Versatility of Protocols for Resistance Training and Assessment using Static and Dynamic Ladders in Animal Models.

Resistance training is a physical exercise model with profound benefits for health throughout life. The use of resistance exercise animal models is a way to gain insight into the underlying molecular mechanisms that orchestrate these adaptations. The aim of this article is to describe exercise models and training protocols designed for strength training and evaluation of resistance in animal models and provide examples. In this article, strength training and resistance evaluation are based on ladder climbing activity, using static and dynamic ladders. These devices allow a variety of training models as well as provide precise control of the main variables which determine resistance exercise: volume, load, velocity, and frequency. Furthermore, unlike resistance exercise in humans, this is a forced exercise. Thus, aversive stimuli must be avoided in this intervention to preserve animal welfare. Prior to implementation, a detailed design is necessary, along with an acclimatization and learning period. Acclimatization to training devices, such as ladders, weights, and clinical tape, as well as to the manipulations required, is necessary to avoid exercise rejection and to minimize stress. At the same time, the animals are taught to climb up the ladder, not down, to the resting area on the top of the ladder. Resistance evaluation can characterize physical strength and permit adjusting and quantifying the training load and the response to training. Furthermore, different types of strength can be evaluated. Regarding training programs, with appropriate design and device use, they can be sufficiently versatile to modulate different types of strength. Furthermore, they should be flexible enough to be modified depending on the adaptive and behavioral response of the animals or the presence of injuries. In conclusion, resistance training and assessment using ladders and weights are versatile methods in animal research.