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Treinamento para sprinter baseado na pse

Treinamento para sprinter baseado na pse






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    Treinamento para sprinter baseado na pse Treinamento para sprinter baseado na pse Document Transcript

    • Journal of Strength and Conditioning Research, 2006, 20(11, 36-42© 2006 National Strength &. Conditioning AssociationPROGRAM DESIGN BASED ON A MATHEMATICALMODEL USING RATING OF PERCEIVED EXERTION FORAN ELITE JAPANESE SPRINTER: A CASE STUDYSHOZO SUZUKI, TASUKU SATO,^ AKINOBU MAEDA,^ AND YASUO TAKAHASHI^Human Performance Laboratories, Faculty of Physical Education, Sendai College, Miyagi, Japan; -Faculty ofHuman Informatics, Tohoku-Gakuin University, Miyagi, Japan.ABSTRACT. Suzuki S., T. Sato, A. Maeda, and Y. Takahashi. heen documented hetween actual and predicted values ofProgram design based on a mathematical model using Rating of physical performance. In addition, the time constant hasPerceived Exertion for an elite Japanese sprinter: A case study. heen reported at 38-60 days for positives and 2-13 daysJ. Strength Comi. Has. 20(l>:36^2. 2006.—We investigated the for negatives, suggesting tbat tbe rate of negative cbangeeffects of program design on 400-m sprint time by applying aRating of Perceived Exertion (RPE) mathematical model to is faster than the rate of positive change. Fitz-Clarke ettraining performance. The subject was 24 years old and had al. (14) conducted a simulation study using a model toheen training for 9 years. His hest performance in 400-m sprint calculate the duration of training required to maximizecompetitions was 45.50 seconds. Body weight, resting heart rate, physical performance. In tbis manner, by calculating co-training time and RPE wore monitored daily after training ses- efficients tbat could improve or worsen training effectssions. Similarly, performance in 400-m races was recorded 9 hased on a given model, tbe effectiveness of tapering intimes during 2003. At the World Championships in Athletics in maximizing physical performance bas been scientificallyFrance, the subjects team placed eighth in the 1,600-m relay. verified.The RPE mathematical model was ahle to predict changes inperformance. Rate of matching was statistically significant (r^ = A device that monitors changes in heart rate (HR)0.83, F ratio = 34.27, p < 0.0011. Application ofthe RPE math- during training must be worn in order to apply tbe train-ematical model to tbe design of a training program specific to ing impulse (TRIMP) matbematical model, developed bythe needs of a 400-m sprinter indicates a potentially powerful Bannister et al. and described in previous studies (3-5,tool that can he applied to accurately assess the effects of train- 23), to routine sports training. Without using this deviceing on athletic performance. in combination with an HR monitor to determine total amount of daily training, the TRIMP matbematical modelKKY WORDS, monitoring, performance, conditioning, monotony cannot be easily applied. We therefore previously inves- tigated wbetber the TRIMP mathematical model, wbicb is capable of predicting performance, could be applied toINTRODUCTION Japanese suhjects (28). he uitimate goal of training is to prepare ath- Borg et al. (6) reported tbat the Category Ratio Scale letes to perform at their hest at important com- (CR-10) is responsive to changes in HR and blood tactate petitions. To achieve this goal, athletes must level. To utilize tbis scale at training sites in a convenient train to improve their competitiveness over a manner, TRIMP, which is calculated as the product oftheperiod of 1 or several years. Designing a suitahly strin- coefficient of blood lactate level, exercise %HR,,,,,.,, andgent training program requires an appreciation of the training time, was replaced with tbe rating of perceivedneed for implementing, analyzing, assessing, and modi- exertion (RPE) (16), a modification ofthe CR-10 scale de-fying training regimens hased on the specific require- veloped by Horg et al. to calculate training volume ac-ments ofthe sport under consideration. The potential to cording to tbe following formula: training load = (trainingsubsequently assess the effectiveness of these different time X session RPE). A study comparing performancecomponents would he particularly helpful. predictions displayed strong positive correlations between Calvert et al. (12) investigated relationships between tbe 2 models (27).training and performance using a mathematical model Tbe present study investigated whether tbe RPEthat manages training as input data, and changes in matbematical model, wbicb is easily applied to routinephysical performance due to training as output data. training, is useful in preparing, implementing, analyzing,Here, input impulses were training stimuli, and impulse and assessing a yearlong training program for a top 400-responses were changes in physical performance due to m sprinter. We also wanted to determine whether tbetraining. By incorporating these 2 antagonistic functions; model was capable of evaluating athlete condition basednamely, the negatives of training (fatigue) and the posi- on conventional pbysiological parameters.tives of training (fitness! into impulse responses, changesin physical performance were determined as the sum of METHODStraining inputs and impulse responses. Using this model,the relationship between training and physical perfor- Experimental Approach to the Prohlemmance bas been clarified in sports including long-distance Training volume, fatigue, recovery, and performancerunning (5), triathlon (4), swimming (3, 22), cycling (7, 9), were assessed daily in an elite Japanese 400-m sprinterrunning (23, 27), hammer throw (8), weightlifting (10, to monitor cbanges in tbese parameters over a period of11), and rowing (30). A strong, positive correlation has 1 year. Furtbermore, a case study was conducted to as-
    • DESK",N ON A M A T H F M A T I C A I . MtlDEL 37 1 1 1Months JAN ; FEE MAK APK MAY : .HINE i JULY AUG iSEPTi OCT 1 HOV CKCPeak 1 u1 y 2 L U a 4 E 1^i •: 7 djii -. 1 9 11 Rating Descriptor 1- ChBini.-Ji.pBn Inici.! Tisili CanpetiUcti T Fukushima Champiornhipa 0 RestSchedule 3ThtjhofcuSludBntC«npstititjn 8 9th Wstlii ChsmpionEhip^iti ALhlatBi i Mjto InUtnatiMiJC^mpstiUon ff Habanal Chsmpionships 1 Very Easy 4 Th^hsku intetc^llaguUChanipiaiKhips S Kalisnal UiiinUuning;Bmps l^-S 2 Easy 3 ModerateMacro Preparation C DID petition R 4 Somewhat Hard 3 4 5 6 7 fl 9 1 lo: 11 12 13 1 2 5 Hard ! ! 1 6Technique Arm dri-v/RvJJtrrt Aonad/FiTn/[>riva 7 Very HardPhysical 8 g Very, Very Hard 10 MaximalFIGURE 1. Program design in 2003. FKJURE 2. Tbe subjects rating nf perceived exertion I RPE) was obtained with the use of a modified Borg scale (16). Thecertain wbetber a performance-peaking program de- suhject was shown the scale approximately 30 minutes follow-signed hased on an RPE mathematical model would he ing the conclusion of the training hout and asked "How was our training today?"effective in actual competition. Tbe training program de-signed in 2003 for tbe subject by his coach comprised sev-eral macrocycles (rests, preparations, and competitions)and 13 mesocycles (Figure 1). In 2003, tbe subject com- mediately weighed bimself to an accuracy of 50 g usingpeted in the following competitions: China-Japan Indoor UC-300 scales (Measurement Specialists, Huntsville, AL).Track Competition {Fehruary 22). Toboku Student Com- To assess the effects of training on tbe body, the sub-petition (April 12), Mito International Competition (May ject was asked RPE following tbe completion of each5), Toboku Intercollegiate Cbampionships (May 18), Jap- training session using tbe session RPE scale developedanese Track and Field Cbampionsbips (June 8), Japanese by Foster and Lebmann (16; Figure 2). Subjective muscleIntercollegiate Cbampionships (July 4), Fukusbima pain was assessed using the CPS scale developed by Ar-Cbampionships (July 13), Ninth World Championships in vidsson et al. (2; Figure 3). To assess RPE and CPS dur-Athletics (August 23-31), and National Cbampionsbips ing exercise sessions, standard instruction and anchoring(October 28). For a period of almost 1 year, from January procedures were explained during a familiarization ses-7 to December 20, 2003, the suhject was instructed to sion (25). At 30 minutes after the end of eacb trainingkeep a training journal recording morning HR, body session, the subject was asked, "How was your trainingweight, RPE, category ratio pain scale (CPS), and total today?" to determine RPE score, and "How are your mus-quality recovery (TQR). Whether tbe RPE matbematical cles?" to determine CPS. Tbe suhject tben stated scoresmodel could predict actual performance was ascertained for all activities during tbe training session. Similarly, toin four competitions up to May 2003. Because the model determine recovery from the training ofthe previous day,was shown to he able to predict performance, a perfor- subjective recovery was assessed every morning using tbemance-peaking training program was designed using tbe TQR scale developed hy Kentta and Hassmen (21; Figuremodel in an attempt to yield optimal performance during 4) and the CPS scale.subsequent important competitions. The TQR scale was used in the same manner as the All data were analyzed using Excel software (Excel RPE and CPS scales. The subject was shown tbe scaleSoftware, Placitas, NM) and were available to the sports before breakfast and was asked "How is your conditionscientist, coacb, strengtb-and-conditioning specialist, and now?" to determine his TQR score.athlete so tbat they could visually ohserve daily changes Table 1 sbows tbe contents of microcycle training andin the ahove-mentioned parameters. assessment of tbe training progiam in terms of load, mo- notony, and strain, whicb were quantified according toSubject tbe metbods reported by Foster and Lehmann (15, 16).The suhject was a 24-year-old track athlete (height, 174.5 The subject was required to record in bis trainingcm; weight, 63 kg) witb a 9-year background in training journal tbe duration of training and RPE las subjectivewbo won the 400-m sprint in tbe Japanese Track and exertion) 30 minutes after the end of daily training.Field Championship in 2003 and 2004, was in tbe team Training load was calculated by multiplying duration ofthat finisbed eighth in tbe 1,600-m relay at the Ninth training by RPE. For example, the suhject performed rep-World Cbampionsbips in Atbletics in 2003, and was se- etition training on Tuesdays, so tbe load for Tuesdays waslected to represent Japan in the 2004 Athens Olympics. 180 X 6 = 1,080 units. Mean (± SD} weekly load wasInformed consent was obtained after thorough explana- calculated as 686 ± 661 units.tion ofthe study objectives and methods. Study protocols Monotony was calculated by dividing the weekly av-were approved by the Ethics Review Board of Sendai Col- erage by the standard deviation (1.04). In other words,lege, Japan. training monotony resulted in a small standard deviation and a high monotony value. A small monotony value in-Parameters Measured dicated a high degree of training variation.Tbe subject measured HR hy palpation ofthe radial pulse Strain was calculated by multiplying the mean weeklyfor 1 minute while still in bed in the morning, then im- load hy monotony (4,994). In otber words, training with
    • 38 SUZUKI, SATO, MAEDA ET AL. Rating Descriptor Rating Descriptor 20 = Extremely strong 6 151 7 Very, Very Poor Recovery 8 121 Very strong 10- 9 Very Poor Recovery 9- 10 8- 11 Poor Recovery 7- 12 6 - Strong 5~ 13 Reasonable Recovery 14 4— 15 Good Recovery 3 - Moderate 16 17 Very Good Recovery Q 18 19 Very, Very Good Recovery Light 20 1.0- FIGURE 4. Subjective recovery was assessed using Kentta and Hassmens total quality recovery scale 121), The subject was shown the scale hefore hreakfa.st and was asked, "How is Very light your condition now?" 0.5- (1, 15). If long, low-intensity training was performed the day after short, high-intensity training to reduce fatigue, Extremely light training load would be consistently comparable, increas- ing monotony. If this type of training monotony continued for periods of weeks, months, and years, the degree of oJ —No pain physical stress would increase, diminishing training ef- fects and increasing the risk of overtraining (19).FKiUKK 3. Subjective muscle pain was assessed using the Mathematical Model Using Rating of Perceivedcategory ratio pain scale (CPS) developed by Arvidsson 12). At Exertion30 minutes after the end of each training session and heforebreakfast, the subject was asked "How are your muscles?" to Recorded training parameters were used for a systemdetermine the CPS score. model adapted from the model developed hy Morton et al. (23). Levels of fitness and fatigue, p(t) and f(t), were ob- tained by convolving training load (w(t) ^ training timea high degree of" variation resulted in a low monotony X session RPE), with training responses g(t) and h(t), asvalue and thus low strain. described by Banister and Hamilton (5). The value w(t) Even if total weekly load was low, repeated training is expressed in arbitrary units; so that:monotony (long, low-intensity training! performed on adaily basis would increase the level of monotony and pit) = w(t)-g(t), and (1)strain and could result in overtraining and sports injury fit) - wi.t)-h(t). (2) TABLE 1. Evaluation of the load, monotony, and strain associated with a training program. Duration Day Training session Load (min)Monday Rest 0 0 0Tuesday High-tempo training 180 6 1080Wednesday Short interval. Resistance training 120 6 720Thursday Rest 0 0 0Friday Up-and-down hill training 180 9 1620Saturday Jump training 180 7 1260Sunday Jog 5 km, easy 30 4 120Mean weekly load 686Standard deviation of mean weekly load 661Monotony (mean weekly load/standard deviation of mean weekly load) 1.04Total weekly load (mean weekly load x 7) 4802Strain (total weekly load X monotony) 4994 * RPE = rating of perceived exertion.
    • Pi«x~,KAM DFSICN BASFD ON A MATKFMATICAL MOD[-:I_ 39 300 r RPE -a-CPSl 1 Competitions 20 1(12 a i K •.il]3 * 1 2 6112 7/12 8IIZ 9/12 10/12 11/12 12/12 Date inv, 2/lK 3/12 A/2 6112 6/12 7/12 B/12 9/lE 10/lZ ll/lf. 12/12 DateFICURE 5. Weekly changes in training time, rating of per-ceived exertion (RPE), and category ratio pain scale (CPS). FIGURE 6. Weekly changes in morning category ratio pain scale (CPS) and total quality recovery (TQRl including the peaking periods.In the description by Banister and Hamilton (5), themathematical form of the functions g(t) and hit) were asfollows: •W.>Bht-»-R.=t-HR [ I Competitions git) = e "•., and (3) h(t) - e "^^ (4) I . M U 11 i Iwhere T, and T,^ represent decay time constants for fitnessand fatigue (first estimated as 45 days and 15 days, re-spectively, then refined by iteration), and t is time. ANiu i An index of performance was obtained from differencehetween levels of fitness and fatigue weighted by a coef-ficient k: ail) = k,-p(t^ - ^2-/f^) fj) 1/12 2IIZ HII7. 4/12 fiil2 6/12 H/IH 9(12 I(V12 11/12 !2/IKwhere fe, and k.^ represent the proportionality factors of Datefitness and fatigue (first estimated as /;, = 1 and k.^ = 2,then refined by iteration). FIGURE 7. Weekly changes in morning pulse rate and weight. In our apphcation, the mathematical form of responsefunctions were as follows: gU) = and (6) RPE, and CPS decreased hefore the Japanese and World Championships in 2003. Mean annual RPE and CPS were hit) = (7) high at 5.6 and 6.6, respectively, indicating that the sub- ject underwent physically demanding training during thePerformance ait) was determined as the difference be- training season. In addition, during these 2 major cham-tween fatigue and fitness levels, as such: pionships, CPS was 0 (no pain) and TQR was 17 to 20 ait) - pit) - fit) (8) (favorable recovery), indicating that the subject competed in the 2 major championships after having sufficientlyBy recurrence, p(t), f(t), and thus a(t) could be calculated recovered from muscle pain and fatigue (Figure 6).using previous successive training loads and individualparameters T,, T^, ^i, and k.^. Morning Heart Rate and Body Weight Model parameters were determined by fitting modelperformances to the 400-m races during the 9 competition Figure 7 shows weekly changes in morning HR and bodyperiods. These parameters were obtained by minimizing weigbt, which decreased as the subject prepared for thethe residual sum of squares (RSS) hetween modeled and 2 major championships. Morning HR and body weight onactual performances. A multiple linear regression method the day of the 2 major championships were 58 b-min was used after decay time constants were fixed. and 61.8 kg, respectively. In 2003, the degree of daily fiuc- tuation in morning HR and body weight in 2003 was 10Statistical Analyses b-min and 3.1 kg, respectively.Indicators of goodness-of-fit were estimated for the levelsof model. Coefficients of determination ir) hetween mod- Load, Monotony, and Straineled and actual performances were calculated. Statistical Figure 8 shows weekly changes in load, monotony, andsignificance of fit was tested using analysis of variance strain in 2003. As an indicator of total amount of training,on the RSS. The statistical F test was used to estimate load decreased as the subject prepared for the 2 majorlevel of significance for model fit. championships. Monotony, indicating training variation, decreased from 1.02 to 0.8 hefore the national champi-RESULTS onships and from 1.4 to 0.8 before tbe world champion-Training Time and Subjective Parameters ships. Furthermore, strain also decreased before both ma-Figure 5 shows weekly changes in training time, RPE 30 jor championships.minutes after training, and CPS for 2003. Training time, Mean monotony was 0.74 ± 0.4, suggesting that the
    • 40 SUZUKI, SATO, MAEDA ET AL. 15000 r FaUgue ^ 80000 10000 5000 1112 Sn2 3112 Alls SI12 ll2 8fia 9(12 ions 11112 1211?. 1(13 3(ia 3(13 1(13 5(13 «(13 1112 BII3 9(13 10(13 11 § 30000 g - 44 3 o 46 B o s - I 48 J I ! T(13 8(13 9tl3 1(U13 11(13 1(18 2(12 3(12 4(ia 5(12 «1S 7(12 WIK 9(12 10(12 11(12 12(18 Date 20000 FiGURK 9. Changes in actual and predicted performance. 16000 championships. According to introspective reports on per- I 12000 formance, the subject wrote that he could win the 400-m sprint at the Japanese championships, his most impor- W 8000 tant competition in 2003, and achieved this in a time of 40O0 45.63 seconds. In the preliminary race, his time was 0.13 0 [•-"-AAAA ; seconds faster than in the final race, and satisfied the A standard (45.55 seconds) for the World Championships in 1(12 2(12 3(12 4(12 5(12 «12 7(12 8112 9(12 10(12 11(12 12(12 Athletics. Based on results from the Japanese champi- Date onships, his coach redesigned the training program toFicuRE 8. Weekly changes in load monotony and strain. prepare him for the Ninth World Championships in Ath- letics in August. With a time of 46.53 seconds, approxi- mately 1 second slower than his personal best, the suhjectsubject underwent training with a high degree of varia- failed to make the 400-m final in the world champion-tion. ships. However, in the 1,600-m relay, he ran anchor leg and finished eighth with a time of 3 minutes 2.35 seconds.Actual and Predicted Performance Thayer (31) found that stimulation, overloading, ad-Figure 9 shows the relationship between actual perfor- aptation, and training effects correlated with fast recov-mance and the performance curve derived using the RPE ery, stating that alternating periods of training and restmodel and times for 400-m sprints in the 9 competitions. are important to maximize cyclic training. This is partic-The mathematical model was prepared using the follow- ularly important when designing a yearlong traininging coefficients and time constants for fatigue and fitness plan. Thayer also stated that a yearlong training programin the subject: with a high degree of variation can maintain a low mo- notony level. Regarding the yearlong training program FitU) = l-w(t}-e "^ and designed for the present subject, a 5-mesocycle block was scheduled before each important competition. In other Failt) = 2-w(t)-e "^. words, the program specified the amount of trainingThese coefficients were calculated to achieve minimal (load) to be tapered before each important competition.RSS between actual and predicted values. While predict- As to changes in monotony and strain, these parametersed value was lower than the actual value for the first were low before the national and world championships,indoor competition, actual and predicted values were sim- enabling the subject to enter while undergoing trainingilar for outdoor competitions (r- = 0.83; F ratio = 34.27, with a high degree of variation, to reduce the amount ofp < 0.0011. physical stress. Foster and Lehman (15, 16) followed the load, monotony, and strain of elite long-distance runnersDISCUSSION for 2 years and reported that training with a high degreeThe 2003 training plan for the subject, comprising several of variation and low level of monotony improved compet-macrocycles containing rests, preparations, competitions, itive performance. As a result, they designed the secondand 13 distinct mesocycles, was designed to ensure that year of the training program to minimize training mo-the condition of the subject would peak at the major notony. In our previous research on competitive rowers,
    • DI;SIC;N ON A MAIHrMATICAL MODFl. 41level of monotony was >3 before a competition, and per- designed utilizing tbe RPE matbematical model can sim-formance was not observed to peak at tbe competition (26, ulate performance fluctuations in terms of intensity, du-29). Mean monotony for tbe present subject was much ration, and frequency. Overtraining can tbus be prevent-lower, at 0.74, indicating tbat tbe yearlong training pro- ed and periodization used to maximize performance at agram incorporated a bigb degree of variation. particular competition. Furtbermore, maximization of Morning HR, serving as an objective physiological pa- performance at a particular competition requires not onlyrameter, decreased before tbe 2 major cbampionsbips. On utilization of tbe RPE mathematical model, but also tbetbe day of tbe cbampionsbips, morning HR was 58 combination of objective and subjective parameters sucbb-min , lower tban tbe mean morning HR of 60 b min . as morning HR, CPS, TQR, and monotony. Program de-Dressendorfer et al. (13) reported tbat wben fatigue sign accounting for these parameters sbould prove usefulsymptoms worsened, morning HR increased by more tban in routine training for top atbletes.10 b min . Wbile morning HR did not increase by more For a program such as the described model to functiontban 10 b-min for our subject before any of tbe impor- optimally, tbe sport scientist, sport coach, and strengtb-tant competitions, subjective and objective parameters of and-conditioning professional must plan the program to-monotony, strain, TQR, and CPS were poor at times wben gether and share goals and strategies.morning HR did increase by >10 b-min . Tbese findingssuggest tbat wben planning and assessing yearlong train- PRACTICAL APPLICATIONSing programs, monitoring basic pbysical parameters isimportant for determining pbysicai conditioning of atb- In practical terms, program design involves manipulatingletes. training intensity and volume wbile being respectful of Fry et al. (17, 18) reported tbat tbe major objectives the seasonal demands of the specific sport and athlete.of periodization, wbicb is at the core of training program Many coaches prepare training programs to peak atbleticdesign, are to prevent overtraining and to ensure peak or performance during important competitions. To maximizemaximized performance at appropriate times. Further- performance during important competitions, the qualitymore, tbe key for successful program design is to ensure of training programs must be improved. An RPE mathe-recovery from fatigue (18, 22). matical model was used as a tool for designing training programs, and combined witb sucb subjective and objec- Loren et al. (20) suggested tbat training effects will tive parameters sucb as CPS, TQH, and monotony, tbebe maximized wben tbe fitness-fatigue model is effective- model was sbown to function as an effective tool in tbely utilized witbin any yearlong program design. field. In the present study, the RPE model, wbicb reflected This system comprising a mathematical model andtbe aftereffects of fatigue and fitness, was used to predict pbysical condition assessments runs on Excel, and dailyperformance in 400-m sprints, and predicted and actual changes in performance can be visually cbecked in theperformances were compared in 4 competitions up to form of figures and charts. In addition, maximal perfor-May. The results showed that the model could predict mance during important competitions can be simulatedperformance ir^ = 0.88; F ratio = 52.04; p < 0.001}. Fur- by adjusting training time, intensity, and frequency. Thethermore, the sports scientist, coach, and strength-and- present results show that by adding performance predic-conditioning specialist each comprehensively examined tions based on a matbematical model to tbe existing pe-the performance curve derived from tbe matbematical riodization metbod, optimal performance can be targetedmodel and cbanges in various parameters, sucb as morn- during important competitions while preventing over-ing HR, CPS, TQR, and monotony, and concluded tbat tbe training. In addition, by collecting more data, tbe presentRPE mathematical model could be utilized as a tool for system sbould contribute to improving tbe quality ofaiding tbe design of training progiams. Next, the sports training programs designed by coaches.scientist performed a simulation study using the RPEmathematical model to maximize subject performanceduring tbe Japanese Track and Field Cbampionships by REFERENCESaltering training volume, and tben tbe idea of preparing 1. ANI)KI{.S()N, L., T. TRTPLETT-MrBmnE, C. FOSTER, S. DOGER-a microcycle peaking program was provided as feedback STEIN, AND G. BKKE. Impact of training patterns on incidenceto tbe field. Based on tbese data, tbe coacb prepared the of illness and injury during a womens c-ollegiate basketballfinal program. During training, the sports scientist and season. J, Strength Cond. Res. 17:734-738. 2003.strength-and-conditioning specialist analyzed and as- 2. AjiViiissoN, I. Rehabilitation of athletes knee. Med. Sport Sci.sessed changes in various parameters and reported tbe 26:238-246. 1987.results back to tbe coacb. Based on tbis feedback, tbe 3. BANISTER, E.W.. AND T.W. CALVEKT. 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