Health Technology/clinical-trials

Completed Cohort Study: Machine Learning-Guided Workload Optimisation in Elite Track Athletes — Injury Reduction and Sprint Performance Outcomes

247 Health News Editorial

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23 July 2026

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4 min read

·clinical-trials

A completed prospective cohort study from Debre Berhan University evaluated whether adaptive machine learning-guided workload optimisation could reduce time-loss injuries and improve sprint performance in 120 elite track and field athletes over a 9-month competitive season. The findings have significant implications for sports medicine clinicians and high-performance sport practitioners seeking evidence-based, individualised training prescription tools.

Originally reported by ClinicalTrials.gov

References

  1. 1.Debre Berhan University. (2023). A machine learning-guided training approach to reduce injuries and enhance performance in elite athletes: A prospective cohort evaluation. ClinicalTrials.gov. https://clinicaltrials.gov/study/NCT07683091
  2. 2.Jacobsson, J., Timpka, T., Kowalski, J., Nilsson, S., Ekberg, J., & Renström, P. (2012). Injury patterns in Swedish elite athletics: Annual incidence, injury types and risk factors. British Journal of Sports Medicine, 46(16), 1164–1170. https://doi.org/10.1136/bjsports-2011-090725
  3. 3.Claudino, J. G., Capanema, D. O., de Souza, T. V., Serrão, J. C., Machado Pereira, A. C., & Nassis, G. P. (2019). Current approaches to the use of artificial intelligence for injury risk assessment and performance prediction in team sports: A systematic review. Sports Medicine Open, 5(1), 28. https://doi.org/10.1186/s40798-019-0202-3
  4. 4.Gabbett, T. J. (2016). The training-injury prevention paradox: Should athletes be training smarter and harder? British Journal of Sports Medicine, 50(5), 273–280. https://doi.org/10.1136/bjsports-2015-095788
  5. 5.Meeuwisse, W. H., Tyreman, H., Hagel, B., & Emery, C. (2007). A dynamic model of etiology in sport injury: The recursive nature of risk and causation. Clinical Journal of Sport Medicine, 17(3), 215–219. https://doi.org/10.1097/JSM.0b013e3180592a48
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