Pace and Stride Frequency Kit for Running, Utilizing Auditory and Visual Cues to Direct the Run at the Specified Frequency, Pace and Stride Length
Keywords:
cadence and stride length regulation system, stride frequency, stride length, running economyAbstract
The aims of this research were to (1) create a pacing and stride length device and (2) evaluate the device’s accuracy based on stride frequency and stride length measurements. The device analyzes picture data from the runner’s movements to ascertain the optimal settings for the individual. The research approach encompassed (1) analyzing design needs and specifications, (2) delineating essential characteristics, (3) selecting components, (4) developing and constructing the light and sound device, and (5) evaluating the device’s functionality. A stopwatch with a resolution of 0.01 seconds was employed to assess accuracy at stride frequencies of 120, 160, 200, 240, 280, and 320 strides per minute. A millimeter-scale measuring tape was employed to ascertain the stride length of the device at intervals of 30, 60, 90, 120, and 180 cm. The accuracy percentage (% was utilized to denote the device’s performance, with an acceptable margin of error not exceeding ± 5%. The experimental findings indicated that (1) the maximum accuracy for stride frequency was 1.32%, and (2) the maximum accuracy for stride length display was 1.62%. Both stride frequency and stride length precision achieved the predetermined aim established in this investigation, which was no greater than 5%. This research yields a prototype apparatus for measuring stride frequency and stride length, applicable for analyzing energy-efficient running metrics and functioning as a training instrument to identify the best frequency and stride length for individual runners.
References
Barnes, K. R., & Kilding, A. E. (2015). Running economy: measurement, norms, and determining factors. Sports Medicine - Open, 1(1), 8. https://doi.org/10.1186/s40798-015-0007-y
Díaz, J. J., Fernández‐Ozcorta, E. J., & Santos‐Concejero, J. (2018). The influence of pacing strategy on marathon world records. European Journal of Sport Science, 18(6), 781–786. https://doi.org/10.1080/17461391.2018.1450899
dos Anjos Souza, V. R., Seffrin, A., da Cunha, R. A., Vivan, L., de Lira, C. A. B., Vancini, R. L., Weiss, K., Knechtle, B., & Andrade, M. S. (2023). Running economy in long-distance runners is positively affected by running experience and negatively by aging. Physiology & Behavior, 258, 114032. https://doi.org/10.1016/j.physbeh.2022.114032
Hoogkamer, W., Snyder, K. L., & Arellano, C. J. (2019). Reflecting on Eliud Kipchoge’s marathon world record: an update to our model of cooperative drafting and its potential for a Sub-2-Hour performance. Sports Medicine, 49(2), 167–170. https://doi.org/10.1007/s40279-019-01056-2
HoGberg, P. (1952). Length of stride, stride frequency, ?flight? period and maximum distance between the feet during running with different speeds. European Journal of Applied Physiology, 14(6), 431–436. https://doi.org/10.1007/bf00934422
Lockie, R. G., Murphy, A. J., Schultz, A. B., Knight, T. J., & De Jonge, X. a. J. (2011). The effects of different speed training protocols on sprint acceleration kinematics and muscle strength and power in field sport athletes. The Journal of Strength and Conditioning Research, 26(6), 1539–1550. https://doi.org/10.1519/jsc.0b013e318234e8a0
Praditpod, N., & Tantipoon, P. (2017). Effects of age and gender on reference value of kinematic gait parameters among healthy Thai adults aged 20-69 years. Journal of Medical Technology, 28(3), 308–315. Retrieved from https://www.tci-thaijo.org/index.php/ams/article/download/76281/61326
Schubert, A. G., Kempf, J., & Heiderscheit, B. C. (2013). Influence of stride frequency and length on running mechanics. Sports Health a Multidisciplinary Approach, 6(3), 210–217. https://doi.org/10.1177/1941738113508544
Sha, J., Yi, Q., Jiang, X., Wang, Z., Cao, H., & Jiang, S. (2024). Pacing Strategies in Marathons: A Systematic review. Heliyon, 10(17), e36760. https://doi.org/10.1016/j.heliyon.2024.e36760
Souza, V. R. D. A., Seffrin, A., Da Cunha, R. A., Vivan, L., De Lira, C. a. B., Vancini, R. L., & Andrade, M. S. (2022). Running economy in long-distance runners is positively affected by running experience and negatively by aging. Physiology & Behavior, 258, 114032.
https://doi.org/10.1016/j.physbeh.2022.114032
Stöhr, A., Nikolaidis, P. T., Villiger, E., Sousa, C. V., Scheer, V., Hill, L., & Knechtle, B. (2021). An analysis of participation and performance of 2067 100-km Ultra-Marathons worldwide. International Journal of Environmental Research and Public Health, 18(2), 362. https://doi.org/10.3390/ijerph18020362
Suwankan, S., Suwankan, S., Theanthong, A., & Kemarat, S. (2024). Prediction of 10 km running time by physical and training characteristics in recreational runners. Journal of Human Sport and Exercise, 19(1). https://doi.org/10.14198/jhse.2024.191.17
Taylor, J., Atkinson, G., & Best, R. (2021). Paced to perfection: Exploring the potential impact of WaveLight Technology in athletic. The Sport and Exercise Scientist , 68, 8-9. Retrieved from https://bit.ly/4oXgbNT
Van Hooren, B., & Meijer, K. (2024). Dataset of running kinematics, kinetics and muscle activation at different speeds, surface gradients, cadences and with forward trunk lean. Data in Brief, 54, 110312. https://doi.org/10.1016/j.dib.2024.110312
Watanabe, T., Kondo, S., Kakinoki, K., Fukusaki, C., & Hatta, H. (2023). Stride-to-stride variability and fluctuations at intensities around lactate threshold in distance runners. Heliyon, 9(6), e17437. https://doi.org/10.1016/j.heliyon.2023.e17437
