A., Zdunek, R., Amari, S.I. (2007). Hierarchical ALS Algorithms for Nonnegative Matrixand 3D Tensor Factorization. in ICA07, London, UK, September 9-12, Lecture Notes in Computer Science, 4666,169-176. https://doi.org/10.1007/978-3-540-74494-8_22

Cichocki, A., Zdunek, R., Phan, A.H., Amari, S. (2009). Nonnegative Matrix and Tensor Factorizations. John Wiley & Sons Ltd: Chichester, UK. https://doi.org/10.1002/9780470747278

D'Avella, A., Portone, A., Fernandez, L., Lacquaniti, F. (2006). Control of fast-reaching Movements by Muscle synergy combinations. J Neurosci., 26(30), 7791-810. https://doi.org/10.1523/JNEUROSCI.0830-06.2006

Draper, N., Bird, E. L., Coleman, I., & Hodgson, C. (2006). Effects of Active Recovery on Lactate Concentration, Heart Rate and RPE in Climbing. Journal of Sports Science & Medicine, 5(1), 97–105.

Elena, S., Georgeta, N., Cecilia, G. (2014). Traditional and Modern Means of Recovery in Sports: Survey on a Sample of Athletes. Social and Behavioral Sciences, 117, 498-504. https://doi.org/10.1016/j.sbspro.2014.02.252

Frère, J., & Hug, F. (2012). Between-subject variability of muscle synergies during a complex motor skill. Frontiers in Computational Neuroscience, 6, 99. https://doi.org/10.3389/fncom.2012.00099

Higgins, T.R., Heazlewood, I.T., Climstein, M. (2011). A Random Control Trial of Contrast Baths and Ice Baths for Recovery during Competition in U/20 Rugby Union. Journal of Strength and Conditioning Research, 25(4), 1046-51. https://doi.org/10.1519/JSC.0b013e3181cc269f

Huang, A. & Owen, K. (2012). Role of supplementary L-carnitine in exercise and exercise recovery. Med Sport Sci., 59, 135-42. https://doi.org/10.1159/000341934

Hurst, P., Foad, A., Coleman, D., Beedie, C. (2016). Development and validation of the sports supplements beliefs scale. Performance enhancement & health. https://doi.org/10.1016/j.peh.2016.10.001

Ingram, J., Dawson, B., Goodman, C., Wallman, K., Beilby, J. (2009). Effect of water immersion methods on post-exercise recovery from simulated team sport exercise, J. Sci. Med. Sport., 12(3), 417-21. https://doi.org/10.1016/j.jsams.2007.12.011

Kaboodvand, N., Towhidkhah, F., Gharibzadeh, S. (2013). Extracting and study of synchronous muscle synergies during fast arm reaching movements. Biomedical Engineering (ICBME), 20th Iranian Conference on. https://doi.org/10.1109/ICBME.2013.6782210

Koohestani, A., Kobravi, H., Koohestani, M. (2014). Identifying the muscle synergy pattern during human grasping. Journal of Biomedical Engineering and Medical Imaging, 33-39. https://doi.org/10.14738/jbemi.16.779

Kyle, U.G., Bosaeus, I., De Lorenzo, A.D., Deurenberg, P., Elia, M., Gómez, J.M. (2004). Bioelectrical impedance analysis part I: review of principles and methods. Clin Nutr., 23(5), 1226-43. https://doi.org/10.1016/j.clnu.2004.06.004

Lattier, G., Millet, G.Y., Martin, A., Martin, V. et al. (2004). Fatigue and recovery after high-intensity exercise. Part II: Recovery interventions. Int. J. Sports. Med., 25(7), 509-15. https://doi.org/10.1055/s-2004-820946

Manuel, R., Tillaar, R., Pereira, A., Marquesa, M. (2016). The effect of fatigue and duration knowledge of exercise on kicking performance in soccer players. Journal of Sport and Health Science, 1–7.

Mori, H., Ohsawa, H., Tanaka, T.H., Taniwaki, E., Leisman, G., Nishijo, K. (2004). Effect of massage on blood flow and muscle fatigue following isometric lumbar exercise. Med. Sci. Monit., 10(5), CR173-8.

Robertson, A., Watt, J.M., Galloway, S.D. (2004). Effects of leg massage on recovery from high intensity cycling exercise. Br. J. Sports. Med., 38, 173-176. https://doi.org/10.1136/bjsm.2002.003186

Tresch, M.C., Cheung, V.C.K., d'Avella, A. (2006). Matrix factorization algorithms for the identification of synergies: evaluation on simulated and experimental data sets. J. Neurophysiol., 95, 2199 –212. https://doi.org/10.1152/jn.00222.2005

Tresch, M.C., Jarc, A. (2009). The case for and against muscle synergies. Curr. Opin. Neurobiol., 19, 601. https://doi.org/10.1016/j.conb.2009.09.002

Zhuang, C., Marquez, J.C., Qu, H.E., x.He, N.Ln (2015). A neuromuscular electrical stimulation strategy based on muscle synergy for stroke rehabilitation Neural Engineering (NER), 7th International IEEE/EMBS Conference on, 10.1109/NER.2015.7146748.




DOI: https://doi.org/10.14198/jhse.2017.121.15