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|[[File:Supercompensation-fatigue-small.png|none|thumb|x300px|Without sufficient recovery time, the fatigue builds up until injury or [[Overtraining Syndrome]] occurs.]]

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=Quantifying monotony=

One approach<ref name="OTMonotony"/> to measuring monotony is statistically analyze the variation in workouts. The first stage is to work out a measure of the daily [[TRIMP]] ([[Training Impulse|TRaining IMPulse]]). From this daily [[TRIMP]] it's possible to calculate the standard deviation for each 7 day period. The relationship between the daily average [[TRIMP]] value and the standard deviation can provide a metric for monotony. The monotony value combined with the overall training level can be used to evaluate the likelihood of [[Overtraining Syndrome]].

=Monotony Calculations=

The original work<ref name="OTMonotony"/> on training monotony used [[TRIMP]]^cr10 and [[TRIMP]]^zone, but I substitute [[TRIMP]]^exp for [[TRIMP]]^zone because of the advantages noted in [[TRIMP]]. From the daily [[TRIMP]] values for a given 7 day period the standard deviation can be calculated. (If there is more than one workout in a day, the [[TRIMP]] values for each are simply added together.) The monotony can be calculated using

Monotony = average([[TRIMP]])/stddev([[TRIMP]])

This gives a value of monotony that tends towards infinity as stddev([[TRIMP]]) tends towards zero, so I cap Monotony to a maximum value of 10. Without this cap, the value tends to be unreasonably sensitive to high levels of monotony. Values of Monotony over 2.0 are generally considered too high, and values below 1.5 are preferable. A high value for Monotony indicates that the training program is ineffective. This could be because the athlete is doing a low level of training; an extreme example would be a well-trained runner doing a single easy mile every day. This would allow for complete recovery, but would not provide the stimulus for improvement and would likely lead to rapid detraining. At the other extreme, doing a hard work out every day would be monotonous and not allow sufficient time to recover. The Training Strain below can help determine the difference between monotonous training that is inadequate and monotonous training that is excessive.

=Training Strain Calculations=

A similar calculation can be used to calculate a value for Training Strain.

Training Strain = sum([[TRIMP]]) * Monotony

The value of Training Strain that leads to actual [[Overtraining Syndrome]] would be specific to each athlete. An elite level athlete will be able to train up much higher levels than a beginner. However this Training Strain provides a better metric of the overall stress that an athlete is undergoing than simply looking at training volume.

=A simple [[TRIMP]]^cr10 based calculator=

This calculator will show the [[TRIMP]]^cr10 values for each day, the Monotony, the total [[TRIMP]]^cr10 for the week and the Training Strain.

i = numArr.length,

v = 0;

while( i-- ){

v += Math.pow( (numArr[ i ] - avg), 2 );

return getNumWithSetDec( stdDev, numOfDec );

};

function doTrimp(dur, cr10, trimp)

{

}

</script>

<form style="font-family: Helvetica,Arial,sans-serif;border: solid 2px #40a0c0" id="MonotonyForm">

<table style="text-align: left;" border="1" cellpadding="1" cellspacing="1">

</form>

</html>

=TRIMP^exp Examples=

For these examples we will use just a few simple workouts. Let's assume a male athlete with a [[Maximum Heart Rate]] of 180 and a [[Resting Heart Rate]] of 40, giving a [[Heart Rate Reserve]] of 140. Let's assume our hypothetical athlete does his easy runs at a 9 min/mile pace and heart rate of 130. We'll use only one of the type of workout, a tempo run his easy runs at a 7 min/mile pace and heart rate of 160. This gives us some TRIMP^exp values for some workouts

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=References=

<references>

<ref name="variabledose">Variable dose-response relationship bet... [Med Sci Sports Exerc. 2003] - PubMed - NCBI http://www.ncbi.nlm.nih.gov/pubmed/12840641 </ref>

</references>