Adaptive training fine-tunes training according to recovery status
It is a well-known phenomenon that the effects of strength and endurance training can vary between individuals even when the training has been similar. Behind this are several possible factors such as individual differences in training background, nutrition, sleep quality and genetics. External stress from life outside training can also weaken training responses. It is important to understand that even a single person’s training responses are not always the same from one training period to another. Training programmes are naturally built around an individual’s needs and goals, but it is equally important to fine-tune training to match the current situation. This principle is followed in adaptive training, where training load is adjusted according to the individual’s recovery and readiness.
Recovery is a broad concept
Recovery is a broad concept involving both physical and psychological dimensions. As useful as it is, there is no single metric that captures all aspects of recovery. When assessing recovery status, we can rely on subjective measures based on the individual’s own assessment, as well as objective measures that assess physiological functions at rest or during exertion. In general, subjective measures react more sensitively to changes in training load than objective measures. At the same time, a change in training load does not automatically mean a change in an individual’s recovery status, because similar training can stress people differently. The most reliable outcome is achieved when information from different perspectives is combined to build a complete picture.
How should recovery be measured?
Perceived recovery status can be assessed simply using a numerical scale, for example from 1 to 10. In endurance training, a question about perceived readiness to train has been found to be useful in identifying overreaching. Perception of performance relative to expectations can also provide information about load status. Asking about muscle soreness, in turn, can provide information on muscle-level recovery that may not always be captured by objective measures. Numerical estimates should always be interpreted in relation to the individual’s usual results. Persistently low mood beyond what is expected or a greater-than-expected decline in perceived recovery are situations worth considering in training planning.
With the widespread use of smartwatches, resting heart rate and heart rate variability have become increasingly used indicators of physiological recovery. Heart rate variability can be used to indirectly assess autonomic nervous system activity, especially the parasympathetic branch. In a good recovery state and with low stress load, parasympathetic activity is high, whereas in the opposite situation sympathetic activity dominates. Heart rate variability is influenced by both physical and psychological stress, so it does not reflect only the load caused by training. The general idea is that when parasympathetic activity is lower than normal, the body’s ability to adapt to training is also reduced.
Evidence for adaptive training based on recovery status
In endurance training research, training adjusted on the basis of heart rate variability has produced somewhat better training responses than fixed training prescriptions. There has also been the advantage that a larger proportion of individuals have developed at least moderately. In the case of strength training, the evidence for heart rate variability-guided programming is not yet as strong, and in some studies the training adaptations in fixed and adaptive training groups have been quite similar. However, in strength training there is positive evidence for so-called autoregulation, meaning adjustments to training content based on the day’s readiness, such as perceived exertion or movement speed, rather than a tightly pre-scheduled program.
In summary
Reliable assessment of recovery status requires sufficiently regular monitoring. This helps us learn the normal range of variation in an individual’s results and identify deviations from it. When monitoring recovery, it is important to be willing to react to results that deviate from expectations. Measurement should not be an end in itself; the information should also be used actively as part of training.
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