Difference between revisions of "Training Monotony"

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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.]]
 
|[[File:Supercompensation-fatigue-small.png|none|thumb|x300px|Without sufficient recovery time, the fatigue builds up until injury or [[Overtraining Syndrome]] occurs.]]
 
|}
 
|}
−
 
 
=Quantifying monotony=
 
=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]].  
 
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=
 
=Monotony Calculations=
 
The original work<ref name="OTMonotony"/> 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
 
The original work<ref name="OTMonotony"/> 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]])
 
  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.  
 
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=
 
=Training Strain Calculations=
 
A similar calculation can be used to calculate a value for Training Strain.  
 
A similar calculation can be used to calculate a value for Training Strain.  
 
  Training Strain = sum([[TRIMP]]) * Monotony
 
  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.  
 
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]]<sup>cr10</sup> based calculator=
 
=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.  
 
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.  
Line 48: Line 44:
 
i = numArr.length,
 
i = numArr.length,
 
v = 0;
 
v = 0;
−
 
 
while( i-- ){
 
while( i-- ){
 
v += Math.pow( (numArr[ i ] - avg), 2 );
 
v += Math.pow( (numArr[ i ] - avg), 2 );
Line 60: Line 55:
 
return getNumWithSetDec( stdDev, numOfDec );
 
return getNumWithSetDec( stdDev, numOfDec );
 
};
 
};
−
 
 
function doTrimp(dur, cr10, trimp)
 
function doTrimp(dur, cr10, trimp)
 
{
 
{
Line 91: Line 85:
 
}
 
}
 
</script>
 
</script>
−
 
 
<form style="font-family: Helvetica,Arial,sans-serif;border: solid 2px #40a0c0" id="MonotonyForm">
 
<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">
 
   <table style="text-align: left;" border="1" cellpadding="1" cellspacing="1">
Line 149: Line 142:
 
</form>
 
</form>
 
</html>
 
</html>
−
 
 
=TRIMP<sup>exp</sup> Examples=
 
=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
 
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"
 
{| class="wikitable"
−
!Type!!Miles!!Name!!Duration!!TRIMP<sup>exp</sup>
+
!  
 +
! Miles
 +
! Duration
 +
! TRIMP<sup>exp</sup>  
 
|-
 
|-
−
|Easy ||4||Easy 4||36||11,132
+
| Easy  
 +
| 4
 +
| 36
 +
| 79
 
|-
 
|-
−
|Easy ||6||Easy 6||54||16,699
+
| Easy  
 +
| 6
 +
| 54
 +
| 119
 
|-
 
|-
−
|Easy ||10||Easy 10||90||27,831
+
| Easy  
 +
| 10
 +
| 90
 +
| 198
 
|-
 
|-
−
|Easy ||20||Easy 20||180||55,662
+
| Easy  
 +
| 20
 +
| 180
 +
| 396
 
|-
 
|-
−
|Tempo||4||Tempo 4||28||17,421
+
| Tempo
−
|-
+
| 4
−
|Tempo||8||Tempo 8||56||34,841
+
| 28
 +
| 62
 
|-
 
|-
 +
| Tempo
 +
| 8
 +
| 56
 +
| 123
 
|}
 
|}
−
 
+
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.
−
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, total TRIMP<sup>exp</sup> of 145K, Monotony of 1.35 and a Training Strain of 197K.
 
 
{| class="wikitable"
 
{| class="wikitable"
−
|Monday||Tempo 4||17,421
+
! Monday
 +
! Tempo 4
 +
! 62
 
|-
 
|-
−
|Tuesday||Easy 4||11,132
+
| Tuesday
 +
| Easy 4
 +
| 79
 
|-
 
|-
−
|Wednesday||Easy 10||27,831
+
| Wednesday
 +
| Easy 10
 +
| 198
 
|-
 
|-
−
|Thursday||Easy 4||11,132
+
| Thursday
 +
| Easy 4
 +
| 79
 
|-
 
|-
−
|Friday||Easy 4||11,132
+
| Friday
 +
| Easy 4
 +
| 79
 
|-
 
|-
−
|Saturday||Easy 20||55,662
+
| Saturday
 +
| Easy 20
 +
| 396
 
|-
 
|-
−
|Sunday||Easy 4||11,132
+
| Sunday
 +
| Easy 4
 +
| 79
 
|-
 
|-
−
|Stdev||||15,353
+
| <span style='color:#FF0000'>Stdev</span>
 +
| <span style='color:#FF0000'> </span>
 +
| <span style='color:#FF0000'>113</span>
 
|-
 
|-
−
|Avg||||20,777
+
| <span style='color:#00B050'>Avg</span>
 +
| <span style='color:#00B050'> </span>
 +
| <span style='color:#00B050'>139</span>
 
|-
 
|-
−
|Total||||145,442
+
| Total
 +
|
 +
| 972
 
|-
 
|-
−
|Monotony||||1.35
+
| Monotony
−
|-
+
|
−
|Training Strain||||196,824
+
| 1.23
 
|-
 
|-
 +
| Training Strain
 +
| 
 +
| 1,191
 
|}
 
|}
−
 
+
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 79, but the Monotony of drops more significantly to 1.04 and a Training Strain drop 260. So the mileage has dropped about 9%, but the Training Strain has dropped by 22%.  
−
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 134K, but the Monotony of drops more significantly to 1.14 and a Training Strain to 154K. So the mileage has dropped about 9%, but the Training Strain has dropped by 28%.  
 
 
{| class="wikitable"
 
{| class="wikitable"
−
|Monday||Tempo 4||17,421
+
! Monday
 +
! Tempo 4
 +
! 62
 
|-
 
|-
−
|Tuesday||Easy 4||11,132
+
| Tuesday
 +
| Easy 4
 +
| 79
 
|-
 
|-
−
|Wednesday||Easy 10||27,831
+
| Wednesday
 +
| Easy 10
 +
| 198
 
|-
 
|-
−
|Thursday||Easy 4||11,132
+
| Thursday
 +
| Easy 4
 +
| 79
 
|-
 
|-
−
|Friday||Easy 4||11,132
+
| Friday
 +
| Easy 4
 +
| 79
 
|-
 
|-
−
|Saturday||Easy 20||55,662
+
| Saturday
 +
| Easy 20
 +
| 396
 
|-
 
|-
−
|Sunday||Rest||0
+
| Sunday
 +
| Rest
 +
| 0
 
|-
 
|-
−
|Stdev||||16,780
+
| <span style='color:#FF0000'>Stdev</span>
 +
| <span style='color:#FF0000'> </span>
 +
| <span style='color:#FF0000'>122</span>
 
|-
 
|-
−
|Avg||||19,187
+
| <span style='color:#00B050'>Avg</span>
 +
| <span style='color:#00B050'> </span>
 +
| <span style='color:#00B050'>128</span>
 
|-
 
|-
−
|Total||||134,310
+
| Total
 +
|
 +
| 893
 
|-
 
|-
−
|Monotony||||1.14
+
| Monotony
−
|-
+
|
−
|Training Strain||||153,574
+
| 1.04
 
|-
 
|-
 +
| Training Strain
 +
| 
 +
| 932
 
|}
 
|}
−
 
+
A further rest day on Tuesday drops the Training Strain by a further 22%.  
−
A further rest day on Tuesday drops the Training Strain by a further 27%.  
 
 
{| class="wikitable"
 
{| class="wikitable"
−
|Monday||Tempo 4||17,421
+
! Monday
 +
! Tempo 4
 +
! 62
 
|-
 
|-
−
|Tuesday||Rest||0
+
| Tuesday
 +
| Rest
 +
| 0
 
|-
 
|-
−
|Wednesday||Easy 10||27,831
+
| Wednesday
 +
| Easy 10
 +
| 198
 
|-
 
|-
−
|Thursday||Easy 4||11,132
+
| Thursday
 +
| Easy 4
 +
| 79
 
|-
 
|-
−
|Friday||Easy 4||11,132
+
| Friday
 +
| Easy 4
 +
| 79
 
|-
 
|-
−
|Saturday||Easy 20||55,662
+
| Saturday
 +
| Easy 20
 +
| 396
 
|-
 
|-
−
|Sunday||Rest||0
+
| Sunday
 +
| Rest
 +
| 0
 
|-
 
|-
−
|Stdev||||17,955
+
| <span style='color:#FF0000'>Stdev</span>
 +
| <span style='color:#FF0000'> </span>
 +
| <span style='color:#FF0000'>130</span>
 
|-
 
|-
−
|Avg||||17,597
+
| <span style='color:#00B050'>Avg</span>
 +
| <span style='color:#00B050'> </span>
 +
| <span style='color:#00B050'>116</span>
 
|-
 
|-
−
|Total||||123,178
+
| Total
 +
|
 +
| 814
 
|-
 
|-
−
|Monotony||||0.98
+
| Monotony
−
|-
+
|
−
|Training Strain||||120,723
+
| 0.90
 
|-
 
|-
 +
| Training Strain
 +
| 
 +
| 730
 
|}
 
|}
−
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 lower total TRIMP<sup>exp</sup> (128K v 135K), but the monotony is remarkably high at 4.7 and the training strain is more than three times 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.  
+
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 1.6x 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"
 
{| class="wikitable"
−
|Monday||Easy 6||16,699
+
! Monday
−
|-
+
! Easy 6
−
|Tuesday||Easy 6||16,699
+
! 54
 
|-
 
|-
−
|Wednesday||Easy 6||16,699
+
| Tuesday
 +
| Easy 6
 +
| 54
 
|-
 
|-
−
|Thursday||Easy 6||16,699
+
| Wednesday
 +
| Easy 10
 +
| 90
 
|-
 
|-
−
|Friday||Easy 6||16,699
+
| Thursday
 +
| Easy 6
 +
| 54
 
|-
 
|-
−
|Saturday||Easy 10||27,831
+
| Friday
 +
| Easy 6
 +
| 54
 
|-
 
|-
−
|Sunday||Easy 6||16,699
+
| Saturday
 +
| Easy 6
 +
| 54
 
|-
 
|-
−
|Stdev||||3,895
+
| Sunday
 +
| Easy 6
 +
| 54
 
|-
 
|-
−
|Avg||||18,289
+
| <span style='color:#FF0000'>Stdev</span>
 +
| <span style='color:#FF0000'> </span>
 +
| <span style='color:#FF0000'>13</span>
 
|-
 
|-
−
|Total||||128,025
+
| <span style='color:#00B050'>Avg</span>
 +
| <span style='color:#00B050'> </span>
 +
| <span style='color:#00B050'>59</span>
 
|-
 
|-
−
|Monotony||||4.70
+
| Total
 +
|
 +
| 414
 
|-
 
|-
−
|Training Strain||||601,092
+
| Monotony
 +
|
 +
| 4.69
 
|-
 
|-
 +
| Training Strain
 +
| 
 +
| 1,944
 
|}
 
|}
−
 
 
=References=
 
=References=
 
<references>
 
<references>
Line 287: Line 387:
 
<ref name="variabledose">Variable dose-response relationship bet... [Med Sci Sports Exerc. 2003] - PubMed - NCBI http://www.ncbi.nlm.nih.gov/pubmed/12840641 </ref>
 
<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>
 
</references>
 +
[[Category:Advanced]]
 +
[[Category:Science]]

Revision as of 10:03, 14 November 2014

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[1][2]. There is also evidence[3] 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 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.

1 Training Monotony and Supercompensation

Training Monotony is related to Supercompensation and the need for adequate rest to recover from training.

Exercise produces a temporary decrease in fitness, followed by a recovery and Supercompensation.
With sufficient rest between workouts, fitness improves.
Without sufficient recovery time, the fatigue builds up until injury or Overtraining Syndrome occurs.

2 Quantifying monotony

One approach[4] 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). 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.

3 Monotony Calculations

The original work[4] on training monotony used TRIMPcr10 and TRIMPzone, but I substitute TRIMPexp for TRIMPzone 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.

4 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.

5 A simple TRIMPcr10 based calculator

This calculator will show the TRIMPcr10 values for each day, the Monotony, the total TRIMPcr10 for the week and the Training Strain.

Day Duration (min) CR10 Rating TRIMP(CR10)
1
2
3
4
5
6
7



6 TRIMPexp 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 TRIMPexp values for some workouts

Miles Duration TRIMPexp
Easy 4 36 79
Easy 6 54 119
Easy 10 90 198
Easy 20 180 396
Tempo 4 28 62
Tempo 8 56 123

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 62
Tuesday Easy 4 79
Wednesday Easy 10 198
Thursday Easy 4 79
Friday Easy 4 79
Saturday Easy 20 396
Sunday Easy 4 79
Stdev 113
Avg 139
Total 972
Monotony 1.23
Training Strain 1,191

If we give our athlete a single day's rest on Sunday, we reduce the mileage by 4 miles to 46 miles, total TRIMPexp goes down 79, but the Monotony of drops more significantly to 1.04 and a Training Strain drop 260. So the mileage has dropped about 9%, but the Training Strain has dropped by 22%.

Monday Tempo 4 62
Tuesday Easy 4 79
Wednesday Easy 10 198
Thursday Easy 4 79
Friday Easy 4 79
Saturday Easy 20 396
Sunday Rest 0
Stdev 122
Avg 128
Total 893
Monotony 1.04
Training Strain 932

A further rest day on Tuesday drops the Training Strain by a further 22%.

Monday Tempo 4 62
Tuesday Rest 0
Wednesday Easy 10 198
Thursday Easy 4 79
Friday Easy 4 79
Saturday Easy 20 396
Sunday Rest 0
Stdev 130
Avg 116
Total 814
Monotony 0.90
Training Strain 730

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 TRIMPexp (414 v 927), but the monotony is remarkably high at 4.7 and the training strain is 1.6x 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.

Monday Easy 6 54
Tuesday Easy 6 54
Wednesday Easy 10 90
Thursday Easy 6 54
Friday Easy 6 54
Saturday Easy 6 54
Sunday Easy 6 54
Stdev 13
Avg 59
Total 414
Monotony 4.69
Training Strain 1,944

7 References

  1. ↑ Prevention, diagnosis and treatment of the Overtraining Syndrome http://www.ingentaconnect.com/content/tandf/tejs/2006/00000006/00000001/art00001
  2. ↑ The unknown mechanism of the overtraining syndrom... [Sports Med. 2002] - PubMed - NCBI http://www.ncbi.nlm.nih.gov/pubmed/11839081
  3. ↑ Variable dose-response relationship bet... [Med Sci Sports Exerc. 2003] - PubMed - NCBI http://www.ncbi.nlm.nih.gov/pubmed/12840641
  4. ↑ 4.0 4.1 Monitoring training in athletes with re... [Med Sci Sports Exerc. 1998] - PubMed - NCBI http://www.ncbi.nlm.nih.gov/pubmed/9662690
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