Abstract
This work investigated the suitability of a new tool for decision support in training programs of high performance athletes. The aim of this study was to find a reliable and robust measure of the fitness of an athlete for use as a tool for adjusting training schedules. We examined the use of heart rate recovery percentage (HRr%) for this purpose, using a two- phased approach. Phase 1 consisted of testing the suitability of HRr% as a measure of aerobic fitness, using a modified run- ning test specifically designed for high- performance team running sports such as football. Phase 2 was conducted over a 12-week training program with two different training loads. HRr% measured aerobic fitness and a running time -trial measured pe rformance. Consecutive measures of HRr% during phase 1 indi- cated a Pear son’s r of 0.92, suggesting a robust measure of aerobic fitness. Dur- Material published as part of this publication, either on-line or in print, is copyrighted by the Informing Science Institute. Permission to make digital or paper copy of part or all of these works for personal or classroom use is granted without fee provided that the copies are not made or distributed for profit or commercial advantage AND that copies 1) bear this notice in full and 2) give
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Heart Rate Recovery in Decision Support 194 ing phase 2, HRr% reflected the training load and significantly increased when the training load was reduced between weeks 4 to 5. This work shows that HRr% is a robust indicator of aerobic fitness and provides an on- the-spot index that is useful for training load adjustment of elite- performance athletes. Keywords: heart rate recovery, decision support, training schedule, high perfor mance sport, aer- obic fitness Introduction The aim of this study was to determine whether heart rate recovery percentage (HRr%) is a robust measure of athlete fitness over time, and whether HRr% changes reflect changes in fitness, per- formance, and training load over a 12 -week training program, in the context of a professional team sport environment. The training environment in this study closely reflects actual on-field conditions of the athletes. Results show that HRr% is a robust indicator of aerobic fitness and match performance. Also, HRr% provides an on- the-spot index that is useful for making adjust- ments to the training load of elite-performance athletes. A successful pre- season training program aims to increase the fitness of athletes in preparation for the upcoming competition season. This is achieved through the application of training protocols that are similar to those encountered in performance situations during the competitive season. These stressful conditions may take a number of different formats from traditional running to game-based conditioning drills. The training program for elite athletes needs to allow each athlete to reach peak performance at the appropriate time and to maintain this level of performance throughout the competition season. At the same time, the training program must avoid over - training and fatigue by controlling the total amount of exercise, or load. Athletes have a finite capacity for adaptation to training load before fatigue and over-reaching occurs (Coutts, Reaburn, Piva, & Rowsell, 2007). Fatigue and over-reaching during training can limit the athlete’s perfor- mance capacity and may take several weeks to recover from (Kreider, Fry, & O’Toole, 1998). The ability to monitor and adjust athletes’ training load according to their training status and/or their health status
capacity for adaptation to training load before fatigue and over-reaching occurs (Coutts, Reaburn, Piva, & Rowsell, 2007). Fatigue and over-reaching during training can limit the athlete’s perfor- mance capacity and may take several weeks to recover from (Kreider, Fry, & O’Toole, 1998). The ability to monitor and adjust athletes’ training load according to their training status and/or their health status is therefore essential. An appropriate and sensitive measure allows for an opti- mal training load to be applied and over-training to be avoided. Any measurement tool used to determine an athlete’s training and health status must be accurate, reliable, and able to determine real change appropriate for high-performance athletes. Several models have addressed how to determine the correct level of training to obtain the best perfor- mance outcomes and how these can be monitored (Busso, 2003). Some of these monitoring tools attempt to measure the overall well-being of the athlete using questionnaire data, such as the Pro- file of Mood Status (POMS) questionnaire (McNair, Lorr, & Droppleman, 1971), the Daily Ana l- ysis of Life Demands for Athletes Questionnaire (DALDA) (Rushall, 1990) and Kenttä’s passive and/or active recovery scale (Kenttä & Hassmen, 1998). Autonomic Nervous System Assessment in Athlete Performance An alternative way of monitoring training status is to analyze the response of the autonomic nervous system to training load. A variety of measurement tools for monitoring and predicting changes in training status have been developed (Borresen & Lambert, 2008; Lamberts, Rietjens, Tijdink, Noakes, & Lambert, 2010; Lamberts, Swart, Capostagno, Noakes, & Lambert , 2009). A period of submaximal exercise has been shown to be associated with a prolonged period of in- creased heart rate as a function of increased sympathetic activity extending up to 45 minutes post exercise with parasympathetic reactivation peaking at two minutes post exercise (Wang et al., 2011). Heart Rate recovery (HRr) is defined as the fall in heart rate over a given period following
Cornforth, Robinson, Spence, & Jelinek 195 exercise. HRr is strongly correlated to parasympathetic activity early in the recovery period, and thus autonomic regulation of the heart rate can provide important information on athlete psycho- logical stress, fatigue, fitness, and over-stretching (Kannankeril, Le, Ka dish, & Goldberger, 2004; Savin, Davidson, & Haskell , 1982). Several studies have reported autonomic nervous system changes in association with training performance outcomes using HRr as a measure of autonomic response (Borresen & Lambert, 2008; Hottenrott, Hoos, & Esperer, 2006; Lamberts et al., 2009). In other studies, a decrease in submaximal exercise heart rate (SubHR) (Barbeau, Serresse, & Boulay, 1993; Scharhag-Rosenberger, Meyer, Walitzek, & Kindermann, 2009), quicker HRr (Buchheit et al., 2008; Sugawara, Murakami, Maed, Kuno, & Matsuda, 2001) and greater post - exercise heart rate variability (HRV) (Buchheit, Duche, Laursen, & Ratel, 2010) have been relat- ed to changes in aerobic fitness as a measure of performance. The usefulness of a marker to assess physiological adaptation to training ideally requires it to be easy to administer so that frequent monitoring is possible with little inconvenience to the athlete (Borresen & Lambert, 2008). From the literature, we can identify suitable candidates such as SubHR (Buchheit et al., 2008; Lamberts, Lemmink, Durandt, & Lambert, 2004; Lamberts et al., 2009), post-exercise HRr (Bosquet, Gamelin, & Berthoin, 2008; Buchheit et al., 2008; Lamberts et al., 2004) and HRV (Buchheit et al., 2008; Buchheit, Papelier, Laur sen, &Ahmaidi, 2007; Javorka, Zila, Balharek, & Javorka, 2002; Martinmäki & Rusko, 2007). The response of the car- diac autonomic nervous system can be assessed non-invasively using these measures, all of which can provide useful information regarding the functional adaptations to a given training stimulus. The reliability and the associated error of measurement of these markers have been previously described mainly in individual sports such as cycling and swimming using treadmill or bicycle programs for performance and aerobic fitness testing. The utilization of the warm-up period to collect submaximal heart rate (SubHR), post-exercise HRr and/or HRV recordings minimizes the disturbance to the athletes (Buchheit et al., 2010). To be able to adapt training loads
these markers have been previously described mainly in individual sports such as cycling and swimming using treadmill or bicycle programs for performance and aerobic fitness testing. The utilization of the warm-up period to collect submaximal heart rate (SubHR), post-exercise HRr and/or HRV recordings minimizes the disturbance to the athletes (Buchheit et al., 2010). To be able to adapt training loads based on the measurements within the warm-up requires confi- dence with the results. Therefore the ‘normal’ day- to-day variations of these indices need to be taken into account (Hopkins & Hewson, 2001). Heart rate recovery is a robust indicator of cardi- ac function and measures of HRr rather than of HRV avoid saturation effects and allow assess- ment of exercise outcomes (Kiviniemi et al., 2004). In individual high- performance sport, HRr is used to gauge aerobic fitness and the effect of training. Fitter athletes may have a faster HRr, with HRr increasing with VO2max (Aziz, Kilding, & Teh, 2006; Lamberts et al., 2004). Previous studies on athletes from team sports utilizing a variety of measures have produced con- flicting results in part due to a wide variation in baseline fitness of athletes and exercise protocols (Buchheit, Simpson, Al Haddad, Bourdon, & Mendez -Villanueva, 2012; Edwards, MacFayden, & Clark, 2003; Hoffman, Epstein, Einbinder, & Weinstein, 1999). These studies also differed with respect to the time at which HRr was measured and whether HRr was measured following sub-maximal or maximal exercise exertion. No systematic study has addressed the reliability of HRr nor investigated the relationship between HRr and training load and fatigue in high- performance athletes during a pre- season training regimen. HRr% has not previously been used to monitor the effect of pre-season training load and compared to performance outcomes of elite athletes at an individual or team level. HRr%, used to assess fitness/fatigue in elite athletes, is dependent on various factors such as training load and needs to be considered in conjunction with athlete performance as part of a pre- season training program. An optimum balance between training load and recovery, as measured by heart rate parameters, is essential to
elite athletes at an individual or team level. HRr%, used to assess fitness/fatigue in elite athletes, is dependent on various factors such as training load and needs to be considered in conjunction with athlete performance as part of a pre- season training program. An optimum balance between training load and recovery, as measured by heart rate parameters, is essential to an effective training schedule and on-field performance (Baumert, Brechtel, Lock, Voss, & Abbott, 2006; Borresen & Lambert, 2008; Meeusen, Watson, Hasegawa, Roelands, & Piacentini, 2006).
Heart Rate Recovery in Decision Support 196 The work reported in this paper is in two phases. The first phase establishes the reliability of the HRr% assessment procedure; the second phase used HRr% to assess performance and fatigue in a cohort of high- performance athletes during a 12-week pre- season training period with varying load. Research Objectives From the foregoing, we have shown the need for a robust measure of fitness in elite athletes, a measure that will reflect the changes in athlete fitness during training. It is also required that the measure is easy to administer on the field, and that results are quickly obtained and in a meaning- ful format, so that the team coach can interpret these results and adjust the athletes training pro- gram on the spot. As the literature has drawn attention to HRr%, this measure forms the focus of this study. The objectives of this research are: 1. To find a reliable and robust measure of the fitness of an athlete 2. To test the suitability of HRr% as a measure of aerobic fitness 3. To determine whether HRr% is robust, that is, whether changes reflect changes in fitness, performance, and training load over a training program 4. To find evidence to support the use of HRr% as an on-the-spot index that is useful for training load adjustment of elite-performance athletes Methods Participants in both parts of the study were professional Australian Football players. Before the testing period, all participants completed a medical screening questionnaire and underwent a bat- tery of medical and musculoskeletal tests. This work was carried out in two phases. Phase 1: Development of the Heart Rate Recovery Test The first phase took place during a 4- day period prior to the commencement of the pre-season training program. Athletes underwent two recovery tests separated by three days, one at the b e- ginning, and the other at the end of this period. On each of these two occasions, heart rate meas- urements were made during the training warm-up period to minimize the disturbance to the ath- letes. The heart rate recovery
the commencement of the pre-season training program. Athletes underwent two recovery tests separated by three days, one at the b e- ginning, and the other at the end of this period. On each of these two occasions, heart rate meas- urements were made during the training warm-up period to minimize the disturbance to the ath- letes. The heart rate recovery test, modified from the submaximal running test, is also known as a HIMS test (Heart rate Interval Monitoring System). This test was designed to be performed fre- quently in the warm-up of a training session and has to be submaximal and non- aversive for the athletes (Lamberts, Maskell, Borresen, & Lambert, 2011). The principal change from the original HIMS test made for the purposes of this study was to use continuous running instead of a stop- start protocol. The pace of running within each of the 4 stages (9.3, 11.1, 12.8, and 14.6 km/h, respectively) was increased from the original HIMS test to retain 85-90% maximal heart rate as recommended by Lamberts et al. (2009) and Dellal et al. (2010). Running speeds were adjusted based on the fitness of the athletes in the study. The test was also changed to continuous running in order to minimize the stress to the groin region, as seen in a continual stop- start test, with changes in direction of running as in the HIMS protocol. The heart rate recovery test was conducted at the same time on each day to avoid any confound- ing effects due to circadian changes in heart rate. The test consisted of four 2-minute periods of running at progressively increasing speeds (9.3, 11.1, 12.8, and 14.6 km/h) with athletes resting for one minute between each of the running periods (Reilly, Robinson, & Minors, 1984). The pro- tocol was designed so that athletes achieved 85-90% of maximum HR at the end of the fourth period. The distance was broken into 50- metre blocks, each covered in a set time by a pacing runner in order to maintain the correct speed.
Robinson, & Minors, 1984). The pro- tocol was designed so that athletes achieved 85-90% of maximum HR at the end of the fourth period. The distance was broken into 50- metre blocks, each covered in a set time by a pacing runner in order to maintain the correct speed.
Cornforth, Robinson, Spence, & Jelinek 197 All athletes were familiar with the training ground and surface used for the testing and had a min- imum of four repetitions for familiarization to the test procedures. The participants performed a submaximal continuous running test in Indian file formation of 4 stages with duration of 2 minutes each, followed by a rest period of 1minute. The total duration of the test was 12 minutes. During the three initial rest periods, the participants stretched. At the end of the run athletes re- mained standing and stationary. Athletes measured their carotid pulse for 15 seconds (HR Stage 4) and again 1 minute later (HR 1 min post) manually as this was more practical than using elec- tronic recording devices linked with GPS systems, and it had the advantage of providing on the spot results. All athletes were trained in the procedure and verified that they all correctly identi- fied the pulse and counted accurately. A 3-second count into sampling the pulse was used at both time points to allow for correct location of the pulse (Chatterjee, Chatterjee, &Bandyopadhyay, 2005). The two recordings (HR Stage 4 and HR 1 min post) were used to calculate the heart rate recov- ery percentage (HRr%) using the formula: The test was conducted twice, three days apart, to test the reliability/robustness of the measure- ments. Phase 2: Application of the Heart Rate Recovery Test After the 4-day warm up period (phase 1), athletes participated in a minimum of 15 hours per week of training during the twelve-week pre-season study. Athletes undertook a 2 km time trial on an Athletics Australia approved track utilizing the track photo- finish timing system at the times indicated in Table 1. Within 5 seconds of completing the time trial, the athletes measured their carotid pulse for 15- seconds and heart rate (HR) in beats per minute (bpm) was calculated. This provided heart rate data for weeks 1, 3, 6 and 12, and a measure of performance for weeks 1, 6 and 12. Table 1. Testing protocol. Note the absence of a time trial in week
completing the time trial, the athletes measured their carotid pulse for 15- seconds and heart rate (HR) in beats per minute (bpm) was calculated. This provided heart rate data for weeks 1, 3, 6 and 12, and a measure of performance for weeks 1, 6 and 12. Table 1. Testing protocol. Note the absence of a time trial in week 3. Week 1 Week 3 Week 6 Week 12 HRr test % Body fat Height HRr test HRr test HRr test 2km Time Trial 2km TT 2km TT The training schedule consisted of two blocks of heavy training (Table 2) separated by a two- week block of lighter training (Table 3). The training regimen was based on best practice and by consultation with the high- performance training coach. On the second day of the trial each player’s height was measured with a calibrated Lufkin tape measure, and body mass was obtained via a calibrated scale (Life Measurement Instruments, Concord, CA). Percent body fat determination was obtained using air-displacement plethysmog- raphy with the BOD POD body- composition system (Life Measurement Instruments) using the basic methods previously described (Kraemer et al., 2005). On test days, the body mass of the subjects and the ambient temperature and humidity were measured before each test.
Description
The study examines heart rate recovery as a measure for adjusting training schedules in elite athletes.