How to monitor training and fatigue

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We live in an age of data, so you might imagine that training should be simple. How could anyone get overtrained if they’re diligently monitoring their heart-rate variability, omega-wave brain activity, hormonal profile, blood levels and lactate kinetics?

Of course, it’s not that simple in practice. Lots of studies have shown promise in linking various types of physiological data to fatigue or training outcomes, but it’s proven to be much harder than expected to nail down exactly what signs to watch for. As a new paper in the British Journal of Sports Medicine points out, this inconsistency could result from “intra-assay and interassay variability, intraindividual and interindividual variability, the influence of circadian and pulsatile rhythms, nutrition and hydration status, climate, psychosocial factors and particular exercise characteristics” - which is another way of saying “We have no idea.”

The paper, by Anna Saw and her colleagues at Deakin University in Australia, pulls together the results of 56 training studies in which both “objective” and “subjective” measures were recorded. The objective measures include categories such as endocrine (cortisol, testosterone...), erythrocytes (haematocrit, haemoglobin...), immune (leukocytes, interleukins...), inflammation and muscle damage (creatine kinase, oxidative stress...), physiological (lactate levels, heart rate, VO2max...), and performance.

The subjective measures, in contrast, basically consist of asking the athletes “How do you feel?” More specifically, various questionnaires look at perceptions of mood, stress and recovery.

So which is better? You can probably see it coming: “Subjective measures reflected acute and chronic training loads with superior sensitivity and consistency than objective measures.” The subjective measures were able to reliably pick up changes resulting from acute increases or decreases in training, and also the more subtle changes resulting from long-term increases.

In about half of the studies, the subjective and objective measures performed equally well. Of the rest of the studies, the subjective measures were more responsive in 85 per cent of them.

Of course, the researchers aren’t proposing we abandon all objective measures. They have their place, and they may well continue to get more useful as research progresses. The point is that subjective measures deserve a little more respect. If you take the time to record even a few comments about how you feel each day in your training log, you’ll eventually notice that big crashes in training rarely appear out of nowhere. Usually you “know” in advance - you just have to make sure you’re listening to yourself.