A brief history of submaximal testing
Note: This is part of a chapter in 'The Development of an evidenced-based submaximal cycle test designed to monitor and predict cycling performance' R.P. Lamberts (ISBN: 978-90-9024959-9) - please use this reference if you use info from this webpage. © Copyright 2009: R.P. Lamberts.
Performing at the highest level in professional sports is dependent on finding the optimal balance between the training workload and the recovery period, so that training effects and adaptations are maximized. In an attempt to maintain this fine balance, it is important to monitor the training load, changes in performance and symptoms of fatigue which may develop when this balance is disturbed. Perhaps the biggest challenge is to monitor these changes on a regular basis so that intervention can be implemented as soon as there is evidence that the balance is disturbed. Tests that are able to predict cycling performance, for example a peak power output (PPO) test or a 40-km time trial (40-km TT), require a maximal or near maximal exertion. Therefore these tests are not performed on a regular basis as they may interfere with the prescribed training or racing programme of an elite cyclist (Jeukendrup 2002; Lucia et al. 2000). It follows therefore that a less exhaustive submaximal test should rather be used for ongoing regular monitoring of well-trained and elite cyclists (Jeukendrup 2002). However, as a submaximal test only predicts performance, the accuracy of this prediction will influence how useful the test may be as a monitoring tool for already well-trained or elite cyclists. To understand the current status of performance testing, it is important to know the origins of exercise physiology and trace the development of submaximal tests which were designed to predict performance.
A brief history of performance testing
When sport became more professional in the early 1700’s and research in the field of physiology was gaining momentum, it was logical that the interest in exercise physiology also started to develop. An important contributor in this process was a Frenchman Antione Laurent Lavoisier (1743-1794), who was one of the first researchers to conduct and document exercise physiology studies (Duveen and Klickstein 1955). Although Lavoisier conducted the pioneering research, it was his wife Marie-Anne Pierette Paulze who documented all the experiments to ensure that his work was published. In the late 1800’s the German Eduard Friedrich Wilhelm Pflüger (1829-1910) contributed substantially to knowledge in respiratory physiology and electrophysiology. This work subsequently formed the basis for the development of electromyography. In addition he founded the ‘Archive für die gesamte Physiologie’ in 1868. This journal has developed into one of the current prominent journals publishing research on exercise physiology and sports medicine (Pflügers Archives - European Journal of Physiology). One of Pflüger’s students, Nathan Zuntz (1847-1920), became a professor at the Landwirtschaftliche Hochschule in Berlin. He led a research group which conducted innovative research on exercise metabolism, nutrition and respiratory responses (Gunga and Kirsch 1995a; Gunga and Kirsch 1995b). This group developed the ‘Laufband’, the predecessor of the treadmill, and the ‘Zuntz-Geppert respiratory apparatus’ which is the predecessor of modern metabolic gas analyzers.
A similar device, which also measures oxygen consumption during exercise, was developed by Claude Gordon Douglas. This device, known as the Douglas bag, was able to capture the expired air of a subject during exercise (Douglas 1911). After the bout of exercise the volume and composition of the exhaled air could be analyzed for the determination of oxygen consumption, carbon dioxide production and metabolic rate. This research methodology was frequently used by Archibald Vivian Hill (1886-1977) to study questions in the field of exercise physiology and medicine.
This researcher, together with Otto Fritz Meyerhof, was awarded the Nobel Prize in 1922 for their contributions to the field of physiology and medicine. Even today there is still a lively debate about how this physiological model explains maximal exercise capacity (Bassett, Jr. and Howley 1997; Ekblom 2009; Meeusen et al. 2007; Noakes et al. 2001; Noakes and Marino 2009; Wagner 2000). It can be concluded with confidence that this research was the catalyst for laboratory testing of elite athletes, and has made a substantial contribution to the understanding of high performance physiology (Hill and Lupton 1923).
As a direct consequence of Hill’s work, performance testing in men started to gain popularity from the early 1900’s. However, it was only in the 1960’s that a Swedish PhD Student, Per-Olaf Åstrand, started conducting maximal performance tests in females (Åstrand 1952). Prior to this it was regarded as dangerous and socially unethical for females to undergo maximal exercise. The research of Åstrand showed that a maximal performance test might not always be the most appropriate test for a subject (male or female), as it could interfere with health in certain clinical populations, and in the context of athletes, would interfere with their normal training habits.
Submaximal performance testing
As a consequence of the limitations of maximal performance testing, Åstrand and Ryhming developed a submaximal cycle test to predict VO2max as a measurement of “aerobic fitness” (Åstrand and Ryhming 1954). The test, known as the Åstrand test, is currently widely used. A similar submaximal cycle test, developed in the 1970’s, also predicts VO2max by estimating the workload coinciding with a heart rate of 170 beats per minute. This test, known as the Physical Work Capacity (PWC 170) test, is still used regularly (Haber et al. 1976; Trudeau et al. 2003).
As the Åstrand test gained popularity in the 1960’s, researchers also started to develop submaximal walking and running tests. One of the first submaximal running tests was developed by Kenneth Cooper in 1968. In this test subjects are asked to cover as much distance as possible within 12 minutes, and VO2max is then predicted from the completed distance (Cooper 1968). This test was a catalyst for the development of a variety of submaximal walking and running tests which were designed to accommodate the specific needs of special populations. Examples of these tests are the six-minute walk test for cardiac and respiratory patients (Butland et al. 1982), and the shuttle walk test for respiratory patients and patients with chronic obstructive pulmonary disease (Singh et al. 1992). To accommodate subjects with a relatively low exercise capacity, such as elderly people or people with severe obesity, Kline et al. (1987) and Laukkanen et al. (1992) developed the Rockport Fitness Walk and UKK walk test respectively.
In contrast to the development and specialization of submaximal running tests, submaximal cycle tests have not evolved further beyond the Åstrand test and the PWC 170 test. However, as explained earlier, a limitation of both these tests when applied to highly trained athletes is that both tests predict VO2max. While the VO2max of highly trained athletes is higher than that of lesser trained athletes (Arts and Kuipers 1994; Lucia et al. 2002b), the practical application of this measurement to monitor changes over time in well-trained and elite cyclists is questionable. For example, there is evidence that VO2max varies by over 25% in professional cyclists (Lucia et al. 2002; Mujika and Padilla 2001), and has shown to have a limited relationship with athletic ability (Lucia et al. 2002a; Noakes 2008). In addition, a study by Lucia et al. (2002) showed that VO2max in professional road cyclists only changes by 1.1% between pre-competition and in-competition, while the current typical error of measurement (TEM) of VO2max is about 2 to 3% (Paton and Hopkins 2006). Despite these limitations, VO2max is still regarded, in some quarters, as being an important measurement for predicting fitness in well-trained and elite athletes.
The development of a novel submaximal cycle test
The limitations of applying VO2max to monitoring the response to training have been clearly exposed. As discussed earlier, finding the optimal balance between training load and recovery is a fundamental requirement for achieving peak performance. When this balance is disrupted by either a training load which is too high and or a recovery period which is insufficient, a subject will start to accumulate fatigue. This will initially manifest as acute fatigue which can lead to a state of functional overreaching (Meeusen et al. 2006). This state can cause a stagnation or decrement in performance, however is sometimes used as a training technique to improve performance (Halson et al. 2003). The goal of this approach is to reach ‘super-compensation’ in the following recovery period, which is associated with enhanced performance (Meeusen et al. 2006). However when imbalance persists over a longer period, the accumulated fatigue becomes chronic with more exacerbated and longer lasting effects on performance (non-functional overreaching). This state can even progress into a more serious condition, known as the overtraining syndrome, which has been associated with severe impairment in performance and recovery times ranging from months to up to over a year (Meeusen et al. 2006).
Although this scenario may only seem relevant for well-trained athletes, it is also applicable to certain population groups who are encouraged to lower their risk of disease by increasing their physical activity. Maintaining the balance between training and recovery in these patient groups is as important for them as it is for well-trained athletes. In the case of the special population groups, for example in cardiac patients, it prevents them from developing serious health consequences, and in the case of the well-trained athlete it prevents them from reaching their optimal peak performance.
Monitoring this balance however is complex as both factors (training load and recovery) are influenced by multiple factors. For example, training load is influenced by the intensity, volume, frequency and duration of exercise (Jeukendrup 2002). The recovery is determined by less quantifiable factors such as stress, sleeping patterns, nutrition and psychological and sociological well-being (Jeukendrup 2002; Kenttä and Hassmén 1998). With it being so complex, it seems unrealistic to expect one single parameter to detect or indicate an imbalance between training load and recovery with any accuracy (Borresen and Lambert 2007).
Incidence of overreaching and overtraining
Due to the different definitions for overtraining and overreaching (Halson and Jeukendrup 2004; Lehmann et al. 1997; Robson 2003; Roelands et al. 2008; Silva 2009; Tenenbaum et al. 2003a; Tenenbaum et al. 2003b; Urhausen 2001) it is hard to accurately quantify the incidence of overreaching and overtraining. In addition, the preference for certain terminology between USA and Europe seem to further complicate this. For example, the term ‘staleness’ or ‘stale’ is often used in the USA and seems to refer to a state of overtraining or overtraining syndrome.
Lehman et al (1997) has estimated that between 20 to 60% of athletes will at least once during their career develop a state of overtraining. Although this percentage will be substantially lower on a yearly basis, Halson and Jeukendrup still expected the incidence of overtraining to be relatively high (Halson and Jeukendrup 2004). Research on competitive swimmers has shown that about 5-10% of the swimmers were ‘stale’ (Morgan et al. 1987; O'Connor et al. 1989; Raglin and Morgan 1994). Associated with this ‘staleness’ were increased ratings of fatigue, while subjects indicated a decreased performance. Objective measurements of changes in performance were however only measured in one study on swimmers (Hooper et al. 1997).
A study of 257 athletes of the British National Teams and/or Olympic squad showed an incidence of 38 cases (15%) over a 12 month period in which a state of ‘overtraining’ was diagnosed (Koutedakis and Sharp 1998). Although the incidence was slightly higher in males (17%) then in females (11%), there was an even distribution of these incidences within the different sports. Also, in this study no objective measurements were performed to confirm the anecdotal claim of a decrease in performance with increased ratings of fatigue.
Power output and fatigue
As peak power output has shown to correlate well with cycling performance (Arts and Kuipers 1994; Bentley et al. 2001; Faria et al. 2005; Hawley and Noakes 1992) and performance seems to be effected as fatigue accumulates, the measurement of changes in power seem to be important. This concept was adopted in a protocol designed to distinguish between normal training status and an overreached status (Meeusen et al. 2004). The protocol used a double peak power output performance test with each test being separated by 4 hours. When subjects were overreached, a larger decrement in peak power output was observed between the 2 tests (Meeusen et al. 2004).
Although this technique has the potential to confirm the status of an overreached or overtrained athlete, the physically exhausting nature of the test precludes it from being used on a weekly basis for monitoring purposes. Therefore a submaximal cycle test, which is able to monitor changes in power and be administered on a weekly basis, would be of importance to a cyclist, sports scientist and coach alike.
Overall wellbeing and ratings of perceived exertion
In contrast to the lack of actual performance tests to confirm a state of overtraining, most studies do use questionnaires to confirm a general state of fatigue. A frequently used and widely excepted questionnaire to assess psychological well-being is the Profile of Mood Status (POMS) questionnaire (McNair et al. 1971). Although this questionnaire measures aspects of tension, depression, anger, vigor, fatigue and confusion to assess the general mood status, it has shown mixed results to quantify an overreaching status (Halson et al. 2002; Martin et al. 2000; Nederhof et al. 2007; Rietjens et al. 2005; Uusitalo 2001). A possible explanation for this might be that this general questionnaire is not sufficiently sport specific.
A more specific questionnaire designed to assess the sport specific stress of training is the Daily Analysis of Life Demands for Athletes (DALDA) questionnaire (Rushall 1990). This questionnaire has evolved into its current format after being validated and tested for reliability. Halson et al. (2002) showed a change in DALDA scores after 2 weeks of high intensity training. These scores were associated with the accumulation of fatigue and a decrease in peak power output and 40km time trial time. Although these data suggest that this is possibly a better questionnaire for monitoring training stress in athletes compared to the POMS, the DALDA is rarely used in overtraining studies.
A more direct way of assessing an athlete’s well-being can be performed by using a 6-20 Borg Scale (Borg 1970) or the subsequently developed 10 point scale (0-10) Borg Scale (Borg 1982). This method is normally used during or directly after a performance test when a subject rates his/her overall rate of perceived exertion on a 6-20 or 0-10 scale respectively. One of the first studies that used RPE as a monitoring tool, showed that during steady state running the athlete’s RPE (6-20 scale) correlated well with average heart rate during that training session (Robinson et al. 1991). Recent studies (Rietjens et al. 2005; Uusitalo 2001) however, have also proposed that increased RPE levels at the same constant workload can play an important role in detecting a state of overreaching.
Heart rate recovery
The regulation of the autonomic nervous system has also shown potential for detecting the imbalance between training and recovery because the autonomic nervous system is interlinked with both physiological and psychological systems and has been able to predict mortality in healthy subjects (Cole et al. 1999). The autonomic nervous system is composed of the parasympathetic and sympathetic nervous systems. Whereas the parasympathetic nervous system is mainly active during resting conditions, the sympathetic nervous system becomes active with exercise or a fight-or-fright response (Brooks et al. 2005). Therefore activation of the sympathetic nervous system and withdrawal of the parasympathetic nervous system can be observed with increasing exercise intensity. In contrast, heart rate recovery after the cessation of exercise is regulated by parasympathetic reactivation and sympathetic withdrawal (Borresen and Lambert 2007; Bunc et al. 1988).
Two measurements, heart rate recovery and heart rate variability have been associated with the regulation of the autonomic nervous system (Borresen and Lambert 2007; Buchheit et al. 2007b; Buchheit and Gindre 2006; Bunc et al. 1988; Kaikkonen et al. 2008; Lamberts et al. 2004; Seiler et al. 2007; Shetler et al. 2001). Support for the measurement of heart rate recovery can be found in studies which have shown a change in heart rate recovery with a change in training status (Buchheit et al. 2007a; Buchheit et al. 2008; Otsuki et al. 2007; Sugawara et al. 2001; Yamamoto et al. 2001).
In addition, a recent study showed a decrease in heart rate recovery after a sudden increase in training load (55%) which was possibly a result of accumulating fatigue (Borresen and Lambert, 2007).
The measurement of heart rate variability also reflects the functioning of the autonomic nervous system. Although this method is based on sound theoretical principles, there is still much debate on how to measure and analyse these data. This possibly contributes to the inconclusive results that are found in studies which have measured heart rate variability (Aubert et al. 2003;; Tulppo et al. 1998). In addition age (Carter and Jeukendrup 2002; Levy et al. 1998; Tulppo et al. 1998), respiration (Tulppo et al. 1998) and temperature (Carter and Jeukendrup 2002) influence the measurement of heart rate variability. Recent studies suggest Buchheit (2007b; 2008) that the heart rate variability and heart rate recovery reflect different adaptations of the autonomic nervous system in a response to a change in training status. Where the indices of heart rate variability seem to reflect a more long term modulation of the autonomic nervous system, the indices of heart rate recovery seem to be more responsive to recently applied training loads (Buchheit et al. 2007b; Buchheit et al. 2008).
Based on the knowledge that indices of heart rate recovery are more sensitive measures of small changes over time, the measurement of heart rate recovery seems to be a more appropriate method for monitoring small changes as part of an ongoing monitoring system (Borresen and Lambert 2008).
Lamberts and Lambert Submaximal Cycle test (LSCT)
The development of technical devices such as cycle ergometers and heart rate monitors has provided the opportunity to measure a wider range of variables simultaneously.This offers the potential to overcome the limitation of being unable to accurately detect symptoms of an imbalance between training and recovery as a result of being able to measure only one marker.
Based on the above discussed potential parameters and the improved accuracy of ergometers, the LSCT was designed and tested since 2005. In an attempt to make the submaximal test both evidenced-based and practically useable for high performance cyclists, the LSCT had to fulfil the following criteria:
- should be non-invasive and submaximal
- should not interfere with the subject’s normal training or racing habits
- should be able to use the test for warming up before a performance test, training session, or racing event
- should have a maximal duration of a ‘normal’ warming up period (< 20 minutes)
- should be able to measure both objective and subjective data simultaneously
- have sufficient sensitivity to be able to reflect meaningful changes in performance parameter
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