Development of an IoT-Based Training Monitoring System for Optimizing Athlete Performance

Ayub Baharuddin; Achmad Widodo; Donny Ardy Kusuma; Irmantara Subagiao

Authors

Ayub Baharuddin Universitas Negeri Surabaya Author
Achmad Widodo Universitas Negeri Surabaya Author
Donny Ardy Kusuma Universitas Negeri Surabaya Author
Irmantara Subagiao Universitas Negeri Surabaya Author

Keywords:

IoT-Based Monitoring; Training Load Management; Fitness–Fatigue Model; Athlete Performance; Sport Analytics

Abstract

This study aimed to develop and evaluate an Internet of Things (IoT)-based training monitoring system designed to optimize athlete performance through integrated and real-time training load analysis. The research employed a Research and Development (R&D) approach using the Four-D model (define, design, develop, disseminate). The system integrates key internal load indicators, including Rating of Perceived Exertion (RPE), Training Load, Acute Training Load (ATL), Chronic Training Load (CTL), Training Stress Balance (TSB), Monotony, and Strain. The platform was developed using accessible cloud-based tools (Google Sites, Google Forms, Google Sheets, and Looker Studio) to ensure scalability and cost-effectiveness in a regional multi-sport context. Content validity was assessed by five experts in sport science, coaching methodology, and educational technology using Aiken’s V, resulting in coefficients ranging from 0.75 to 1.00 with an average of 0.93, indicating very high validity. Field trials involved 172 athletes from 24 sports and 62 coaches from 40 sports. Athlete evaluation yielded an overall feasibility score of 79.63% (feasible category), with the highest score in motivation and workload awareness (81.24%). Coach evaluation resulted in an overall score of 78.20% (feasible category), confirming practical usability and decision-making support. These findings indicate that the developed IoT-based system is valid, feasible, and effective in supporting structured load management and performance optimization.

References

Allen, H., & Coggan, A. R. (2010). Training and racing with a power meter (2nd ed.). VeloPress. https://www.velopress.com

Bourdon, P. C., Cardinale, M., Murray, A., et al. (2017). Monitoring athlete training loads: Consensus statement. International Journal of Sports Physiology and Performance, 12(S2), S2-161–S2-170. https://doi.org/10.1123/ijspp.2017-0208

Coutts, A. J. (2016). Working fast and working slow: The benefits of embedding research in high-performance sport. International Journal of Sports Physiology and Performance, 11(1), 1–2. https://doi.org/10.1123/ijspp.2015-0781

Drew, M. K., & Finch, C. F. (2016). The relationship between training load and injury in athletes. Sports Medicine, 46(6), 861–883. https://doi.org/10.1007/s40279-015-0454-8

Etikan, I., Musa, S. A., & Alkassim, R. S. (2015). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1–4. https://doi.org/10.11648/j.ajtas.20160501.11

Fox, J. L., Stanton, R., Sargent, C., Wintour, S.-A., & Scanlan, A. T. (2020). The association between training load and performance. Sports Medicine, 50, 181–192. https://doi.org/10.1007/s40279-019-01181-4

Gabbett, T. J. (2016). The training—injury prevention paradox. British Journal of Sports Medicine, 50(5), 273–280. https://doi.org/10.1136/bjsports-2015-095788

Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision and future directions. Future Generation Computer Systems, 29(7), 1645–1660. https://doi.org/10.1016/j.future.2013.01.010

Halson, S. L. (2014). Monitoring training load to understand fatigue. Sports Medicine, 44(S2), 139–147. https://doi.org/10.1007/s40279-014-0253-z

Hulin, B. T., Gabbett, T. J., Lawson, D. W., et al. (2016). The acute:chronic workload ratio predicts injury. British Journal of Sports Medicine, 50(4), 231–236. https://doi.org/10.1136/bjsports-2015-094817

Impellizzeri, F. M., Rampinini, E., Coutts, A. J., et al. (2004). Use of RPE-based training load in soccer. Medicine & Science in Sports & Exercise, 36(6), 1042–1047. https://doi.org/10.1249/01.MSS.0000128199.23901.2F

Malone, S., Roe, M., Doran, D. A., et al. (2017). High chronic training loads and injury risk. British Journal of Sports Medicine, 51(3), 204–210. https://doi.org/10.1136/bjsports-2015-095859

McLaren, S. J., Macpherson, T. W., Coutts, A. J., et al. (2018). The relationships between internal and external load. Sports Medicine, 48(3), 641–658. https://doi.org/10.1007/s40279-017-0830-z

Meeusen, R., Duclos, M., Foster, C., et al. (2013). Prevention, diagnosis and treatment of overtraining syndrome. European Journal of Sport Science, 13(1), 1–24. https://doi.org/10.1080/17461391.2012.730061

Peake, J. M., Kerr, G., & Sullivan, J. P. (2018). A critical review of wearable technology in sport. Sports Medicine, 48(3), 541–551. https://doi.org/10.1007/s40279-017-0840-x

Riduwan. (2015). Skala pengukuran variabel-variabel penelitian. Alfabeta.

Saw, A. E., Main, L. C., & Gastin, P. B. (2016). Monitoring the athlete training response. Sports Medicine, 46(6), 773–789. https://doi.org/10.1007/s40279-015-0430-3

Sugiyono. (2023). Metode penelitian kuantitatif, kualitatif, dan R&D. Alfabeta.

Vanrenterghem, J., Nedergaard, N. J., Robinson, M. A., & Drust, B. (2017). Training load monitoring in team sports. Sports Medicine, 47(11), 2245–2258. https://doi.org/10.1007/s40279-017-0711-5

Windt, J., & Gabbett, T. J. (2017). How do training and competition loads relate to injury? British Journal of Sports Medicine, 51(5), 428–435. https://doi.org/10.1136/bjsports-2016-096040

Xu, L. D., He, W., & Li, S. (2014). Internet of Things in industries. IEEE Transactions on Industrial Informatics, 10(4), 2233–2243. https://doi.org/10.1109/TII.2014.2300753