International Journal of Sports Physiology and Performance, 2015, 10, 1023 -1028 http://dx.doi.org/10.1123/ijspp.2014-0557 © 2015 Human Kinetics, Inc.
ORIGINAL INVESTIGATION
Training-Load Distribution in Endurance Runners: Objective Versus Subjective Assessment Vincenzo Manzi, Antonio Bovenzi, Carlo Castagna, Paola Sinibaldi Salimei, Maurizio Volterrani, and Ferdinando Iellamo Purpose: To assess the distribution of exercise intensity in long-distance recreational athletes (LDRs) preparing for a marathon and to test the hypothesis that individual perception of effort could provide training responses similar to those provided by standardized training methodologies. Methods: Seven LDRs (age 36.5 ± 3.8 y) were followed during a 5-mo training period culminating with a city marathon. Heart rate at 2.0 and 4.0 mmol/L and maximal heart rate were used to establish 3 intensity training zones. Internal training load (TL) was assessed by training zones and TRIMPi methods. These were compared with the session-rating-of-perceived-exertion (RPE) method. Results: Total time spent in zone 1 was higher than in zones 2 and 3 (76.3% ± 6.4%, 17.3% ± 5.8%, and 6.3% ± 0.9%, respectively; P = .000 for both, ES = 0.98, ES = 0.99). TL quantified by session-RPE provided the same result. The comparison between session-RPE and training-zones-based methods showed no significant difference at the lowest intensity (P = .07, ES = 0.25). A significant correlation was observed between TL RPE and TL TRIMPi at both individual and group levels (r = .79, P < .001). There was a significant correlation between total time spent in zone 1 and the improvement at the running speed of 2 mmol/L (r = .88, P < .001). A negative correlation was found between running speed at 2 mmol/L and the time needed to complete the marathon (r = –.83, P < .001). Conclusions: These findings suggest that in recreational LDRs most of the training time is spent at low intensity and that this is associated with improved performances. Session-RPE is an easy-to-use training method that provides responses similar to those obtained with standardized training methodologies. Keywords: exercise, rating of perceived exertion, training impulse, heart rate, performance Individuation of factors that could limit or improve performance in endurance sports has been a matter of several studies.1 The main objective of these studies was to obtain practical information to optimize the methodology and design of the training schedule to achieve the best performance. At present, there is a general consensus that the best performance in endurance athletes is achieved through a workout consisting of optimal amounts of physical conditioning and appropriate recovery periods to permit to the physiological adaptations to reach the maximum before a competition.2 Accumulation of large amounts of intensive exercise with insufficient recovery between training bouts could provide performance lower than expected.3 Within this framework, however, the modalities by which the daily training should be planned to obtain the best result still remain to be defined. Among the essential variables of workouts to be taken into account, exercise intensity and its distribution are likely the crucial factors and the most widely investigated, inasmuch as they represent the actual physiological load to which athletes are exposed during everyday training sessions.4 Many studies have emphasized the importance of varying the daily training load (TL) in the short to medium term (alternating periods of training with high and low intensities) for optimal performance.5 Usually, in long-distance endurance athletes exercise intensity is assessed on Manzi, Volterrani, and Iellamo are with the San Raffaele Pisana Scientific Inst for Hospitalization and Care, Rome, Italy. Bovenzi and Castagna are with the University of Rome II, Rome, Italy. Salimei is with the University of Rome II, Rome, Italy. Address author correspondence to Ferdinando Iellamo at [email protected].
the basis of distinct training zones, according to heart rate or blood lactate concentrations obtained during specific laboratory tests.4,6 Recently, the volume distribution with respect to intensity of TL has been investigated in endurance athletes in some descriptive studies.1,4,7 Analyzing the training responses of subelite endurance runners (5- to 10-km racers) where training volume was 4 to 5 h/ wk, one of these descriptive studies7 showed that over a prolonged preparation period (ie, a 6-mo macrocycle), 71% of the total training time was spent at low intensity, 21% at moderate intensity, and 8% at high intensity. These data are similar to those reported during training in professional cyclists,8 elite marathoners,9 and professional soccer players.10 These studies would indicate that in endurance athletes (eg, long-distance runners [LDRs], cyclists, and cross-country skiers) there is a sport-specific pattern in TL distribution. In addition, longitudinal studies have shown that when the training is equated as a function of individual responses, low-intensity training (ie, below the lactate zone) is more effective than moderate training (ie, within the lactate zone), provided that the contribution of high-intensity training (ie, above the lactate zone) is enough.1 Although many studies have examined the distribution of the TL in elite endurance athletes,4,7 no research has focused on the distribution of TL and its effectiveness in nonelite recreational endurance athletes, who represent a large population among physically active individuals. The purpose of this observational study was to assess the distribution of exercise intensity and the individual response profile in recreational athletes preparing for a marathon race. The hypothesis tested was that even in recreational, like in elite, athletes, training time would be based mainly on low-intensity physical activities and 1023
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would result in a better performance at the marathon race. Within this main framework, we also tested the hypothesis that an easyto-use training method based on the individual perception of effort provides responses similar to those of more usual heart-rate- and lactate-targeted training methodologies.
Methods
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Subjects Seven long-distance recreational male runners, (age 36.5 ± 3.8 y, height 177.3 ± 6.3 cm, weight 71.0 ± 4.2 kg) who had 5 to 6 years of running training experience volunteered to participate in the study. Inclusion criteria were the absence of clinical signs or symptoms of infection, cardiovascular disease, or metabolic disorders and a minimum weekly training distance of 50 km/wk. All subjects provided informed written consent to the experimental procedures after the possible benefits and risks of participation had been explained to them. The study protocol was approved by the local institutional review board and conformed to the Codes of Ethics of the World Medical Association (Declaration of Helsinki).
Methodology Before the beginning of the study, all the recreational LDRs abstained from any scheduled physical activity program aimed at improving performance for 4 weeks to avoid possible effects of training status on the experimental intervention. Thus, for the purpose of the study they were considered (partially) detrained. The LDRs abstained from alcohol and caffeinated beverages and refrained from training in the 24 hours before the experimental sessions. They consumed their last meal at least 3 hours before a treadmill test, and a report of nutrient intake was taken to ensure a sufficient carbohydrate intake during the week before testing. Throughout the study, all testing sessions took place at the same time as the training sessions to avoid possible circadian influences on the parameters under investigation. No athlete was considered overtrained at the time of the recording sessions, based on the lack of the following signs: inability to sustain usual training program or reduced performance and the presence of symptoms such as increased feeling of fatigue during daily training routine, sleeping disorders, apathy, or restlessness. No subject was taking drugs at the time of the recording sessions.
Fitness Assessment and Training Subject underwent a 2-phase progressive treadmill test (Technogym Run Race 1400 HC, Gambettola, Italy) for the assessment of individual blood-lactate-concentration profile and maximal heart rate (HR), respectively, on 2 occasions: at the start of the study and after 8 weeks of training. The progressive treadmill test consisted of 4 or 5 submaximal exercise bouts at an initial running speed of 10 km/h and interspersed with 1-minute recoveries, followed by a maximal incremental test to volitional fatigue. The treadmill running velocity was increased during the submaximal test by 1 km/h every 5 minutes. Once capillary blood lactate concentrations were elevated above 4 mmol/L, the treadmill speed was increased 0.5 km/h every 30 seconds until exhaustion, as done in previous studies.4 In the 1-minute interval between bouts during submaximal exercise test and 3 minutes after exhaustion in the maximal incremental test, capillary blood samples (25 μL) were taken from the earlobe and immediately analyzed to assess blood lactate concentration using
an electroenzymatic technique (YSI 1500 Sport, Yellow Springs Instruments, Yellow Springs, OH, USA). Before each treadmill test, the analyzer was calibrated following the instructions of the manufacturer using standard lactate solutions of 0, 5, 15, and 30 mmol/L. The highest 5-second mean HR measured during the maximal incremental test was used as maximum reference value (HRmax). Criteria for HRmax achievement were blood lactate concentrations higher than 8 mmol/L and HR plateau attainment despite speed increments.
Quantification of TL To quantify TL we used the method advanced by Foster et al,11 referred to as session-RPE. By this method, internal TL is quantified by multiplying the whole training-session rating of perceived exertion (RPE) using the 10-point Borg category ratio scale (CR10scale)12 by its duration. This product represents in a single number the magnitude of internal TL in arbitrary units (AU) and has been used and validated in athletes of different sport disciplines2,13; as such it could be considered an accurate indicator of global internal TL.14 Each subject reported the perception of training-session effort by indicating the number on the CR10 scale. To ensure that the perceived effort referred to the whole training session rather than the most recent exercise intensity, each subject was asked to provide a rating of the overall difficulty of the exercise bout, and each individual RPE was recorded within 30 minutes after completion of each training session. We explained to the subjects that we wanted a global rating of the entire training bout using whatever cues they felt to be appropriate. Resting HR (HRrest) was measured with subjects in a resting state (ie, quiet room, supine position after 24 h of no exercise). The HRrest was assumed as the lowest 5-second value within a 5-minute monitoring period. Individual blood lactate concentrations versus running speeds were obtained in each subject with speeds at 2 and 4 mmol/L used as exercise paradigm.15,16 Blood lactate concentrations were plotted against running speeds, fractional HR elevation (ΔHR, ie, HR reserve), and RPE values, and individual blood-lactate-concentration profiles (speed at 2 and 4 mmol/L and ΔHR at 2.0 mmol/L and 4 mmol/L) were identified via exponential interpolation.17 The responses of the session-RPE were compared with respect to 2 objective methods based on the HR, that described by Edwards18 and that reported by Manzi et al,6 referred to as the individual TRaining IMPulse (TRIMPi).6 These 2 methods have been employed as criteria of validity. The evolution of the profile of the TL of these recreational runners was examined over a 5-month macrocycle (from late October to early March) designed to achieve peak performance during a city marathon. The 3 zones of intensity were determined according to the results of the tests carried out on a treadmill at the beginning of the training period. In all subjects, HR was continuously recorded during each training session for the entire period of 5 months to determine the following variables: total training dose (by TRIMPi), total time spent in each of the 3 intensity zones (zone 1, HR ≤ HRS2; zone 2, HR between HRS2 and HRS4; zone 3, HR ≥ HRS4), and total value of session-RPE in each of the 3 intensity zones (zone 1, ≤4, zone 2, between 4 and 5.5; zone 3, ≥5.5, with RPE = 4 corresponding to S2 and RPE = 5.5 corresponding to S4). Quantification of TL based on session-RPE scores, divided according to Seiler and Kjerland,4 was compared with that based on the training zones defined by HR values. In this study, 380 to 420 training sessions were analyzed. As a performance measure, we considered the time obtained at the marathon.
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HR was assessed every 5 seconds with a short-range telemetry system (Polar Team System, Polar Electro Oy, Kempele, Finland), and data were downloaded to a portable PC and analyzed using dedicated software (Polar ProTrainer 5, Polar Electro Oy, Kempele, Finland) and an electronic spreadsheet (Excel, Microsoft Corp, Redmond, WA, USA). Recreational LDRs trained 4 to 6 times a week according to the training schedule depicted in Table 1. Training mileage and intensity (ie, distance to be covered at selected paces) were prescribed to LDRs by an experienced marathon coach according to treadmill-test results (Table 2). Speeds at selected blood lactate concentrations were used by LDRs as a training cue, and no HR feedback was provided to LDRs during training sessions. The prescribed training schedule represents the typical training program performed by recreational marathon runners to prepare for a marathon race at the end of the training period. The repeatability of the session-RPE method was assessed before the beginning of the study by intraclass correlation coefficient (ICC) and coefficient of variation (CV). The corresponding values were .94 and 1.2%, respectively.
Statistical Analysis The results are expressed as mean ± SD and 95% confidence intervals (95% CI). Before using parametric tests, the assumption of normality was verified using the W test of Shapiro-Wilk. One-way ANOVA was used to compare the total time spent in each of the 3 zones over the 5-month training period. The Bonferroni test was used as a post hoc test. The Wilcoxon test was used to determine any statistically significant differences in training-intensity distributions determined using HR and session-RPE. The effect size (ES) was
calculated to assess meaningfulness of differences. Effect sizes of 0.1, 0.1 to 0.20, 0.20 to 0.50, 0.50 to 0.80, and >0.80 were considered trivial, small, moderate, large, and very large, respectively. Pearson product–moment correlation was used for both within and between groups, separately, to examine whether there was a significant relationship between training variables and performance time. According to Hopkins19 the magnitudes for correlation coefficients were considered trivial (r < .1), small (.1< r < .3) moderate (.3< r < .5), large (.5 < r < .7), very large (.75 < r < .9), nearly perfect (r > .9), and perfect (r = 1). Significance was set at P ≤ .05. The statistical package SPSS (SPSS Inc, version 17 for Windows, Chicago, IL, USA) was used for all statistical analyses.
Results Adherence to the specific training content in each training session and compliance with the program were 100%, as inferred from the HR-monitoring device that recorded, in addition to HR, the date of each training session. The results of the treadmill test at baseline were HRrest 54 ± 5 beats/min, HRS2 155 ± 6 beats/min, HRS4 170 ± 7 beats/min, and HRmax 184 ± 9 beats/min, and the percentages of HR at S2 and S4 were 82% ± 2.6% and 90.6% ± 1.9% of HRmax, respectively. The percentage of total time spent in each of the 3 training zones of intensity during the training period is shown in Figure 1. Statistically significant differences were found between the total time spent in zone 1 and those in zones 2 and 3 (P = .000 for both, ES = 0.98, ES = 0.99) and between the total time spent in zones 2 and 3 (P = .002, ES = 0.91). Similarly, when the TL was quantified
Table 1 Results for the Natural-Interval, Continuous Running on a Hilly Course at Constant Speed (>2 and ≤ 4 mmol/L); an Easy Run, Continuous Running on a Hilly Course at Constant Speed (4 mmol/L) Training zone
Distance (km)
Time (min)
Eighth week
Training zone
Natural-interval run
10
50
Day 1
Recovery
Easy run
9
50
Day 2
Natural-interval run
Day 3
Recovery
Day 4 Day 5
Recovery Natural-interval run
10
50
Recovery
Distance (km)
Time (min)
15
70
Intervals (6 × 2000 m + 6 × 1000 m recovery)
18
70
Easy run
26
130
20
80
79
350
Moderate run
12
50
Day 6
Recovery
Easy run
9
50
Day 7
Moderate run
Total
50
250
Table 2 Training Intensities and Workouts Blood lactate zone
Blood lactate concentration (mmol/L)
% HRmax
Workouts
≤2
≤80 ± 3.8
Continuous running (time 80–120 min)
>2 ≤ 4
≤80 ± 3.8 to ≤91 ± 3.9
Continuous running (time 70–100 min)
>4
>91 ± 3.9
Low lactate Lactate accommodation Lactate accumulation
Interval training (6 × 2000 m with 1000 m in 4–5 min recovery)
Abbreviation: HRmax, maximal heart rate. IJSPP Vol. 10, No. 8, 2015
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by means of the session-RPE, 69.6% ± 9.0% of the sessions were performed at a value less than or equal to 4 on the 10-point scale, 27.8% ± 8.0% between the values 4 and 5.5, and finally 2.6% ± 2.0% of the training was performed at a session-RPE score ≥5.5. The comparison between the 2 methods of TL quantification showed significant differences as far as the percentage of time spent in zone 2 and 3 is concerned (P = .02, ES = 0.36 and P = .001, ES = 0.64 respectively), whereas no significant difference was detected for the lowest intensity (P = .07, ES = 0.25; Figure 2). Large to very large correlations were found between sessionRPE- and HR-based TL at the individual level. Similarly, large to very large correlation was observed between TL as assessed by
session-RPE and TRIMPi methods at both individual (Table 3) and group levels (r = .79, P < .000; Figure 3). The total time of training spent in zone 1 showed very large association with the percentage of improvement at the running speed of 2 mmol/L (r = .88, P = .008; Figure 4). In addition, a very large correlation was found between the running speed at 2 mmol/L and the performance at the marathon race (r = –.83, P = .04; Figure 5).
Table 3 Individual Relationships Between Training Loads Based on Session Rating of Perceived Exertion (RPE) and the Edwards Methods and Between Training Loads Based on Session-RPE and TRIMPi Methods Subject
r
95% confidence interval
S1
.67
.86–.30
S2
.78
.86–.66
S3
.70
.82–.52
S4
.80
.89–.65
S5
.69
.80–.53
S6
.82
.89–.70
S7
.72
.83–.56
S1
.84
.94–.60
S2
.79
.86–.72
S3
.77
.86–.68
S4
.86
.93–.77
S5
.71
.80–.62
S6
.87
.93–.81
S7
.82
.87–.73
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Edwards vs session-RPE
TRIMPi vs Session-RPE
Figure 1 — Total time spent in each of the 3 intensity zones on the basis of heart rate during the training period. The percentage of time spent in each zone is also indicated. *P < .00 vs zone 1.
Figure 2 — Comparison of percentages of time spent in each training zone between heart-rate (white columns) and session-rating-of-perceivedexertion (black columns) -based training methods quantification. *P = .02 and †P = .001 between the methods.
Figure 3 — Correlation between mean subject rating-of-perceivedexertion-based training load (session-RPE) and mean heart-rate-based training load (TRIMPi) (pooled data, r = .79, P = .000, 95% CI 0.68–0.86).
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Training-Load Assessment Methods 1027
Figure 4 — Relationship between the total time of training performed in zone 1 and percentage of improvement in running speed at 2 mmol/L (r = .88, P = .08, 95% CI 0.39–0.98).
This discrepant finding could be explained by the different level of performance, in that recreational athletes are capable of sustaining less of their training at the highest intensity than are elite athletes. The part of the training carried out at low relative intensity (zone 1 with a serum lactate concentration ≤2.0 mmol/L) was found to be quantitatively similar to those seen in other studies.4 Several studies showed that these intensities are the most effective in stimulating mitochondrial biogenesis, enhancing oxidative processes, and increasing mobilization of energy reserves.7 These are the potential mechanisms by which low-intensity training improved the performance in our recreational marathon runners, like in elite athletes, as well. Our findings are in keeping with those reported by EsteveLanao et al1 in subelite distance runners during the preparatory phase of the competitive season. Those authors observed beneficial effect of low-intensity training volume (ie, below the ventilatory threshold) on performance (ie, races 4 and 10 km). In our study, we demonstrated a strong and significant correlation between the amount of training spent in zone 1 (low intensity) and the percentage of improvement at a running speed of 2 mmol/L, with a significant relationship also between the running speed at 2 mmol/L and performance, that is, the competition time at the marathon (Figures 4 and 5). These findings suggest that when dealing with subelite or recreational endurance runners, attention should be paid to choosing the right method to monitor internal TL. Specifically, only methods that show an association (ie, convergent construct validity) between internal TL and performance and/or fitness variables should be considered.20 In addition, the results of this study indicate that session-RPE, that is, a method based on the individual perception of effort, can be considered a valid tool for assessing individual responses to training in recreational endurance runners. In fact, a strong and significant relationship was found between session-RPE score and both the method of Edwards and the TRIMPi, regarded as the gold standard in evaluation of TL. This finding extends those previously reported in endurance athletes4 and in team-sport players.2,13
Conclusions and Practical Application Figure 5 — Correlation between the time needed to complete the marathon and the running speed at 2 mmol/L (r = –.83, P = .04, 95% CI –0.98 to –0.06). Entries are for the 6 athletes who completed the marathon.
Discussion The main and novel finding of this study is the demonstration that in recreational LDRs, the time of training spent at low intensity is greater than that spent at higher intensities and is effective in improving performance. Another key finding is the observation that an easy-to-use, inexpensive training method like the sessionRPE provides responses similar to those of the more usual HR- and lactate-targeted training methodologies. In agreement with previous studies conducted in high-level endurance athletes, even in recreational marathon runners most of the training was spent in the low-intensity zone 1.4 At variance with elite athletes, in whom TL distribution in the 3 areas of intensity is polarized at lowest and highest intensities (eg, 75% –5%, –20%),4 the recreational runners of our study spent more time at moderate (zone 2) than at high intensity.
The findings ensuing from this study suggest that in recreational LDRs most of the training time is spent at low intensity and that this is associated with improved performances. Recreational longdistance running is a worldwide exercise activity that celebrates its popularity with mass participation in city marathons. In light of the current study, the session-RPE might be considered an easy-touse, valid, and inexpensive training method to monitor individual responses to TL that provides responses similar to those obtained with standardized training methodologies. As a consequence, session-RPE could be used for everyday endurance physical activity programs.
Limitations of the Study The main limitation of the current investigation is the small sample size, which is a common characteristic of studies carried out in athletes. This limitation is compensated for, in part, by the strong consistency of our observations, which led to statistically significant results. Finally, this study was observational in nature, aiming to examine the distribution of spontaneous TL in recreational LDRs. Although in this study approximately 400 training sessions were analyzed, further research employing an experimentally designed
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training program—with enhancements or reductions of low-intensity training time—is warranted to obtain definitive conclusions on the effectiveness of TL distribution during preparation for a marathon.
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References 1. Esteve-Lanao J, Foster C, Seiler S, Lucia A. Impact of training intensity distribution on performance in endurance athletes. J Strength Cond Res. 2007;21:943–949. PubMed 2. Impellizzeri FM, Rampinini E, Coutts AJ, Sassi A, Marcora SM. Use of RPE-based training load in soccer. Med Sci Sports Exerc. 2004;36:1042– 1047. PubMed doi:10.1249/01.MSS.0000128199.23901.2F 3. Busso T. Variable dose-response relationship between exercise training and performance. Med Sci Sports Exerc. 2003;35:1188–1195. PubMed doi:10.1249/01.MSS.0000074465.13621.37 4. Seiler KS, Kjerland GO. Quantifying training intensity distribution in elite endurance athletes: is there evidence for an “optimal” distribution? Scand J Med Sci Sports. 2006;16:49–56. PubMed doi:10.1111/j.1600-0838.2004.00418.x 5. Robinson DM, Robinson SM, Hume PA, Hopkins WG. Training intensity of elite male distance runners. Med Sci Sports Exerc. 1991;23:1078–1082. PubMed doi:10.1249/00005768-19910900000013 6. Manzi V, Iellamo F, Impellizzeri F, D’Ottavio S, Castagna C. Relation between individualized training impulse and performance in distance runners. Med Sci Sports Exerc. 2009;41:2090–2096. PubMed doi:10.1249/MSS.0b013e3181a6a959 7. Esteve-Lanao J, San Juan AF, Earnest CP, Lucia A. How do endurance runners actually train?: relationship with competition performance. Med Sci Sports Exerc. 2005;37:496–504. PubMed doi:10.1249/01. MSS.0000155393.78744.86 8. Luciá A, Hoyos J, Carvajal A, Chicharro JL. Heart rate response to professional road cycling: the Tour de France. Int J Sports Med. 1999;20:167–172. PubMed doi:10.1055/s-1999-970284
9. Billat VL, Demarle A, Slawinski J, Paiva M, Koralsztein JP. Physical and training characteristics of top-class marathon runners. Med Sci Sports Exerc. 2001;33:2089–2097. PubMed doi:10.1097/00005768200112000-00018 10. Castagna C, Impellizzeri FM, Chaouachi A, Bordon C, Manzi V. Effect of training intensity distribution on aerobic fitness variables in elite soccer players: a case study. J Strength Cond Res. 2011;25(1):66–71. PubMed doi:10.1519/JSC.0b013e3181fef3d3 11. Foster C, Daines E, Hector L, Snyder AC, Welsh R. Athletic performance in relation to training load. Wis Med J. 1996;95:370–374. PubMed 12. Borg G, Hassmén P, Lagerström M. Perceived exertion related to heart rate and blood lactate during arm and leg exercise. Eur J Appl Physiol Occup Physiol. 1987;56:679–685. PubMed doi:10.1007/BF00424810 13. Foster C, Florhaug JA, Franklin J, et al. A new approach to monitoring exercise training. J Strength Cond Res. 2001;15:109–115. PubMed 14. Morgan WP. Psychological components of effort sense. Med Sci Sports Exerc. 1994;26:1071–1077. PubMed doi:10.1249/00005768199409000-00001 15. Hughson RL, Weisiger KH, Swanson GD. Blood lactate concentration increases as continuous function in progressive exercise. J Appl Physiol. 1987;62:1975–1981. PubMed 16. Banister EW. Modeling elite athletic performance. In: Green HJ, McDougal JD, Wenger H, eds. Physiological Testing of Elite Athletes. Champaign, IL: Human Kinetics. 1991:403–424. 17. Mader A, Heck H. A theory of the metabolic origin of “anaerobic threshold.” Int J Sports Med. 1986;7(Suppl 1):45–65. PubMed doi:10.1055/s-2008-1025802 18. Edwards S. High performance training and racing. In: Edwards S, ed. The Heart Rate Monitor Book. Sacramento, CA: Feet Fleet Press; 1993:113–123. 19. Hopkins WG. (2002). A scale of magnitudes for effect statistics. http:// www.sportsci.org/resource/stats/index.html 20. Thomas JR, Nelson JK, Silverman J. Research Methods in Physical Activity. Champaign, IL: Human Kinetics; 2005.
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ORIGINAL INVESTIGATION
Training-Load Distribution in Endurance Runners: Objective Versus Subjective Assessment Vincenzo Manzi, Antonio Bovenzi, Carlo Castagna, Paola Sinibaldi Salimei, Maurizio Volterrani, and Ferdinando Iellamo Purpose: To assess the distribution of exercise intensity in long-distance recreational athletes (LDRs) preparing for a marathon and to test the hypothesis that individual perception of effort could provide training responses similar to those provided by standardized training methodologies. Methods: Seven LDRs (age 36.5 ± 3.8 y) were followed during a 5-mo training period culminating with a city marathon. Heart rate at 2.0 and 4.0 mmol/L and maximal heart rate were used to establish 3 intensity training zones. Internal training load (TL) was assessed by training zones and TRIMPi methods. These were compared with the session-rating-of-perceived-exertion (RPE) method. Results: Total time spent in zone 1 was higher than in zones 2 and 3 (76.3% ± 6.4%, 17.3% ± 5.8%, and 6.3% ± 0.9%, respectively; P = .000 for both, ES = 0.98, ES = 0.99). TL quantified by session-RPE provided the same result. The comparison between session-RPE and training-zones-based methods showed no significant difference at the lowest intensity (P = .07, ES = 0.25). A significant correlation was observed between TL RPE and TL TRIMPi at both individual and group levels (r = .79, P < .001). There was a significant correlation between total time spent in zone 1 and the improvement at the running speed of 2 mmol/L (r = .88, P < .001). A negative correlation was found between running speed at 2 mmol/L and the time needed to complete the marathon (r = –.83, P < .001). Conclusions: These findings suggest that in recreational LDRs most of the training time is spent at low intensity and that this is associated with improved performances. Session-RPE is an easy-to-use training method that provides responses similar to those obtained with standardized training methodologies. Keywords: exercise, rating of perceived exertion, training impulse, heart rate, performance Individuation of factors that could limit or improve performance in endurance sports has been a matter of several studies.1 The main objective of these studies was to obtain practical information to optimize the methodology and design of the training schedule to achieve the best performance. At present, there is a general consensus that the best performance in endurance athletes is achieved through a workout consisting of optimal amounts of physical conditioning and appropriate recovery periods to permit to the physiological adaptations to reach the maximum before a competition.2 Accumulation of large amounts of intensive exercise with insufficient recovery between training bouts could provide performance lower than expected.3 Within this framework, however, the modalities by which the daily training should be planned to obtain the best result still remain to be defined. Among the essential variables of workouts to be taken into account, exercise intensity and its distribution are likely the crucial factors and the most widely investigated, inasmuch as they represent the actual physiological load to which athletes are exposed during everyday training sessions.4 Many studies have emphasized the importance of varying the daily training load (TL) in the short to medium term (alternating periods of training with high and low intensities) for optimal performance.5 Usually, in long-distance endurance athletes exercise intensity is assessed on Manzi, Volterrani, and Iellamo are with the San Raffaele Pisana Scientific Inst for Hospitalization and Care, Rome, Italy. Bovenzi and Castagna are with the University of Rome II, Rome, Italy. Salimei is with the University of Rome II, Rome, Italy. Address author correspondence to Ferdinando Iellamo at [email protected].
the basis of distinct training zones, according to heart rate or blood lactate concentrations obtained during specific laboratory tests.4,6 Recently, the volume distribution with respect to intensity of TL has been investigated in endurance athletes in some descriptive studies.1,4,7 Analyzing the training responses of subelite endurance runners (5- to 10-km racers) where training volume was 4 to 5 h/ wk, one of these descriptive studies7 showed that over a prolonged preparation period (ie, a 6-mo macrocycle), 71% of the total training time was spent at low intensity, 21% at moderate intensity, and 8% at high intensity. These data are similar to those reported during training in professional cyclists,8 elite marathoners,9 and professional soccer players.10 These studies would indicate that in endurance athletes (eg, long-distance runners [LDRs], cyclists, and cross-country skiers) there is a sport-specific pattern in TL distribution. In addition, longitudinal studies have shown that when the training is equated as a function of individual responses, low-intensity training (ie, below the lactate zone) is more effective than moderate training (ie, within the lactate zone), provided that the contribution of high-intensity training (ie, above the lactate zone) is enough.1 Although many studies have examined the distribution of the TL in elite endurance athletes,4,7 no research has focused on the distribution of TL and its effectiveness in nonelite recreational endurance athletes, who represent a large population among physically active individuals. The purpose of this observational study was to assess the distribution of exercise intensity and the individual response profile in recreational athletes preparing for a marathon race. The hypothesis tested was that even in recreational, like in elite, athletes, training time would be based mainly on low-intensity physical activities and 1023
1024 Manzi et al
would result in a better performance at the marathon race. Within this main framework, we also tested the hypothesis that an easyto-use training method based on the individual perception of effort provides responses similar to those of more usual heart-rate- and lactate-targeted training methodologies.
Methods
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Subjects Seven long-distance recreational male runners, (age 36.5 ± 3.8 y, height 177.3 ± 6.3 cm, weight 71.0 ± 4.2 kg) who had 5 to 6 years of running training experience volunteered to participate in the study. Inclusion criteria were the absence of clinical signs or symptoms of infection, cardiovascular disease, or metabolic disorders and a minimum weekly training distance of 50 km/wk. All subjects provided informed written consent to the experimental procedures after the possible benefits and risks of participation had been explained to them. The study protocol was approved by the local institutional review board and conformed to the Codes of Ethics of the World Medical Association (Declaration of Helsinki).
Methodology Before the beginning of the study, all the recreational LDRs abstained from any scheduled physical activity program aimed at improving performance for 4 weeks to avoid possible effects of training status on the experimental intervention. Thus, for the purpose of the study they were considered (partially) detrained. The LDRs abstained from alcohol and caffeinated beverages and refrained from training in the 24 hours before the experimental sessions. They consumed their last meal at least 3 hours before a treadmill test, and a report of nutrient intake was taken to ensure a sufficient carbohydrate intake during the week before testing. Throughout the study, all testing sessions took place at the same time as the training sessions to avoid possible circadian influences on the parameters under investigation. No athlete was considered overtrained at the time of the recording sessions, based on the lack of the following signs: inability to sustain usual training program or reduced performance and the presence of symptoms such as increased feeling of fatigue during daily training routine, sleeping disorders, apathy, or restlessness. No subject was taking drugs at the time of the recording sessions.
Fitness Assessment and Training Subject underwent a 2-phase progressive treadmill test (Technogym Run Race 1400 HC, Gambettola, Italy) for the assessment of individual blood-lactate-concentration profile and maximal heart rate (HR), respectively, on 2 occasions: at the start of the study and after 8 weeks of training. The progressive treadmill test consisted of 4 or 5 submaximal exercise bouts at an initial running speed of 10 km/h and interspersed with 1-minute recoveries, followed by a maximal incremental test to volitional fatigue. The treadmill running velocity was increased during the submaximal test by 1 km/h every 5 minutes. Once capillary blood lactate concentrations were elevated above 4 mmol/L, the treadmill speed was increased 0.5 km/h every 30 seconds until exhaustion, as done in previous studies.4 In the 1-minute interval between bouts during submaximal exercise test and 3 minutes after exhaustion in the maximal incremental test, capillary blood samples (25 μL) were taken from the earlobe and immediately analyzed to assess blood lactate concentration using
an electroenzymatic technique (YSI 1500 Sport, Yellow Springs Instruments, Yellow Springs, OH, USA). Before each treadmill test, the analyzer was calibrated following the instructions of the manufacturer using standard lactate solutions of 0, 5, 15, and 30 mmol/L. The highest 5-second mean HR measured during the maximal incremental test was used as maximum reference value (HRmax). Criteria for HRmax achievement were blood lactate concentrations higher than 8 mmol/L and HR plateau attainment despite speed increments.
Quantification of TL To quantify TL we used the method advanced by Foster et al,11 referred to as session-RPE. By this method, internal TL is quantified by multiplying the whole training-session rating of perceived exertion (RPE) using the 10-point Borg category ratio scale (CR10scale)12 by its duration. This product represents in a single number the magnitude of internal TL in arbitrary units (AU) and has been used and validated in athletes of different sport disciplines2,13; as such it could be considered an accurate indicator of global internal TL.14 Each subject reported the perception of training-session effort by indicating the number on the CR10 scale. To ensure that the perceived effort referred to the whole training session rather than the most recent exercise intensity, each subject was asked to provide a rating of the overall difficulty of the exercise bout, and each individual RPE was recorded within 30 minutes after completion of each training session. We explained to the subjects that we wanted a global rating of the entire training bout using whatever cues they felt to be appropriate. Resting HR (HRrest) was measured with subjects in a resting state (ie, quiet room, supine position after 24 h of no exercise). The HRrest was assumed as the lowest 5-second value within a 5-minute monitoring period. Individual blood lactate concentrations versus running speeds were obtained in each subject with speeds at 2 and 4 mmol/L used as exercise paradigm.15,16 Blood lactate concentrations were plotted against running speeds, fractional HR elevation (ΔHR, ie, HR reserve), and RPE values, and individual blood-lactate-concentration profiles (speed at 2 and 4 mmol/L and ΔHR at 2.0 mmol/L and 4 mmol/L) were identified via exponential interpolation.17 The responses of the session-RPE were compared with respect to 2 objective methods based on the HR, that described by Edwards18 and that reported by Manzi et al,6 referred to as the individual TRaining IMPulse (TRIMPi).6 These 2 methods have been employed as criteria of validity. The evolution of the profile of the TL of these recreational runners was examined over a 5-month macrocycle (from late October to early March) designed to achieve peak performance during a city marathon. The 3 zones of intensity were determined according to the results of the tests carried out on a treadmill at the beginning of the training period. In all subjects, HR was continuously recorded during each training session for the entire period of 5 months to determine the following variables: total training dose (by TRIMPi), total time spent in each of the 3 intensity zones (zone 1, HR ≤ HRS2; zone 2, HR between HRS2 and HRS4; zone 3, HR ≥ HRS4), and total value of session-RPE in each of the 3 intensity zones (zone 1, ≤4, zone 2, between 4 and 5.5; zone 3, ≥5.5, with RPE = 4 corresponding to S2 and RPE = 5.5 corresponding to S4). Quantification of TL based on session-RPE scores, divided according to Seiler and Kjerland,4 was compared with that based on the training zones defined by HR values. In this study, 380 to 420 training sessions were analyzed. As a performance measure, we considered the time obtained at the marathon.
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HR was assessed every 5 seconds with a short-range telemetry system (Polar Team System, Polar Electro Oy, Kempele, Finland), and data were downloaded to a portable PC and analyzed using dedicated software (Polar ProTrainer 5, Polar Electro Oy, Kempele, Finland) and an electronic spreadsheet (Excel, Microsoft Corp, Redmond, WA, USA). Recreational LDRs trained 4 to 6 times a week according to the training schedule depicted in Table 1. Training mileage and intensity (ie, distance to be covered at selected paces) were prescribed to LDRs by an experienced marathon coach according to treadmill-test results (Table 2). Speeds at selected blood lactate concentrations were used by LDRs as a training cue, and no HR feedback was provided to LDRs during training sessions. The prescribed training schedule represents the typical training program performed by recreational marathon runners to prepare for a marathon race at the end of the training period. The repeatability of the session-RPE method was assessed before the beginning of the study by intraclass correlation coefficient (ICC) and coefficient of variation (CV). The corresponding values were .94 and 1.2%, respectively.
Statistical Analysis The results are expressed as mean ± SD and 95% confidence intervals (95% CI). Before using parametric tests, the assumption of normality was verified using the W test of Shapiro-Wilk. One-way ANOVA was used to compare the total time spent in each of the 3 zones over the 5-month training period. The Bonferroni test was used as a post hoc test. The Wilcoxon test was used to determine any statistically significant differences in training-intensity distributions determined using HR and session-RPE. The effect size (ES) was
calculated to assess meaningfulness of differences. Effect sizes of 0.1, 0.1 to 0.20, 0.20 to 0.50, 0.50 to 0.80, and >0.80 were considered trivial, small, moderate, large, and very large, respectively. Pearson product–moment correlation was used for both within and between groups, separately, to examine whether there was a significant relationship between training variables and performance time. According to Hopkins19 the magnitudes for correlation coefficients were considered trivial (r < .1), small (.1< r < .3) moderate (.3< r < .5), large (.5 < r < .7), very large (.75 < r < .9), nearly perfect (r > .9), and perfect (r = 1). Significance was set at P ≤ .05. The statistical package SPSS (SPSS Inc, version 17 for Windows, Chicago, IL, USA) was used for all statistical analyses.
Results Adherence to the specific training content in each training session and compliance with the program were 100%, as inferred from the HR-monitoring device that recorded, in addition to HR, the date of each training session. The results of the treadmill test at baseline were HRrest 54 ± 5 beats/min, HRS2 155 ± 6 beats/min, HRS4 170 ± 7 beats/min, and HRmax 184 ± 9 beats/min, and the percentages of HR at S2 and S4 were 82% ± 2.6% and 90.6% ± 1.9% of HRmax, respectively. The percentage of total time spent in each of the 3 training zones of intensity during the training period is shown in Figure 1. Statistically significant differences were found between the total time spent in zone 1 and those in zones 2 and 3 (P = .000 for both, ES = 0.98, ES = 0.99) and between the total time spent in zones 2 and 3 (P = .002, ES = 0.91). Similarly, when the TL was quantified
Table 1 Results for the Natural-Interval, Continuous Running on a Hilly Course at Constant Speed (>2 and ≤ 4 mmol/L); an Easy Run, Continuous Running on a Hilly Course at Constant Speed (4 mmol/L) Training zone
Distance (km)
Time (min)
Eighth week
Training zone
Natural-interval run
10
50
Day 1
Recovery
Easy run
9
50
Day 2
Natural-interval run
Day 3
Recovery
Day 4 Day 5
Recovery Natural-interval run
10
50
Recovery
Distance (km)
Time (min)
15
70
Intervals (6 × 2000 m + 6 × 1000 m recovery)
18
70
Easy run
26
130
20
80
79
350
Moderate run
12
50
Day 6
Recovery
Easy run
9
50
Day 7
Moderate run
Total
50
250
Table 2 Training Intensities and Workouts Blood lactate zone
Blood lactate concentration (mmol/L)
% HRmax
Workouts
≤2
≤80 ± 3.8
Continuous running (time 80–120 min)
>2 ≤ 4
≤80 ± 3.8 to ≤91 ± 3.9
Continuous running (time 70–100 min)
>4
>91 ± 3.9
Low lactate Lactate accommodation Lactate accumulation
Interval training (6 × 2000 m with 1000 m in 4–5 min recovery)
Abbreviation: HRmax, maximal heart rate. IJSPP Vol. 10, No. 8, 2015
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by means of the session-RPE, 69.6% ± 9.0% of the sessions were performed at a value less than or equal to 4 on the 10-point scale, 27.8% ± 8.0% between the values 4 and 5.5, and finally 2.6% ± 2.0% of the training was performed at a session-RPE score ≥5.5. The comparison between the 2 methods of TL quantification showed significant differences as far as the percentage of time spent in zone 2 and 3 is concerned (P = .02, ES = 0.36 and P = .001, ES = 0.64 respectively), whereas no significant difference was detected for the lowest intensity (P = .07, ES = 0.25; Figure 2). Large to very large correlations were found between sessionRPE- and HR-based TL at the individual level. Similarly, large to very large correlation was observed between TL as assessed by
session-RPE and TRIMPi methods at both individual (Table 3) and group levels (r = .79, P < .000; Figure 3). The total time of training spent in zone 1 showed very large association with the percentage of improvement at the running speed of 2 mmol/L (r = .88, P = .008; Figure 4). In addition, a very large correlation was found between the running speed at 2 mmol/L and the performance at the marathon race (r = –.83, P = .04; Figure 5).
Table 3 Individual Relationships Between Training Loads Based on Session Rating of Perceived Exertion (RPE) and the Edwards Methods and Between Training Loads Based on Session-RPE and TRIMPi Methods Subject
r
95% confidence interval
S1
.67
.86–.30
S2
.78
.86–.66
S3
.70
.82–.52
S4
.80
.89–.65
S5
.69
.80–.53
S6
.82
.89–.70
S7
.72
.83–.56
S1
.84
.94–.60
S2
.79
.86–.72
S3
.77
.86–.68
S4
.86
.93–.77
S5
.71
.80–.62
S6
.87
.93–.81
S7
.82
.87–.73
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Edwards vs session-RPE
TRIMPi vs Session-RPE
Figure 1 — Total time spent in each of the 3 intensity zones on the basis of heart rate during the training period. The percentage of time spent in each zone is also indicated. *P < .00 vs zone 1.
Figure 2 — Comparison of percentages of time spent in each training zone between heart-rate (white columns) and session-rating-of-perceivedexertion (black columns) -based training methods quantification. *P = .02 and †P = .001 between the methods.
Figure 3 — Correlation between mean subject rating-of-perceivedexertion-based training load (session-RPE) and mean heart-rate-based training load (TRIMPi) (pooled data, r = .79, P = .000, 95% CI 0.68–0.86).
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Training-Load Assessment Methods 1027
Figure 4 — Relationship between the total time of training performed in zone 1 and percentage of improvement in running speed at 2 mmol/L (r = .88, P = .08, 95% CI 0.39–0.98).
This discrepant finding could be explained by the different level of performance, in that recreational athletes are capable of sustaining less of their training at the highest intensity than are elite athletes. The part of the training carried out at low relative intensity (zone 1 with a serum lactate concentration ≤2.0 mmol/L) was found to be quantitatively similar to those seen in other studies.4 Several studies showed that these intensities are the most effective in stimulating mitochondrial biogenesis, enhancing oxidative processes, and increasing mobilization of energy reserves.7 These are the potential mechanisms by which low-intensity training improved the performance in our recreational marathon runners, like in elite athletes, as well. Our findings are in keeping with those reported by EsteveLanao et al1 in subelite distance runners during the preparatory phase of the competitive season. Those authors observed beneficial effect of low-intensity training volume (ie, below the ventilatory threshold) on performance (ie, races 4 and 10 km). In our study, we demonstrated a strong and significant correlation between the amount of training spent in zone 1 (low intensity) and the percentage of improvement at a running speed of 2 mmol/L, with a significant relationship also between the running speed at 2 mmol/L and performance, that is, the competition time at the marathon (Figures 4 and 5). These findings suggest that when dealing with subelite or recreational endurance runners, attention should be paid to choosing the right method to monitor internal TL. Specifically, only methods that show an association (ie, convergent construct validity) between internal TL and performance and/or fitness variables should be considered.20 In addition, the results of this study indicate that session-RPE, that is, a method based on the individual perception of effort, can be considered a valid tool for assessing individual responses to training in recreational endurance runners. In fact, a strong and significant relationship was found between session-RPE score and both the method of Edwards and the TRIMPi, regarded as the gold standard in evaluation of TL. This finding extends those previously reported in endurance athletes4 and in team-sport players.2,13
Conclusions and Practical Application Figure 5 — Correlation between the time needed to complete the marathon and the running speed at 2 mmol/L (r = –.83, P = .04, 95% CI –0.98 to –0.06). Entries are for the 6 athletes who completed the marathon.
Discussion The main and novel finding of this study is the demonstration that in recreational LDRs, the time of training spent at low intensity is greater than that spent at higher intensities and is effective in improving performance. Another key finding is the observation that an easy-to-use, inexpensive training method like the sessionRPE provides responses similar to those of the more usual HR- and lactate-targeted training methodologies. In agreement with previous studies conducted in high-level endurance athletes, even in recreational marathon runners most of the training was spent in the low-intensity zone 1.4 At variance with elite athletes, in whom TL distribution in the 3 areas of intensity is polarized at lowest and highest intensities (eg, 75% –5%, –20%),4 the recreational runners of our study spent more time at moderate (zone 2) than at high intensity.
The findings ensuing from this study suggest that in recreational LDRs most of the training time is spent at low intensity and that this is associated with improved performances. Recreational longdistance running is a worldwide exercise activity that celebrates its popularity with mass participation in city marathons. In light of the current study, the session-RPE might be considered an easy-touse, valid, and inexpensive training method to monitor individual responses to TL that provides responses similar to those obtained with standardized training methodologies. As a consequence, session-RPE could be used for everyday endurance physical activity programs.
Limitations of the Study The main limitation of the current investigation is the small sample size, which is a common characteristic of studies carried out in athletes. This limitation is compensated for, in part, by the strong consistency of our observations, which led to statistically significant results. Finally, this study was observational in nature, aiming to examine the distribution of spontaneous TL in recreational LDRs. Although in this study approximately 400 training sessions were analyzed, further research employing an experimentally designed
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training program—with enhancements or reductions of low-intensity training time—is warranted to obtain definitive conclusions on the effectiveness of TL distribution during preparation for a marathon.
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References 1. Esteve-Lanao J, Foster C, Seiler S, Lucia A. Impact of training intensity distribution on performance in endurance athletes. J Strength Cond Res. 2007;21:943–949. PubMed 2. Impellizzeri FM, Rampinini E, Coutts AJ, Sassi A, Marcora SM. Use of RPE-based training load in soccer. Med Sci Sports Exerc. 2004;36:1042– 1047. PubMed doi:10.1249/01.MSS.0000128199.23901.2F 3. Busso T. Variable dose-response relationship between exercise training and performance. Med Sci Sports Exerc. 2003;35:1188–1195. PubMed doi:10.1249/01.MSS.0000074465.13621.37 4. Seiler KS, Kjerland GO. Quantifying training intensity distribution in elite endurance athletes: is there evidence for an “optimal” distribution? Scand J Med Sci Sports. 2006;16:49–56. PubMed doi:10.1111/j.1600-0838.2004.00418.x 5. Robinson DM, Robinson SM, Hume PA, Hopkins WG. Training intensity of elite male distance runners. Med Sci Sports Exerc. 1991;23:1078–1082. PubMed doi:10.1249/00005768-19910900000013 6. Manzi V, Iellamo F, Impellizzeri F, D’Ottavio S, Castagna C. Relation between individualized training impulse and performance in distance runners. Med Sci Sports Exerc. 2009;41:2090–2096. PubMed doi:10.1249/MSS.0b013e3181a6a959 7. Esteve-Lanao J, San Juan AF, Earnest CP, Lucia A. How do endurance runners actually train?: relationship with competition performance. Med Sci Sports Exerc. 2005;37:496–504. PubMed doi:10.1249/01. MSS.0000155393.78744.86 8. Luciá A, Hoyos J, Carvajal A, Chicharro JL. Heart rate response to professional road cycling: the Tour de France. Int J Sports Med. 1999;20:167–172. PubMed doi:10.1055/s-1999-970284
9. Billat VL, Demarle A, Slawinski J, Paiva M, Koralsztein JP. Physical and training characteristics of top-class marathon runners. Med Sci Sports Exerc. 2001;33:2089–2097. PubMed doi:10.1097/00005768200112000-00018 10. Castagna C, Impellizzeri FM, Chaouachi A, Bordon C, Manzi V. Effect of training intensity distribution on aerobic fitness variables in elite soccer players: a case study. J Strength Cond Res. 2011;25(1):66–71. PubMed doi:10.1519/JSC.0b013e3181fef3d3 11. Foster C, Daines E, Hector L, Snyder AC, Welsh R. Athletic performance in relation to training load. Wis Med J. 1996;95:370–374. PubMed 12. Borg G, Hassmén P, Lagerström M. Perceived exertion related to heart rate and blood lactate during arm and leg exercise. Eur J Appl Physiol Occup Physiol. 1987;56:679–685. PubMed doi:10.1007/BF00424810 13. Foster C, Florhaug JA, Franklin J, et al. A new approach to monitoring exercise training. J Strength Cond Res. 2001;15:109–115. PubMed 14. Morgan WP. Psychological components of effort sense. Med Sci Sports Exerc. 1994;26:1071–1077. PubMed doi:10.1249/00005768199409000-00001 15. Hughson RL, Weisiger KH, Swanson GD. Blood lactate concentration increases as continuous function in progressive exercise. J Appl Physiol. 1987;62:1975–1981. PubMed 16. Banister EW. Modeling elite athletic performance. In: Green HJ, McDougal JD, Wenger H, eds. Physiological Testing of Elite Athletes. Champaign, IL: Human Kinetics. 1991:403–424. 17. Mader A, Heck H. A theory of the metabolic origin of “anaerobic threshold.” Int J Sports Med. 1986;7(Suppl 1):45–65. PubMed doi:10.1055/s-2008-1025802 18. Edwards S. High performance training and racing. In: Edwards S, ed. The Heart Rate Monitor Book. Sacramento, CA: Feet Fleet Press; 1993:113–123. 19. Hopkins WG. (2002). A scale of magnitudes for effect statistics. http:// www.sportsci.org/resource/stats/index.html 20. Thomas JR, Nelson JK, Silverman J. Research Methods in Physical Activity. Champaign, IL: Human Kinetics; 2005.
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