Correlations of physiological responses in squash players during competition

Resumen physiological responses in squash players during competition

The knowledge of physiological variables that intervene in squash competition is not very well-known. Physiological and perceptual measures in squash are no well understood. This relation were studied in cycling during high-intensity interval exercise (Green, 2006) and athletes (Seiler, 2004) Laboratory testing is commonly used to determine metabolic profile of athletes, to evaluate physiological training induced changes and/or determine appropriate training intensities. In racket sports such us squash, success is largely dependent of technical, tactical, and motor skills (Lees, 2003). Squash is a moderate – to high-intensity intermittent exercise. Players are active 50 to 70% of the playing time and 80% of the time; the ball is in play 10 seconds or less. The rest intervals fit a normal distribution with an average duration of 8 seconds. Heart rate increases rapidly in the first minutes of play and remains stable at approximately 160 beats/min for the whole match no matter what levels the players are. (Montpetit, 1990).

Introduction

The knowledge of physiological variables that intervene in squash competition is not very well-known. Physiological and perceptual measures in squash are no well understood. This relation were studied in cycling during high-intensity interval exercise (Green, 2006) and athletes (Seiler, 2004) Laboratory testing is commonly used to determine metabolic profile of athletes, to evaluate physiological training induced changes and/or determine appropriate training intensities. In racket sports such us squash, success is largely dependent of technical, tactical, and motor skills (Lees, 2003). Squash is a moderate – to high-intensity intermittent exercise. Players are active 50 to 70% of the playing time and 80% of the time; the ball is in play 10 seconds or less. The rest intervals fit a normal distribution with an average duration of 8 seconds. Heart rate increases rapidly in the first minutes of play and remains stable at approximately 160 beats/min for the whole match no matter what levels the players are. (Montpetit, 1990). In sports with intermittent activity such us squash, physiological demands imposed on the players during competition cannot be simulated in controlled laboratory settings and is important to considerate the physiological characteristics of squash, including many dynamic leg movements (repeated accelerations, decelerations, turns and jumps) and arm (shoulder internal rotation, forearm pronation, wrist flexion) and other movements and therefore have to be determined during real match play. Only limited data on squash play performance exists in literature. Some studies have monitored cardiorespiratory responses in incremental treadmill test versus specific incremental test (Girard, 2005); physiological characteristics (VO2 max, anaerobic thresholds, running speed) of squash players (Chin, 1995,; Steininger, 1987) The maximal oxygen consumption (VO2max) and the anaerobic threshold (ventilatory thresholds, (VT), and respiratory compensation point, is not valid in order to explain the performance in competition and this is not sufficient for a valid estimate of competition fitness (Steininger, 1987; Girard, 2005). Other authors (Chin, 1995) present a results that cardiorespiratory specific fitness and a muscular strenght may be one a key factors that contributed to the success in squash play The aim of this study were to evaluate (correlate) several physiological variables (anthropometric, mean heart rate, lactate levels, ratings of perceived exertion (RPE), and training variables) considerate important in the course of several matches in squash players.

Subjects and methods

Thirteen healthy male experienced squash players volunteered for this investigation and written informed consent was obtained from each participant. The protocol was approved by the University of Malaga Ethical Committee (Spain) Measurements of anthropometric variables to estimate body composition: Sum 4 skinfolds (triceps, subscapular, ileospinal and abdominal) Body fat (Faulkner, modified of Yuhasz, 1987), skeletal muscle mass (Lee, 2000) and Heath-Carter anthropometric somatotype (Heath-Carter, 1990) according to ISAK’s methodology (Norton, 2000) Measurements of continuously heart rate (Polar 610i, 710i by Polar Electro Oy, Finland), lactate levels (Dr Lange LP20 Miniphotometer, Berlin, Germany), after extraction of 20 ?l of hyperhemized sample blood from the earlobe in the first minute of concluding the match. Same we recorded ratings of perceived exertion (RPE) (modified Borg scale of 0-10) (Chen, 2002), total time game (s) and the dichotomic variable: loser (L= 0) ó winner (W= 1), everything it analyzed in a total of 25 matches. Statistical analyses were conducted using a SPSS version 14.0 and a MedCalc Statistical Software. The results are expressed as mean (SD). Paired t-tests were used to determine significant differences between variables. Pearson product moment correlations were used to examine the relationships among variables (anthropometric, mean heart rate, lactate levels, RPE, and training variables), and level p< 0.05 was considered significant. Variables such as lactate concentrations, RPE Borg Scale, HR mean, time and win (1) or loss (2) were included in a forward stepwise multiple regression procedure and techniques of logistic regression (for dichotomous variable WIN or LOSS) Pampel, 2000

Results

Thirteen squash players participate in this study. Table 1 (mean (SD)) shows descriptive data for age, height, weight, percent body fat, Heath-Carter somatotype components, BMI and training characteristics.

Table 1: General data: Age, height, weight, percent fat, BMI, somatotype components and training data.

Paired t-test: Significant differences were found between Borg Scale levels between W (5.5 (2.3)) and L (7.6 (2.46)) (p= 0.0377), lactate levels between W (3.41 (1.58)) and L (6.54 (2.86)) (p= 0.0028). No significant differences were found between HR mean W (167 (12)) and L (175 (9.56)), between W or L and in variables like percent fat, BMI, somatotype components, age, weight, height, training years and week training unit sessions (Table 2) (Fig 1)

Table 2: Values are mean (SD) of lactate concentration (mMol/l), RPE Borg Scale and mean Heart Rate (bpm) between Winners and Losers.

Fig 1: Box and Whisker means of lactate levels (p< 0,0028) and Borg scale (p<0,037) between Winners (W) and Losers (L).

Significant correlations for Pearson’s product moment coefficient (table 3) were found between Lactate concentrations and Borg Scale, W or L and HRmean (table 3).

Table 3: Pearson’s correlation coefficient (r) and p < = significance level among Lactate concentrations, Borg Scale, Winner or Loser (dichotomous variable), Heart rate mean (bpm) and play time (seconds); ns: no significant .

Week training sessions are related with percent fat (r = -0,423; p< 0,035), endomorphy (r = -0,54; p< 0,005) and ectomorphy (r = 0,42; p< 0,034) Regional ranking are related with percent fat (r = -0, 48; p< 0, 01) and endomorphy (r = 0,47; p< 0,018). By means techniques of multiple regression is possible to predict: ? LACT = 0.70388 (BORG) + 0,39442 F-ratio= 16,93; SEE: 2,16 p< 0,001, coeff of determination: 0,424, mult corr coeff: 0,65 ? BORG = 0.09661 (HR mean) + 0.40031 (LACT) + 0.0347 (TIME) -13.69 F-ratio= 20,14; SEE: 1,40 p< 0,001, coeff of determination: 0,74, mult corr coeff: 0,86 ? HRmean = 3.28875 (BORG) + 149.61 F-ratio= 25.83; SEE: 8,18 p< 0,001, coeff of determination: 0,529, mult corr coeff: 0,72 By means techniques of logistic regression for dichotomous variable: (Pampel, 2000) ? WINNER OR LOSER = – 0.6004 (LACT) + 2.7701 (SE= 0.2476) p =0,0153 OR: 0.5486 95% CI 0.3377- 0.8912 Chi-square = 9,6901; DF = 1; p = 0,0019 This model correctly predicts 80% of the cases ? WINNER OR LOSER = – 0.3708 (BORG) + 2.3539 (SE= 0.1884) p =0,049 OR: 0.6902 95% CI 0.4771- 0.9984 Chi-square = 4,6115; DF = 1; p = 0,0318 This model correctly predicts 64% of the cases

Discussion

In squash, maximal oxygen uptake has traditionally been considered the key factor in aerobic performance, and this sport is largely dependent of a technical motor abilities. The basic anthropometric variables of our athletes are in agreement with Chin et al, (Chin, 1995) and Girard (Girard, 2005) in relation to weight and height and more percent fat. Limited data are in relation to competition parameters, and only limited data are available on sport specific fitness test, and other sports (Green, 2006; Seiler, 2004) The results of present study shows a large relationship between variables that they are related with fitness, mean heart rate, lactate levels, competition time and the ratings of perceived exertion. The relationship between lactate and RPE, lactate and HR mean are in accordance with data of Green et al. (Green, 2006), This relations are direct (r= 0.44-0,65) and this suggest that RPE is a sensitive variable of acute exercise. High values of Borg Scale and lactate levels are related inversely to win or loss. Approximately exist a lower level of RPE and lactate for win. The RPE and the condition of winner or loser are independent variables that explain very well, the lactate levels in a match. This study indicates a stronger RPE- lactate and RPE-HRmean associations. Other exercise circumstances such as a longer duration, steady workload, repeated exercise bouts may alter these associations (Green, 2005; Weltman, 1998; Seiler, 2004). In our study this good relation could be owed to that the collection of the RPE is after exercise and is valued on the whole the perception of the effort, that in finally picks up a sensation of a maintained state of exercise. Significant increases in RPE occurring concurrently with significant decreases in blood lactate during later stages of exercise reveal a divergence relationships indicating lactate does not serve as a strong RPE mediator during 60 min of workload cycling (Green, 2005) The half time was from 8,3 (3,7) min., for the realization of 2 or 3 games, with a high percentage of mean heart rate. The changes in mean heart rate and lactate levels during competitive squash are important and may be regulated for duration of match and competitor level. The Borg Scale level is a dependent variable of heart rate, lactate level and time of match. The lactate levels and HRmean are a dependent variable of a RPE. Logistic regression is a technique for analyzing problems in which there are one or more independent variables which determine an outcome that is measures with a dichotomous in which there are only two possible outcomes. The goal of logistic regression is to find the best fitting model to describe the relationship between the dichotomous characteristics of interest and a set of independent variables. Regulating the intensity of the competition, could we modify the heart rate and lactate levels, and the result of match? Are necessary more investigations to demonstrate the relationship between these parameters and their changes with the sport training and their effect in the competition?

Conclusions

1. The performance level of squash matches seem to have great relationship to variables like lactate levels, the mean heart rate and perceived exertion 2. It seem to acquire great importance a relative intensity of game characterized by aerobic lactate levels an mean heart rate 3. This study indicates a stronger RPE- lactate and RPE-HRmean associations.

Bibliografía

• Baron R, Petschnig R, Bachl N, Raberger G, Smekal G, Kastner P.1992. “Catecholamine excretion and heart rate as factors of psychophysical stress in table tennis”. Int J Sports Med. 13:501-5.
• Beaudin P, Zapiec C, Montgomery D. 1978 “Heart rate response and lactic acid concentration in squash players”. Res Q. 49:406-12.
• Bergeron MF, Maresh CM, Kraemer WJ, Abraham A, Conroy B, Gabaree C. 1991. “Tennis: a physiological profile during match play”. Int J Sports Med.;12:474-9.
• Blanksby BA, Elliott BC, Davis KH, Mercer MD.1980. “Blood pressure and rectal temperature responses of middle-aged sedentary, middle-aged active and “A”-grade competitive male squash players”. Br J Sports Med.14:133-8
• Brady HR, Kinirons M, Lynch T, Ohman EM, Tormey W, O’Malley KM, Horgan JH.1989. “Heart rate and metabolic response to competitive squash in veteran players: identification of risk factors for sudden cardiac death”. Eur Heart J.;10:1029-35. Brigden GS, Hughes LO, Broadhurst P, Raftery EB.1992. “Blood pressure changes during the game of squash”. Eur Heart J. 13:1084-7
• Chen MJ, Fan X, Moe ST. 2002.” Criterion related validity of the Borg ratings of perceived exertion scale in healthy individuals: a meta-analysis”. J Sports Sci 20: 873-899
• Chin MK, Wong AS, So RC, Siu OT, Steininger K, Lo DT. 1995 “Sport specific fitness testing of elite badminton players”. Br J Sports Med, 29:153-7.
• Docherty D.1982. “A comparison of heart rate responses in racquet games”. Br J Sports Med.,16:96-100
• Ferrauti A, Bergeron MF, Pluim BM, Weber K. 2001. “Physiological responses in tennis and running with similar oxygen uptake”. Eur J Appl Physiol.;85(1-2):27-33
• Girard O, Sciberras P, Habrard M, Hot P, Chevalier R, Millet GP. 2005.”Specific incremental test in squash players”. Br J Sports Med. 39:921-926.
• Green JM, McLester JR, Crews TR, Wickwire PJ, Pritchett RC, Lomax RG. 2006 “RPE association with lactate and heart rate during high intensity interval cycling”. Med Sci Sports Exerc, 38:167-172Carter JEL, Heath BH 1990. Somatotyping. Development and Applications. Cambridge. University Press
• Kindermann W, Schnabel A, Schnitt WM, Flothner K, Biro G Lehmann M. 1981. “Behaviour of pulse rate and metabolism in tennis and squash”. Dtsh Z Sportsmed, 9:229-238
• Konig D, Huonker M, Schmid A, Halle M, Berg A, Keul J.. 2001. “Cardiovascular, metabolic, and hormonal parameters in professional tennis players”. Med Sci Sports Exerc. ;33:654-8.
• Lee RC, Wang ZM, Heo M, Ross R, Janssen I, Heymsfield SB. 2000. “Total-body skeletal muscle mass: development and cross-validation of anthropometric prediction models”. Am J Clin Nutr, 72: 796-803
• Lees A. 2003. “Science and the major racket sports: a review”. J Sports Sci. 21:707-732
• Locke S, Colquhoun D, Briner M, Ellis L, O’Brien M, Wollstein J, Allen G.1997 ”Squash racquets. A review of physiology and medicine”. Sports Med.;23:130-8
• Majumdar P, Khanna GL, Malik V, Sachdeva S, Arif M, Mandal M.1997. “Physiological analysis to quantify training load in badminton”. Br J Sports Med. 31:342-345
• Montpetit RR. 1990. “Applied physiology of squash”. Sports Med. 10:31-41.
• Northcote RJ, MacFarlane P, Ballantyne D.1983 “Ambulatory electrocardiography in squash players”. Br Heart J.;50:372-7.
• Norton K, Olds T. 1996. Anthropometrica. Sidney, Southwood Press
• Pampel,FC .2000. Logistic regression: A primer. Sage University Papers on Quantitative Applicattions in Social Sciences, 7-32. Thousand Oaks, CA: Sage
• Salmoni AW, Sidney K, Michel R, Hiser J, Langlotz K.1991.”A descriptive analysis of elite-level racquetball. Res Q Exerc Sport”. 62:109-14.
• Seiler S, Sjursen JE.2004. “Effect of work duration on physiological and rating scale of perceived exertion responses during self-paced interval training”. Scand J Med Sci Sports 14:318-325
• Smekal G, Pokan R, von Duvillard SP, Baron R, Tschan H, Bachl N. 2000.“Comparison of laboratory and “on-court” endurance testing in tennis”. Int J Sports Med. 21:242-249.
• Smekal G, von Duvillard SP, Pokan R, Tschan H, Baron R, Hofmann P, Wonisch M, Bachl N. 2003. “Changes in blood lactate and respiratory gas exchange measures in sports with discontinuous load profiles”. Eur J Appl Physiol. 89:489-95.
• St Clair Gibson A, Broomhead S, Lambert MI, Hawley JA. 1998. “Prediction of maximal oxygen uptake from a 20-m shuttle run as measured directly in runners and squash players”. J Sports Sci. 16:331-5.
• Smekal G, Baron R, Pokan R, Dirninger K, Bachl N.1995. “Metabolic and cardiorespiratory reactions in tennis-players in laboratory testing and under sport-specific conditions”. Wien Med Wochenschr. 145:611-5. Steininger K, Wodick RE. 1987. “Sports-specific fitness testing in squash”. Br J Sports Med. 21:23-26.
• Struthers AD, Quigley C, Brown MJ.1988 “Rapid changes in plasma potassium during a game of squash”. Clin Sci (Lond);74:397-401
• Weltman A, Weltman JY, Kanaley JA.Rogol AD, Veldhuis JD. 1998. “Repeated bouts of exercise alter the blood lactate-RPE relation”. Med Sci Sports Exerc 30: 1113-1117
• Yuhasz MS. 1962. The effects of sports training in man with prediction of optimal body weight. Unpublished Doctoral Thesis. Urbana. University of Illinois