How pacing patterns influence middle-distance performance Perspectives from object tracking and oxygen uptake
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Abstract
This study explores the impact of kinematic and physiological factors on middle-distance running performance to optimize training strategies. Six runners participated in experiments with three pacing patterns (F-S, Even, and S-F), monitored using a drone, camera, and tracking technology. VO2master measured oxygen uptake, and post-race PBLa and RPE were assessed. Results showed that the Even pattern had the lowest speed variation and most consistent trajectory. The running distances for the three pacing patterns were similar: F-S (807.30 ± 0.88 m), Even (806.52 ± 0.66 m), and S-F (806.37 ± 1.63 m). Although the Even strategy required less work, the F-S pattern had lower oxygen uptake, indicating higher efficiency. Heart rate and oxygen uptake stabilized fastest in the Even pattern, while the F-S pattern led to the lowest blood lactate (15.0 ± 2.56 mmol/L). The Even pacing is optimal for consistent performance, while F-S may optimize energy efficiency. This study emphasizes tailoring pacing strategies to an athlete’s profile, suggesting further research on personalized pacing to enhance performance.
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References
Abdel-Aziz, Y. I., Karara, H. M., & Hauck, M. (2015). Direct linear transformation from comparator coordinates into object space coordinates in close-range photogrammetry. Photogrammetric engineering & remote sensing, 81(2), 103-107. https://doi.org/10.14358/PERS.81.2.103
Ariyoshi, M., Yamaji, K., &Shephard, R.J.(1979).Influence of Running Pace upon Performance: Effects upon Treadmill Endurance Time and Oxygen Cost. European Journal of Applied Physiology and Occupational Physiology,41, 83-91. https://doi.org/10.1007/BF00421655
Casado, A.,Hanley, B.,Jimenez-Reyes, P.,& Renfree, A.(2021).Pacing profiles and tactical behaviors of elite runners. Journal of Sport and Health Science,10(5),537-549. https://doi.org/10.1016/j.jshs.2020.06.011
De Koning J. J., Bobbert M.F., &Foster C. (1999). Determination of optimal pacing strategy in track cycling with an energy flow model. Journal of Science and Medicine in Sport,2(3),266-277. https://doi.org/10.1016/S1440-2440(99)80178-9
Dearden, A., Demiris, Y., & Grau, O. (2006). Tracking football player movement from a single moving camera using particle filters. European Conference on Visual Media Production (CVMP),29-37. https://doi.org/10.1049/cp:20061968
Draper, S. B., & Wood, D. M. (2005). The V̇O2 response for an exhaustive treadmill run at 800-m pace: a breath-by-breath analysis. European journal of applied physiology, 93, 381-389. https://doi.org/10.1007/s00421-004-1278-z
Enomoto, Y., Ae, M., Morioka, Y., Sugita, M., & Matsuo, A. (2005). Comparison of the race pattern for the World and Japanese elite 800 m runners. Bulletin of studies in athletics of JAAF, 1, 16-22.
Enomoto, Y.,Sugita, M.,Matsuo, A.,&Ae, M.(2006).The 1500 m race analysis of the elite women's middle-distance runners. Bulletin of studies in athletics of JAAF,2,104-106.
Foster, C., Schrager, M., Snyder, A.C., &Thompson, N.N. (1994). Pacing strategy and athletic performance. Sports Medicine,17(2),77-85. https://doi.org/10.2165/00007256-199417020-00001
Ghasemzadeh, H., & Jafari, R. (2010). Coordination analysis of human movements with body sensor networks: A signal processing model to evaluate baseball swings. IEEE Sensors Journal, 11(3), 603-610. https://doi.org/10.1109/JSEN.2010.2048205
Hanon, C., Thomas, C., Le Chevalier, J. M., Gajer, B., & Vandewalle, H. (2002). How does VO2 evolve during the 800 m?. New Studies in Athletics, 17(2), 61-68.
Hanon, C., Leveque, J.M., Vivier, L., & Thomas, C. (2007). Oxygen uptake in the 1500 metres. New Studies in Athletics, IAAF,2007, 22 (1),15-22.
Jones, A. M., & Whipp, B.J. (2002). Bioenergetic constraints on tactical decision making in middle distance running. British journal of sports medicine, 36(2), 102-104. https://doi.org/10.1136/bjsm.36.2.102
Jones, A. M., Wilkerson, D. P., Vanhatalo, A., & Burnley, M. (2008). Influence of pacing strategy on O2 uptake and exercise tolerance. Scandinavian journal of medicine and science in sports, 18(5), 615-626. https://doi.org/10.1111/j.1600-0838.2007.00725.x
Kadono, H.,Ae, M.,Enomoto, Y.,Sugita, M.,&Morioka, Y.(2008).The racing patterns of male 800 m runners of different record levels. Japan Journal of Physical Education, Health and Sport Sciences,53, 247-263. https://doi.org/10.5432/jjpehss.a530211
Lu, W. L., Ting, J. A., Little, J. J., & Murphy, K. P. (2013). Learning to track and identify players from broadcast sports videos. IEEE transactions on pattern analysis and machine intelligence, 35(7), 1704-1716. https://doi.org/10.1109/TPAMI.2012.242
Matsuo, A., Sugita, M., Kobayashi, K., & Ae, M. (1992). Changes in speed, stride frequency and stride length of top 1500 m runners. Abstracts of Japan Society of Physical Education, Health and Sport Sciences Conference 43A(0), 429.
Manafifard, M., Ebadi, H., & Moghaddam, H. A. (2017). A survey on player tracking in soccer videos. Computer Vision and Image Understanding, 159, 19-46. https://doi.org/10.1016/j.cviu.2017.02.002
Sheng, M., Wang, W., Qin, H., Wan, L., Li, J., & Wan, W. (2020). A novel changing athlete body real-time visual tracking algorithm based on distractor-aware siamRPN and HOG-SVM. Electronics, 9(2), 378. https://doi.org/10.3390/electronics9020378
Spencer, M. R., Gastin, P. B., & Payne, W. (1996). Energy system contribution during 400 to 1500 metres running. New Studies in Athletics, 11(4), 59-66.
Suzuki,Y.,Murata,M.,&Masumura,M.(2020).Development of an automatic ball trajectory acquisition system for volleyball serves using a convolutional neural network. Japan Journal of Physical Education, Health and Sport Sciences,65 (0), 273-279. https://doi.org/10.5432/jjpehss.19096
Thomas, C., Hanon, C., Perrey, S., Le Chevalier, J. M., Couturier, A., & Vandewalle, H. (2005). Oxygen uptake response to an 800-m running race. International journal of sports medicine, 26(04), 268-273. https://doi.org/10.1055/s-2004-820998
Victor, B., He, Z., Morgan, S., & Miniutti, D. (2017). Continuous video to simple signals for swimming stroke detection with convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops,66-75. https://doi.org/10.1109/CVPRW.2017.21
Walton, J. S. (1981). Close-range cine-photogrammetry: A generalized technique for quantifying gross human motion. The Pennsylvania State University.
Wang, D., Fang, W., Chen, W., Sun, T., & Chen, T. (2019). Model update strategies about object tracking: A state of the art review. Electronics, 8(11), 1207. https://doi.org/10.3390/electronics8111207
Winter, D. A., Sidwall, H. G., & Hobson, D. A. (1974). Measurement and reduction of noise in kinematics of locomotion. Journal of biomechanics, 7(2), 157-159. https://doi.org/10.1016/0021-9290(74)90056-6
Xing, J., Ai, H., Liu, L., & Lao, S. (2010). Multiple player tracking in sports video: A dual-mode two-way bayesian inference approach with progressive observation modeling. IEEE Transactions on Image Processing, 20(6), 1652-1667. https://doi.org/10.1109/TIP.2010.2102045
Yang, Y.,& Enomoto,Y. (2022a). Comparison of the Pace Pattern for the World and Japanese Elite 1500 m Runners. Journal of Humanities, Arts and Social Science, 6(4), 632-642. https://doi.org/10.26855/jhass.2022.12.017
Yang, Y., & Enomoto, Y. (2022b). The pace pattern of the men's 1500 m race with different levels. Journal of Physical Education and Sport, 22(10),2549-2556. https://doi.org/10.7752/jpes.2022.10323
Yang, Y., Lee, X., & Enomoto, Y. (2023). Implementing the tracking of 1500 m runners using Open CV. Journal of Physical Education and Sport, 23(7), 1698-1705. https://doi.org/10.7752/jpes.2023.07208