This article focuses on programming \u2014 specifically how to organize loading week by week within a VBT strength training block.<\/p>\n
The 4 strength training programming models<\/h2>\n
The study by Riscart-L\u00f3pez and collaborators (2021) compared four programming models for 8 weeks, with 2 weekly full squat sessions. All groups worked with a average intensity of 67.5% of 1RM<\/strong> and one 20% maximum speed loss per set<\/strong> \u2014 that is, the total volume and intensity was identical in all groups. The only thing that changed was how those loads were distributed throughout the sessions:<\/p>\n The intensity progressively increases<\/strong> week by week as the volume decreases. It is the most classic and well-known model: you start with lighter loads and many repetitions, and you end with maximum loads and few repetitions. Intuitively it seems the most logical to gain strength, but as we will see, it has no advantage over the others.<\/p>\n Unlike the linear one: the volume increases<\/strong> progressively as the intensity decreases. Less common, but useful in certain contexts, especially when you want to accumulate volume of work towards the end of the block.<\/p>\n The intensity and volume fluctuate from session to session<\/strong>, alternating high intensity\/low volume days with low intensity\/high volume days. It is the model that best adapts to the daily variability of the athlete’s performance.<\/p>\n The relative intensity is keeps fixed<\/strong> (67.5% RM) throughout the block. However, the absolute load in kg is adjusted each session to keep that percentage constant \u2014 because with adaptations to training, the 1RM increases and you have to increase the kilos to continue at the same %RM. This is where VBT is essential: without measuring speed, it would be impossible to know when to adjust the load.<\/p>\n The main finding of the study is clear: all groups improved similarly<\/strong> in all the variables analyzed \u2014 1RM in the squat, speed with all loads, vertical jump (CMJ) and 20-meter sprint time.<\/p>\n The authors’ conclusion is direct: when relative intensity and volume are equivalent, the programming model does not significantly influence the final result<\/strong>. All four models are equally effective at improving performance.<\/p>\n This result may seem disappointing at first glance \u2014 why debate models if they all work the same? But in reality it is a excellent news for coaches<\/strong>, because it removes the pressure of finding the \u201cright\u201d model and shifts the focus to what really matters: control the actual intensity and effective volume of each session<\/strong>.<\/p>\n The study itself emphasizes that this was only possible because the execution speed was recorded in all repetitions. Without that objective control, the groups would not have had the same actual intensity or the same effective volume \u2014 and the results would have been different and difficult to compare.<\/p>\n If all models produce similar results, the choice should be based on contextual factors of the athlete and the time of the season:<\/p>\n The most important fact that this study reveals is not which model to choose, but why all models worked equally well<\/strong>: because in all groups the speed of execution in each repetition was controlled.<\/p>\n This allowed two fundamental things:<\/p>\n Without measuring speed, the four models would have produced different actual intensities and volumes between subjects, making comparison impossible. VBT isn’t just a way to program \u2014 it’s the tool that makes any programming model work accurately.<\/strong><\/p>\n If you want to apply any of these models with the same rigor as in the study, the ADR Encoder<\/a> It allows you to record the speed of each repetition in real time and estimate the 1RM at the beginning of each session. The app ADR System<\/a> It automatically calculates intra-set speed loss and notifies you when you reach the configured threshold, so that volume and intensity control is objective and consistent regardless of the model you use.<\/p>\n The evidence is clear: There is no programming model superior to the others when relative intensity and volume are equivalent<\/strong>. Linear, reverse, undulating and constant produce similar improvements in strength, speed, vertical jump and sprint.<\/p>\n The most important practical implication is not which model to choose, but to ensure that the chosen model is executed accurately \u2014 and that is only possible by controlling the speed of execution on each repetition. The model matters less than the quality of the load control.<\/p>\n Los siguientes estudios respaldan los datos y conclusiones de este art\u00edculo sobre modelos de programaci\u00f3n del entrenamiento de fuerza con VBT:<\/p>\n![\"\"](\"https:\/\/adrencoder.com\/blog\/wp-content\/uploads\/2022\/11\/4-Formas-de-programar-el-entrenamiento-de-fuerza-basado-en-velocidad-1.jpg\")
<\/figure>\n1. Linear programming<\/h3>\n
2. Reverse programming<\/h3>\n
3. Wave programming<\/h3>\n
4. Constant programming<\/h3>\n
Study result: all 4 models work equally well<\/h2>\n
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\n \nVariable<\/th>\n Linear<\/th>\n Reverse<\/th>\n Wavy<\/th>\n Constant<\/th>\n<\/tr>\n<\/thead>\n \n Improve 1RM<\/td>\n \u2713<\/td>\n \u2713<\/td>\n \u2713<\/td>\n \u2713<\/td>\n<\/tr>\n \n Improve speed<\/td>\n \u2713<\/td>\n \u2713<\/td>\n \u2713<\/td>\n \u2713<\/td>\n<\/tr>\n \n CMJ improvement<\/td>\n \u2713<\/td>\n \u2713<\/td>\n \u2713<\/td>\n \u2713<\/td>\n<\/tr>\n \n 20m sprint improvement<\/td>\n \u2713<\/td>\n \u2713<\/td>\n \u2713<\/td>\n \u2713<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n ![\"\"](\"https:\/\/adrencoder.com\/blog\/wp-content\/uploads\/2022\/11\/4-Formas-de-programar-el-entrenamiento-de-fuerza-basado-en-velocidad-2.jpg\")
<\/figure>\nWhat does this mean in practice? The freedom to choose<\/h2>\n
When to use each model? Practical guide<\/h2>\n
Use linear programming when\u2026<\/h3>\n
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Use ripple programming when\u2026<\/h3>\n
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Use constant scheduling when\u2026<\/h3>\n
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Use reverse programming when\u2026<\/h3>\n
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The key role of VBT in any programming model<\/h2>\n
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Conclusion<\/h2>\n
Literature<\/h2>\n
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