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[[File:Tired athlete.jpg|right|thumb|350px|Monotonous training produces increased fatigue and is a risk factor for [[Overtraining]] and [[Overtraining Syndrome]].]]
Training Monotony is not about boredom, but is a way of measuring the similarity of daily training. By calculating a simple number, it's easy to evaluate a training program, and understand its effectiveness. Training Monotony can be calculated using a spreadsheet or via my [[SportTracks Dailymile Plugin]]. The calculation is based on [[TRIMP| each day's training stress]], dividing the average by the standard deviation for each rolling seven day period. =Training Monotony and Overtraining=It is long been recognized the athletes cannot train hard every day. Modern training plans recommend a few hard days per week, with the other days as easier or rest days. A lack of variety in training stress, known as training monotonyTraining Monotony, is considered a key factor in causing [[Overtraining Syndrome]]<ref name="Meeusen-2013"/><ref name="Armstrong-2002"/>. There is also evidence<ref name="Busso-2003"/> that increased training frequency results in reduced performance benefits from identical training sessions as well as increased fatigue. Training monotony can be mathematically evaluated by [[TRIMP| measuring each day's training stress]], then dividing the average by the standard deviation for each seven day period. Monotony can be used to modify [[Training Stress Balance]], a method for evaluating the effect of training over time. Training Monotony is calculated as part of the [[SportTracks Dailymile Plugin]].
=Training Monotony and Supercompensation=
Training Monotony is related to [[Supercompensation]] and the need for adequate rest to recover from training.
One approach<ref name="Foster-1998"/> 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="Foster-1998"/> on training monotony used [[TRIMP]]<sup>cr10</sup> and [[TRIMP]]<sup>zone</sup>, but I substitute [[TRIMP]]<sup>exp</sup> for [[TRIMP]]<sup>zone</sup> because of the advantages noted in [[TRIMP]]. (Simply using daily mileage or duration could be used to get an estimate of Training Monotony.) 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.
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<sup>exp</sup> values for some workouts.
{| class="wikitable"
!
! Miles
! Duration
|-
| <span style='color:#FF0000'>Stdev</span>
| <span style='color:#FF0000'> </span>
| <span style='color:#FF0000'>70</span>
|-
| <span style='color:#00B050'>Avg</span>
| <span style='color:#00B050'> </span>
| <span style='color:#00B050'>95</span>
|-
| Total
|
| 665
|-
| Monotony
|
| 1.36
|-
| Training Strain
|
| 903
|}
|-
| <span style='color:#FF0000'>Stdev</span>
| <span style='color:#FF0000'> </span>
| <span style='color:#FF0000'>77</span>
|-
| <span style='color:#00B050'>Avg</span>
| <span style='color:#00B050'> </span>
| <span style='color:#00B050'>88</span>
|-
| Total
|
| 614
|-
| Monotony
|
| 1.15
|-
| Training Strain
|
| 704
|}
|-
| <span style='color:#FF0000'>Stdev</span>
| <span style='color:#FF0000'> </span>
| <span style='color:#FF0000'>82</span>
|-
| <span style='color:#00B050'>Avg</span>
| <span style='color:#00B050'> </span>
| <span style='color:#00B050'>80</span>
|-
| Total
|
| 563
|-
| Monotony
|
| 0.98
|-
| Training Strain
|
| 553
|}
|-
| <span style='color:#FF0000'>Stdev</span>
| <span style='color:#FF0000'> </span>
| <span style='color:#FF0000'>13</span>
|-
| <span style='color:#00B050'>Avg</span>
| <span style='color:#00B050'> </span>
| <span style='color:#00B050'>59</span>
|-
| Total
|
| 414
|-
| Monotony
|
| 4.69
|-
| Training Strain
|
| 1,944
|}
