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IntroductionPeriodization is the strategic organization of training to optimize progress while avoiding setbacks and injuries (1). This method of organizing training elicits superior results when compared to non-periodized training (2). However, even the best planning cannot account for all variables that affect athletes’ acute readiness.
Sleep, emotional stress, illness, and diet all significantly influence training. Furthermore, professionals who work with teams train groups of athletes. In this case, not only must they account for individual variability, but also they must try to apply training strategies to many athletes each potentially at a different level of readiness.Elite athletes have extraordinary abilities of perception, intuition, split-decision making, and anticipation (3).
This mind and body awareness manifests from innate talent and years of experience, allowing them to train near optimal capacity, if self-regulation is encouraged. The question becomes, how can a coach transfer this ability to athletes without this innate talent or experience?One method that attempts to achieve these ends by matching training stress to athlete-readiness is Auto-Regulatory Training (AT) (4, 5). This approach to training is designed to adapt to individual changing needs to allow optimal training more frequently. Sometimes a workout intended to be hard can be easy if the athlete is particularly well recovered or energetic.
Likewise, a workout intended to be light can be fatiguing if under-recovered. A practitioner implementing AT uses specific criteria that dictate training variables based on athlete readiness, theoretically improving training efficiency (6, 7).Due to the flexible nature of AT, its investigation is rare because it is difficult to set up appropriate study designs and controls. Despite this, existing research is promising (4, 5) and several reviewers have noted its potential as a training tool (6, 8). Furthermore, numerous practitioners already utilize AT with success, evident in commercial training (9) and in observational research (10).This article focuses on subjective measures of readiness and ways to use them to guide training. Depending on the resources of the practitioner, objective measures such as heart rate reserve (11), salivary testosterone and cortisol ratio (12), or others may be available to further augment these measures and inform training. Following is a discussion of the literature on AT, a system of AT currently used in the private sector and practical methods to implement AT for strength development.Auto-Regulatory Progressive Resistance ExerciseOver 60 years ago, DeLorme et al. (13) first detailed the Progressive Resistance Exercise (PRE) method to rehabilitate quadriceps strength after injury, and it became the basis for contemporary resistance training prescription.
Nearly 30 years later, Knight et al. (14) modified PRE to adjust to patients’ daily abilities, naming it Daily Adjustable Progressive Resistance Exercise (DAPRE). In this 1979 study, DAPRE proved more efficient in strength rehabilitation than traditional methods. This was a novel modification to PRE at the time and over thirty years later, DAPRE is still lauded as safe and efficient for rehabilitation of patients at all levels (7).Most recently, Mann et al. (4) modified DAPRE for strength training. Coined Auto-regulatory Progressive Resistance Exercise (APRE), APRE was compared to traditional Linear Periodization (LP) in training college football athletes. The LP group progressed weekly for six weeks from three sets of eight at 70% 1RM, eventually to 4 sets of 5 at 85% of 1RM, with a test of maximal strength on week six. Volume and intensity were not matched since the APRE group’s protocol was dictated daily by individual performance. The 6RM protocol outlined in Table 1 was used for the majority of the 6 weeks, and the 3RM and 10RM protocols were utilized less.
The APRE group improved by an average of 21 lb more in the 1RM bench press test, 35 lb more in the 1RM squat test, and three repetitions more in the bench press to fatigue test than the LP group. Auto-regulatory progressive resistance exercise is a four set system in which the first two sets are preparatory sets for two subsequent sets taken to failure. The repetitions performed in the third set determine the load used in the fourth. There are 3, 6, and 10RM protocols, designed to elicit adaptations in strength and power, strength and hypertrophy, and hypertrophy, respectively. By utilizing Table 1 and cross-referencing Table 2, load assignments can be made and put into practice in training.
A limitation of APRE to consider is how the fourth set adjustment is made. While a 5–15 lb adjustment may be appropriate for some athletes and lifts, it may be too much for some and not enough for others. Consider the load difference with maximal deadlifts between an experienced 200-lb male strength athlete and an inexperienced 130-lb female team sport athlete. Conceivably, one could be triple that of the other. Instead of adjusting in absolute poundage, it may be more versatile to adjust by percentage as seen in Table 3 so the system can be applied to wider varieties of individuals and lifts.
Flexible Nonlinear PeriodizationAnother method of AT is Flexible Nonlinear Periodization (FNP)(5). Flexible nonlinear periodization takes Nonlinear Periodization (NP) and adds elements of AT. In nonlinear periodization, training parameters are varied acutely each workout or weekly (15). Nonlinear periodization is suggested by some researchers to be superior to linear periodization (8, 16), however the results of other studies are to the contrary (17). Nonetheless, researchers continue to explore NP for its potential to optimize training.McNamara et al. (5) compared NP to FNP in a college weight training class for 12 weeks. The volume, intensity, exercise selection, and number of exercises were the same in both groups. Both utilized 10, 15, and 20RM loads in their programming. The NP group followed these protocols in a pre-planned fashion, while the FNP group chose one protocol each session based on daily readiness. To quantify daily readiness, they assessed their energy from 0 to 10; with 10 indicating maximal energy and readiness, and zero indicating no energy.
The premise being that when energy was high, heavier loads would be used and training would be more successful. The FNP group increased their leg press by an average of 62 kg over 12 weeks, while the NP group increased on average by 16 kg. This simple system is detailed in Table 4 and it only requires athletes to gauge daily energy. Presumably, this skill improves as athletes gain self-awareness and improve their ability to self-monitor.
A limitation of this method arises from the potential to misunderstand load selection. A true repetition maximum, be it 10 or 20 repetitions, is still demanding if the set is taken to failure. Thus, it may be better to apply the concept more broadly. A coach can simply match the difficulty of the workout to the energy of the athlete.
For example instead of a 10, 15 or 20RM load selection as shown in Table 4, a workout could be assigned based on a qualitative evaluation of its difficulty (i.e., light, moderate, or hard, respectively).The specific loading parameters in this study are also designed for a beginning weight training class. Higher intensity loading assignments more specific to strength and power could be implemented for a more experienced population. Another limitation is that this method may not be applicable to athletes training in-season or at the end of pre-season that are required to compete at specific times.
Athletes with these requirements need to be at peak performance on game day. Even if high energy levels allow for a high-intensity session, it may be inappropriately timed if too close to competition. Thus, it may be more appropriate for off-season training designed to provide general fitness and strength rather than preparation for competition at a specific date.Ratings of Perceived Exertion in Resistance TrainingAn assumption of AT is that the athlete is in the best position to determine their own abilities. Therefore, if this awareness is translated into programming, performance may improve. This premise requires a Rating of Perceived Exertion (RPE), or in the case of McNamara et al. (5) a rating of readiness. The study of RPE began over thirty years ago (18, 19) to assess aerobic training, but more recently it has been studied in resistance training.While not all researchers deem RPE a reliable measure of performance (20), the majority conclude it is a reliable way to quantify resistance training intensity (21-25). A rating of perceived exertion correlates with training strain as measured by hormonal response (26), can be used to estimate percentage of 1RM (27), and can be used for load selection in certain populations (28-30).However, the use of RPE in resistance training is not without limitations. Some researchers suggest RPE does not reliably report set-to-set intensity, and is more effective at measuring the difficulty of entire sessions (31). In adolescents, reports of RPE can be inconsistent (32).
Also, RPE accuracy is enhanced with training experience (33, 34), indicating it is less effective for novices. Furthermore, RPE without training structure provides differing results depending on the population. For example, untrained women who self-selected the intensities of their workout chose loads too light to promote optimal adaptations even though RPE accurately reflected the loads chosen (35).To put RPE in context, it is likely a better rating of fatigue than intensity per se, as reducing inter-set (36) or inter-repetition (37) rest intervals results in a higher RPE, which is related to many variables affecting fatigue, including multi- versus single-joint exercise, range of motion, isolated muscle fatigue, training to failure, loads above 90% of 1RM, and total volume (38).Testa et al. (34) showed that with enough volume performed, novice and experienced lifters can accurately gauge RPE. However, with lower volume, RPE is more accurately gauged by experienced lifters. A RPE may be a function of the volume performed relative to the volume of which the subject is capable.
Singh et al. (38) found strength (3 sets of 5 reps at 90% 1RM) and hypertrophy (3 sets of 10 reps at 70% 1RM) protocols provided nearly equal RPE values (5.9 ± 1.8 and 6.4 ± 1.6) while power training (3 sets of 5 reps at 50% 1RM) provided a lower session RPE (3.2 ± 1.4) in men with at least one year of resistance training experience. It appears that RPE measures training stress experienced, rather than quantifying training itself. Thus, RPE should be used in the context of a structured program. Also, RPE appears to be more effective when applied to experienced populations, and becomes more accurate with use.Literature currently validates RPE as a monitoring tool; however, it has not been researched in exercise prescription. The theoretical benefits of RPE warrant discussion of ways to implement it as such. In an effort to provide a framework, a RPE system currently used for training powerlifters and strength athletes known as, Reactive Training Systems (RTS) is detailed below (9). Reactive training systems utilize RPE adapted to strength training as outlined in Table 5.
Programs based on percentage of 1RM can be converted to RPE using Table 6. Find the repetitions prescribed along the top axis, and scroll down to the corresponding percentage of 1RM prescribed. Along the left axis is the RPE for those prescriptions. Thus, 6 repetitions at 75% of 1RM would convert most closely to 6 repetitions with the final repetition at RPE 9.
In theory, adapting programs to RPE allows training at desired stress levels, even when readiness is not what is anticipated. Traditional programs do not have this flexibility; invariably there are times when more or fewer repetitions than prescribed can be performed. With that said, until research compares this system (or those like it) to traditional programs, the benefits remain theoretical.ConclusionMatching training stress to athlete readiness with AT may be an effective approach to optimizing strength and these frameworks can assist in implementation. Science often follows practice and if professionals use AT with success, researchers will step in to study, validate, and hopefully devise novel applications for AT that advance the science of strength development.
Eric Helms, MS, CSCS, is the co-owner of the consulting business "3D Muscle Journey LLC" where he is a coach and trainer for natural bodybuilders and power lifters. He has been a trainer for over a decade and is a competitive natural bodybuilder and power lifter himself. He has his MS in Exercise Science with a concentration in performance enhancement from the California University of Pennsylvania and is continuing his graduate work at the Auckland University of Technology in New Zealand. His graduate work focuses on nutritional aspects of strength, hypertrophy and fat loss. Copyright (c) 1999-2012 National Strength and Conditioning Association. Use with permission. All rights reserved.