Marta Avalos

Modeling the Training-Performance Relationship Using a Mixed Model in Elite Swimmers

PURPOSE The aim of this study was to model the relationship between training and performance in 13 competitive swimmers, over three seasons, and to identify individual and group responses to training. METHODS A linear mixed model was used as an alternative to the Banister model. Training effect on performance was studied over three training periods: short-term, the average of training load accomplished during the 2 wk preceding each performance of the studied period; mid-term, the average of training load accomplished during weeks 3, 4, and 5 before each performance; and long-term, weeks 6, 7, and 8. RESULTS Cluster analysis identified four groups of subjects according to their reactions to training. The first group corresponded to the subjects who responded well to the long-term training period, the second group to the long- and mid-term periods, the third to the short- and mid-term periods, and the fourth to the combined periods. In the model, the intersubject differences and the evolution over the three seasons were statistically significant for the identified groups of swimmers. Influence of short-term training was negative on performance in the four groups, whereas mid- and long-term training had, on the average, a positive effect in three groups out of four. Between seasons 1 and 3, the effect of mid-term training declined, whereas the effect of long-term training increased. The fit between real and modeled performances was significant for all swimmers (0.15 </= r2 </= 0.65; P </= 0.01). CONCLUSION The mixed model described a significant relationship between training and performance both for individuals and for groups of swimmers. This relationship was different over the 3 yr. Personalized training schedules could be prescribed on the basis of the model results.

Assessing the limitations of the Banister model in monitoring training.

Abstract The aim of this study was to carry out a statistical analysis of the Banister model to verify how useful it is in monitoring the training programmes of elite swimmers. The accuracy, the ill-conditioning and the stability of this model were thus investigated. The training loads of nine elite swimmers, measured over one season, were related to performances with the Banister model. First, to assess accuracy, the 95% bootstrap confidence interval (95% CI) of parameter estimates and modelled performances were calculated. Second, to study ill-conditioning, the correlation matrix of parameter estimates was computed. Finally, to analyse stability, iterative computation was performed with the same data but minus one performance, chosen at random. Performances were related to training loads for all participants (R 2 = 0.79 ± 0.13, P < 0.05) and the estimation procedure seemed to be stable. Nevertheless, the range of 95% CI values of the most useful parameters for monitoring training was wide: τ a = 38 (17, 59), τ f = 19 (6, 32), tn = 19 (7, 35), tg = 43 (25, 61). Furthermore, some parameters were highly correlated, making their interpretation worthless. We suggest possible ways to deal with these problems and review alternative methods to model the training – performance relationships.

Kinematic measures and stroke rate variability in elite female 200-m swimmers in the four swimming techniques: Athens 2004 Olympic semi-finalists and French National 2004 Championship semi-finalists

Abstract The aim of this study was to assess stroke rate variability in elite female swimmers (200-m events, all four techniques) by comparing the semi-finalists at the Athens 2004 Olympic Games (n = 64) and semi-finalists at the French National 2004 Championship (n = 64). Since swimming speed (V) is the product of stroke rate (SR) and stroke length (SL), these three variables and the coefficient of variation of stroke rate (CVSR) of the first and second 100 m were determined (V1, V2; SR1, SR2; SL1, SL2; CVSR1, CVSR2) and differences between the two parts of the events were calculated (ΔV; ΔSR; ΔSL; ΔCVSR). When the results for the four 200-m events were analysed together, SR1, SR2, SL1, and SL2 were higher (α = 0.05, P < 0.001) and ΔV, ΔSR, and ΔCVSR were lower (P < 0.01) in the Olympic group than in the National group. The Olympic-standard swimmers exhibited faster backstrokes and longer freestyle strokes (P < 0.05). Both CVSR1 and CVSR2 were lower for freestyle and backstroke races in the Olympic group than in the National group (P < 0.001). Our results suggest that stroke rate variability is dependent on an interaction between the biomechanical requisites of the task (techniques) and the standard of the swimmer.

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 the residual effects and threshold saturation of training: a case study of Olympic swimmers.

The aim of this study was to model the residual effects of training on the swimming performance and to compare a model that includes threshold saturation (MM) with the Banister model (BM). Seven Olympic swimmers were studied over a period of 4 ± 2 years. For 3 training loads (low-intensity wLIT, high-intensity wHIT, and strength training wST), 3 residual training effects were determined: short-term (STE) during the taper phase (i.e., 3 weeks before the performance [weeks 0, 1, and 2]), intermediate-term (ITE) during the intensity phase (weeks 3, 4, and 5), and long-term (LTE) during the volume phase (weeks 6, 7, and 8). ITE and LTE were positive for wHIT and wLIT, respectively (p < 0.05). Low-intensity training load during taper was related to performances by a parabolic relationship (p < 0.05). Different quality measures indicated that MM compares favorably with BM. Identifying individual training thresholds may help individualize the distribution of training loads.

Regularization methods for additive models

This paper tackles the problem of model complexity in the context of additive models. Several methods have been proposed to estimate smoothing parameters, as well as to perform variable selection. However, these procedures are inefficient or computationally expensive in high dimension. To answer this problem, the lasso technique has been adapted to additive models, but its experimental performance has not been analyzed.

Variable selection on large case-crossover data: Application to a registry-based study of prescription drugs and road traffic crashes

In exploratory analyses of pharmacoepidemiological data from large populations with large number of exposures, both a conceptual and computational problem is how to screen hypotheses using probabilistic reasoning, selecting drug classes or individual drugs that most warrant further hypothesis testing.

Prescription-drug-related risk in driving: comparing conventional and lasso shrinkage logistic regressions.

Background: Large data sets with many variables provide particular challenges when constructing analytic models. Lasso-related methods provide a useful tool, although one that remains unfamiliar to most epidemiologists. Methods: We illustrate the application of lasso methods in an analysis of the impact of prescribed drugs on the risk of a road traffic crash, using a large French nationwide database (PLoS Med 2010;7:e1000366). In the original case-control study, the authors analyzed each exposure separately. We use the lasso method, which can simultaneously perform estimation and variable selection in a single model. We compare point estimates and confidence intervals using (1) a separate logistic regression model for each drug with a Bonferroni correction and (2) lasso shrinkage logistic regression analysis. Results: Shrinkage regression had little effect on (bias corrected) point estimates, but led to less conservative results, noticeably for drugs with moderate levels of exposure. Carbamates, carboxamide derivative and fatty acid derivative antiepileptics, drugs used in opioid dependence, and mineral supplements of potassium showed stronger associations. Conclusion: Lasso is a relevant method in the analysis of databases with large number of exposures and can be recommended as an alternative to conventional strategies.

Sparse conditional logistic regression for analyzing large-scale matched data from epidemiological studies: a simple algorithm.

This paper considers the problem of estimation and variable selection for large high-dimensional data (high number of predictors p and large sample size N, without excluding the possibility that N < p) resulting from an individually matched case-control study. We develop a simple algorithm for the adaptation of the Lasso and related methods to the conditional logistic regression model. Our proposal relies on the simplification of the calculations involved in the likelihood function. Then, the proposed algorithm iteratively solves reweighted Lasso problems using cyclical coordinate descent, computed along a regularization path. This method can handle large problems and deal with sparse features efficiently. We discuss benefits and drawbacks with respect to the existing available implementations. We also illustrate the interest and use of these techniques on a pharmacoepidemiological study of medication use and traffic safety.

Analysis of multiple exposures in the case‐crossover design via sparse conditional likelihood

We adapt the least absolute shrinkage and selection operator (lasso) and other sparse methods (elastic net and bootstrapped versions of lasso) to the conditional logistic regression model and provide a full R implementation. These variable selection procedures are applied in the context of case-crossover studies. We study the performances of conventional and sparse modelling strategies by simulations, then empirically compare results of these methods on the analysis of the association between exposure to medicinal drugs and the risk of causing an injurious road traffic crash in elderly drivers. Controlling the false discovery rate of lasso-type methods is still problematic, but this problem is also present in conventional methods. The sparse methods have the ability to provide a global analysis of dependencies, and we conclude that some of the variants compared here are valuable tools in the context of case-crossover studies with a large number of variables.