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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[[Overtraining Syndrome]]<ref name="OTEcssPosMeeusen-2013"/><ref name="OTDepressionArmstrong-2002"/>. There is also evidence<ref name="variabledoseBusso-2003"/> that increased training frequency results in reduced performance benefits from identical training sessions as well as increased fatigue. =Training Monotony and Supercompensation=Training Monotony is related to [[Supercompensation]] and the need for adequate rest to recover from training. {| class="wikitable" |- valign="top"|[[File:Supercompensation-small.png|none|thumb|x300px|Exercise produces a temporary decrease in fitness, followed by a recovery and [[Supercompensation]].]]|[[File:Supercompensation-continued-small.png|none|thumb|x300px|With sufficient rest between workouts, fitness improves.]]|[[File:Supercompensation-fatigue-small.png|none|thumb|x300px|Without sufficient recovery time, the fatigue builds up until injury or [[Overtraining Syndrome]] occurs.]]|}
=Quantifying monotony=
One approach<ref name="OTMonotonyFoster-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[[Overtraining Syndrome]].
=Monotony Calculations=
The original work<ref name="OTMonotonyFoster-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.
=Training Strain Calculations=
A similar calculation can be used to calculate a value for Training Strain.
Training Strain = sum([[TRIMP]]) * MonotonyThe value of Training Strain that leads to actual overtraining [[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]]<sup>cr10</sup> based calculator=This calculator will show the [[TRIMP]]<sup>cr10</sup> values for each day, the Monotony, the total [[TRIMP]]<sup>cr10</sup> for the week and the Training Strain.
<html>
<script type="text/javascript">
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">
<tr>
<td>1</td>
<td><input maxlength="3" size="3" id="dur_1" value="100"></td> <td><input maxlength="3" size="3" id="cr10_1" value="40"></td>
<td><label id="trimp_1"></label></td>
</tr>
<tr>
<td>2</td>
<td><input maxlength="3" size="3" id="dur_2" value="190"></td> <td><input maxlength="3" size="3" id="cr10_2" value="30"></td>
<td><label id="trimp_2"></label></td>
</tr>
</form>
</html>
=TRIMP<sup>exp</sup> 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<sup>exp</sup> values for some workouts.
{| class="wikitable"
!Type! !Miles!!Name!!Duration!!TRIMP<sup>exp</sup>
|-
|Easy ||4||Easy 4||36||11,13251
|-
|Easy ||6||Easy 6||54||16,69976
|-
|Easy ||10||Easy 10||90||27,831127
|-
|Easy ||20||Easy 20||180||55,662254
|-
|Tempo||4||Tempo 4||28||17,421|-|Tempo||8||Tempo 8||56||34,84180
|-
| Tempo
| 8
| 56
| 159
|}
Here is a sample week's workout with three harder workouts, a 4 mile tempo, a 10 mile mid-long run and a 20 mile long run with four mile easy runs on the other days. This is 50 miles, a total TRIMP<sup>exp</sup> of 145K, Monotony of 1.35 and a Training Strain of 197K50 miles.
{| class="wikitable"
|-
|Wednesday|Tuesday|Easy 10|4|27,83151
|-
|Thursday|Wednesday|Easy 4|10|11,132127
|-
|Friday|Thursday|Easy 4||11,13251
|-
|Saturday|Friday|Easy 20|4|55,66251
|-
|Sunday|Saturday|Easy 4|20|11,132254
|-
|StdevSunday|Easy 4|||15,35351
|-
|Avg<span style='color:#FF0000'>Stdev</span>|<span style='color:#FF0000'> </span>|||20,777<span style='color:#FF0000'>70</span>
|-
|Total<span style='color:#00B050'>Avg</span>|<span style='color:#00B050'> </span>|||145,442<span style='color:#00B050'>95</span>
|-
|MonotonyTotal| |||1.35665
|-
|Training StrainMonotony| |||196,8241.36
|-
| Training Strain
|
| 903
|}
If we give our athlete a single day's rest on Sunday, we reduce the mileage by 4 miles to 46 miles, total TRIMP<sup>exp</sup> goes down 9K to 134Kby 51, but the Monotony of drops more significantly to 1.14 15 and a the Training Strain to 154Kdrops by 199. So the mileage has dropped about 9%, but the Training Strain has dropped by 2822%.
{| class="wikitable"
|-
|Wednesday|Tuesday|Easy 10|4|27,83151
|-
|Thursday|Wednesday|Easy 4|10|11,132127
|-
|Friday|Thursday|Easy 4||11,13251
|-
|Saturday|Friday|Easy 20|4|55,66251
|-
|SundaySaturday|Easy 20|Rest||0254
|-
|StdevSunday|Rest|||16,7800
|-
|Avg<span style='color:#FF0000'>Stdev</span>|<span style='color:#FF0000'> </span>|||19,187<span style='color:#FF0000'>77</span>
|-
|Total<span style='color:#00B050'>Avg</span>|<span style='color:#00B050'> </span>|||134,310<span style='color:#00B050'>88</span>
|-
|MonotonyTotal| |||1.14614
|-
|Training StrainMonotony| |||153,5741.15
|-
| Training Strain
|
| 704
|}
A further rest day on Tuesday drops the Training Strain by a further 2721%.
{| class="wikitable"
|-
|Tuesday||Rest||0
|-
|Wednesday||Easy 10||27,831127
|-
|Thursday||Easy 4||11,13251
|-
|Friday||Easy 4||11,13251
|-
|Saturday||Easy 20||55,662254
|-
|Sunday||Rest||0
|-
|<span style='color:#FF0000'>Stdev</span>|<span style='color:#FF0000'> </span>|||17,955<span style='color:#FF0000'>82</span>
|-
|<span style='color:#00B050'>Avg</span>|<span style='color:#00B050'> </span>|||17,597<span style='color:#00B050'>80</span>
|-
|Total| |||123,178563
|-
|Monotony||| |0.98|-|Training Strain||||120,723
|-
| Training Strain
|
| 553
|}
If we compare this with an extreme example of a monotonous training plan, we have a slightly lower mileage (46 v 50), and a considerably 57% lower total TRIMP<sup>exp</sup> (128K 414 v 135K927), but the monotony is remarkably high at 4.7 and the training strain is more than three times 2.2x higher (601K v 197K). In practice, there would be greater day to day variations, even within the same 6 mile easy run, so the results would not be quite so dramatic.
{| class="wikitable"
|-
|Wednesday|Tuesday|Easy 6||16,69954
|-
|Thursday|Wednesday|Easy 6|10|16,69990
|-
|Friday|Thursday|Easy 6||16,69954
|-
|Saturday|Friday|Easy 10|6|27,83154
|-
|Sunday|Saturday|Easy 6||16,69954
|-
|StdevSunday|Easy 6|||3,89554
|-
|Avg<span style='color:#FF0000'>Stdev</span>|<span style='color:#FF0000'> </span>|||18,289<span style='color:#FF0000'>13</span>
|-
|Total<span style='color:#00B050'>Avg</span>|<span style='color:#00B050'> </span>|||128,025<span style='color:#00B050'>59</span>
|-
|MonotonyTotal| |||4.70414
|-
|Training StrainMonotony| |||601,0924.69
|-
| Training Strain
|
| 1,944
|}
=References=
<references>
<ref name="OTEcssPosMeeusen-2013">R. Meeusen, M. Duclos, C. Foster, A. Fry, M. Gleeson, D. Nieman, J. Raglin, G. Rietjens, J. Steinacker, Prevention, diagnosis , and treatment of the Overtraining Syndrome overtraining syndrome: joint consensus statement of the European College of Sport Science and the American College of Sports Medicine., Med Sci Sports Exerc, volume 45, issue 1, pages 186-205, Jan 2013, doi [http://wwwdx.ingentaconnectdoi.comorg/content10.1249/tandfMSS.0b013e318279a10a 10.1249/tejsMSS.0b013e318279a10a], PMID [http:/2006/00000006www.ncbi.nlm.nih.gov/00000001pubmed/art00001 23247672 23247672]</ref><ref name="OTMonotonyFoster-1998">C. Foster, Monitoring training in athletes with rereference to overtraining syndrome... [, Med Sci Sports Exerc. , volume 30, issue 7, pages 1164-8, Jul 1998] - PubMed - NCBI , PMID [http://www.ncbi.nlm.nih.gov/pubmed/9662690 9662690]</ref><ref name="OTDepressionArmstrong-2002">LE. Armstrong, JL. VanHeest, The unknown mechanism of the overtraining syndromsyndrome: clues from depression and psychoneuroimmunology... [, Sports Med. , volume 32, issue 3, pages 185-209, 2002] - PubMed - NCBI , PMID [http://www.ncbi.nlm.nih.gov/pubmed/11839081 11839081]</ref><ref name="variabledoseBusso-2003">T. Busso, Variable dose-response relationship betbetween exercise training and performance... [, Med Sci Sports Exerc, volume 35, issue 7, pages 1188-95, Jul 2003, doi [http://dx.doi.org/10.1249/01.MSS.0000074465.13621. 200337 10.1249/01.MSS.0000074465.13621.37] - PubMed - NCBI , PMID [http://www.ncbi.nlm.nih.gov/pubmed/12840641 12840641]</ref>
</references>
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[[Category:Science]]
