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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]].]]
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 monotony, is considered a key factor in causing [[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 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.
|}
=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 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]]. 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.
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=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"
!
! Miles
! Duration
| 4
| 36
| 7951
|-
| Easy
| 6
| 54
| 11976
|-
| Easy
| 10
| 90
| 198127
|-
| Easy
| 20
| 180
| 396254
|-
| Tempo
| 4
| 28
| 6280
|-
| Tempo
| 8
| 56
| 123159
|}
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, a total of 50 miles.
! Monday
! Tempo 4
! 6280
|-
| Tuesday
| Easy 4
| 7951
|-
| Wednesday
| Easy 10
| 198127
|-
| Thursday
| Easy 4
| 7951
|-
| Friday
| Easy 4
| 7951
|-
| Saturday
| Easy 20
| 396254
|-
| Sunday
| Easy 4
| 7951
|-
| <span style='color:#FF0000'>Stdev</span>
| <span style='color:#FF0000'> </span>
| <span style='color:#FF0000'>11370</span>
|-
| <span style='color:#00B050'>Avg</span>
| <span style='color:#00B050'> </span>
| <span style='color:#00B050'>13995</span>
|-
| Total
|
| 972665
|-
| Monotony
|
| 1.2336
|-
| Training Strain
|
| 1,191903
|}
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 79by 51, but the Monotony of drops more significantly to 1.04 15 and a the Training Strain drop 260drops by 199. So the mileage has dropped about 9%, but the Training Strain has dropped by 22%.
{| class="wikitable"
! Monday
! Tempo 4
! 6280
|-
| Tuesday
| Easy 4
| 7951
|-
| Wednesday
| Easy 10
| 198127
|-
| Thursday
| Easy 4
| 7951
|-
| Friday
| Easy 4
| 7951
|-
| Saturday
| Easy 20
| 396254
|-
| Sunday
| <span style='color:#FF0000'>Stdev</span>
| <span style='color:#FF0000'> </span>
| <span style='color:#FF0000'>12277</span>
|-
| <span style='color:#00B050'>Avg</span>
| <span style='color:#00B050'> </span>
| <span style='color:#00B050'>12888</span>
|-
| Total
|
| 893614
|-
| Monotony
|
| 1.0415
|-
| Training Strain
|
| 932704
|}
A further rest day on Tuesday drops the Training Strain by a further 2221%.
{| class="wikitable"
! Monday
! Tempo 4
! 6280
|-
| Tuesday
| Wednesday
| Easy 10
| 198127
|-
| Thursday
| Easy 4
| 7951
|-
| Friday
| Easy 4
| 7951
|-
| Saturday
| Easy 20
| 396254
|-
| Sunday
| <span style='color:#FF0000'>Stdev</span>
| <span style='color:#FF0000'> </span>
| <span style='color:#FF0000'>13082</span>
|-
| <span style='color:#00B050'>Avg</span>
| <span style='color:#00B050'> </span>
| <span style='color:#00B050'>11680</span>
|-
| Total
|
| 814563
|-
| Monotony
|
| 0.9098
|-
| Training Strain
|
| 730553
|}
If we compare this with an extreme example of a monotonous training plan, we have a slightly lower mileage (46 v 50), and a 57% lower total TRIMP<sup>exp</sup> (414 v 927), but the monotony is remarkably high at 4.7 and the training strain is 12.6x 2x higher. 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"
! Monday
=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]]
