Developing an Autoregulation Framework

Key Takeaway:

Research suggests that autoregulated training may be more effective than structured, predetermined loading strategies for improving physical qualities [1].

Introduction

Autoregulation in training is a systematic approach that adjusts programming based on real-time assessments of an athlete's performance and perceived exertion. While the concept dates back to the 1940s, research has often been fragmented, with inconsistencies in terminology—such as adaptation, readiness, and fatigue—complicating its practical application. This article builds on the work of Greig et al. (2020) by synthesizing existing knowledge, proposing a framework for operational definitions, and outlining how different autoregulation methods can be applied in practice [2].

The Fitness-Fatigue Model (FFM)

The Fitness-Fatigue Model (FFM) is one of the most influential frameworks in sports science, linking training stimuli to performance outcomes [3]. Developed by Banister et al. [4], the model forms the foundation of many contemporary training approaches. It suggests that a single training session produces two opposing effects:

• A long-lasting, low-magnitude fitness effect
• A short-lasting, high-magnitude fatigue effect [5]

An athlete's performance on any given day is the sum of these effects, along with their baseline performance (Fig. 1).

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In practical terms, this model reinforces the idea that larger training doses lead to greater adaptations, though they also require more recovery time. Since its inception [4], refinements have been made to account for real-world complexities, such as the carryover effects of previous sessions. Despite limitations noted by Hellard et al. [6], the FFM remains a useful foundation for understanding autoregulation.

Using the Fitness-Fatigue Model to Apply Autoregulation

The FFM originally proposed by Banister et al. [4] operates on the premise that performance changes are directly linked to training stimuli [7]. However, later iterations have acknowledged that non-training-related factors—such as sleep, nutrition, and illness—also impact performance [8]. These external stressors can be conceptualized as readiness, a key component of training autoregulation [9].

Research by Greig et al. proposed a reformulated model where an athlete’s daily performance is determined by both training-related and non-training-related influences [2]:

p(t)=p0+FitnessΣ(t)+FatigueΣ(t)+Readiness(t)
where:

• p(t) represents an individual’s performance on day 
• P0  is baseline performance,
• FitnessΣ(t) is the cumulative fitness effect from training sessions,
• FatigueΣ(t) is the cumulative fatigue effect from training,
• Readiness(t) accounts for fluctuations in performance caused by non-training-related factors.

This model suggests that training modifications should only be made when performance deviates beyond expected variations within a structured plan. Understanding whether a performance change is due to fatigue, fitness adaptation, or external factors can help refine how autoregulation is applied.

Practical Application of Autoregulation

Autoregulation can be implemented before a session (pre-session adjustments) or within a session (real-time modifications based on performance feedback).

Pre-Session Autoregulation

Readiness Testing

Assessing an athlete's readiness before training can help guide session adjustments. Common assessments include:

• Countermovement Jump (CMJ)
• RSI (Drop Jump Test or 10-5 Reactive Hops Test)

The CMJ is widely used to monitor neuromuscular readiness and fatigue [10]. Studies suggest that while CMJ height reliably tracks long-term physical changes (≥3 weeks) [11], it may be less sensitive to short-term fluctuations [12]. Fatigue can alter jump mechanics, allowing athletes to maintain height despite reduced force output [12]. Different CMJ calculation methods—such as peak velocity or time in the air—may further influence sensitivity [13]. Despite these limitations, CMJ remains a practical and accessible tool for daily monitoring [10] due to the simplicity in administering.

Using CMJ for Autoregulation

One method of autoregulating training volume is the Minimal Individual Difference (MID) method, as outlined by Claudino et al. [14]. This involves calculating the standard deviation of an athlete’s CMJ height over multiple trials to establish a baseline confidence interval. If pre-session CMJ height deviates beyond this range, training volume can be adjusted accordingly [14].

However, CMJ sensitivity challenges must be considered, particularly in short-term monitoring [12]. Additionally, autoregulating based on CMJ may not be appropriate during overreaching phases, where short-term fatigue is expected. Instead, practitioners must differentiate between expected performance decrements and maladaptive fatigue.

‍Using RSI for Autoregulation‍‍‍

An increasingly popular tool to assess readiness is the Reactive Strength Index (RSI) assessment, often measured using a drop jump or the 10-5 Repeated Jump test. Research by Southey et al. (2023) demonstrates that the 10/5 Repeated Jump Test exhibits good intraday reliability (ICC = 0.87–0.95), making it a practical tool for monitoring changes in performance. This level of reliability allows for the assessment of the smallest worthwhile change (SWC), providing a strong rationale for its use as a monitoring tool, although further research is still needed [15]. Additionally, other studies indicate that the 10-5 Repeated Jump test is sensitive to periods of heightened stress and fatigue, making it a valuable metric for monitoring athletes during demanding training phases [16].

While more research is required, and no single metric can fully indicate readiness, tests assessing neuromuscular fatigue and performance should be used in conjunction with other metrics, such as subjective assessments. These tests can serve as a high-level indicator for coaches and help initiate important conversations about athlete readiness and training adjustments.

In-Session Autoregulation

The most immediate form of autoregulation occurs within a training session. The two most commonly used methods are velocity-based training (VBT) and the repetitions in reserve (RIR) scale.

Velocity-Based Training (VBT)

VBT autoregulates training intensity and volume based on barbell velocity, which has a direct relationship with an athlete’s 1RM [17]. Common VBT applications include:

1. Velocity Zones – Instead of prescribing loads based on %1RM, training is assigned by target velocity ranges (e.g., 0.45–0.55 m/s for ~80% 1RM) [18].
2. Indicator Sets – A multi-set warm-up is used to estimate daily strength levels, allowing for real-time load adjustments [19].
3. Velocity Drop-Off – Training sets are terminated when velocity drops by a set percentage (e.g., 20% or 40%) to manage fatigue and optimize adaptation [20].

Studies show that different velocity drop thresholds produce different outcomes—lower drop-offs (~20%) prioritize power, while higher drop-offs (~40%) enhance hypertrophy [20].

Repetitions in Reserve (RIR)

RIR is a subjective autoregulation tool adapted from the Borg RPE scale [21]. It estimates how close an athlete is to muscular failure based on rep-based effort perception [22].

• Load Adjustments: If a session prescribes 3 sets of 10 at an RIR of 2, the athlete selects a weight where they would fail at 12 reps but stop at 10. Adjustments are made if the perceived effort deviates from the target RIR [22].
• Volume Adjustments: The RIR stop-point method regulates training volume instead of load. For example, if an athlete completes a set with 2 reps left in reserve, they perform another set. If they reach failure sooner, the session ends [21].

Research suggests that Repetitions in Reserve (RIR)-based autoregulation is effective for compound lifts such as squats, deadlifts, and bench press [23]. While RIR requires minimal equipment to administer and can be an effective tool, it relies heavily on athletes having a strong understanding of their training intensity. Athletes with limited training age and experience may struggle to accurately gauge what different RIR levels "feel like," which can reduce the reliability of this method. Additionally, RIR is inherently subjective, making it challenging to hold athletes accountable. For example, athletes who wish to "go through the motions" may overestimate their proximity to failure. Although this issue can be mitigated through education, it remains a potential limitation to consider. Further research is needed to explore the application of RIR across a wider range of exercises, particularly those where proximity to failure may be harder to assess [24].

Conclusion

Autoregulation allows for individualized training adjustments, optimizing performance while managing fatigue. By incorporating frameworks like the Fitness-Fatigue Model and tools such as VBT and RIR, practitioners can tailor training based on an athlete’s daily readiness. While autoregulation methods offer flexibility, their effectiveness depends on proper implementation—understanding how to differentiate between fitness, fatigue, and readiness ensures the best application in both strength training and sport performance settings. Continued research will refine these methods, enhancing their practical use in elite and sub-elite environments.

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References

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