Technology-assisted training load monitoring and injury risk in elite and professional team sports: a systematic review
DOI:
https://doi.org/10.47197/retos.v80.118930Keywords:
Workload, athletic injury, sports, global positioning system, monitoring, machine learningAbstract
Introduction: Technology-assisted monitoring of training load is widely used in team sports, yet its association with injury risk remains unclear.
Objective: To systematically review the evidence on the association between technology-assisted training load monitoring and injury risk in elite and professional team-sport athletes.
Methodology: This systematic review followed PRISMA 2020 guidelines and was registered in PROSPERO (CRD420251161886). Searches were conducted in PubMed, Scopus, Web of Science, and ScienceDirect from inception to 5 October 2025. Eligible studies involved adult elite or professional team-sport athletes, used technological systems to monitor training load, and reported injury outcomes. Risk of bias was assessed using the Newcastle–Ottawa Scale and PROBAST. Due to heterogeneity, a narrative synthesis was performed.
Results: Eleven longitudinal studies were included (eight prospective cohorts and three prediction studies). High acute workload exposure and abrupt workload increases were consistently associated with an increased risk of non-contact and time-loss injuries. In contrast, higher chronic workload exposure, when progressively accumulated, was associated with a reduced injury risk in some contexts. Machine-learning models improved injury prediction but showed moderate risk-of-bias concerns.
Conclusions: Technology-assisted workload monitoring is associated with injury risk in elite team sports. Managing acute workload spikes while progressively developing chronic load capacity may help reduce injury risk, although further research is required to validate predictive models.
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