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Standardized Protocol For Monitoring Muscle Fatigue and Biomechanics In Amateur Cyclists Using Infrared Thermography
Published on: March 20, 2026
Resistance Training for Cyclists: Scientific Evidence and Practical Recommendations
Raúl de Pablos1,2, Iván R Sánchez-Redondo1, Lidia B Alejo1,3
1Department of Sports Sciences. Faculty of Medicine, Health and Sports, Universidad Europea de Madrid, Madrid, Spain.
Abstract:
Growing evidence supports strength or resistance training (RT) as an effective method for enhancing cycling performance, but many cyclists remain reluctant to incorporate it due to perceived detrimental effects, such as excessive residual fatigue and unwanted hypertrophy. Furthermore, controversy persists regarding optimal training methodologies. This narrative review aims to summarize current evidence on the effects of RT on cycling performance, as well as to address its potential impact on body composition and bone health. We also provide evidence-based recommendations on best practices for RT implementation. Overall, the addition of 1-2 weekly sessions of RT with moderate-to-high loads (60%-85% 1RM), 2-4 sets per exercise across 3-5 compound lower-body movements, and limited intra-set fatigue (10%-20% velocity loss) has proven beneficial for improving key cycling performance indicators. Recent evidence also suggests that high-load on-bike torque training may serve as an effective strategy when conventional RT is not feasible. When implemented as described, RT does not appear to negatively affect performance through unwanted hypertrophy. Although further research is warranted, RT also emerges as a potentially effective strategy to counteract the low bone mineral density commonly observed in competitive cyclists. However, the magnitude of these benefits largely depends on precise programming, particularly intensity, proximity to failure, and temporal separation from endurance training sessions. In summary, current evidence supports the role of RT for improving both health and performance in cyclists, but further research is warranted to determine the optimal training parameters to maximize its benefits.
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