Modeled responses to training and taper in competitive swimmers

The Effects of Eccentric Training on Undulatory Underwater Swimming Performance and Kinematics in Competitive Swimmers

This study aimed to evaluate the effects of a five-week training program on undulatory underwater swimming (UUS) in swimmers and to compare the specific effects prompted by two different training protocols on UUS performance and kinematics. Swimmers (n = 14) were divided into in-water only (WO) (18.61 ± 2.62 years, FINA points: 507 ± 60) and water + dry-land training groups (with conical pulleys) (WD) (18.38 ± 2.67 years, FINA points: 508 ± 83). Three countermovement jumps (CMJ) and three maximal UUS trials were performed before and after a five-week training period. The training program comprised 14 × 30-min sessions. The WO group repeated the same 15-min block twice, while the WD group performed one block of 15 min in the water and the other block on land performing lower limb exercises with conical pulleys. Seven body landmarks were auto-digitalized during UUS by a pre-trained neural network and 21 kinematic variables were calculated. The level of statistical significance was set at p < 0.05. Significant time × group interaction in favour of the WD group was observed for mean vertical toe velocity (p = 0.035,η p 2 = 0.32). The WD group experienced enhancements in mean and maximum underwater velocity, kick frequency, maximum shoulder angular velocity, as well as mean and maximum vertical toe velocity (p < 0.05). The WO group exhibited an enhancement in CMJ height (p < 0.05). In conclusion, UUS performance was improved in adolescent swimmers after five weeks of specific training, only when combining water and conical pulley exercises. Coaches should include dry-land specific lower limb exercises in addition to in-water training to improve UUS performance.

Understanding the effects of training on underwater undulatory swimming performance and kinematics

In swimming, the underwater phase after the start and turn comprises gliding and dolphin kicking, with the latter also known as underwater undulatory swimming (UUS). Swimming performance is highly dependent on the underwater phase; therefore, understanding the training effects in UUS and underwater gliding can be critical for swimmers and coaches. Further, the development of technique in young swimmers can lead to exponential benefits in an athlete's career. This study aimed to evaluate the effects of a training protocol on UUS and underwater gliding performance and kinematics in young swimmers. Seventeen age group swimmers (boys = 10, girls = 7) performed maximal UUS and underwater gliding efforts before and after a seven-week training protocol. Time to reach 10 m; intra-cyclic mean, peak, and minimum velocities; and gliding performance improved significantly after the training protocol. The UUS performance improvement was mostly produced by an improvement of the upbeat execution, together with a likely reduction of swimmers' hydrodynamic drag. Despite the changes in UUS and gliding, performance was also likely influenced by growth. The findings from this study highlight kinematic variables that can be used to understand and quantify changes in UUS and gliding performance.

Validity and Accuracy of Impulse-Response Models for Modeling and Predicting Training Effects on Performance of Swimmers

PURPOSE

The aim of this study was to compare the suitability of models for practical applications in training planning.

METHODS

We tested six impulse-response models, including Banister's model (Model Ba), a variable dose-response model (Model Bu), and indirect-response models differing in the way they account or not for the effect of previous training on the ability to respond effectively to a given session. Data from 11 swimmers were collected during 61 wk across two competitive seasons. Daily training load was calculated from the number of pool-kilometers and dry land workout equivalents, weighted according to intensity. Performance was determined from 50-m trials done during training sessions twice a week. Models were ranked on the base of Aikaike's information criterion along with measures of goodness of fit.

RESULTS

Models Ba and Bu gave the greatest Akaike weights, 0.339 ± 0.254 and 0.360 ± 0.296, respectively. Their estimates were used to determine the evolution of performance over time after a training session and the optimal characteristics of taper. The data of the first 20 wk were used to train these two models and predict performance for the after 8 wk (validation data set 1) and for the following season (validation data set 2). The mean absolute percentage error between real and predicted performance using Model Ba was 2.02% ± 0.65% and 2.69% ± 1.23% for validation data sets 1 and 2, respectively, and 2.17% ± 0.65% and 2.56% ± 0.79% with Model Bu.

CONCLUSIONS

The findings showed that although the two top-ranked models gave relevant approximations of the relationship between training and performance, their ability to predict future performance from past data was not satisfactory for individual training planning.

Effects of tapering on performance in endurance athletes: A systematic review and meta-analysis

OBJECTIVE

To assess the responses to taper in endurance athletes using meta-analysis.

METHODS

Systematic searches were conducted in China National Knowledge Infrastructure, PubMed, Web of Science, SPORTDiscus, and EMBASE databases. Standardized mean difference (SMD) and 95% confidence interval (CI) of outcome measures were calculated as effect sizes.

RESULTS

14 studies were included in this meta-analysis. Significant improvements were found between pre- and post-tapering in time-trial (TT) performance (SMD = -0.45; P < 0.05) and time to exhaustion (TTE) performance (SMD = 1.28; P < 0.05). However, There were no improvements in maximal oxygen consumption ([Formula: see text]) and economy of movement (EM) (P > 0.05) between pre- and post-tapering. Further subgroup analysis showed that tapering combined with pre-taper overload training had a more significant effect on TT performance than conventional tapering (P < 0.05). A tapering strategy that reduced training volume by 41-60%, maintained training intensity and frequency, lasted ≤7 days, 8-14 days, or 15-21 days, used a progressive or step taper could significantly improve TT performance (P < 0.05).

CONCLUSIONS

The tapering applied in conjunction with pre-taper overload training seems to be more conducive to maximize performance gains. Current evidence suggests that a ≤21-day taper, in which training volume is progressively reduced by 41-60% without changing training intensity or frequency, is an effective tapering strategy.

Biophysical Impact of 5-Week Training Cessation on Sprint Swimming Performance

PURPOSE

To assess changes in swimming performance, anthropometrics, kinematics, energetics, and strength after 5-week training cessation.

METHODS

Twenty-one trained and highly trained swimmers (13 males: 17.4 [3.1] y; 50-m front crawl 463 [77] FINA points; 8 females: 16.7 [1.7] y; 50-m front crawl 535 [48] FINA points) performed a 50-m front-crawl all-out swim test, dryland and pool-based strength tests, and 10-, 15-, 20-, and 25-m front-crawl all-out efforts for anaerobic critical velocity assessment before and after a 5-week training cessation. Heart rate and oxygen uptake (V˙O2) were continuously measured before and after the 50-m swim test (off-kinetics).

RESULTS

Performance was impaired 1.9% (0.54 s) for males (P = .007, d = 0.91) and 2.9% (0.89 s) for females (P = .033, d = 0.93). Neither the anthropometrical changes (males: r2 = .516, P = .077; females: r2 = .096, P = .930) nor the physical activities that each participant performed during the off-season (males: r2 = .060, P = .900; females: r2 = .250, P = .734) attenuated performance impairments. Stroke rate and clean swimming speed decreased (P < .05), despite similar stroke length and stroke index (P > .05). Blood lactate concentrations remained similar (P > .05), but V˙O2 peak decreased in females (P = .04, d = 0.85). Both sexes showed higher heart rate before and after the 50-m swim test after 5 weeks (P < .05). Anaerobic metabolic power deterioration was only observed in males (P = .035, d = 0.65). Lower in-water force during tethered swimming at zero speed was observed in males (P = .033, d = 0.69). Regarding dryland strength, lower-body impairments were observed for males, while females showed upper-body impairments (P < .05).

CONCLUSIONS

A 5-week training cessation yielded higher heart rate in the 50-m front crawl, anaerobic pathways, and dryland strength impairments. Coaches should find alternatives to minimize detraining effects during the off-season.

The Fitness-Fatigue Model: What's in the Numbers?

PURPOSE

The purpose of this commentary is to outline some of the pitfalls when using the fitness-fatigue model to unravel the interaction between training load and performance. By doing so, we encourage sport scientists and coaches to interpret the parameters from the model with some extra caution.

CONCLUSIONS

Caution is needed when interpreting the fitness-fatigue model since the parameter values are influenced by the starting parameter values, the modeling technique, and the input of the model. Also, the use of general constants should be avoided since they do not account for interindividual differences and differences between training-load methods. Therefore, we advise sport scientists and coaches to use the model as a way to work more data-informed rather than working data-driven.

The Use of Fitness-Fatigue Models for Sport Performance Modelling: Conceptual Issues and Contributions from Machine-Learning

The emergence of the first Fitness-Fatigue impulse responses models (FFMs) have allowed the sport science community to investigate relationships between the effects of training and performance. In the models, athletic performance is described by first order transfer functions which represent Fitness and Fatigue antagonistic responses to training. On this basis, the mathematical structure allows for a precise determination of optimal sequence of training doses that would enhance the greatest athletic performance, at a given time point. Despite several improvement of FFMs and still being widely used nowadays, their efficiency for describing as well as for predicting a sport performance remains mitigated. The main causes may be attributed to a simplification of physiological processes involved by exercise which the model relies on, as well as a univariate consideration of factors responsible for an athletic performance. In this context, machine-learning perspectives appear to be valuable for sport performance modelling purposes. Weaknesses of FFMs may be surpassed by embedding physiological representation of training effects into non-linear and multivariate learning algorithms. Thus, ensemble learning methods may benefit from a combination of individual responses based on physiological knowledge within supervised machine-learning algorithms for a better prediction of athletic performance.In conclusion, the machine-learning approach is not an alternative to FFMs, but rather a way to take advantage of models based on physiological assumptions within powerful machine-learning models.

Training load responses modelling and model generalisation in elite sports

This study aims to provide a transferable methodology in the context of sport performance modelling, with a special focus to the generalisation of models. Data were collected from seven elite Short track speed skaters over a three months training period. In order to account for training load accumulation over sessions, cumulative responses to training were modelled by impulse, serial and bi-exponential responses functions. The variable dose-response (DR) model was compared to elastic net (ENET), principal component regression (PCR) and random forest (RF) models, while using cross-validation within a time-series framework. ENET, PCR and RF models were fitted either individually (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$M_{I}$$\end{document}MI) or on the whole group of athletes (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$M_{G}$$\end{document}MG). Root mean square error criterion was used to assess performances of models. ENET and PCR models provided a significant greater generalisation ability than the DR model (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p = 0.018$$\end{document}p=0.018, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p < 0.001$$\end{document}p<0.001, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p = 0.004$$\end{document}p=0.004 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p < 0.001$$\end{document}p<0.001 for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ENET_{I}$$\end{document}ENETI, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ENET_{G}$$\end{document}ENETG, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$PCR_{I}$$\end{document}PCRI and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$PCR_{G}$$\end{document}PCRG, respectively). Only \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ENET_{G}$$\end{document}ENETG and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$RF_{G}$$\end{document}RFG were significantly more accurate in prediction than DR (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p < 0.001$$\end{document}p<0.001 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p < 0.012$$\end{document}p<0.012). In conclusion, ENET achieved greater generalisation and predictive accuracy performances. Thus, building and evaluating models within a generalisation enhancing procedure is a prerequisite for any predictive modelling.

The Influence of Different Training Load Quantification Methods on the Fitness-Fatigue Model

PURPOSE

Numerous methods exist to quantify training load (TL). However, the relationship with performance is not fully understood. Therefore the purpose of this study was to investigate the influence of the existing TL quantification methods on performance modeling and the outcome parameters of the fitness-fatigue model.

METHODS

During a period of 8 weeks, 9 subjects performed 3 interval training sessions per week. Performance was monitored weekly by means of a 3-km time trial on a cycle ergometer. After this training period, subjects stopped training for 3 weeks but still performed a weekly time trial. For all training sessions, Banister training impulse (TRIMP), Lucia TRIMP, Edwards TRIMP, training stress score, and session rating of perceived exertion were calculated. The fitness-fatigue model was fitted for all subjects and for all TL methods.

RESULTS

The error in relating TL to performance was similar for all methods (Banister TRIMP: 618 [422], Lucia TRIMP: 625 [436], Edwards TRIMP: 643 [465], training stress score: 639 [448], session rating of perceived exertion: 558 [395], and kilojoules: 596 [505]). However, the TL methods evolved differently over time, which was reflected in the differences between the methods in the calculation of the day before performance on which training has the biggest positive influence (range of 19.6 d).

CONCLUSIONS

The authors concluded that TL methods cannot be used interchangeably because they evolve differently.

Effects of an experimental taper period on male and female swimmers

BACKGROUND

This study investigated the possible influence of the gender on the responses of swimmers during a taper period (TP).

METHODS

Ten males (19 ± 3 years and 73.5 ± 7.8 kg) and ten females (17 ± 2 years and 54.7 ± 7.2 kg) swimmers were submitted to a 12-week training, followed by three weeks of the TP. Before and after the TP we evaluated the performance at 100 m freestyle, stroke parameters and lactacidemic responses; lactate minimum intensity (LMI) and stroke parameters associated with LMI and the propulsive force in tethered swimming. TP consisted of 14 sessions with mean volume 2,253 ± 1,213 m•session-1 at an intensity below than the LMI, 1,730 ± 327 m•session-1 at an intensity near the LMI and 1,530 ± 1,019 m•session-1 at an intensity above the LMI.

RESULTS

Significant effects of the genders were observed for LMI and stroke parameters (p-value < 0.001 and η2 > 0.52 [large]) and propulsive force (p-value = 0.001; η2 = 0.59 [large]). However, no significant effects of the TP were identified in the performance of the 100 m freestyle (p-value = 0.66; η2 = 0.006 [small]), propulsive force (p-value > 0.63; η2 < 0.006 [small]), aerobic parameters (LMI: p-value = 0.32 and η2 = 0.03 [small]) and mechanical parameters (p-value > 0.23; η2 = 0.01 [small]). Nonetheless, the peak blood lactate concentrations were improved after TP (p-value = 0.014; η2 = 0.16 [large]), without significant interactions (p-value = 0.38; η2 = 0.02 [small]), as well as the mechanical parameters during maximum 100 m freestyle (p-value < 0.04 and η2 > 0.10 [medium]).

CONCLUSIONS

Hence, men and women presenting significantly different values in the age group studied, the responses observed after the TP investigated were the same independent of gender.

Biophysical Follow-up of Age-Group Swimmers During a Traditional Three-Peak Preparation Program

Zacca, R, Azevedo, R, Ramos, VR, Abraldes, JA, Vilas-Boas, JP, Castro, FAdS, Pyne, DB, and Fernandes, RJ. Biophysical follow-up of age-group swimmers during a traditional three-peak preparation program. J Strength Cond Res 34(9): 2585-2595, 2020-The aim of this study was to quantify changes and contributions of bioenergetic, technique, and anthropometric profiles across a traditional 3-peak swimming season. Twenty-four age-group swimmers (11 boys: 15 years 6 months ± 1 year 1 month; 13 girls: 14 years 5 months ± 10 months) of equal maturational stage were monitored through a 400-m test in front crawl (T400). Bioenergetic, technique, and anthropometric characteristics were compared before and after macrocycles I, II and III. Sex interaction was verified only for amplitude of the fast oxygen uptake component and height (moderate). Multiple linear regressions and principal component analysis were used to identify the most influential variables and the relative contribution of each domain (bioenergetics, technique, and anthropometrics) to changes in swimming performance of T400. The relative contributions for the performance of T400 after macrocycles I, II, and III were, respectively, 6, 18, and 27% for bioenergetics, 88, 69, and 54% for technique, and 6, 13, and 20% for anthropometrics. Technique was the biggest contributor (71%) for changes in the performance of T400 over the training season, followed by bioenergetics (17%) and anthropometrics (12%). Technique played the main role during the competitive season, regardless of gradual increase in the contribution of bioenergetics and anthropometrics. Despite that, bioenergetics and technique are closely connected, thus a powerful and endurable metabolic base and cannot be overlooked. Changes and contribution of bioenergetics, technique, and anthropometrics on age-group swimmers' performance over a traditional 3-peak swimming season could be described by the T400 swimming test, providing a comprehensive biophysical overview of the main contributors to swimming performance.

The relationship between training load and pain, injury and illness in competitive swimming: A systematic review

BACKGROUND

Research suggests that the frequency of training, combined with the repetitive motion involved in high volume swimming can predispose swimmers to symptoms of over-training. The prevention of pain, injury and illness is of paramount importance in competitive swimming in order to maximise a swimmer's ability to train and perform consistently. A significant factor in the prevention of pain, injury or illness is the appropriate load monitoring and management practices within a training programme.

OBJECTIVE

The purpose of this systematic review is to investigate the relationship between training load and pain, injury and illness in competitive swimmers.

METHODS

The databases SPORTDiscus, CINAHL, Scopus, MEDLINE and Embase were searched in accordance with PRISMA guidelines. Studies were included if they reported on competitive swimmers and analysed the link between training load and either pain, injury or illness. The methodological quality and study bias were assessed using the Joanna Briggs Institute Critical Appraisal Checklist.

RESULTS

The search retrieved 1,959 articles, 15 of which were included for review. The critical appraisal process indicated study quality was poor overall. Pain was the most explored condition (N = 12), with injury (N = 2) and illness (N = 1) making up the remaining articles. There was no evidence of an association between training load and pain, while there may be some evidence to suggest a relationship between training load and injury or illness.

CONCLUSIONS

The relationship between training load and pain, injury or illness is unclear owing to a host of methodological constraints. The review highlighted that youth, masters and competitive swimmers of a lower ability (e.g. club versus international) may need particular consideration when planning training loads. Winter periods, higher intensity sessions and speed elements may also need to be programmed with care. Monitoring practices need to be developed in conjunction with consensus guidelines, with the inclusion of internal training loads being a priority. Future research should focus on longitudinal prospective studies, utilising the session Rating of Perceived Exertion (sRPE) monitoring method and investigating the applicability of Acute/Chronic Workload Ratio (ACWR) and exponentially weighted moving average (EWMA). Improved methods and study design will provide further clarity on the relationship between load and pain, injury, and illness.

Monitoring Age-Group Swimmers Over a Training Macrocycle: Energetics, Technique, and Anthropometrics

Zacca, R, Azevedo, R, Chainok, P, Vilas-Boas, JP, Castro, FAdS, Pyne, DB, and Fernandes, RJ. Monitoring age-group swimmers over a training macrocycle: energetics, technique, and anthropometrics. J Strength Cond Res 34(3): 818-827, 2020-The aim of this study was to quantify changes and contributions of energetic, technique, and anthropometric profiles across the first training macrocycle (16-week) in a traditional 3-peak swimming season. Twenty-four age-group swimmers (10 boys and 14 girls age 14.4 ± 0.9 years) of equal maturational stage were monitored through a 400-m test in front crawl (T400). Energetic, technique, and anthropometric characteristics were compared before (experimental testing 1, E1) and after the preparatory (E2), specific (E3), and competitive (E4) training periods. Sex interaction was not significant for any variable. Multiple linear regressions and principal component analysis were used to identify the most influential variables and the relative contribution of each domain (energetics, technique, and anthropometrics) to changes in swimming performance of T400. The relative contributions for performance of T400 at E1, E2, E3, and E4 were 15, 12, 6, and 13% for energetics, 78, 85, 75, and 70% for technique, and 7, 3, 19 and 17% for anthropometrics, respectively. Technique played the main role during the first 16-week macrocycle in a competitive season, regardless of small fluctuations in the influence of energetics and anthropometrics. Changes and influence of energetics, technique, and anthropometric on age-group swimmers' performance could be described by the T400 swimming test, providing a comprehensive biophysical overview of the main contributors to swimming performance.

Effects of short-term resistance training and tapering on maximal strength, peak power, throwing ball velocity, and sprint performance in handball players

The purpose of this study was to assess the effect of short-term resistance training and two weeks of tapering on physical performances in handball players. Following a ten-week progressive resistance training program, subjects were divided between an experimental (n = 10) and a control group (n = 10). The experimental group completed a resistance training program, followed by a two-week period when the training intensity was tapered by 60%, while the control group maintained their typical pattern of training. Muscle power (force-velocity test and squat and counter-movement jump tests), sprinting ability (10m and 30m), ability to change direction (T-half test) and throwing velocity (a 3-step throw with a run, and a jump throw) were evaluated before training, at the end of training and after tapering. The experimental group showed significantly larger interaction effects for the 10-week training period (12/15, 80%), than for the following 2 weeks of tapering (10/15, 67%), with the largest gains being in 15 m sprint times (d = 3.78) and maximal muscular strength in the snatch (d = 3.48). Although the performance of the experimental group generally continued to increase over tapering, the mean effect size for the training period was markedly higher (d = 1.92, range: 0.95-3.78) than that seen during tapering (d = 1.02, range: -0.17-2.09). Nevertheless the ten weeks of progressive resistance training followed by two weeks of tapering was an effective overall tactic to increase muscle power, sprint performance and ball throwing velocity in handball players.

Elite Swimmers' Training Patterns in the 25 Weeks Prior to Their Season's Best Performances: Insights Into Periodization From a 20-Years Cohort

Background

This study investigated the periodization of elite swimmers’ training over the 25 weeks preceding the major competition of the season.

Methods

We conducted a retrospective observational study of elite male (n = 60) and female (n = 67) swimmers (46 sprint, 81 middle-distance) over 20 competitive seasons (1992–2012). The following variables were monitored: training corresponding to blood lactate <2 mmol⋅L^-1, 2 to ≤4 mmol⋅L^-1, >4–6 mmol⋅L^-1, >6 mmol⋅L^-1, and maximal swimming speed; general conditioning and maximal strength training hours; total training load (TTL); and the mean normalized volumes for both in-water and dryland workouts. Latent class mixed modeling was used to identify various TTL pattern groups. The associations between pattern groups and sex, age, competition event, Olympic quadrennial year, training contents, and relative performance were quantified.

Results

For the entire cohort, ∼86–90% of the training was swum at an intensity of [La]_b ≤ 4 mmol⋅L^-1. This training volume was divided into 40–44% at <2 mmol⋅L^-1 and 44–46% at 2 to ≤4 mmol⋅L^-1, leaving 6–9.5% at >4–6 mmol⋅L^-1, and 3.5–4.5% at >6 mmol⋅L^-1. Three sprint TTL patterns were identified: a pattern with two long ∼14–15-week macrocycles, one with two ∼12–13 week macrocycles each composed of a balanced training load, and one with a single stable flat macrocycle. The long pattern elicited the fastest performances and was most prevalent in Olympic quadrennials (i.e., 4 seasons preceding the 2004, 2008, and 2012 Olympic Games). This pattern exhibited moderate week-to-week TTL variability (6 ± 3%), progressive training load increases between macrocycles, and more training at ≤4 mmol⋅L^-1 and >6 mmol⋅L^-1. This fastest sprint pattern showed a waveform in the second macrocycle consisting of two progressive load peaks 10–11 and 4–6 weeks before competition. The stable flat pattern was the slowest and showed low TTL variability (4 ± 3%), training load decreases between macrocycles (P < 0.01), and more training at 4–6 mmol⋅L^-1 (P < 0.01).

Conclusion

Progressive increases in training load, macrocycles lasting about 14–15 weeks, and substantial volume of training at intensities ≤4 mmol⋅L^-1 and >6 mmol⋅L^-1, were associated with peak performance in elite swimmers.

An Improved Version of the Classical Banister Model to Predict Changes in Physical Condition

In this paper, we formulate and provide the solutions to two new models to predict changes in physical condition by using the information of the training load of an individual. The first model is based on a functional differential equation, and the second one on an integral differential equation. Both models are an extension to the classical Banister model and allow to overcome its main drawback: the variations in physical condition are influenced by the training loads of the previous days and not only of the same day. Finally, it is illustrated how the first model works with a real example of the training process of a cyclist.

Relationships Between Model-Predicted and Actual Match-Play Exercise-Intensity Performance in Professional Australian Footballers During a Preseason Training Macrocycle

PURPOSE

To assess and compare the validity of internal and external Australian football (AF) training-load measures for predicting preseason variation of match-play exercise intensity (MEI sim/min) using a variable dose-response model.

METHODS

A total of 21 professional male AF players completed an 18-wk preseason macrocycle. Preseason internal training load was quntified using the session rating-of-perceived-exertion method (sRPE) and external load from satellite (as distance [Dist] and high-speed distance [HS Dist]) and accelerometer (Player Load [PL]) data. Using a training-impulse (TRIMPs) calculation, external load expressed in arbitrary units was represented as TRIMPs_Dist, TRIMPs_HSDist, and TRIMPs_PL. Preseason training load and MEI sim/min data were applied to a variable dose-response model, which provided estimates of MEI sim/min. Model estimates of MEI sim/min were correlated with actual measures from each match-play drill performed during the preseason macrocycle. Magnitude-based inferences (effect size [90% confidence interval]) were calculated to determine practical differences in the precision of MEI sim/min estimates using each of the internal- and external-load inputs.

RESULTS

Estimates of MEI sim/min demonstrated very large and large associations with actual MEI sim/min with models constructed from external and internal training inputs (r [90% confidence interval]; TRIMPs_Dist .73 [.72-.74], TRIMPs_PL .72 [.71-.73], and sRPE_Skills .67 [.56-.78]). There were trivial differences in the precision of MEI sim/min estimates between models constructed from TRIMPs_Dist and TRIMPs_PL and between internal input methods.

CONCLUSIONS

Variable dose-response models from multiple training-load inputs can predict the within-individual variation of MEI sim/min across an entire preseason macrocycle. Models informed by external training inputs (TRIMPs_Dist and TRIMPs_PL) exhibited predictive power comparable to those of sRPE_Skills models.

Effects of a 6-Week Period of Polarized or Threshold Training on Performance and Fatigue in Elite Swimmers

PURPOSE

To quantify the impact of a polarized distribution of training intensity on performance and fatigue in elite swimmers.

METHODS

Twenty-two elite junior swimmers (12 males, age = 17 [3] y, and 10 females, age = 17 [3] y) participated in a crossover intervention study over 28 wk involving 2- × 6-wk training periods separated by 6 wk. Swimmers were randomly assigned to a training group for the first period: polarized (81% in zone 1, blood lactate concentration, [La]_b ≤ 2 mmol·L^-1; 4% in zone 2, 2 mmol·L^-1 < [La]_b ≤ 4 mmol·L^-1; and 15% in zone 3, [La]_b > 4 mmol·L^-1) or threshold (65%/25%/10%). Before and after each period, they performed a 100-m maximal swimming test to determine performance, maximal [La]_b, and oxygen consumption and an incremental swimming test to determine speed corresponding to [La]_b = 4 mmol·L^-1 (V4 mmol·L^-1). Self-reported indices of well-being were collected with a daily questionnaire.

RESULTS

Polarized training elicited small to moderately greater improvement than threshold training on 100-m performance (within-group change ± 90% confidence interval: 0.97% ± 1.02% vs 0.09% ± 0.94%, respectively) with less fatigue and better quality of recovery. There was no substantial gender effect. No clear differences were observed in physiological adaptations between groups.

CONCLUSIONS

In elite junior swimmers, a 6-wk period of polarized training induced small improvements in 100-m time-trial performance and, in combination with less perceived fatigue, forms a viable option for coaches preparing such cohorts of swimmers for competition.

Training methods and analysis of races of a top level Paralympic swimming athlete

Training methods for Paralympic swimmers must take into account different pathologies, competitions classes, athlete’s individual circumstances and peculiar physical adaptation mechanisms, hence general guidelines cannot be found in literature. In this study we present a training program, implemented for the physical preparation of a top level Paralympic swimmer. The athlete under study, affected by infantile cerebral palsy within a clinical picture of a spastic tetraparesis, by the end of 2016 was holder of Italian, European, world and Paralympic titles in the 400-m freestyle competition, S6 class. The training macrocycle was structured in a 3-fold periodization (three mesocycles), in view of the preparation to three international competitions. The 4-month training mesocycles prior to each competition differed substantially in terms of mileage load, intensity and recovery times. The first mesocycle was characterized by a sizeable low-intensity mileage load, the second one was shifted to lower mileage load, carried out at middle-to-high intensity levels, the third one entailed increased effort intensity, counterbalanced by lower mileage load. In all cases, recovery times were balanced to obtain optimized performance through physical adaptation to training stimuli, keeping into account the physiopatological response. Tapering phases were adjusted to maximize performance at competition. As an assessment of the effectiveness of the training method, correspondence between chronometric and technical parameters in the three competitions and the respective mesocycle training programs was found. The results of the present study may support the development of training guidelines for athletes affected by upper motor neuron lesions.

Effects of Different Training Intensity Distributions Between Elite Cross-Country Skiers and Nordic-Combined Athletes During Live High-Train Low

Purpose: To analyze the effects of different training strategies (i.e., mainly intensity distribution) during living high – training low (LHTL) between elite cross-country skiers and Nordic-combined athletes.

Methods: 12 cross-country skiers (XC) (7 men, 5 women), and 8 male Nordic combined (NC) of the French national teams were monitored during 15 days of LHTL. The distribution of training at low-intensity (LIT), below the first ventilatory threshold (VT1), was 80% and 55% in XC and NC respectively. Daily, they filled a questionnaire of fatigue, and performed a heart rate variability (HRV) test. Prior (Pre) and immediately after (Post), athletes performed a treadmill incremental running test for determination of V˙O_2max and V˙O₂ at the second ventilatory threshold (V˙O_{2V T2}), a field roller-skiing test with blood lactate ([La-]) assessment.

Results: The training volume was in XC and NC, respectively: at LIT: 45.9 ± 6.4 vs. 23.9 ± 2.8 h (p < 0.001), at moderate intensity: 1.9 ± 0.5 vs. 3.0 ± 0.4 h, (p < 0.001), at high intensity: 1.2 ± 0.9 vs. 1.4 ± 02 h (p = 0.05), in strength (and jump in NC): 7.1 ± 1.5 vs. 18.4 ± 2.7 h, (p < 0.001). Field roller-skiing performance was improved (-2.9 ± 1.6%, p < 0.001) in XC but decreased (4.1 ± 2.6%, p < 0.01) in NC. [La-] was unchanged (-4.1 ± 14.2%, p = 0.3) in XC but decreased (-27.0 ± 11.1%, p < 0.001) in NC. Changes in field roller-skiing performance and in [La-] were correlated (r = -0.77, p < 0.001). V˙O_2max increased in both XC and NC (3.7 ± 4.2%, p = 0.01 vs. 3.7 ± 2.2%, p = 0.002) but V˙O_{2V T2} increased only in XC (7.3 ± 5.8%, p = 0.002). HRV analysis showed differences between XC and NC mainly in high spectral frequency in the supine position (HF_SU). All NC skiers showed some signs of overreaching at Post.

Conclusion: During LHTL, despite a higher training volume, XC improved specific performance and aerobic capacities, while NC did not. All NC skiers showed fatigue states. These findings suggest that a large amount of LIT with a moderate volume of strength and speed training is required during LHTL in endurance athletes.

Relation Between Training Load and Recovery-Stress State in High-Performance Swimming

Background: The relation between training load, especially internal load, and the recovery-stress state is of central importance for avoiding negative adaptations in high-performance sports like swimming. The aim of this study was to analyze the individual time-delayed linear effect relationship between training load and recovery-stress state with single case time series methods and to monitor the acute recovery-stress state of high-performance swimmers in an economical and multidimensional manner over a macro cycle. The Acute Recovery and Stress Scale (ARSS) was used for daily monitoring of the recovery-stress state. The methods session-RPE (sRPE) and acute:chronic workload-ratio (ACWR) were used to compare different methods for quantifying the internal training load with regard to their interrelationship with the recovery-stress state. Methods: Internal load and recovery-stress state of five highly trained female swimmers [with a training frequency of 13.6 ± 0.8 sessions per week and specializing in sprint (50 and 100 m), middle-distance (200 and 400 m), or long distance (800 and 1,500 m) events] were daily documented over 17 weeks. Two different types of sRPE were applied: RPE^∗duration (sRPE^h) and RPE^∗volume (sRPE^km). Subsequently, we calculated the ratios ACWR^h and ACWR^km (sRPE last week: 4-week exponentially weighted moving average). The recovery-stress state was measured by using the ARSS, consisting of eight scales, four of which are related to recovery [Physical Performance Capability (PPC), Mental Performance Capability (MPC), Emotional Balance (EB), Overall Recovery (OR)], and four to stress [Muscular Stress (MS), Lack of Activation (LA), Negative Emotional State (NES), Overall Stress (OS)]. To examine the relation between training load and recovery-stress state a cross correlation (CCC) was conducted with sRPE^h, sRPE^km, ACWR^h, and ACWR^km as lead and the eight ARSS-scales as lag variables. Results: A large variation of training load can be observed in the individual week-to-week fluctuations whereby the single fluctuations can significantly differ from the overall mean of the group. The range also shows that the CCC individually reaches values above 0.3, especially with sRPE^km as lead variable. Overall, there is a large range with significant differences between the recovery and stress dimensions of the ARSS and between the training load methods, with sRPE^km having the largest span (Range = 1.16). High inter-individual differences between the athletes lie in strength and direction of the correlation | 0.66|≤ CCC ≥|-0.50|. The time delayed effects (lags 0-7) are highly individual, however, clear patterns can be observed. Conclusion: The ARSS, especially the physical and overall-related scales (PPC, OR, MS, OS), is a suitable tool for monitoring the acute recovery-stress state in swimmers. MPC, EB, LA, and NES are less affected by training induced changes. Comparably high CCC and Ranges result from the four internal load methods, whereby sRPE, especially sRPE^km, shows a stronger relation to recovery-stress state than ACWR. Based on these results and the individual differences in terms of time delay in training response, we recommend for swimming to use sRPE to monitor the internal training load and to use the ARSS, with a focus at the physical and overall-scales, to monitor the recovery-stress state.

Relationships Between Model Estimates and Actual Match-Performance Indices in Professional Australian Footballers During an In-Season Macrocycle

PURPOSE

To assess and compare the validity of internal and external Australian football (AF) training-load measures for predicting match exercise intensity (MEI/min) and player-rank score (PR_Score) using a variable dose-response model.

METHODS

A cohort of 25 professional AF players (23 ± 3 y, 188.3 ± 7.2 cm, 87.7 ± 8.4 kg) completed a 24-wk in-season macrocycle. In-season internal training and match load were quantified using session rating of perceived exertion (sRPE) and external load from satellite and accelerometer data. Using a training-impulse (TRIMP) calculation, external load (au) was represented as distance (TRIMP_Dist), distance ≥4.16 m/s (TRIMP_HSDist), and PlayerLoad (TRIMP_PL). In-season training load, MEI/min, and PR_Score were applied to a variable dose-response model, which provided estimates of MEI/min and PR_Score. Predicted MEI/min and PR_Score were correlated with actual measures from each match. The magnitude of the difference between MEI/min and PR_Score estimates for each model input and the difference between the precision of internal and external load measures to predict MEI/min and PR_Score were calculated using the effect size ± 90% confidence interval (CI).

RESULTS

Estimates of MEI/min demonstrated very large associations with actual MEI/min (r, 90% CI) (eg, TRIMP_Dist .76 ± .13, and sRPE_Skills .73 ± .14). Estimates of PR_Score demonstrated associations of large magnitude with actual PR_Score using the same inputs. Precision of actual MEI/min was lowest using sRPE compared with (ES ± 90% CI) TRIMP_Dist, -.67 ± .34, and TRIMP_PL, -.91 ± .39. There were trivial and unclear differences in the precision of PR_Score estimates between TRIMP and sRPE inputs.

CONCLUSIONS

Dose-response models from multiple training-load inputs can predict within-individual variation of MEI/min and PR_Score. Internal and external training-input methods exhibited comparable predictive power.

Long-term swimming training modifies acute immune cell response to a high-intensity session

PURPOSE

Long-term training influence on athletes' immune cell response to acute exercise has been poorly studied, despite the complexity of both chronic and acute adaptations induced by training. The purpose of the study is to study the influence of a 4-month swimming training cycle on the immune cell response to a high-intensity training session, during 24 h of recovery, considering sex, maturity, and age group.

METHODS

Forty-three swimmers (16 females, 14.4 ± 1.1 years; 27 males, 16.2 ± 2.0) performed a standardized high-intensity session, after the main competition of the first (M1), and second (M2) macrocycles. Blood samples were collected before (Pre), immediately after (Post), 2 h after (Post2h) and 24 h after (Post24h) exercise. Haemogram and lymphocytes subsets were assessed by an automatic cell counter and by flow cytometry, respectively. Subjects were grouped according to sex, competitive age groups, or pubertal Tanner stages. Results express the percentage of relative differences from Pre to Post, Post2h and Post24h. Upper respiratory symptoms (URS) and training load were quantified.

RESULTS

At M2, we observed smaller increases of leukocytes (M1: 14.0 ± 36.3/M2: 2.33 ± 23.0%) and neutrophils (M1: 57.1 ± 71.6/M2: 38.9 ± 49.9%) at Post; and less efficient recoveries of total lymphocytes (M1: - 22.0 ± 20.1/M2: - 30.0 ± 18.6%) and CD19⁺ (M1: 4.09 ± 31.1/M2: - 19.1 ± 24.4%) at Post2h. At Post2h, the increment of CD4⁺/CD8⁺ was smaller in youth (M1: 21.5 ± 16.0/M2: 9.23 ± 21.4%), and bigger in seniors (M1: 3.68 ± 9.21/M2: 23.2 ± 15.0%); and at Post24h late pubertal swimmers' CD16⁺56⁺ recovered less efficiently (M1: - 0.66 ± 34.6/M2: - 20.5 ± 34.2%).

CONCLUSIONS

The training cycle induced an attenuated immune change immediately after exercise and a less efficient recovery of total lymphocytes, involving an accentuated CD19⁺ decrease. The concomitant higher URS frequency suggests a potential immune depression and a longer interval of susceptibility to infection.

Modelling of optimal training load patterns during the 11 weeks preceding major competition in elite swimmers

Periodization of swim training in the final training phases prior to competition and its effect on performance have been poorly described. We modeled the relationships between the final 11 weeks of training and competition performance in 138 elite sprint, middle-distance, and long-distance swimmers over 20 competitive seasons. Total training load (TTL), strength training (ST), and low- to medium-intensity and high-intensity training variables were monitored. Training loads were scaled as a percentage of the maximal volume measured at each intensity level. Four training periods (meso-cycles) were defined: the taper (weeks 1 to 2 before competition), short-term (weeks 3 to 5), medium-term (weeks 6 to 8), and long-term (weeks 9 to 11). Mixed-effects models were used to analyze the association between training loads in each training meso-cycle and end-of-season major competition performance. For sprinters, a 10% increase between ∼20% and 70% of the TTL in medium- and long-term meso-cycles was associated with 0.07 s and 0.20 s faster performance in the 50 m and 100 m events, respectively (p < 0.01). For middle-distance swimmers, a higher TTL in short-, medium-, and long-term training yielded faster competition performance (e.g., a 10% increase in TTL was associated with improvements of 0.1–1.0 s in 200 m events and 0.3–1.6 s in 400 m freestyle, p < 0.01). For sprinters, a 60%–70% maximal ST load 6–8 weeks before competition induced the largest positive effects on performance (p < 0.01). An increase in TTL during the medium- and long-term preparation (6–11 weeks to competition) was associated with improved performance. Periodization plans should be adapted to the specialty of swimmers.

From an indirect response pharmacodynamic model towards a secondary signal model of dose-response relationship between exercise training and physical performance

The aim of this study was to test the suitability of using indirect responses for modeling the effects of physical training on performance. We formulated four different models assuming that increase in performance results of the transformation of a signal secondary to the primary stimulus which is the training dose. The models were designed to be used with experimental data with daily training amounts ascribed to input and performance measured at several dates ascribed to output. The models were tested using data obtained from six subjects who trained on a cycle ergometer over a 15-week period. The data fit for each subject was good for all of the models. Goodness-of-fit and consistency of parameter estimates favored the model that took into account the inhibition of production of training effect. This model produced an inverted-U shape graphic when plotting daily training dose against performance because of the effect of one training session on the cumulated effects of previous sessions. In conclusion, using secondary signal-dependent response provided a framework helpful for modeling training effect which could enhance the quantitative methods used to analyze how best to dose physical activity for athletic performance or healthy living.

Acute Oxidative Effect and Muscle Damage after a Maximum 4 Min Test in High Performance Athletes

The purpose of this investigation was to determine lipid peroxidation markers, physiological stress and muscle damage in elite kayakers in response to a maximum 4-min kayak ergometer test (KE test), and possible correlations with individual 1000m kayaking performances. The sample consisted of twenty-three adult male and nine adult female elite kayakers, with more than three years’ experience in international events, who voluntarily took part in this study. The subjects performed a 10-min warm-up, followed by a 2-min passive interval, before starting the test itself, which consisted of a maximum 4-min work paddling on an ergometer; right after the end of the test, an 8 ml blood sample was collected for analysis. 72 hours after the test, all athletes took part in an official race, when then it was possible to check their performance in the on site K1 1000m test (P1000m). The results showed that all lipoproteins and hematological parameters tested presented a significant difference (p≤0.05) after exercise for both genders. In addition, parameters related to muscle damage such as lactate dehydrogenase (LDH) and creatine kinase (CK) presented significant differences after stress. Uric acid presented an inverse correlation with the performance (r = -0.76), while CK presented a positive correlation (r = 0.46) with it. Based on these results, it was possible to verify muscle damage and the level of oxidative stress caused by indoor training with specific ergometers for speed kayaking, highlighting the importance of analyzing and getting to know the physiological responses to this type of training, in order to provide information to coaches and optimize athletic performance.

Relationship Between Training Volume and Ratings of Perceived Exertion in Swimmers

The markers of external training load (ETL), distance and intensity, do not take into account the athletes’ psychophysiological stress, i.e., internal training load (ITL). Thus, the aim of this study was to evaluate the relationship between ETL and ITL using the rating of perceived exertion (RPE) and session-RPE in swimmers. Seventeen young swimmers (10 male, 15.8 ± 0.87 yr and 7 female, 15.1 ± 0.46 yr) belonging to one national level youth team took part in this study over 4 wk. The external training load was planned using swimming distance (in meters) at seven different training intensities. Swimmers’ RPE was assessed 30 min after each training session. Session-RPE was calculated by multiplying RPE by session duration (min). The relationship between the variables was analyzed with Pearson correlations and a multiple linear regression was performed to predict the session-RPE as a function of the independent variables (aerobic and anaerobic volume). The swimming distance at different intensities correlated strongly with RPE and very largely with session-RPE (.64, p < .05 and .71, p < .05, respectively). Regression analysis indicated that the aerobic and anaerobic volumes together explained more than 50% of the ITL variability. In conclusion, the swimming distance in each training session was significantly associated with RPE and session-RPE in swimmers. In other words, based on these results, the use of high-volume training at lower intensities affects the RPE and Session-RPE more than the anaerobic volume.

The training-injury prevention paradox: should athletes be training smarter and harder?

Background

There is dogma that higher training load causes higher injury rates. However, there is also evidence that training has a protective effect against injury. For example, team sport athletes who performed more than 18 weeks of training before sustaining their initial injuries were at reduced risk of sustaining a subsequent injury, while high chronic workloads have been shown to decrease the risk of injury. Second, across a wide range of sports, well-developed physical qualities are associated with a reduced risk of injury. Clearly, for athletes to develop the physical capacities required to provide a protective effect against injury, they must be prepared to train hard. Finally, there is also evidence that under-training may increase injury risk. Collectively, these results emphasise that reductions in workloads may not always be the best approach to protect against injury.

Main thesis

This paper describes the ‘Training-Injury Prevention Paradox’ model; a phenomenon whereby athletes accustomed to high training loads have fewer injuries than athletes training at lower workloads. The Model is based on evidence that non-contact injuries are not caused by training per se, but more likely by an inappropriate training programme. Excessive and rapid increases in training loads are likely responsible for a large proportion of non-contact, soft-tissue injuries. If training load is an important determinant of injury, it must be accurately measured up to twice daily and over periods of weeks and months (a season). This paper outlines ways of monitoring training load (‘internal’ and ‘external’ loads) and suggests capturing both recent (‘acute’) training loads and more medium-term (‘chronic’) training loads to best capture the player's training burden. I describe the critical variable—acute:chronic workload ratio—as a best practice predictor of training-related injuries. This provides the foundation for interventions to reduce players risk, and thus, time-loss injuries.

Summary

The appropriately graded prescription of high training loads should improve players’ fitness, which in turn may protect against injury, ultimately leading to (1) greater physical outputs and resilience in competition, and (2) a greater proportion of the squad available for selection each week.

Training-related risk of common illnesses in elite swimmers over a 4-yr period

PURPOSE

The objective of this study is to investigate the relation between sport training and the risk of common illnesses: upper respiratory tract and pulmonary infections (URTPI), muscular affections (MA), and all-type pathologies in highly trained swimmers.

METHODS

Twenty-eight French professional swimmers were monitored weekly for 4 yr. Training variables included 1) in-water and dryland intensity levels: low-load, high-load, resistance, maximal strength, and general conditioning training (expressed as the percentage of the maximal load performed by each subject, at each intensity level over the study period); and 2) training periods: moderate, intensive, taper, competition, and postcompetition. Illnesses were diagnosed by a sports physician using a standardized questionnaire. Mixed-effects logistic regression analyses were used to model odds ratios for the association between common illnesses and training variables, adjusted for sport season, semiseason (summer or winter), age, competition level, sex, and history of recent events, whereas controlling for heterogeneity among swimmers.

RESULTS

The risk of common illnesses was significantly higher in winter months, for national swimmers (for URTPI), and in cases of history of recent event (notably for MA). The odds of URTPI increased 1.08 (95% CI, 1.01-1.16) and 1.10 (95% CI, 1.01-1.19) times for every 10% increase in resistance and high-load trainings, respectively. The odds of MA increased by 1.49 (95% CI, 1.14-1.96) and 1.63 (95% CI, 1.20-2.21) for each 10% increase in high load and general conditioning training, respectively. The odds of illnesses were 50%-70% significantly higher during intensive training periods.

CONCLUSION

Particular attention must be paid to illness prevention strategies during periods of intensive training, particularly in the winter months or in case of the recent medical episode.

Modeling of performance and ANS activity for predicting future responses to training

PURPOSE

Our aim was to assess whether we can predict satisfactorily performance in swimming and high frequency power (HF power) of heart rate variability from the responses to previous training. We have tested predictions using the model of Banister and the variable dose-response model.

METHODS

Data came from ten swimmers followed during 30 weeks of training with performance and HF power measured each week. The first 15-week training period was used to estimate the parameters of each model for both performance and HF power. Both were then predicted in response to the training done during the second 15-week training period. The bias and precision were estimated from the mean and SD of the difference between prediction and actual value expressed as a percentage of performance or HF power at the first week.

RESULTS

With the variable-dose response model, the bias for performance prediction was -0.24 ± 0.06 and the precision 0.69 ± 0.24% (mean ± between-subject SD). For HF power, the bias was 0 ± 21 and the precision 22 ± 8%. When HF power was transformed into performance using a quadratic relation in each swimmer established from the first 15-week period, the bias was 0.18 ± 0.74 and the precision 0.80 ± 0.30%. No clear trend in the error was observed during the second period.

CONCLUSIONS

This study showed that the modeling of training effects on performance allowed accurate performance prediction supporting its relevance to control and predict week after week the responses to future training.

Changes in naïve and memory T-cells in elite swimmers during a winter training season

High intensity training regimens appear to put athletes at a higher risk of illness. As these have been linked to alterations in the proportions of differentiated T cells, how training load affects these populations could have important implications for athlete susceptibility to disease. This study examined the effect of a winter training season on the proportions of circulating naïve and memory T cells subsets of high competitive level swimmers. Blood samples were taken at rest at 4 time-points during the season: before the start of the season (t0-September), after 7weeks of an initial period of gradually increasing training load (t1-November), after 6weeks of an intense training cycle (t2-February) and 48h after the main competition (t3-April) and from eleven non-athlete controls at 2 similar time-points (t2 and t3). CD4, CD8 and gamma-delta (γδ) T cells expressing the naïve (CCR7(+)CD45RA(+)), central-memory (CM-CCR7(+)CD45RA(-)), effector-memory (EM-CCR7(-)CD45RA(-)) and terminal effector (TEMRA-CCR7(-)CD45RA(+)) were quantified by flow cytometry. Statistical analyses were performed using multilevel modeling regression. Both T CD4(+) naïve and CM presented a linear increase in response to the first moment of training exposure, and had an exponential decrease until the end of the training exposure. As for TCD4(+) EM, changes were observed from t2 until the end of the training season with an exponential trend, while TCD4(+) TEMRA increased linearly throughout the season. TCD8(+) naïve increased at t1 and decreased exponentially thereafter. TCD8(+) TEMRA values decreased at t1 and increased exponentially until t3. γδT-EM had an increase at t1 and an exponential decrease afterwards. In contrast, γδT-TEMRA decreased at t1 and exponentially increased during the remaining 20weeks of training. An increase in TEMRA and EM T cells alongside a decrease in naïve T cells could leave athletes more susceptible to illness in response to variation in training stimulus during the season.

Effects of intermittent training on anaerobic performance and MCT transporters in athletes

This study examined the effects of intermittent hypoxic training (IHT) on skeletal muscle monocarboxylate lactate transporter (MCT) expression and anaerobic performance in trained athletes. Cyclists were assigned to two interventions, either normoxic (N; n = 8; 150 mmHg PIO2) or hypoxic (H; n = 10; ∼3000 m, 100 mmHg PIO2) over a three week training (5×1 h-1h30 x week(-1)) period. Prior to and after training, an incremental exercise test to exhaustion (EXT) was performed in normoxia together with a 2 min time trial (TT). Biopsy samples from the vastus lateralis were analyzed for MCT1 and MCT4 using immuno-blotting techniques. The peak power output (PPO) increased (p<0.05) after training (7.2% and 6.6% for N and H, respectively), but VO2max showed no significant change. The average power output in the TT improved significantly (7.3% and 6.4% for N and H, respectively). No differences were found in MCT1 and MCT4 protein content, before and after the training in either the N or H group. These results indicate there are no additional benefits of IHT when compared to similar normoxic training. Hence, the addition of the hypoxic stimulus on anaerobic performance or MCT expression after a three-week training period is ineffective.

Competitive performance, training load and physiological responses during tapering in young swimmers

The study examined the changes of training load and physiological parameters in relation to competitive performance during a period leading to a national championship. The training content of twelve swimmers (age: 14.2±1.3 yrs) was recorded four weeks before the national championship (two weeks of normal training and two weeks of the taper). The training load was calculated: i) by the swimmer’s session-RPE score (RPE-Load), ii) by the training intensity levels adjusted after a 7×200-m progressively increasing intensity test (LA-Load). Swimmers completed a 400-m submaximal intensity test, a 15 s tethered swimming and hand-grip strength measurements 34–35 (baseline: Test 1), 20–21 (before taper: Test 2) and 6–7 (Test 3) days before the national championship. Performance during the national championship was not significantly changed compared to season best (0.1±1.6%; 95% confidence limits: −0.9, 1.1%; Effect Size: 0.02, p=0.72) and compared to performance before the start of the two-week taper period (0.9±1.7%; 95% confidence limits: 0.3, 2.1%; Effect size: 0.12, p=0.09). No significant changes were observed in all measured physiological and performance related variables between Test 1, Test 2, and Test 3. Changes in RPE-Load (week-4 vs. week-1) were correlated with changes in performance (r=0.63, p=0.03) and the RPE-Load was correlated with the LA-Load (r=0.80, p=0.01). The estimation of the session-RPE training load may be helpful for taper planning of young swimmers. Increasing the difference between the normal and last week of taper training load may facilitate performance improvements.

Rationale and resources for teaching the mathematical modeling of athletic training and performance

A number of professions rely on exercise prescription to improve health or athletic performance, including coaching, fitness/personal training, rehabilitation, and exercise physiology. It is therefore advisable that the professionals involved learn the various tools available for designing effective training programs. Mathematical modeling of athletic training and performance, which we henceforth call "performance modeling," is one such tool. Two models, the critical power (CP) model and the Banister impulse-response (IR) model, offer complementary information. The CP model describes the relationship between work rates and the durations for which an individual can sustain them during constant-work-rate or intermittent exercise. The IR model describes the dynamics by which an individual's performance capacity changes over time as a function of training. Both models elegantly abstract the underlying physiology, and both can accurately fit performance data, such that educating exercise practitioners in the science of performance modeling offers both pedagogical and practical benefits. In addition, performance modeling offers an avenue for introducing mathematical modeling skills to exercise physiology researchers. A principal limitation to the adoption of performance modeling is a lack of education. The goal of this report is therefore to encourage educators of exercise physiology practitioners and researchers to incorporate the science of performance modeling in their curricula and to serve as a resource to support this effort. The resources include a comprehensive review of the concepts associated with the development and use of the models, software to enable hands-on computer exercises, and strategies for teaching the models to different audiences.

Changes in natural killer cell subpopulations over a winter training season in elite swimmers

Immune changes and increased susceptibility to infection are often reported in elite athletes. Infectious episodes can often impair training and performance with consequences for health and sporting success. This study monitored the occurrence of episodes of upper respiratory symptoms (URS) and the variation in circulating NK cells, CD56(bright) and CD56(dim) NK cells subpopulations, over a winter swimming season. Nineteen national elite swimmers and 11 non-athlete controls participated in this study. URS episodes were monitored using daily log books. Blood samples were taken at rest at four time points during the season: before the start of the season (t1--middle September), after 7 weeks of an initial period of gradually increasing training load (t2--early November), after 6 weeks of an intense training cycle (t3--late February) and 48 h after the main competition (t4--early April) and from the controls at three similar time points (t1--early November; t2--late February; t3--early April). In the swimmers, the occurrence of URS clustered around the periods of elevated training load (67 %). No URS were reported at equivalent time points in the non-athletes. Athletes showed a decrease in the percentage (t2 = 21 %; t3 = 27 %; t4 = 17 %) and absolute counts of circulating NK cells (t2 = 35 %; t3 = 22 %; t4 = 22 %), coinciding with the periods of increased training load, never recovering to the initial values observed at the start of the season. The reduction in the CD56(dim) and an increase in the CD56(bright) NK cell subpopulations were significant at t2 and t3 (p < 0.05). Concomitant with the fall in values of NK cells, in athletes that shown more than three URS episodes, a moderate correlation (r = 0.493; p = 0.036) was found between CD56(bright)/CD56(dim) ratio and the number of URS episodes after the more demanding training phase (t3). At t3, a lower value of CD56 cell counts was found in the group who reported three or more URS episodes (t = 2.239; p = 0.032). A progressive significant decrease in the expression of CD119, the receptor for IFN-γ, on the CD56(dim) cells was found over the season and an elevation in Granzyme B expression was coincident with the more demanding training phases. Periods of highly demanding training seem to have a negative impact on innate immunity mediated by NK cell subsets, which could partially explain the higher frequency of URS observed during these training phases.

A model for the training effects in swimming demonstrates a strong relationship between parasympathetic activity, performance and index of fatigue

Competitive swimming as a physical activity results in changes to the activity level of the autonomic nervous system (ANS). However, the precise relationship between ANS activity, fatigue and sports performance remains contentious. To address this problem and build a model to support a consistent relationship, data were gathered from national and regional swimmers during two 30 consecutive-week training periods. Nocturnal ANS activity was measured weekly and quantified through wavelet transform analysis of the recorded heart rate variability. Performance was then measured through a subsequent morning 400 meters freestyle time-trial. A model was proposed where indices of fatigue were computed using Banister’s two antagonistic component model of fatigue and adaptation applied to both the ANS activity and the performance. This demonstrated that a logarithmic relationship existed between performance and ANS activity for each subject. There was a high degree of model fit between the measured and calculated performance (R² = 0.84±0.14,p<0.01) and the measured and calculated High Frequency (HF) power of the ANS activity (R² = 0.79±0.07, p<0.01). During the taper periods, improvements in measured performance and measured HF were strongly related. In the model, variations in performance were related to significant reductions in the level of ‘Negative Influences’ rather than increases in ‘Positive Influences’. Furthermore, the delay needed to return to the initial performance level was highly correlated to the delay required to return to the initial HF power level (p<0.01). The delay required to reach peak performance was highly correlated to the delay required to reach the maximal level of HF power (p = 0.02). Building the ANS/performance identity of a subject, including the time to peak HF, may help predict the maximal performance that could be obtained at a given time.

Influence of altitude training modality on performance and total haemoglobin mass in elite swimmers

We compared changes in performance and total haemoglobin mass (tHb) of elite swimmers in the weeks following either Classic or Live High:Train Low (LHTL) altitude training. Twenty-six elite swimmers (15 male, 11 female, 21.4 ± 2.7 years; mean ± SD) were divided into two groups for 3 weeks of either Classic or LHTL altitude training. Swimming performances over 100 or 200 m were assessed before altitude, then 1, 7, 14 and 28 days after returning to sea-level. Total haemoglobin mass was measured twice before altitude, then 1 and 14 days after return to sea-level. Changes in swimming performance in the first week after Classic and LHTL were compared against those of Race Control (n = 11), a group of elite swimmers who did not complete altitude training. In addition, a season-long comparison of swimming performance between altitude and non-altitude groups was undertaken to compare the progression of performances over the course of a competitive season. Regardless of altitude training modality, swimming performances were substantially slower 1 day (Classic 1.4 ± 1.3% and LHTL 1.6 ± 1.6%; mean ± 90% confidence limits) and 7 days (0.9 ± 1.0% and 1.9 ± 1.1%) after altitude compared to Race Control. In both groups, performances 14 and 28 days after altitude were not different from pre-altitude. The season-long comparison indicated that no clear advantage was obtained by swimmers who completed altitude training. Both Classic and LHTL elicited ~4% increases in tHb. Although altitude training induced erythropoeisis, this physiological adaptation did not transfer directly into improved competitive performance in elite swimmers.

The development and application of an injury prediction model for noncontact, soft-tissue injuries in elite collision sport athletes

Limited information exists on the training dose-response relationship in elite collision sport athletes. In addition, no study has developed an injury prediction model for collision sport athletes. The purpose of this study was to develop an injury prediction model for noncontact, soft-tissue injuries in elite collision sport athletes. Ninety-one professional rugby league players participated in this 4-year prospective study. This study was conducted in 2 phases. Firstly, training load and injury data were prospectively recorded over 2 competitive seasons in elite collision sport athletes. Training load and injury data were modeled using a logistic regression model with a binomial distribution (injury vs. no injury) and logit link function. Secondly, training load and injury data were prospectively recorded over a further 2 competitive seasons in the same cohort of elite collision sport athletes. An injury prediction model based on planned and actual training loads was developed and implemented to determine if noncontact, soft-tissue injuries could be predicted and therefore prevented in elite collision sport athletes. Players were 50-80% likely to sustain a preseason injury within the training load range of 3,000-5,000 units. These training load 'thresholds' were considerably reduced (1,700-3,000 units) in the late-competition phase of the season. A total of 159 noncontact, soft-tissue injuries were sustained over the latter 2 seasons. The percentage of true positive predictions was 62.3% (n = 121), whereas the total number of false positive and false negative predictions was 20 and 18, respectively. Players that exceeded the training load threshold were 70 times more likely to test positive for noncontact, soft-tissue injury, whereas players that did not exceed the training load threshold were injured 1/10 as often. These findings provide information on the training dose-response relationship and a scientific method of monitoring and regulating training load in elite collision sport athletes.