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Abstract Background Inspiratory muscle training (IMT) has brought great benefits in terms of improving physical performance in healthy individuals. However, there is no consensus regarding the best training load, as in most cases the maximal inspiratory pressure (MIP) is used, mainly the intensity of 60% of MIP. Therefore, prescribing an IMT protocol that takes into account inspiratory muscle strength and endurance may bring additional benefits to the commonly used protocols, since respiratory muscles differ from other muscles because of their greater muscular resistance. Thus, IMT using critical inspiratory pressure (PThC) can be an alternative, as the calculation of PThC considers these characteristics. Therefore, the aim of this study is to propose a new IMT protocol to determine the best training load for recreational cyclists. Methods Thirty recreational cyclists (between 20 and 40 years old) will be randomized into three groups: sham (SG), PThC (CPG) and 60% of MIP, according to age and aerobic functional capacity. All participants will undergo the following evaluations: pulmonary function test (PFT), respiratory muscle strength test (RMS), cardiopulmonary exercise test (CPET), incremental inspiratory muscle endurance test (iIME) (maximal sustained respiratory pressure for 1 min (PThMAX)) and constant load test (CLT) (95%, 100% and 105% of PThMÁX) using a linear load inspiratory resistor (PowerBreathe K5). The PThC will be calculated from the inspiratory muscle endurance time (TLIM) and inspiratory loads of each CLT. The IMT will last 11 weeks (3 times/week and 55 min/session). The session will consist of 5-min warm-up (50% of the training load) and three sets of 15-min breaths (100% of the training load), with a 1-min interval between them. RMS, iIME, CLT and CPET will be performed beforehand, at week 5 and 9 (to adjust the training load) and after training. PFT will be performed before and after training. The data will be analyzed using specific statistical tests (parametric or non-parametric) according to the data distribution and their respective variances. A p value