Entries in Heart Rate (5)

Tuesday
Oct252011

7 x 4 min step test protocol

Information for Athletes and NTC Scientists

(To be used from October 2006 - Updated 27/12/08)
Compiled by: Tony Rice
Dept of Physiology: Australian Institute of Sport and Australian Rowing
Office: (02) 6214-7891
Email: Tony.Rice@ausport.gov.au


Introduction

The laboratory test protocol adopted by Rowing Australia aims to provide detailed physiological information of the rower’s submaximal capacity and efficiency and to measure maximal performance parameters in a time efficient manner. To do this a 7 x 4 min protocol has been implemented across the country since 2006. For the 2009-2012 Olympic cycle the standard protocol will change to reflect new information that has been presented by Ivan Hooper and others on the use of sliders and lower drag factors. The main aims of altering the protocol are to 1. more accurately reflect the stroke rate and drive:recovery ratio of on-water rowing and 2. minimise the risk of injury that may result from considerable time being spent on the ergometer with significant lower back load. Implementation of the new drag factor settings and sliders will be mandatory from Nov 2009.

For the 2008-2009 season each SIS/SAS will be able to asked to do complete the laboratory test in one of two ways:
1. If sliders are available then it is asked the laboratory completes the test protocol using sliders and the updated drag factor settings
2. If sliders are not available then it is asked the laboratory completes the test protocol using the old drag factor settings as per the previous Olympic cycle

The standard laboratory test will be completed at least two times within each season (all dates to be communicated early 2009 and confirmed at the beginning of each season). Rowing Australia and the
National Rowing Centre of Excellence (NRCE) require a standard summary of data on all SIS/SAS athletes who are aspiring for National Selection in the current season to be returned to the Sports
Science Coordinator shortly after the completion of each testing period.

NB Testing on Concept IID ergometers - there are no significant differences in the physiological responses to either the IIC or IID ergo and thus tests can be completed on either one with the proviso that all tests for an individual athlete are carried out on the same ergometer throughout the rowing season and every is made to continue using the same ergometer for all subsequent seasons.
The following information is designed as a detailed guide to the testing methods.

7 x 4 min Step Test Protocol Laboratory Environment and Subject Preparation

Training

The athlete must not train at all in the 12 hours preceding the test. On the day before the test, the afternoon training session should consist of no more than 12 km on the water, and should be of low intensity (T3-T2 range). There should be no heavy weight training, or exercise to which the athlete is not accustomed. It is suggested that the athlete replicate as closely as possible similar training loads in the 24 hours leading into each testing block.

Diet

A normal meal (incorporating a high carbohydrate component) should be eaten on the evening preceding the test and, if scheduling allows, also on the day of the test. No alcohol should be consumed in the 24 hours preceding the test. The athlete should give special attention to ensuring good hydration in the lead-up to the test.

Test Preparation

Each laboratory may have information and consent forms that may need to be provided prior to the
test.

Equipment Checklist
• Concept IIC or IID rowing ergometer
• Heart rate monitoring system
• Expired gas analysis system (as per general recommendations)
• Stopwatch
• Lactate analyser (Lactate Pro recommended) and blood gas analyser if possible.
Blood sampling equipment:
• Finalgon ointment or cream
• Autolet, lancets and platforms
• Sterile alcohol swab
• Tissues
• Heparinised capillary tubes
• Pipette (if required)
• Disposable rubber gloves
• Sharps container
• Biohazard bag.

Ergometer Settings
Table 1: Old Ergometer Drag Factor Settings
Category Drag Factor
Junior Female 110
Lightweight Female 110
Heavyweight Female 120
Junior Male 120
Lightweight Male 120
Heavyweight Male 130

New Ergometer Drag Factor Settings
(only to be used if tested on sliders – see Introduction)
Category Drag Factor
Junior Female 95
Lightweight Female 95
Heavyweight Female 105
Junior Male 105
Lightweight Male 105
Heavyweight Male 115

Step - Test Administration:

Athletes will start the 7 step test protocol with a work load based and increment based purely on their previous year’s best 2000m time. The range of times for the 2000m ergometer tests have been
divided on 10 sec increments with the fastest 2000m times having the highest starting work load and increment (see Table 2).

 

See resource list below for a print copy of table 2.  

In order to ensure that individual athlete’s complete the identical amount of work prior to beginning the 7th step (4 min at maximal pace) every 7 x 4 min step test undertaken by the athlete for that seasonal year must use the same starting work load and increment. In other words there will be no increment of starting work load during the season as has been in previous years. The only way an athlete will be able to change their starting work load or increment will be to perform a 2000m test that has a time that places them into a different time bracket.

The workloads and increments have been designed such that the 6th step (i.e. the step immediately preceding the maximal step) produces a blood lactate value in the range of 5-8 mmol/L. Obviously this will change depending on the time of year and the athlete’s current training status but importantly in a single season the athlete will complete an identical amount of work leading into the maximal performance component of the test.

The scientist in charge is given flexibility in choosing if moving to the next 10 sec increment is valuable or not. An example would be that when an athlete has 4 years of data all starting at the same work load and increment but finally betters their 2000m time from 5:50.6 to 5:49.8. Taking the protocol to its exact description would mean the athlete would change their starting work load and increment. However the gain in doing this is far outweighed by the inability to compare the athlete across the 4 years of data using the previous starting work load and increment. In cases such as this it is left to the discretion of the coach and scientist to decide what should be the appropriate starting work load and increment foir that athlete. Scientists can contact either the Head Coaches, HPD or Tony Rice for additional consultation if required.

The 2000m category times used in Table 2 are based on the athlete’s best 2000m time from the previous year and not their all-time personal best 2000m test. If the athlete had a medical exemption for the previous year or Thus it is possible that work loads would change slightly from year to year for a given individual. If this is the case, comparison between years for the same individual, or in the same year between individuals, must only be done using variables such as heart rate, blood lactate, VO2 and perceived exertion at LT1, LT2 and the maximal step.

Distance covered in the final maximal step can only be used as a comparison between athletes when those athletes have completed the same amount of work prior to the maximal step i.e. identical starting work load and increment.

7 x 4 min step test procedure:

1. Complete and give scientist your Informed Consent forms.
2. The scientist will measure the following physical parameters: height; weight; sitting height; arm span; sum of 7 skinfolds (only sum of 7 skinfolds is a requirement of RA but it is good practice if labs have the available resources to complete the additional measurements)
3. Attach a heart rate monitor and ensure it is working correctly.
4. The scientist will place a small quantity of Finalgon on one of your earlobes, or on some fingertips to ensure that the capillary blood is arterialised prior to sampling.
5. The scientist will adjust the ergometer drag factor to that appropriate to your competition category (see Table 1) and provide you with the work loads for your 6 increments (see Table 2).
6. The scientist will position the gas collection apparatus (respiratory valve etc) and ensure that the athlete is as comfortable as possible. Take several light strokes and the scientist will make any necessary adjustment to respiratory hoses or other apparatus to ensure that the hose is not pulling on the breathing apparatus at any stage during the stroke.
7. The scientist will collect a pre-exercise blood sample from the earlobe or fingertip using a Lactate Pro analyser.
8. The scientist set the ergometer output display to show Watts for each stroke as well as set the work load time and rest interval.
9. The scientist will attach the nose clip and prepare to start the test if the test requires gas analysis.
10. Start rowing when instructed.
11. Blood is collected from your earlobe or fingertip during each rest period and analysed. During the 1 min rest period, you are permitted to remove the gas collection apparatus to have a drink. However, it is important to ensure that the breathing apparatus is back in position well before the start of the next work bout (approximately 10–15 s).
12. You are required to complete 6 submaximal work loads prior to beginning the maximal step.
Your starting work load is based on your best 2000m time from the previous year and the work loads for all rowing categories are contained in Table 2.
13. There is only the standard 1 min break between the end of the final submaximal step (6th step) and beginning the 4 min maximal step.
14. Begin the test when instructed. Remember the aim of the test is to cover as many meters as possible in the 4 minutes. You should be exhausted at the completion of the 4 minutes of rowing. If possible try to even split the 4 minutes rather than starting conservatively and then coming home strong. You should be able to hold or better your average 2000m split for the entire 4 minutes. Coincident with the maximal performance assessment will be the attainment of maximal heart rate, blood lactate concentration and oxygen consumption.
15. At the end of the test, the scientist will help you remove the breathing apparatus as rapidly as possible.
16. An earlobe or fingertip blood sample should be collected and analysed at the completion and 4min post completion of the final maximal step. The highest value of these two readings should be used as the peak blood lactate.

Analysis of Test Results: Blood Lactate Profiles

Submaximal oxygen uptakes are calculated by averaging the readings recorded during the final 2 min of each submaximal workload. The maximum oxygen uptake is recorded as the highest value actually attained over a period of a full minute. Thus, if the gas analysis system is based on 30 s sampling periods, the maximum oxygen uptake is the sum, or if all results are expressed in L.min-1, the average of the highest two consecutive readings. If 15 s sampling periods are used, the maximum oxygen uptake is the highest value obtained on the basis of any four consecutive readings.

Submaximal heart rates are the values for the final 30 s of each submaximal workload. The maximum heart rate is the highest value recorded over a 5 s sampling period during the entire test. Computerised analysis allows for quite simple determination of the various blood lactate transition thresholds and associated measures. The ADAPT* (Automatic Data Analysis for Progressive Tests) software package is to be used to calculate these thresholds from the test data.

*(ADAPT software is available to the Australian sport science community from Sport Sciences, Australian Institute of Sport, PO Box 176, Belconnen ACT 2616.)
Data from the final maximal step are included as the final work load values (including peak lactate) and used in combination with values from the submaximal workloads for calculation of the blood lactate thresholds and related measures in ADAPT.

References

Gore CJ. (Editor) (2000) Physiological Tests For Elite Athletes / Australian Sports Commission. Human Kinetics Champaign IL. Chapters 2, 3, 8 and 22.
Medbø JI, Mohn A-C, Tabata I et al. (1988) Anaerobic capacity determined by maximal accumulated O2 deficit. Journal of Applied Physiology 64: 50–60.

 

Resources to conduct test:

For a print copy, see 7x4min step test protocol.

Table 2: Determination of Test Protocol.


Thursday
Oct062011

Heart Rate Variability (HRV), Recovery Index (RI) and Heart Rate Variability Index (HRVI)

By: Eddie Fletcher, Fletcher Sport Science Ltd 2007
A briefing note written by Sports Physiologist and Coach Eddie Fletcher
Accurate tools for assessing Psychological Stress, Physiological Workload and Recovery in Athletes


General Introduction

There are a number of factors which influence training and race performance, ranging from daily living (work and family), diet and hydration, cold, heat and humidity through to the lack of adequate rest and recovery. It is important to understand how stressful a normal training day is and to know the extent of overnight recovery.

The human heart is a wonderful barometer of the overall psychological stress and physical workload experienced by the body. The heart is a muscle, it gets tired and like any other muscle requires time to recover if optimum training and race performance is to be maintained.

The heart responds automatically and immediately to any increase or decrease in stress level. This heart rate response can be used to manage and mitigate the risk of over training, under recovery, illness or injury, to the body.

By monitoring the influence of psychological stress and physiological workload it is possible to use an analysis of heart rate to monitor overnight recovery and to moderate the duration and intensity of training to match the extent of recovery.

The consequences of getting it wrong should not be under estimated. Unless ‘listening to your heart’’ is normal practice deterioration in performance can occur almost unseen.

What are the benefits of measuring daily stress?

• Maximize recovery between training sessions
• Know how travelling, jetlag, high altitude and other stressors influence stress and recovery
• Learn how different daily routines enable and limit recovery
• Measure recovery between training sessions when training in high altitude
• Assess how travelling and jetlag influences recovery after a competition
• Check for social and psychological stressors that influence recovery
• Check athlete's daily routines for arrangements that could be done better to minimize stress during the day
• Interpret results together with athlete to detect stressors that influence recovery and to plan things that could be done differently in the future
• Repeat the daily stress recordings and observe how changes in daily routines influence stress and recovery

What are the benefits of measuring recovery?

• Detect early signs of overtraining or illness
• Optimize training load by finding the balance between training load and recovery
• Evidence based support for critical coaching decisions
• Record individual reference values e.g. during off-season when the body is recovered
• Check the recovery status during hard training periods
• Check recovery status when subjective feelings and fitness level indicates poor recovery
• Make sure that the body is recovered sufficiently before a new hard training period

How does it work?

Tracking daily stress and overnight recovery needs only one physiological signal – beat-by-beat heart rate data (the R-R interval). This measurement may be carried out during normal daily routines, whilst training and whilst sleeping. Although the data collection procedure is simple, the analysis methodology produces accurate recovery information.

Under resting conditions, healthy athletes show a periodic variation in the R-R interval. This rhythmic fluctuation is caused by breathing. Heart rate increases whilst breathing in and decreases when breathing out.

By accurately measuring the time interval between heartbeats (known as Heart Rate Variability HRV) it is possible to use the detected variation in time to measure the psychological and physiological stress and fatigue on the body. Generally speaking the more relaxed and free from fatigue the body is, the more variable the time between heartbeats. Increased Heart Rate Variability is linked to good health; decreased Heart Rate Variability is linked to stress or fatigue.

Heart Rate Variability also distributes as a function of Frequency.

Because of the characteristics of the increase (high frequency HF) and decrease (very low and low frequency LF) of the heart beat, changes in this frequency distribution can be used to monitor overall daily stress and overnight recovery.

Recovery is strongly associated with high frequency reactions and stress with low frequency reactions. These values are highly individual and the most sensitive markers for monitoring stress and recovery status. By looking at the difference from athlete specific baseline values the status of stress and recovery can be monitored and a

Recovery Index or Heart Rate Variability Index created.

The  intensity  of  stress/recovery  is  calculated  from  the  HF,  LF,  Respiration rate and HR.

How easy is it to collect the data?

Very easy, simply wear a Suunto t6 or Suunto Memory Belt during training sessions and overnight. The log is downloaded into Suunto Training manager software and Firstbeat SPORTS or Firstbeat PRO for detailed analysis.

What is a Recovery Index?

The Recovery Index is the relationship between the total duration of the Stress (low frequency) and Recovery (high frequency) reactions during an overnight measurement. The index is generally calculated from the first 4 hours of sleeping time as this time period is the most sensitive time for detecting recovery status. Average values provide information for both stress and recovery reactions during the selected time period indicating the relative strength of the reactions.

The intensity of the Stress/Recovery is calculated from the high and low Heart Rate Frequency mix, Respiration Rate and Heart Rate. The Recovery Index is represented by two numbers i.e. 60/100. The left number represents Stress reactions with the right number representing Recovery reactions.

Athletes need to measure their own individual baseline values at rest and compare subsequent values against the baseline figures.

What is a Heart Rate Variability Index?

Another useful tool for detecting recovery is the Heart Rate Variability Index

This is a single number and reflects the slowing down of the heart. The index can be used to detect recovery from an overnight recording. A high index figure represents increased recovery and a low value poor recovery.

During the day the value should be at least 15 but normally over 25. During the night the value should be at least 50 % higher (20-30) although athletes can have a value of several hundred (athlete above is 100 +). These limits are just guidelines; medication, heritage and training status also influence HRV level. Research indicates that these limits may be associated with burn-out.

As with the Recovery Index an individual baseline Heart Rate Variability Index value would need to be established for comparison purposes.

Example

The ratio for this athlete is 42/100 and represents full recovery. For this athlete normal 100% recovery is 40-110



During a period of high stress for a different athlete a ratio of 117/74 represents under recovery. For this athlete normal 100% recovery range is 60-100

Tracking the Recovery Index

There are some endurance athletes whose heart rate level is so low during the night that despite the changes in HF and LF levels the night recording appears to show mainly recovery reactions.

The overall index may indicate 100% recovery when the underlying values show under recovery. It is important to get a reference level by measuring athlete specific baseline values in a rested state and comparing future results to the baseline figures.

In the example below note 100% recovery during the period 6/11/2007 to 18/11/2007.

Baseline resting values for this athlete 50 (stress)/115 (recovery)

By looking at the individual figures for stress and recovery the true extent of stress or recovery can be determined and compared against baseline level.

The intensity of the stress reactions

The intensity of the recovery reactions.

Normally when recovery increases, stress level decreases and vice versa. It will be noted that although the overall index shows 100% recovery for the 16/11/2007 the Recovery Index is approximately 85/100 which when compared against baseline 50/115.

Am I fully recovered?

More precise answers are obtainable with a long measurement history.

In this example the days when the athlete is recovered are marked on both the Stress and Recovery follow-up charts.

Stress reactions:

Recovery reactions:

Am I tired but training can continue? Am I tired and must rest.

These are the too hardest questions to answer and this is where the experience of the athlete and coach in using the Recovery Index is important. When the goal is to train hard and upset the body’s homeostasis the stress level should increase and recovery decrease.

In the charts above the hard training period was 18/10/07 – 25/10/07 (8 days). Based on the rate of recovery (recovery occurred within two days - see Recovery index 27.10.07) the overreaching period was successful.

The chart below is another athlete training at high altitude 12/10-07 – 27/10/07. The last measurement was 25/10/07. The recovery level was below baseline value all the time and the athlete reported subjective feelings of “big fatigue”. This 15 days hard training period without any easy days may have been too long. Time to reach baseline values after the training period took 10 days (recovery occurred 07/11/07).

When will I know I can train again?

After ending the last hard training period, the recovery level should be measured daily to see when the baseline values are reached again. In the example above, the new training period could be started on 07/11/07 or later.

Conclusion

Measuring recovery is a vital component of any training programme if an athlete is to maintain optimum training and race performance. ‘Listening to your heart’ must become normal practice to avoid deterioration in performance, illness or injury.

More information

Coaches and Athletes are referred to the following articles by Eddie Fletcher for more detailed information

Peak Performance Issues:

• 237 Heart rate variability – what is it and how can it be used to enhance athletic performance
• 246 Using HRV to optimize rest and recovery
• 253 Duration-intensity-recovery: a new training concept

Also see www.fletchersportscience.co.uk for further reference articles.

Eddie Fletcher can be contacted by email eddie@fletchersportscience.co.uk

Note: Some sections of this briefing guide are based upon copyrighted materials owned by Firstbeat Technologies Ltd. They are reproduced with the permission.


Thursday
Sep292011

Intervals, Thresholds, and Long Slow Distance: the Role of Intensity and Duration in Endurance Training

Sportscience - sportsci.org
Perspectives / Training

Stephen Seiler1 and Espen Tønnessen2
Sportscience 13, 32-53, 2009 (sportsci.org/2009/ss.htm)
1 University of Agder, Faculty of Health and Sport, Kristiansand 4604, Norway.  
2 Norwegian Olympic and Paralympic Committee National Training Center, Oslo,  Norway.
Reviewers: Iñigo Mujika, Araba Sport Clinic, Vitoria, Spain; Stephen Ingham, English Institute of Sport, Loughborough University, Leicestershire, LE11 3TU, UK.


Abstract:

Endurance training involves manipulation of intensity, duration, and frequency of training sessions.   The relative impact of short, high-intensity training versus longer, slower distance training has been studied and debated for decades among athletes, coaches, and scientists.  Currently, the popularity pendulum has swung towards high-intensity interval training.  Many fitness experts, as well as some scientists, now argue that brief, high-intensity interval work is the only form of training necessary for performance optimization.   Research on the impact of interval and continuous training with untrained to moderately trained subjects does not support the current interval craze, but the evidence does suggest that short intense training bouts and longer continuous exercise sessions should both be a part of effective endurance training.  Elite endurance athletes perform 80 % or more of their training at intensities clearly below their lactate threshold and use high-intensity training surprisingly spar-ingly.  Studies involving intensification of training in already well-trained ath-letes have shown equivocal results at best.  The available evidence suggests that combining large volumes of low-intensity training with careful use of high-intensity interval training throughout the annual training cycle is the best-practice model for development of endurance performance. KEYWORDS: lactate threshold, maximal oxygen uptake, VO2max, periodization.

Content:

Interval Training: A long History
Exercise Intensity Zones
Training Plans and Cellular Signaling
Training Intensities of Elite Endurance Athletes
Units for Trainong Intensity
The 80-20 Rule for Intensity
Training Volume of Elite Athletes
Intensified-Training Studies
Intensity for Recreational Athletes:
- Case 1-From Soccer Pro to Elite Cyclist
- Case 2 From Modern Pentathete to Runner
Valid Comparisons of Training Interventions
Conclusions
References

The evening before the start of the 2009 European College of Sport Science Congress in Oslo, the two of us were sitting at a doctoral dissertation defense dinner that is part of the time honored tradition of the “doctoral disputas” in Scandinavia. One of us was the relieved disputant (Tønnessen) who had successfully defended his dissertation. The other had played the adversarial role of “førsteopponent.” Tønnessen’s research on the talent development process included extensive empirical analyses of the training characteristics of selected world champion female endurance athletes. His career case-study series systematized training diary logs of over 15,000 training sessions from three World and/or Olympic champions in three sports: distance running, cross-country skiing, and orienteering. Common for all three champions was that over their long, successful careers, about 85 % of their training sessions were performed as continuous efforts at low to moderate intensity (blood lactate ?2 mM). Among the 40 guests sat coaches, scientists, and former athletes who had been directly or indirectly involved in winning more endurance sport Olympic gold medals and world championships than we could count. One guest, Dag Kaas, had coached 12 individual world champions in four different sports. In his toast to the candidate he remarked,  ”My experience as a coach tells me that to become world champion in endurance disciplines, you have to train SMART, AND you have to train a LOT. One without the other is insufficient.”

So what is smart endurance training? The question is timely: research and popular interest in interval training for fitness, rehabilitation, and performance has skyrocketed in recent years on the back of new research studies and even more marketing by various players in the health and fitness industry. Some recent investigations on untrained or moderately trained subjects have suggested that 2-8 wk of 2-3 times weekly intense interval training can induce rapid and substantial metabolic and cardiovascular performance improvements (Daussin et al., 2007; Helgerud et al., 2007; Talanian et al., 2007). Some popular media articles have interpreted these findings to mean that long, steady distance sessions are a waste of time. Whether well founded or not, this interpretation raises reasonable questions about the importance and quantity of high- (and low-) intensity training in the overall training process of the endurance athlete. Our goal with this article is to discuss this issue in a way that integrates research and practice.

In view of the recent hype and the explosion in the number of studies investigating interval training in various health, rehabilitation, and performance settings, one could be forgiven for assuming that this training form was some magic training pill scientists had devised compara-tively recently. The reality is that athletes have been using interval training for at least 60 years.  So, some discussion of interval training research is in order before we address the broader question of training intensity distribution in competitive endurance athletes.

Interval Training: a Long History

International running coach Peter Thompson wrote in Athletics Weekly that clear references to “repetition training” were seen already by the early 1900s (Thompson, 2005).  Nobel Prize winning physiologist AV Hill incorporated intermittent exercise into his studies of exercising humans already in the 1920s (Hill et al., 1924a; Hill et al., 1924b).  About this time, Swede Gosta Holmer introduced Fartlek to distance running (fart= speed and lek= play in Swedish).  The specific term interval training is attributed to German coach Waldemer Gerschler. Influenced by work physiologist Hans Reindell in the late 1930s, he was convinced that alternating periods of hard work and recovery was an effective adaptive stimulus for the heart. They apparently adopted the term because they both believed that it was the recovery interval that was vital to the training effect. Since then, the terms intermittent exercise, repetition training, and interval training have all been used to describe a broad range of training prescriptions involving alternating work and rest periods (Daniels and Scardina, 1984). In the 1960s, Swedish physiologists, led by Per Åstrand, performed groundbreaking research demonstrating how manipulation of work duration and rest duration could dramatically impact physiological responses to intermittent exercise (Åstrand et al., 1960; Åstrand I, 1960; Christensen, 1960; Christensen et al., 1960). As Daniels and Scardina (1984) concluded 25 years ago, their work laid the foundation for all interval training research to follow. In their classic chapter Physical Training in Textbook of Work Physiology, Åstrand and Rodahl (1986) wrote, “it is an important but unsolved question which type of training is most effective: to maintain a level representing 90 % of the maximal oxygen uptake for 40 min, or to tax 100 % of the oxygen uptake capacity for about 16 min.” (The same chapter from the 4th edition, published in 2003, can be read here.)  This quote serves as an appropriate background for defining high intensity aerobic interval training (HIT) as we will use it in this article: repeated bouts of exercise lasting ~1 to 8 min and eliciting an oxygen demand equal to ~90 to 100 % of VO2max, separated by rest periods of 1 to 5 min (Seiler and Sjursen, 2004; Seiler and Hetlelid, 2005). Controlled studies comparing the physiological and performance impact of continuous training (CT) below the lactate turnpoint (typically 60-75 % of VO2max for 30 min or more) and HIT began to emerge in the 1970s. Sample sizes were small and the results were mixed, with superior results for HIT (Henriksson and Reit-man, 1976; Wenger and Macnab, 1975), superior results for CT (Saltin et al., 1976), and little difference (Cunningham et al., 1979; Eddy et al., 1977; Gregory, 1979). Like most published studies comparing the two types of training, the CT and HIT interventions compared in these studies were matched for total work (iso-energetic). In the context of how athletes actually train and perceive training stress, this situation is artificial, and one we will return to.

McDougall and Sale (1981) published one of the earliest reviews comparing the effects of continuous and interval training, directed at coaches and athletes. They concluded that both forms of training were important, but for different reasons. Two physiological assumptions that are now largely disproven influenced their interpretation. First, they concluded that HIT was superior for inducing peripheral changes, because the higher work intensity induced a greater degree of skeletal muscle hypoxia. We now know that in healthy subjects, increased lactate accumulation in the blood during exercise need not be due to increased muscle hypoxia (Gladden, 2004). Second, they concluded that since stroke volume already plateaus at 40-50 %VO2max, higher exercise intensities would not enhance ventricular filling. We now know that stroke volume continues to rise at higher intensities, perhaps even to VO2max, in well trained athletes (Gledhill et al., 1994; Zhou et al., 2001). Assuming a stroke volume plateau at low exercise intensity, they concluded that the benefit of exercise on cardiac performance was derived via stimulation of high cardiac contractility, which they argued peaked at about 75 %VO2max. Thus, moderate-intensity continuous exercise over longer durations and therefore more heart beats was deemed most beneficial for enhancing cardiac performance. While newer research no longer supports  their specific conclusions, they did raise the important point that there are underlying characteristics of the physiological response to HIT and CT that should help explain any differential impact on adaptive responses.

Poole and Gaesser (1985) published a citation classic comparing 8 wk of 3 × weekly training of untrained subjects for either  55 min at 50 %VO2max, 35 min at 75 %VO2max, or 10 × 2 min at 105 %VO2max with 2-min recoveries.  They observed no differences in the magnitude of the increase in either VO2max or power at lactate threshold among the three groups. Their findings were corroborated by Bhambini and Singh (1985) in a study of similar design published the same year. Gorostiaga et al. (1991) reported findings that challenged McDougall and Sale's conclusions regarding the adaptive specificity of interval and continuous training. They had untrained subjects exercise for 30 min, three days a week either as CT at 50 % of the lowest power eliciting VO2max, or as HIT, alternating 30 s at 100 % of power at VO2max and 30 s rest, such that total work was matched. Directly counter to McDougall and Sales conclusions, they found HIT to induce greater changes in VO2max, while CT was more effective in improving peripheral oxidative capacity and the lactate profile. At the beginning of the 1990s, the available data did not support a consensus re-garding the relative efficacy of CT vs HIT in inducing peripheral or central changes related to endurance performance.
Twenty years on, research continues regarding the extent to which VO2max, fractional utilization of VO2max, and work efficiency/economy are differentially impacted by CT and HIT in healthy, initially untrained individuals. Study results continue to be mixed, with some studies showing no differences in peripheral and central adaptations to CT vs HIT (Berger et al., 2006; Edge et al., 2006; Overend et al., 1992) and others greater improvements with HIT (Daussin et al., 2008a; Daussin et al., 2008b; Helgerud et al., 2007). When differences are seen, they lean in the direction that continuous work at sub-maximal intensities promotes greater peripheral adaptations and HIT promotes greater central adaptations (Helgerud et al., 2007).

Controlled studies directly comparing CT and HIT in already well-trained subjects were essentially absent from the literature until recently. However, a few single-group design studies involving endurance athletes did emerge in the 1990s. Acevedo and Goldfarb (1989) reported improved 10-km performance and treadmill time to exhaustion at the same pace up a 2 % grade in well-trained runners who increased their training intensity to 90-95 %VO2max on three of their weekly training days. In these already well-trained athletes, VO2max was unchanged after 8 wk of training intensification, but a right shift in the blood lactate profile was observed. In 1996 -97, South African sport scientists published the results of a single group intervention involving competitive cyclists (Lindsay et al., 1996; Weston et al., 1997). They trained regionally competitive cyclists who were specifically selected for study based on the criteria that they had not undertaken any interval training in the 3-4 months prior to study initiation. When 15 % of their normal training volume was replaced with 2 d.wk-1 interval training for 3-4 wk (six training sessions of six 5-min high intensity work bouts), 40-km time trial performance, peak sustained power output (PPO), and time to fatigue at 150 %PPO were all modestly improved. Physiological measurements such as VO2max and lactate profile changes were not reported. Stepto and colleagues then addressed the question of interval-training optimization in a similar sample of non-interval trained, regional cyclists (Stepto et al., 1999). They compared interval bouts ranging from 80 to 175 % of peak aerobic power (30 s to 8 min duration, 6-32 min total work). Group sizes were small (n=3-4), but the one group that consistently improved endurance test performance (~3 %) had used 4-min intervals at 85 % PPO. These controlled training intensification studies essentially confirmed what athletes and coaches seemed to have known for decades: some high-intensity interval training should be integrated into the training program for optimal performance gains. These studies also seemed to trigger a surge in interest in the role of HIT in athlete performance development that has further grown in recent years.

If doing some HIT (1-2 bouts per week) gives a performance boost, is more even better? Billat and colleagues explored this question in a group of middle distance runners initially training six sessions per week of CT only. They found that a training intensification to four CT sessions, one HIT session, and one lactate threshold (LT) session resulted in improved running speed at VO2max (but not VO2max itself) and running economy. Further intensifi-cation to two CT sessions, three HIT sessions and one LT session each week gave no additional adaptive benefit, but did increase subjective training stress and indicators of impending overtraining (Billat et al., 1999). In fact, training intensification over periods of 2-8 wk with frequent high-intensity bouts (3-4 sessions per week) is an effective means of temporarily compromising performance and inducing overreaching and possibly overtraining symptoms in athletes (Halson and Jeukendrup, 2004).  There is likely an appropriate balance between high- and low-intensity training in the day-to-day intensity distribution of the endurance athlete. These findings bring us to two related questions: how do really good endurance athletes actually train, and is there an optimal training intensity distribution for long-term performance development?

While arguments can be made that tradition, resistance to change and even superstition may negatively influence training methods of elite endurance athletes, sports history tells us that athletes are experimental and innovative. Observing the training methods of the world's best endurance athletes represent a more valid picture of “best practice” than we can develop from short-term laboratory studies of untrained or moderately trained subjects.  In today’s per-formance environment, where promising athletes have essentially unlimited time to train, all athletes train a lot and are highly motivated to optimize the training process. Training ideas that sound good but don't work in practice will fade away. Given these conditions, we argue that any consistent pattern of training intensity distribution emerging across sport disciplines is likely to be a result of a successful self-organization (evolution) towards a “population optimum.” High performance training is an individualized process for sure, but by population optimum, we mean an approach to training organization that results in most athletes staying healthy, making good progress, and performing well in their most important events. 

Exercise Intensity Zones

To describe intensity distribution in endurance athletes we have to first agree on an intensity scale. There are different intensity zone schemes to choose from. Most national sport governing bodies employ an intensity scale based on ranges of heart rate relative to maximum and associated typical blood lactate concentration range.  Research approaches vary, but a number of recent research studies have identified intensity zones based on ventilatory thresholds.  Here we will examine an example of each of these scales.
Table 1 shows the intensity scale used by all endurance sports in Norway. A valid criticism of such a scale is that it does not account for individual variation in the relationship between heart rate and blood lactate, or activity specific variation, such as the tendency for maximal steady state concentrations for blood lactate to be higher in activities activating less muscle mass (Beneke and von Duvillard, 1996; Beneke et al., 2001).

Several recent studies examining training intensity distribution (Esteve-Lanao et al., 2005; Seiler and Kjerland, 2006; Zapico et al., 2007) or performance intensity distribution in multi-day events (Lucia et al., 1999; Lucia et al., 2003) have employed the first and second venti-latory turnpoints to demarcate three intensity zones (Figure 1). The 5-zone scale in the table above and the 3-zone scale below are reasonably super-imposable in that intensity Zone 3 in the 5-zone system coincides well with Zone 2 in the 3-zone model. While defining five “aerobic” intensity zones is likely to be informative in training practice, it is important to note that they are not based on clearly defined physiological markers. Note also that 2-3 additional zones are typically defined to accommodate very high intensity sprint, anaerobic capacity, and strength training. These zones are typically defined as “anaerobic” Zones 6, 7, and 8.

Training Plans and Cellular Signaling

Athletes do not train at the same intensity or for the same duration every day. These va-riables are manipulated from day to day with the implicit goals to maximize physiological capacity over time, and stay healthy. Indeed, the former is quite dependent on the latter. Training frequency is also a critical variable manipulated by the athlete. This is particularly evident when comparing younger (often training 5-8 times per week) and more mature athletes at peak performance level (often training 10-13 sessions per week). Ramping up training frequency (as opposed to training longer durations each session) is responsible for most of the increase in yearly training hours observed as teenage athletes mature. Cycling might be an exception to this general rule, since cycling tradition dictates single daily sessions that often span 4-6 h among professionals. The ultimate targets of the training process are individual cells.  Changes in rates of DNA transcription, RNA translation, and ultimately, synthesis of specific proteins or protein constellations are induced via a cascade of intra-cellular signals induced by the training bout. Molecular exercise biologists are unraveling how manipulation of intensity and duration of exercise specifically modifies intracellular signaling and resulting protein synthetic rates at the cellular or whole muscle/myocardial level (Ahmetov and Rogozkin, 2009; Hoppeler et al., 2007; Joseph et al., 2006; Marcuello et al., 2005; McPhee et al., 2009; Yan, 2009). About 85 % of all publications involving gene expression and exercise are less than 10 y old, so we do not yet know enough to relate results of Western blots to the specific training of an athlete.

The signaling impact of a given exercise stress (intensity×duration) almost certainly decays with training (Hoppeler et al., 2007; Nordsborg et al., 2003).  For example, AMP activated protein kinase a2 (AMPK) activity jumps 9-fold above resting levels after 120 min of cycling at 66 %VO2max in untrained subjects.  However, after only 10 training sessions,  almost no increase in AMPK is seen after the same exercise bout (McConell et al., 2005). Manipulating exercise intensity and duration also impacts the systemic stress responses associated with training. Making this connection is further complicated by recent findings suggesting that muscle glycogen depletion can enhance and antioxidant supplementation can inhibit adaptations to training (Brigelius-Flohe, 2009; Go-mez-Cabrera et al., 2008; Hansen et al., 2005; Ristow et al., 2009; Yeo et al., 2008). It seems fair to conclude that while we suspect important differences exist, we are not yet able to relate specific training variables (e.g., 60 min vs 120 min at 70 %VO2max) to differences in cell signaling in a detailed way. Our view of the adaptive process remains limited to a larger scale. We can still identify some potential signaling factors that are associated with increased exercise intensity over a given duration (Table 2) or increased exercise duration at a given sub-maximal intensity (Table 3). Some of these are potentially adaptive and others maladaptive.  There is likely substantial overlapping of effects between extending exercise duration and increasing exercise intensity.

It may be a hard pill to swallow for some exercise physiologists, but athletes and coaches do not need to know much exercise physiology to train effectively. They do have to be sensitive to how training manipulations impact athlete health, daily training tolerance, and performance, and to make effective adjustments. Over time, a successful athlete will presumably organize their training in a way that maximizes adaptive benefit for a given perceived stress load. That is, we can assume that highly successful athletes integrate this feedback experience over time to maximize training benefit and minimize risk of negative outcomes such as  illness, injury, stagnation, or overtraining.

Training Intensities of Elite Endurance Athletes

Empirical descriptions of the actual distribution of training intensity in well-trained athletes have only recently emerged in the literature. The first time one of us (Seiler) gave a lecture on the topic was in 1999, and there were few hard data to present, but a fair share of anecdote and informed surmise. Carl Foster, Jack Daniels and Seiler published a book chapter the same year, “Perspectives on Correct Approaches to Training” that synthesized what we knew then (read chapter here via Google books). At that time, much of the discussion and research re-lated to the endurance training process focused on factors associated with overtraining (a training control disaster), with little focus on what characterized “successful training.” The empirical foundation for describing successful training intensity distribution is stronger 10 years later. 
Robinson et al. (1991) published what was according to the authors “the first attempt to quantify training intensity by use of objective, longitudinal training data.”  They studied training characteristics of 13 national class male, New Zealand runners with favorite distances ranging from 1500 m to the marathon. They used heart rate data collected during training and related it to results from standardized treadmill determinations of heart rate and running speed at 4-mM blood lactate concentration (misnamed anaerobic threshold at the time). Over a data collection period of 6-8 wk corresponding to the preparation phase, these athletes reported that only 4 % of all training sessions were interval workouts or races. For the remaining training sessions, average heart rate was only 77 % of their heart rate at 4-mM blood lactate. This heart rate translates to perhaps 60-65 % of VO2max. The authors concluded that while their physiological test results were similar to previous studies of well trained runners, the training intensity of these runners was perhaps lower than optimal, based on prevailing recommendations to perform most training at or around the lactate/anaerobic threshold.

In one of the first rigorous quantifications of training intensity distribution reported, Mujika et al. (1995) quantified the training intensity distribution of national and international class swimmers over an entire season based on five blood-lactate concentration zones. Despite specializing in 100-m and 200-m events requiring ~60 to 120 s, these athletes swam 77 % of the 1150 km completed during a season at an intensity below 2 mM lactate.  The intensity distribution of 400- and 1500-m swim specialists was not reported, but was likely even more weighted towards high-volume, low-intensity swimming.

Billat et al. (2001) performed physiological testing and collected data from training diaries of French and Portuguese marathoners. They classified training intensity in terms of three speeds: marathon, 10–km, and 3–km. During the 12 wk preceding an Olympic trials mara-thon, the athletes in this study ran 78 % of their training kilometers at below marathon speed, only 4 % at marathon race speed (likely to be near VT1), and 18 % at 10–km or 3–km speed (likely to be > VT2). This distribution of training intensity was identical in high-level (<2 h 16 min for males and <2 h 38 min for females) and top-class athletes (<2 h 11 min and <2 h 32 min). But the top-class athletes ran more total kilometers and proportionally more distance at or above 10–km speed.

Kenyan runners are often mythologized for the high intensity of their training. It is therefore interesting that with data from another study by Billat et al. (2003), we calculated that elite male and female Kenyan 5- and 10-km runners ran ~85 % of their weekly training kilometers below lactate-threshold speed.

The first study on runners to quantify training intensity using three intensity zones was that of Esteve-Lanao et al. (2005). They followed the training of eight regional- and national-class Spanish distance runners over a six-month period broken into eight, 3-wk mesocycles. Heart rate was measured for every training session to calculate the time spent in each heart-rate zone defined by treadmill testing. All told, they quantified over 1000 heart-rate recordings. On average these athletes ran 70 km.wk-1 during the six-month period, with 71 % of running time in Zone 1, 21 % in Zone 2, and 8 % in Zone 3. Mean training intensity was 64 %VO2max. They also reported that performance times in both long and short races were highly negatively correlated with total training time in Zone 1. They found no significant correlation between the amount of high-intensity training and race performance.

Rowers compete over a 2000-m distance requiring 6-7 min. Steinacker et al. (1998) reported that extensive endurance training (60- to 120-min sessions at <2 mM blood lactate) dominated the training volume of German, Danish, Dutch, and Norwegian elite rowers. Rowing at higher intensities was performed ~4-10 % of the total rowed time. The data also suggested that German rowers preparing for the world championships performed essentially no rowing at threshold intensity, but instead trained either below 2 mM blood lactate or at intensities in the 6-12 mM range.

Seiler collaborated with long time national team rower, coach, and talent development coordinator Åke Fiskerstrand to examine historical developments in training organization among international medal winning rowers from Norway (Fiskerstrand and Seiler, 2004). Using questionnaire data, athlete training diaries, and physiological testing records, they quantified training intensity distribution in 27 athletes who had won world or Olympic medals in the 1970s to 1990s.  They documented that over the three decades: training volume had increased about 20 % and become more dominated by low-intensity volume; the monthly hours of high-intensity training had dropped by one-third; very high intensity overspeed sprint training had declined dramatically in favor of longer interval training at 85-95 %VO2max; and the number of altitude camps attended by the athletes increased dramatically. Over this 30-y timeline, VO2max and rowing ergometer performance improved by ~10 % with no change in average height or body mass.  Most of the changes occurred between the 1970s and 1980s, coinciding with major adjustments in training intensity.

Most recently, Gullich et al. (2009) described the training of world class junior rowers from Germany during a 37-wk period culminating in national championships and qualification races for the world championships. These were very talented junior rowers, with 27 of 36 athletes winning medals in the junior world championships that followed the study period. Remarkably, 95 % of their rowing training was performed below 2 mM blood lactate, based on daily heart-rate monitoring and rowing ergometer threshold determinations performed at the beginning of the season.  This heavy dominance of extensive endurance training persisted across mesocycles. However, the relatively small volume of Zone 2 and Zone 3 work shifted towards higher intensities from the basic preparation phase to the competition phase. That is, the intensity distribution became more polarized. It is important to point out that time-in-zone allocation based on heart-rate cut-offs (the kind of analysis performed by software from heart watch manufacturers) underestimates the time spent performing high-intensity exercise and the impact of that work on the stress load of an exercise session (Seiler and Kjerland, 2006). Although the outcomes are biased by this problem, there was still a clear shift in the intensity distribution towards large volumes of low- to moderate-intensity training. We also evaluated retrospectively whether there were any differences in junior training characteristics between a subgroup of rowers who went on to win international medals as seniors within three years (14 of 36 athletes) and the remainder of the sample, who all continued competing at the national level. The only physical or training characteristic that distinguished the most successful rowers from their peers was a tendency to distribute their training in a more polarized fashion; that is, they performed significantly more rowing at very low aerobic intensities and at the highest intensities. We concluded that the greater polarization observed might have been due to better management of intensity (keeping hard training hard and easy training easy) among the most successful athletes. This polarization might protect against overstress.

Professional road cyclists are known for performing very high training volumes, up to 35,000 km.y-1. Zapico and colleagues (2007) used the 3-intensity zone model to track training characteristics from November to June in a group of elite Spanish under-23 riders. In addi-tion, physiological testing was performed at season start and at the end of the winter and spring mesocycles.  There was an increase in total training volume and a four-fold increase in Zone 3 training between the winter and spring mesocycles (Figure 2), but there was no further improvement in power at VT1, VT2 or at VO2max between the end of the winter and spring mesocycles (Figure 3), despite the training intensification. Anecdotally, this finding is not unusual, despite the fact that athletes feel fitter. It may be that VT2 and VO2max determi-nation using traditional methods can miss an important increase in the duration that can be maintained at the associated workloads.


 

Individual and team pursuit athletes in cycling compete over about 4 min. The event ap-peals to sport scientists because the performance situation is highly controlled and amenable to accurate modeling of the variables on both sides of the power balance equation. Schumacher and Mueller (2002) demonstrated the validity of this approach in predicting “gold medal standards” for physiological testing and power output in track cycling. However, less obvious from the title was the detailed description of the training program followed by the German cyclists monitored in the study, ultimately earning a gold medal in Sydney in world-record time. These athletes trained to maintain 670 W in the lead position and ~450 W when following using a training program dominated by continuous low to moderate intensity cycling on the roads (29-35,000 km.y-1). In the 200 d preceding the Olympics, the athletes performed “low-intensity, high-mileage” training at 50-60 % of VO2max on ~140 d. Stage races took up another ~40 d. Specific track cycling at near competition intensities was performed on less than 20 d between March and September.  In the ~110 d preceding the Olympic final, high-intensity interval track training was performed on only 6 d.

Units for Training Intensity

Cross country skiers have rather legendary status in exercise physiology circles for their aerobic capacity and endurance capacity in arms and legs. Seiler et al. (2006) studied 12 competitive to nationally elite male 17–y old skiers from a special skiing high school in the region. The mean VO2max for the group was 72 ml.kg-1min-1. They were guided by coaches with national team coaching experience and were trained along similar lines to the seniors, but with substantially lower volumes of training. Like Esteve-Lanao (2005) did with runners, we used heart-rate monitoring to quantify all endurance sessions and determined three aerobic intensity zones based on ventilatory turn points. We also recorded the athletes' rating of perceived exertion (RPE) using the methods of Foster et al. (1996; 1998; 2001a) for all training bouts. Finally, we collected blood lactate during one training week to relate heart rate and perceived exertion measurements to blood lactate values.

When comparing the three different intensity quantification methods, we addressed the issue of how training intensity is best quantified. Heart-rate monitoring is clearly appealing. We can save heart rate data, download entire workouts to analysis software, and quantify the time heart rate falls within specific pre-defined intensity zones. Using this “time-in-zone” approach, we found that 91 % of all training time was spent at a heart rate below VT1 intensity, ~6 % between VT1 and VT2, and only 2.6 % of all 15-s heart rate registrations were performed above VT2. We then quantified intensity by allocating each training session to one of the three zones based on the goal of the training and heart rate analysis. We called this the “session-goal approach”. For low-intensity continuous bouts, we used average heart rate for the entire bout. For bouts designed to be threshold training we averaged heart rate during the threshold-training periods. For high-intensity interval-training sessions, we based intensity on the average peak heart rate for each interval bout. Using this approach, intensity distribution derived from heart rate responses closely matched the session RPE (Figure 4), training diary distribution based on workout description, and blood-lactate measurements. The agreement between the session-by-session heart-rate quantification and session RPE-based assignment of intensity was 92 %. In their training diaries, athletes recorded 30-41 training sessions in 32 d and described 75% of their training bouts as low intensity continuous, 5% as threshold wor-kouts, and 17% as intervals.

We have also recently observed the same time-in-zone mismatch when quantifying intensity distribution in soccer training (unpublished data). It seems clear that typical software-based heart-rate analysis methods overestimate the amount of time spent training at low intensity and underestimate the time spent at very high workloads compared to athlete perception of effort. We think this mismatch is important, because the unit of stress perceived and responded to by the athlete is the stress of the entire training session or perhaps training day, not minutes in any given heart-rate zone. 

The 80-20 Rule for Intensity

In spite of differences in the methods for quantifying training intensity, all of the above studies show remarkable consistency in the training distribution pattern selected by successful endurance athletes.  About 80 % of training sessions are performed predominantly at inten-sities under the first ventilatory turn point, or a blood-lactate concentration <=2mM. The remaining ~20 % of sessions are distributed between training at or near the traditional lactate threshold (Zone 2), and training at intensities in the 90-100 %VO2max range, generally as interval training (Zone 3). An elite athlete training 10-12 times per week is therefore likely to dedicate 1-3 sessions weekly to training at intensities at or above the maximum lactate steady state. This rule of thumb coincides well with training studies demonstrating the efficacy of adding two interval sessions per week to a training program (Billat et al., 1999; Lindsay et al., 1996; Weston et al., 1997). Seiler and Kjerland (2006) have previously gone so far as say that the optimal intensity distribution approximated a “polarized distribution” with 75-80 % of training sessions in Zone 1, 5 % in Zone 2, and 15-20 % in Zone 3. However, there is considerable variation in how athletes competing in different sports and event durations distribute their training intensity within Zones 2 and 3. 

Why has this training pattern emerged?  We do not have sufficient research to answer this question, but we can make some reasonable guesses. One group of factors would involve the potential for this distribution to best stimulate the constellation of training adaptations required for maximal endurance performance. For example, large volumes of training at low intensity might be optimal for maximizing peripheral adaptations, while relatively small volumes of high intensity training fulfill the need for optimizing signaling for enhanced cardiac function and buffer capacity. Technically, lots of low intensity training may be effective by allowing lots of repetitions to engrain correct motor patterns. On the other side of the adaptation-stress equation is the stress induced by training. Athletes may migrate towards a strategy where longer duration is substituted for higher intensity to reduce the stress reactions associated with training and facilitate rapid recovery from frequent training (Seiler et al., 2007). Interestingly, Foster and colleagues reported a very similar intensity distribution by professional cyclists during the 3 wk and 80+ racing hours of the grand tours, such as the Tour de France. Perhaps this distribution represents a form of pacing that emerges over the months of elite training (Foster et al., 2005).

”Low intensity”–between 50 %VO2max and just under the first lactate turnpoint–represents a wide intensity range in endurance athletes. There is probably considerable individual varia-tion in where within this range athletes accumulate most of their low-intensity training volume. Technique considerations may play in: athletes have to train at a high enough intensity to allow correct technique. For example, Norwegian Olympic flat-water kayak gold medalist Eric Verås Larsen explained that the reason most of his Zone 1 continuous endurance training tended to be closer to his lactate threshold than normally observed was that he could not paddle with competition technique at lower intensities (Verås Larsen, personal communication).  These qualifiers aside, we conclude that a large fraction of the training within this zone is being performed at ~60-65 %VO2max, We note that this intensity is about the intensity associated with maximal fat utilization in trained subjects (Achten and Jeukendrup, 2003), but it is unclear why optimizing fat utilization would be important for athletes competing over 3-15 min.

Training Volume of Elite Athletes

Obviously, training intensity distribution and training volume together will determine the impact of training. Elite athletes train a lot, but to be more specific requires some common metric for comparing athletes in different sports. Runners and cyclists count kilometers, swimmers count thousands of meters, and rowers and cross-country skiers count training hours.  With a few reasonable assumptions, we can convert these numbers to annual training hours. This physiological metric is appropriate, since the body is sensitive to stress duration. 

Training volume increases with age in high-level performers, mostly through increased training frequency in sports like running and cross-country skiing, but also through increases in average session duration, particularly in cycling. A talented teenage cyclist training five days a week might accumulate 10-15 h.wk-1. A professional cyclist from Italy performing a 1000-km training week will likely be on the bike between 25 and 30 h.

Cycling 30-35,000 kilometers a year at, say, ~35 km.h-1 with occasional sessions of strength training, will add up to ~1000 h.y-1.  An elite male marathoner would likely never run more than about 15 hours in a week. At an average running speed of 15 km.h-1, that would be at most 225 km.  Former world record holder in the 5 km, 10 km, and marathon, Ingrid Kristiansen trained 550 h.y-1 when she was running (Espen Tønnessen, unpublished data). At a younger age, when she competed in the Olympics for Norway as a cross country skier, she actually trained 150 more h.y-1. Bente Skari, one of the most successful female cross country skiers ever, recorded peak annual training loads of 800 h.y-1 (Espen Tønnessen, unpublished data). Annual training volume measured in hours is around 1000 among world class rowers. For example, Olaf Tufte recorded 1100 training hours in 2004, when he took his first gold medal in the single scull event (Aasen, 2008). His monthly training volume for that year is shown in Figure 5. Of these hours, about 92 % were endurance training, with the remainder being primarily strength training. An Olympic champion swimmer like Michael Phelps may record even higher annual training volumes, perhaps as much as 1300 h (a reasonable guess based on training of other swimming medalists).

The Kenyan marathoner, Italian cyclist, Norwegian rower and American swimmer are all at the top of their sport, but when we measure their training volume in hours, they seem quite different, with international success being achieved with a two-fold or larger range in hours per year (Figure 6). What can explain this difference?  One explanation is that the muscle, tendon, and joint loading stress of the different movements vary dramatically. Running imposes severe ballistic loading stress that is not present in cycling or swimming.

There seems to be a strong inverse relationship between tolerated training volume and degree of eccentric or ballistic stress of the sport. Swimming, rowing, and cross-country skiing are all highly technical events with movement patterns that do not draw on the genetically pre-programmed motor pathways of running.  Thus high volumes of training may be as important for technical mastery as for physiological adaptation in these disciplines. Rowers and speed skaters do less movement-specific training than most other athletes, but they accumulate substantial additional hours of strength training and other forms of endurance training.


Intensified-Training Studies

Is the “80-20” training intensity distribution observed for successful athletes really optimal, or would a redistribution of training intensity towards more threshold and high intensity interval training and less long slow distance training stimulate better gains and higher perfor-mance? Proponents of large volumes of interval training might invoke the famous pareto principle and propose that in keeping with this “rule” of effects vs causes, these athletes are achieving 80 % of their adaptive gains with 20 % of their training and wasting valuable training energy. In the last 10 y, several studies have been published addressing this question.

Evertsen et al. (1997; 1999; 2001) published the first of three papers from a study involving training intensification in 20 well-trained junior cross-country skiers competing at the national or international level. All of the subjects had trained and competed regularly for 4-5 years. In the two months before study initiation, 84 % of training was carried out at 60-70 %VO2max, with the remainder at 80-90 %VO2max.  They were then randomized to a moderate-intensity (MOD) or a high-intensity training group (HIGH). MOD maintained essentially the same training-intensity distribution they had used previously, but training volume was increased from 10 to 16 h.wk-1. HIGH reversed their baseline intensity distribution so that 83 % of training time was performed at 80-90 %VO2max, with only 17 % performed as low-intensity training.  This group trained 12 h.wk-1. The training intervention lasted five months. Intensity control was achieved using heart-rate monitoring and blood-lactate sampling.

Despite 60 % more training volume in MOD and perhaps 400 % more training at lactate threshold or above in HIGH, physiological and performance changes were modest in both groups of already well-trained athletes. Findings from the three papers are summarized in Table 4.

Gaskill et al. (1999) reported the results of a 2-y project involving 14 cross-country skiers training within the same club who were willing to have their training monitored and manipu-lated. The design was interesting and practically relevant. During the first year, athletes all trained similarly, averaging 660 training hours with 16 % at lactate threshold or higher (nominal distribution of sessions). Physiological test results and race performances during the first year were used to identify seven athletes who responded well to the training and seven who showed poor VO2max and lactate-threshold progression, and race results. In the second year, the positive responders continued using their established training program. The non-responders performed a markedly intensified training program with a slight reduction in training hours. The non-responders from Year 1 showed significant improvements with the intensified program in Year 2 (VO2max, lactate threshold, race points). The positive responders from Year 1 showed a similar development in Year 2 as in Year 1.

It is interesting in this context to point out that many elite athletes now extend the  peri-odization of their training to a 4-y Olympic cycle. The first year after an Olympics is a “recovery season”, followed by a building season, then a season of very high training volume, culminating with the Olympic season, where training volume is reduced and competition specificity is emphasized more.  Variation in the pattern of training may be important for maximal development, but these large scale rhythms of training have not been studied.

Esteve-Lanao et al. (2007) randomized 12 sub-elite distance runners to one of two training groups (Z1 and Z2) that were carefully monitored for five months. They based their training intensity distribution on the 3-zone model described earlier and determined from treadmill testing. Based on time-in-zone heart-rate monitoring, Z1 performed 81, 12, and 8 % of training in Zones 1, 2, and 3 respectively. Z2 performed more threshold training, with 67, 25, and 8 % of training performed in the three respective zones. That is, Group Z2 performed twice as much training at or near the lactate threshold. In a personal communication, the authors reported that in pilot efforts, they were unable to achieve a substantial increase in the total time spent in Zone 3, as it was too hard for the athletes. Total training load was matched between the groups. Improvement in a cross-country time-trial performed before and after the five-month period revealed that the group that had performed more Zone 1 training showed significantly greater race time improvement (-157 ± 13 vs  122 ± 7 s).

Most recently, Ingham et al. (2008) were able to randomize 18 experienced national standard male rowers from the UK into one of two training groups that were initially equiva-lent based on performance and physiological testing. All the rowers had completed a 25-d post-season training-free period just prior to baseline testing. One group performed “100 %” of all training at intensities below that eliciting 75 %VO2max (LOW). The other group per-formed 70 % training at the same low intensities as well as 30 % of training at an intensity 50 % of the way between power at lactate threshold and power at VO2max (MIX). In practice, MIX performed high intensity training on 3 d.wk-1. All training was performed on a rowing ergometer over the 12 wk. The two groups performed virtually identical volumes of training (~1140 km on the ergometer), with ±10 % individual variation allowed to accommodate for variation in athlete standard. Results of the study are summarized in Table 5.

Sixteen of 18 subjects set new personal bests for the 2000-m ergometer test at the end of the study. The authors concluded that LOW and MIX training had similar positive effects on performance and maximal oxygen consumption. LOW training appeared to induce a greater right-shift in the blood-lactate profile during sub-maximal exercise, which did not translate to a significantly greater gain in performance. If MIX training enhanced or preserved anaerobic capacity more than LOW, this may have compensated for the observed differences in blood-lactate profile.

Intensity for Recreational Athletes

Elite endurance athletes train 10-12 sessions and 15-30h each week.  Is the pattern of 80 % below and 20 % above lactate threshold appropriate for recreational athletes training 4-5 times and 6-10 hours per week?  There are almost no published data addressing this question. Recently Esteve-Lanao (personal communication) completed an interesting study on recreational runners comparing a program that was designed to reproduce the polarized training of successful endurance athletes and compare it with a program built around much more threshold training in keeping with the ACSM exercise guidelines.  The intended intensity distribution for the two groups was: Polarized 77-3-20 % and ACSM 46-35-19 % for Zones 1, 2, and 3. However, heart-rate monitoring revealed that the actual distribution was: Polarized 65-21-14 % and ACSM 31-56-13 %.

Comparing the intended and achieved distributions highlights a typical training error com-mitted by recreational athletes.  We can call it falling into a training intensity “black hole.”  It is hard to keep recreational people training 45-60 min a day 3-5 days a week from accumulating a lot of training time at their lactate threshold. Training intended to be longer and slower becomes too fast and shorter in duration, and interval training fails to reach the desired intensity. The result is that most training sessions end up being performed at the same threshold intensity. Foster et al. (2001b) also found that athletes tend to run harder on easy days and easier on hard days, compared to coaches' training plans.  Esteve Lanao did succeed in getting two groups to distribute intensity very differently. The group that trained more polarized, with more training time at lower intensity, improved their 10-km performance significantly more at 7 and 11 wk. So, recreational athletes could also benefit from keeping low- and high-intensity sessions at the intended intensity.

Interval training can be performed effectively with numerous combinations of work duration, rest duration, and intensity. We have found that when subjects self-select running speed based on a standard prescription, 4-min work duration and 2-min recovery duration combine to give the highest physiological response and maintained speed (Seiler and Sjursen, 2004; Seiler and Hetlelid, 2005). However, perceptual and physiological response differences across the typical work and recovery spectrum are fairly small and performance enhancement differences are unclear at best. Some researchers have proposed that specific interval regimes (e.g., 4 × 4 min at 95 %VO2max) may be superior for achieving adaptive gains (Helgerud et al., 2007; Wisloff et al., 2007), but other research studies and our observations of athlete practice suggest that a variety of combinations of work and rest duration are effective for long-term development. Table 6 shows typical combinations of intensity and effective duration used by elite endurance athletes for workouts in the different aerobic training zones described earlier. All the examples are taken from the training diaries of elite performers. The effective durations for the different zones are utilized by highly trained athletes. For those without the same training base, similar workouts would be performed but with less total effec-tive duration.

Case Studies of Training Manipulation

Case studies are the weakest form of scientific evidence. But, for coaches and high performance athlete support teams, each elite athlete is a case study.  So, we present here two case studies that we think are instructive in demonstrating the potential physiological impact of successfully manipulating training  volume and intensity distribution variables at the individual level. Both cases involve Norwegian athletes who were followed closely by one of the authors (Tønnessen).  Both would be considered already highly trained prior to the training reorganization.

Case 1–From Soccer Pro to Elite Cyclist

Knut Anders Fostervold was a professional soccer player in the Norwegian elite league from 1994 to 2002.  A knee injury ended his soccer career at age 30 and he decided to switch to cycling.  Knut had very high natural endurance capacity and had run 5 km in 17:24 at age 12.  After 15 y of soccer training at the elite level, he adopted a highly intensive training regime for cycling that was focused on training just under or at his lactate threshold and near VO2max; for example, 2-3 weekly training sessions of 4-5 × 4 min at 95 %VO2max.  Weekly training volume did not exceed 10 h.

After 2.5 years of this high-intensity, low-volume training, Fostervold initiated cooperation with the Norwegian Olympic Center and his training program was radically reorganized.  Weekly training volume was doubled from 8-10 h to 18-20.  Training volume in Zone 2 was reduced dramatically and replaced with a larger volume of training in Zone 1.  Training in Zone 5 was replaced with Zones 3 and 4, such that total training volume at intensities at or above lactate threshold was roughly doubled without overstressing the athlete. The typical effective duration of interval sessions increased from ~20 min to ~ 60 min (for example 8 × 8 min at 85-90 %HRmax with 2-min recoveries).  The intensity zones were initially based on heart rate but later adjusted relative to lactate and power output measurements made in the field.  Table 7 shows the training intensity distribution and volume loading for the athlete during the season before and after the change in training to a high-volume program. Table 8 shows the outcome.

  

 

 

 

The athlete responded well to the training load amplification and reorganization.  During the 2005 season, after 2.5 y performing a low-volume, high-intensity program, a season training with higher volume and lower average intensity resulted in marked physiological and performance improvement. Although the athlete’s training de-emphasized both training near his lactate threshold intensity and training at near VO2max, both of these physiological anchors improved markedly.

Fostervold won a bronze medal in the Norwegian national time-trial championships, seconds behind former world under-23 time trial champions and Tour de France stage win-ners Thor Hushovd and Kurt Asle Arvesen.  His failure to perform even better, given his exceptionally high VO2max, was attributed to poorer cycling efficiency and aerodynamics and a lower fractional utilization at lactate threshold compared to the best professionals with many years of specific training. In 2006 and 2007 he represented Norway in the world cham-pionship time trial. His absolute VO2max in 2005 was equal to the highest ever measured in a Norwegian athlete. 

Case 2–From Modern Pentathlete to Runner

Prior to 2003, Øystein Sylta was a military pentathlete (European champion in 2003).  In the Fall of 2003 he decided to focus on distance running and is now nationally competitive, with personal bests for 3000-m steeplechase, 5000-m, and 10000-m of 8:31, 14:04 and 29:12 respectively.  His case is interesting due to the dramatic change in training volume and intensity distribution he undertook from 2003 to 2004 and associated changes in physiolog-ical test results.

Prior to 2003, Sylta trained using a high-intensity, low-volume training structure.  When he agreed to try a new approach, emphasis was placed on increasing training volume with low-intensity sessions and changing his interval training.  He either trained long slow distance or long intense interval sessions. However, his total training distance at intensities above his lactate threshold was reduced and redistributed.  From 2002/2003 to 2003/2004 he increased his total running distance from 3,500 to 5,900 km.  He also reduced his strength training from 100 annual hours to 50.  Table 9 shows a typical hard training week in the Fall of 2003 and Fall of 2004, and Table 10 summarizes the running specific training.  His physiological adaption to the first year of restructured training is documented in Table 11.

From 2003 to 2009, Sylta’s threshold running speed increased from 16.9 to 19.5 km.h-1. From 2002 to 2009,  his 10-km time improved from 31:44 to 29:12, and 3000-m steeplechase from 9:11 to 8:31.  In the first five months of training reorganization, his 3000-m steeple result improved by 30 s.

 

 

 

Both these case studies demonstrate that even in already well trained athletes, meaningful improvements in physiological test results and performance may occur with appropriate training intensity and volume manipulation.  Both athletes showed clear improvements in physiological testing despite reductions in HIT training.  Both seemed to respond positively to an increase in total training volume and specifically, more low-intensity volume.

Valid Comparisons of Training Interventions

Matching training programs based on total work or oxygen consumption seems sensible in a laboratory.  As we noted earlier, this has been the preferred method of matching when com-paring the effects of continuous and interval training in controlled studies. Unfortunately, it is not realistic from the view of athletes pursuing maximal performance. They do not compare training sessions or adjust training time to intensity in this manner. A key issue here is the non-linear impact of exercise intensity on the manageable accumulated duration of intermittent exercise. We have exemplified this in Table 12 by comparing some typical training sessions from the training of elite athletes.

The point we want to make is that the athlete’s perception of the stress of performing 4 × 15 min at 85 %VO2max is about the same as that of performing 6 × 4 min at 95 %VO2max, even though total work performed is very different. To answer a question like, “is near VO2max interval training more effective for achieving performance gains in athletes than training at the maximal lactate steady state?”, the matching of training bouts has to be realistic from the perspective of perceived stress and how athletes train. Future studies of training intensity effects on adaptation and performance should take this issue of ecological validity into account.

Conclusions

Optimization of training methods is an area of great interest for scientists, athletes, and fitness enthusiasts. One challenge for sport scientists is to translate short-term training intervention study results to long-term performance development and fitness training organi-zation. Currently, there is great interest in high-intensity, short-duration interval training programs. However, careful evaluation of both available research and the training methods of successful endurance athletes suggests that we should be cautious not to over-prescribe high-intensity interval training or exhort the advantages of intensity over duration.
Here are some conclusions that seem warranted by the available data and experience from observations of elite performers:

• There is reasonable evidence that an ~80:20 ratio of low to high intensity training (HIT) gives excellent long-term results among endurance athletes training daily.
• Low intensity (typically below 2 mM blood lactate), longer duration training is effective in stimulating physiological adaptations and should not be viewed as wasted training time.
• Over a broad range, increases in total training volume correlate well with improvements in physiological variables and performance.


• HIT should be a part of the training program of all exercisers and endurance athletes. However, about two training sessions per week using this modality seems to be sufficient for achieving performance gains without inducing excessive stress.
• The effects of HIT on physiology and performance are fairly rapid, but rapid plateau effects are seen as well. To avoid premature stagnation and ensure long-term development, training volume should increase systematically as well.
• When already well-trained athletes markedly intensify training with more HIT over 12 to ~45 wk, the impact is equivocal.
• In athletes with an established endurance base and tolerance for relatively high training loads, intensification of training may yield small performance gains at acceptable risk.
• An established endurance base built from reasonably high volumes of training may be an important precondition for tolerating and responding well to a substantial increase in training intensity over the short term.
• Elite athletes achieve periodization of training with reductions in total volume, and modest increases in volume of training above the lactate threshold. An overall polarization of training intensity characterizes the transition from preparation to competition mesocycles. The basic intensity distribution remains similar throughout the year.

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Sunday
Sep182011

Measuring Training Effectiveness

By: Stephen Seiler
From: Measuring Training Effectiveness



At national team testing centers like the Olympic training Center in the U.S, and comparable facilities in Germany, Norway, the United Kingdom etc, elite caliber athletes are evaluated using very expensive oxygen consumption measuring devices, lactate analysis instrumentation etc. The main purpose of all of this is to monitor the effectiveness of training in a systematic, repeatable way. Can we do any kind of testing at the local club or private level that is also quantitative and reproducible without $100,000 in lab equipment? The answer is yes.

If you have access to 1) a Concept II ergometer and 2) a heart rate monitor, you can perform the same performance evaluation that is used during physiological testing of national team candidates in the United States. The test was instituted in 1989 by Fred Hagerman PhD, who has been testing American oarsmen for 30 years, along with then national team coach Kris Korzeniowski. Of course, if you were at the lab, they would also stick a mouthpiece in your mouth, poke your earlobe with needles, and draw blood, but we will save that for the body piercing parlor. Two days are required.

Day One

The first phase of the test is simple. Do a 2000 meter all out trial on the ergometer. Of course you will be interested in your time, but the critical value you need from the test is your average WATTS maintained during the 2000 meters. You will get this from the "Watts Screen", down on the bottom. If you perform the test, wear your heart rate monitor. This way you can get a peak rowing heart rate. If you have a recent 2k time, but don't know the associated watts, here is a way to back-calculate them. 1) Convert your time to seconds, then divide 2000 by that value. This will give your your average velocity in meters/sec. 2) Now raise that value to the 3rd power using your sci calc. 3) Then multiply the resulting value times 2.75. Here's an example. 


   1.Let's say you pull a 2K in 7 minutes. That's 420 seconds. 2000/420 equals 4.76 m/sec.
   2.4.75^3 =107.17.
   3.107.17*2.75= 295 watts.
   4.295 watts would be your 2K power output, and the value you will use for part 2 of the test.

The formula I used (2.75 V^3) is one I have derived. However, if you use the more complex formula from the Concept II computer (They gave it to me) you will get almost identical results. The physics behind these formulas gets into the relationship between power and velocity in rowing. I will discuss this in depth in another article based on some research we have done at the University of Texas. For now, it is important to understand that power output and physiological intensity are linearly related. Physiological intensity and boat (or ergo) velocity are not! Fortunately, the CII ergometer is an excellent tool for measuring power output that sits in most boathouses around the world.

Day Two

Now we have a current max value. Next, multiply that value by 60%, 70%, and 80%. Using my example, 60, 70 and 80% of 295 watts is 177, 209, and 236 watts respectively. Now, with your heart rate monitor on, you will row for three consecutive 5 minute stages, beginning at the 60% workload and finishing at 80%. Monitor your workload by using the watts screen, and maintaining the average as close as possible to appropriate value, without a big burst or reduction in the final minute!. At the end of each stage, record your heart rate. Now you have heart rate data at three quantifiable effort levels based on your current maximal performance.

How Do I use this Information?

These power outputs comprise the range at which the majority of your steady state training should occur to maximize oxygen utilization capacity. In young athletes at 60% of 2k max workload, HR will generally be in the 120-140 range. Seventy percent efforts will elicit a HR of 140-160. And the 80% load will bring HR up to 160-180. Remember, these values are from athletes that average about 25 years old, with a max HR around 185 to 195.

You should use your own peak heart rate and similar percentages as a guide. Eighty percent of max 2k power will correspond quite closely to your power at lactate threshold, if you are well trained. If this is true, you will be able to sustain that workload for 20 minutes or more. The key to the value of this testing is using it as a baseline for subsequent tests. You don't have to keep doing new max tests within a given cycle of training. By periodically (perhaps monthly) performing the submaximal portion of this test, you can quantitatively assess the impact of your training.

If you are making progress, you will see a reduction in heart rate at these standard submaximal workloads as you progress from off season to competitive season. Hagerman monitored 40 elite oarsmen during an Olympic year and observed an average 25 beat/min reduction in heart rate at each workload between December and August.


Saturday
Jul302011

How To Determine Lactate / Anaerobic Threshold

By: Sports Advisor.
Site link: Sports Advisor.

There are several methods used to determine an athletes lactate or anaerobic threshold. While the most accurate and reliable is through the direct testing of blood samples during a graded exercise test, this is often inaccessible to most performers.

There are several field tests that can also be used to estimate lactate threshold. They vary in their reliability but some offer an acceptable alternative for most amateur athletes.

Several terms such as onset of blood lactate accumulation and maximal lactate steady state are used interchangeably with anaerobic threshold. Technically, they do not describe precisely the same thing. In fact, although lactate threshold and anaerobic threshold occur together under most conditions, strictly speaking even these two terms are not the same (1). For this article however, lactate threshold and anaerobic threshold will be used interchangeably. See the lactate threshold article for more details on this topic.

It is also worth bearing in mind that blood lactate and lactic acid are not the same substance. Blood lactate production is actually thought to be beneficial to endurance performance and may delay fatigue. Nevertheless, its accumulation still remains a good marker for the onset of fatigue. 

Laboratory Testing of Anaerobic Threshold

The most accurate way to determine lactate threshold is via a graded exercise test in a laboratory setting (2). During the test the velocity or resistance on a treadmill, cycle ergometer or rowing ergometer is increased at regular intervals (i.e. every 1min, 3min or 4min) and blood samples are taken at each increment. Very often VO2 max, maximum heart rate and other physiological kinetics are measured during the same test (3).

Blood lactate is then plotted against each workload interval to give a lactate performance curve. Heart rate is also usually recorded at each interval often with a more accurate electrocardiogram as opposed to a standard heart rate monitor.

Once the lactate curve has been plotted, the anaerobic threshold can be determined. A sudden or sharp rise in the curve above base level is said to indicate the anaerobic threshold. However, from a practical perspective this sudden rise or inflection is often difficult to pinpoint.

Assuming the inflection is clear (as in the graph above), the relative speed or workload at which it occurs can be determined. In this example, the athletes anaerobic threshold occurs at about 12km/h. This means, in theory they can maintain a pace at or just below 2km/h for a prolonged period, indefinitely. Of course, this is purely hypothetical as there are many other factors involved in fatigue not least the amount of carbohydrate stores an athlete has in reserve (1). A crossover to fat metabolism will significantly reduce the athletes race pace (2).

By recording heart rate data alongside workload and blood lactate levels, an athlete can use a heart rate monitor to plan and complete training sessions. Although monitoring heart rate is never completely reliable and varies greatly between and within individuals (1,2,3) combining it with lactate measurements is probably more reliable than using the Conconi test (see below) for example.

Confirmatory Test

When anaerobic threshold is read from the lactate curve, an additional test can be used to verify its accuracy. Using the example above, the athletes threshold is thought to occur at about 12km/h on the treadmill. The confirmatory test involves running for 15 minutes at a pace just below threshold, 15 minutes at threshold and 15 minutes at a pace just above threshold. See the curves below of three athletes whose thresholds have all been determined to occur at approximately 12kmh.

Notice that for Athlete 1, blood lactate remains steady at their estimated anaerobic threshold (12km/h) and lactate begins to accumulate when the pace is increased to 13km/h in the final 15 minutes. For athlete 1, this is confirmation that their anaerobic threshold pace is reasonably accurate.

For Athlete 2, lactate begins to accumulate during the middle 15minute segment (their estimated threshold) and continues to do so during the final 15 minutes. For this athlete, anaerobic threshold occurs at a slightly slower pace than was determined originally.

Finally, Athlete 3s lactate curve does not rise significantly even during the final 15 minutes segment. Anaerobic threshold for them occurs at a slightly higher pace then was originally determined.

Assuming, you dont have access to facilities that directly monitor your or your athletes lactate response, are there other acceptable alternatives?

Portable Lactate Analyzer

Portable lactate analysers are becoming more popular amongst coaches and athletes at all levels. A good portable analyzer should have the same validity and reliability as laboratory testing equipment. The Accutrend Lactate Analyzer for example, has been tested using guidelines of the European Committee for Clinical Laboratory Standards and is cleared for sports medicine use by the Federal Drug Administration in the United States.

Needless to say a portable analyser is only one half of the equation. A suitable and sport-specific exercise test is still required and selecting the most appropriate protocol takes knowledge and experience. Any physiological test is only as reliable as the testers ability to follow a set protocol. Even when a suitable assessment has been chosen, numerous variables must be kept constant for the test to remain accurate and reliable.   

Conconi Test

In 1982 Conconi et a (4) stated that the anaerobic threshold correlated to a deflection point in the heart rate. Essentially, heart rate and exercise intensity is linear i.e. as exercise intensity increases so will heart rate. However, Conconi et a found that in all their tested subjects, including those in a follow up test (5), heart rate reached a plateau at near maximal exercise intensities.

Although this is a relatively simple field test that would be useful for coaches and athletes at all levels, its accuracy has been contested by subsequent researchers (6,7,8). Studies have found that the deflection point or plateau in heart rate only occurs in a certain number of individuals and that when it does, it significantly overestimates directly measured lactate threshold. Conconi and co-workers (9) themselves acknowledge this controversy and cite studies that both support and contradict their original findings. 

10km Run, 30Km Cycle, 30 Min Time Trial

More experienced athletes often run a 10km race or cycle 30km race at or close to anaerobic threshold. By simulating a race in training and recording heart rate, the anaerobic threshold may be determined. Alternatively, exercising for 30 minutes at the fastest sustainable pace can be used. The key is to sustain a steady pace which is why this test is more suited to experienced athletes who can gauge how fast to set off. A heart rate monitor with split time facility is required to record heart rate at each 1-minute interval. Take the average heart rate over the final 20 minutes as the heart rate corresponding to anaerobic threshold.  

Heart Rate Percentage

A very simply method for estimating the anaerobic threshold is to assume anaerobic threshold occurs at 85-90% maximum heart rate (220-age). As mentioned earlier, heart rate varies greatly between individuals and even within the same individual so this is not a reliable test.

The Lactate Threshold Debate

Some researchers have questioned the validity of determining the lactate or anaerobic threshold even in laboratory settings (10,11). Yet more researchers question whether a definite point or threshold exists at all (10,12,13,14). Instead they suggest blood lactate accumulation is continuous in nature and no specific point can be determined.

Rather than get bogged down in the debate it is sensible to remember that regardless of the underlying mechanisms, the physiological changes that accompany lactate accumulation have important implications for endurance athletes. Any delay in the blood lactate accumulation that can be achieved through training is beneficial to performance.

References

1) Wilmore JH and Costill DL. (2005) Physiology of Sport and Exercise: 3rd Edition. Champaign, IL: Human Kinetics

2) McArdle WD, Katch FI and Katch VL. (2000) Essentials of Exercise Physiology: 2nd Edition Philadelphia, PA: Lippincott Williams & Wilkins

3) Maud PJ and Foster C (eds.). (1995) Physiological Assessment Of Human Fitness. Champaign, IL: Human Kinetics

4) Conconi, F., M. Ferrrari, P. G. Ziglio, P. Droghetti, and L. Codeca. Determination of the anaerobic threshold by a noninvasive field test in runners. J. Appl. Physiol. 1982, 52: 862-873

5) Ballarin, E., C. Borsetto, M. Cellini, M. Patracchini, P. Vitiello, P. G. Ziglio, and F. Conconi. Adaptation of the "Conconi test" to children and adolescents. Int. J. Sports Med. 1989, 10: 334-338

6) Parker, D., R. A. Robergs, R. Quintana, C. C. Frankel, and G. Dallam. Heart rate threshold is not a valid estimation of the lactate threshold. Med. Sci. Sports Exerc. 1997, 29: S235

7) Tokmakidis, S. P., and L. A. Leger. Comparison of mathematically determined blood lactate and heart rate "threshold" points and relationship to performance. Eur. J. Appl. Physiol. 64: 309-317

8) Vachon JA, Bassett Jr DR and Clarke S. Validity of the heart rate deflection point as a predictor of lactate threshold during running. J Appl Physiol. 1999, 87: 452-459

9) Conconi, F., G. Grazze, I. Casoni, C. Guglielmini, C. Borsetto, E. Ballarin, G. Mazzoni, M. Patracchini, and F. Manfredini. The Conconi test: methodology after 12 years of application. Int. J. Sports Med. 1996, 17: 509-519

10) Yeh MP, Gardner RM, Adams TD, Yanowitz FG, Crapo RO. "Anaerobic threshold": problems of determination and validation. J Appl Physiol. 1983, Oct;55(4):1178-86

11) Gladden LB, Yates JW, Stremel RW, Stamford BA. Gas exchange and lactate anaerobic thresholds: inter- and intraevaluator agreement. J Appl Physiol. 1985 Jun;58(6):2082-9

12) Hughson RL, Weisiger KH, Swanson GD. Blood lactate concentration increases as a continuous function in progressive exercise. J Appl Physiol. 1987 May;62(5):1975-81

13) Campbell ME, Hughson RL, Green HJ. Continuous increase in blood lactate concentration during different ramp exercise protocols. J Appl Physiol. 1989 Mar;66(3):1104-7

14) Dennis SC, Noakes TD, Bosch AN. Ventilation and blood lactate increase exponentially during incremental exercise. J Sports Sci. 1993 Oct;11(5):371-5; discussion 377-8