Health conditions and the risk of home injury in French adults: results from a prospective study of the MAVIE cohort.

BACKGROUND: Home injury (HI) is a significant cause of mortality and morbidity in adults of all ages. Health conditions significantly impact HI among old adults, but little is known for other adults. STUDY DESIGN: Prospective cohort study. OBJECTIVE: We assessed the associations between health-related factors and HI's risk in a French study, the MAVIE (Mutualistes pour la recherche contre les Accidents de la VIE courante) cohort. METHODS: Poisson mixed models were fitted using health-related data information (diseases, treatments and disabilities) at baseline and the number of injuries prospectively recorded during the follow-up, adjusting for significant sociodemographics and exposure to a range of home activities. Attributable fractions were estimated based on risk ratio (RR) estimations measured in the fully adjusted models. RESULTS: A total of 6146 dwelling adults aged 15 or older were followed up for 5.1 years on average. Vertigo or dizziness (RR=2.36, 95% CI 1.06 to 5.01) and sciatica or back pain (RR=1.49, 95% CI 1.08 to 2.05) were independently associated with an increased risk of HI. These two groups of conditions showed the most significant associations among people aged 15-49, whereas musculoskeletal diseases other than rachialgias and arthropathies were the most significant health-related risk factor in people aged 50 and older. Sciatica or back pain represented the highest burden of HIs in overall adults (8%) and among people aged 15-49 (12%). CONCLUSION: Our results suggest that adults with musculoskeletal disorders and vertigo or dizziness symptoms have a higher risk of HI, regardless of age.

Lasso regularization for left-censored Gaussian outcome and high-dimensional predictors.

BACKGROUND: Biological assays for the quantification of markers may suffer from a lack of sensitivity and thus from an analytical detection limit. This is the case of human immunodeficiency virus (HIV) viral load. Below this threshold the exact value is unknown and values are consequently left-censored. Statistical methods have been proposed to deal with left-censoring but few are adapted in the context of high-dimensional data. METHODS: We propose to reverse the Buckley-James least squares algorithm to handle left-censored data enhanced with a Lasso regularization to accommodate high-dimensional predictors. We present a Lasso-regularized Buckley-James least squares method with both non-parametric imputation using Kaplan-Meier and parametric imputation based on the Gaussian distribution, which is typically assumed for HIV viral load data after logarithmic transformation. Cross-validation for parameter-tuning is based on an appropriate loss function that takes into account the different contributions of censored and uncensored observations. We specify how these techniques can be easily implemented using available R packages. The Lasso-regularized Buckley-James least square method was compared to simple imputation strategies to predict the response to antiretroviral therapy measured by HIV viral load according to the HIV genotypic mutations. We used a dataset composed of several clinical trials and cohorts from the Forum for Collaborative HIV Research (HIV Med. 2008;7:27-40). The proposed methods were also assessed on simulated data mimicking the observed data. RESULTS: Approaches accounting for left-censoring outperformed simple imputation methods in a high-dimensional setting. The Gaussian Buckley-James method with cross-validation based on the appropriate loss function showed the lowest prediction error on simulated data and, using real data, the most valid results according to the current literature on HIV mutations. CONCLUSIONS: The proposed approach deals with high-dimensional predictors and left-censored outcomes and has shown its interest for predicting HIV viral load according to HIV mutations.

Prescription medicine use by pedestrians and the risk of injurious road traffic crashes: A case-crossover study.

BACKGROUND: While some medicinal drugs have been found to affect driving ability, no study has investigated whether a relationship exists between these medicines and crashes involving pedestrians. The aim of this study was to explore the association between the use of medicinal drugs and the risk of being involved in a road traffic crash as a pedestrian. METHODS AND FINDINGS: Data from 3 French nationwide databases were matched. We used the case-crossover design to control for time-invariant factors by using each case as its own control. To perform multivariable analysis and limit false-positive results, we implemented a bootstrap version of Lasso. To avoid the effect of unmeasured time-varying factors, we varied the length of the washout period from 30 to 119 days before the crash. The matching procedure led to the inclusion of 16,458 pedestrians involved in an injurious road traffic crash from 1 July 2005 to 31 December 2011. We found 48 medicine classes with a positive association with the risk of crash, with median odds ratios ranging from 1.12 to 2.98. Among these, benzodiazepines and benzodiazepine-related drugs, antihistamines, and anti-inflammatory and antirheumatic drugs were among the 10 medicines most consumed by the 16,458 pedestrians. Study limitations included slight overrepresentation of pedestrians injured in more severe crashes, lack of information about self-medication and the use of over-the-counter drugs, and lack of data on amount of walking. CONCLUSIONS: Therapeutic classes already identified as impacting the ability to drive, such as benzodiazepines and antihistamines, are also associated with an increased risk of pedestrians being involved in a road traffic crash. This study on pedestrians highlights the necessity of improving awareness of the effect of these medicines on this category of road user.

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.

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

PURPOSE: 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. METHODS: We report the use of a shrinkage technique, the Lasso, in the exploratory analysis of the data on prescription drugs and road traffic crashes, resulting from the case-crossover matched-pair interval approach described by Orriols and colleagues (PLoS Med 2010; 7:e1000366). To prevent false-positive results, we consider a bootstrap-enhanced version of the Lasso. To highlight the most stable results, we extensively examine sensitivity to the choice of referent window. RESULTS: Antiepileptics, benzodiazepine hypnotics, anxiolytics, antidepressants, antithrombotic agents, mineral supplements, drugs used in diabetes, antiparkinsonian treatment, and several cardiovascular drugs showed suspected associations with road traffic accident involvement or accident responsibility. CONCLUSION: These results, in relation to other findings in the literature, provide new insight and may generate new hypotheses on the association between prescription drugs use and impaired driving ability.

Identifying Optimal Overload and Taper in Elite Swimmers over Time.

The aim of this exploratory study was to identify the most influential training designs during the final six weeks of training (F6T) before a major swimming event, taking into account athletes' evolution over several seasons. Fifteen female and 17 male elite swimmers were followed for one to nine F6T periods. The F6T was divided into two sub-periods of a three-week overload period (OP) and a three-week taper period (TP). The final time trial performance was recorded for each swimmer in his or her specialty at the end of both OP and TP. The change in performances (ΔP) between OP and TP was recorded. Training variables were derived from the weekly training volume at several intensity levels as a percentage of the individual maximal volume measured at each intensity level, and the individual total training load (TTL) was considered to be the mean of the loads at these seven intensity levels. Also, training patterns were identified from TTL in the three weeks of both OP and TP by cluster analysis. Mixed-model was used to analyse the longitudinal data. The training pattern during OP that was associated with the greatest improvement in performance was a training load peak followed by a linear slow decay (84 ± 17, 81 ± 22, and 80 ± 19 % of the maximal training load measured throughout the F6T period for each subject, Mean ± SD) (p < 0.05). During TP, a training load peak in the 1(st) week associated with a slow decay design (57 ± 26, 45 ± 24 and 38 ± 14%) led to higher ΔP (p < 0.05). From the 1(st) to 3(rd) season, the best results were characterized by maintenance of a medium training load from OP to TP. Progressively from the 4(th) season, high training loads during OP followed by a sharp decrease during TP were associated with higher ΔP. Key PointsDuring the overload training period, a medium training load peak in the first week followed by an exponential slow decay training load design was linked to highest performance improvement.During the taper period, a training load peak in the first week associated with a slow decay design led to higher performances.Over the course of the swimmers' athletic careers, better performances were obtained with an increase in training load during the overload period followed by a sharper decrease in the taper period.Training loads schedules during the final six weeks of training before a major swimming event and changes over time could be prescribed on the basis of the model results.

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.

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.

A web-based prospective cohort study of home, leisure, school and sports injuries in France: a descriptive analysis.

BACKGROUND: Home and leisure injuries (HLIs) are a major public health problem. Cohort studies among general population are needed for targeted preventive actions but remain scarce. We quantify and qualify the HLIs collected prospectively in the MAVIE (Mutualists against Home and Leisure Injuries) observatory, a web-based cohort among volunteers of the French general population. METHODS: Participants reported HLIs from November 2014 to December 2019. We calculated crude and standardized incidence rates (SIRs) on the entire cohort, for each of the selected socio-demographic variables and each of the injury circumstances (place and activity), mechanisms, and injury severity levels. We also described other HLIs characteristics and consequences. RESULTS: Out of the 29,931 household members enrolled in the cohort, 12,419 participants completed the questionnaires. Among them, 8640 participants provided follow-up data, leading to a follow-up of 6302 persons for 5.2 years and 2483 HLIs were reported. We obtained a SIR of 85.0 HLIs per 1000 persons-years. Most reported injuries did not require emergency department attendance or hospitalization (64%). SIRs were higher in children (< 15 years of age) (109.1 HLIs per 1000 persons-years; 95% CI, 78.2-140.1) and adults aged 70 years and older (123.7 HLIs per 1000 persons-years; 95% CI, 79.2-168.3). Struck or hit by fall was the most frequent injury mechanism (52%) and also among the most severe injuries (73% of Struck or hit by fall HLIs ending with hospitalization). Sport (without contact with nature), and leisure and play activities were the injury circumstances with higher SIRs, 15.2 HLIs per 1000 persons-years (95% CI, 14.6-15.8) and 11.2 HLIs per 1000 persons-years (95% CI, 10.7-11.6), respectively. Outdoor sport activity (in contact with nature) was the circumstance with the highest proportion of hospitalizations (18% of outdoor sports HLIs ending with hospitalization). CONCLUSION: The incidences, causes, and consequences of HLI differ by age group and are mainly related to the performance of certain activities. Although the participants in the MAVIE cohort were not representative of the French population. Our study identified potential sub-populations and specific types of HLIs that should be targeted by future studies concerning risk factors and prevention programs.

Cohort profile: MAVIE a web-based prospective cohort study of home, leisure, and sports injuries in France.

MAVIE is a web-based prospective cohort study of Home, Leisure, and Sports Injuries with a longitudinal follow-up of French general population volunteers. MAVIE participants are voluntary members of French households, including overseas territories. Participation in the cohort involves answering individual and household questionnaires and relevant exposures and prospectively reporting injury events during the follow-up. Recruitment and data collection have been in progress since 2014. The number of participants as of the end of the year 2019 was 12,419 from 9,483 households. A total of 8,640 participants provided data during follow-up. Respondents to follow-up were composed of 763 children aged 0-14, 655 teenagers and young adults aged 15-29, 6,845 adults, and 377 people aged 75 or more. At the end of the year 2019, 1,698 participants had reported 2,483 injury events. Children, people aged 50 and more, people with poor self-perceived physical and mental health, people who engage in sports activities, and people with a history of injury during the year before recruitment were more likely to report new injuries. An interactive mobile/web application (MAVIE-Lab) was developed to help volunteers decide on personalized measures to prevent their risks of HLIs. The available data provides an opportunity to analyse multiple exposures at both the individual and household levels that may be associated with an increased risk of trauma. The ongoing analysis includes HLI incidence estimates, the determination of health-related risk factors, a specific study on the risk of home injury, another on sports injuries, and an analysis of the role of cognitive skills and mind wandering. Volunteers form a community that constitutes a population laboratory for preventative initiatives.

Respiratory mycobiome and suggestion of inter-kingdom network during acute pulmonary exacerbation in cystic fibrosis.

Lung infections play a critical role in cystic fibrosis (CF) pathogenesis. CF respiratory tract is now considered to be a polymicrobial niche and advances in high-throughput sequencing allowed to analyze its microbiota and mycobiota. However, no NGS studies until now have characterized both communities during CF pulmonary exacerbation (CFPE). Thirty-three sputa isolated from patients with and without CFPE were used for metagenomic high-throughput sequencing targeting 16S and ITS2 regions of bacterial and fungal rRNA. We built inter-kingdom network and adapted Phy-Lasso method to highlight correlations in compositional data. The decline in respiratory function was associated with a decrease in bacterial diversity. The inter-kingdom network revealed three main clusters organized around Aspergillus, Candida, and Scedosporium genera. Using Phy-Lasso method, we identified Aspergillus and Malassezia as relevantly associated with CFPE, and Scedosporium plus Pseudomonas with a decline in lung function. We corroborated in vitro the cross-domain interactions between Aspergillus and Streptococcus predicted by the correlation network. For the first time, we included documented mycobiome data into a version of the ecological Climax/Attack model that opens new lines of thoughts about the physiopathology of CF lung disease and future perspectives to improve its therapeutic management.

Penalized logistic regression with low prevalence exposures beyond high dimensional settings.

Estimating and selecting risk factors with extremely low prevalences of exposure for a binary outcome is a challenge because classical standard techniques, markedly logistic regression, often fail to provide meaningful results in such settings. While penalized regression methods are widely used in high-dimensional settings, we were able to show their usefulness in low-dimensional settings as well. Specifically, we demonstrate that Firth correction, ridge, the lasso and boosting all improve the estimation for low-prevalence risk factors. While the methods themselves are well-established, comparison studies are needed to assess their potential benefits in this context. This is done here using the dataset of a large unmatched case-control study from France (2005-2008) about the relationship between prescription medicines and road traffic accidents and an accompanying simulation study. Results show that the estimation of risk factors with prevalences below 0.1% can be drastically improved by using Firth correction and boosting in particular, especially for ultra-low prevalences. When a moderate number of low prevalence exposures is available, we recommend the use of penalized techniques.

Health administrative data enrichment using cohort information: Comparative evaluation of methods by simulation and application to real data.

BACKGROUND: Studies using health administrative databases (HAD) may lead to biased results since information on potential confounders is often missing. Methods that integrate confounder data from cohort studies, such as multivariate imputation by chained equations (MICE) and two-stage calibration (TSC), aim to reduce confounding bias. We provide new insights into their behavior under different deviations from representativeness of the cohort. METHODS: We conducted an extensive simulation study to assess the performance of these two methods under different deviations from representativeness of the cohort. We illustrate these approaches by studying the association between benzodiazepine use and fractures in the elderly using the general sample of French health insurance beneficiaries (EGB) as main database and two French cohorts (Paquid and 3C) as validation samples. RESULTS: When the cohort was representative from the same population as the HAD, the two methods are unbiased. TSC was more efficient and faster but its variance could be slightly underestimated when confounders were non-Gaussian. If the cohort was a subsample of the HAD (internal validation) with the probability of the subject being included in the cohort depending on both exposure and outcome, MICE was unbiased while TSC was biased. The two methods appeared biased when the inclusion probability in the cohort depended on unobserved confounders. CONCLUSION: When choosing the most appropriate method, epidemiologists should consider the origin of the cohort (internal or external validation) as well as the (anticipated or observed) selection biases of the validation sample.

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.

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 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

Modeling the association between HR variability and illness in elite swimmers.

PURPOSE: To determine whether HR variability (HRV), an indirect measure of autonomic control, is associated with upper respiratory tract and pulmonary infections, muscular affections, and all-type pathologies in elite swimmers. METHODS: For this study, 7 elite international and 11 national swimmers were observed weekly for 2 yr. The indexes of cardiac autonomic regulation in supine and orthostatic position were assessed as explanatory variables by time domain (SD1, SD2) and spectral analyses (high frequency [HF] = 0.15-0.40 Hz, low frequency [LF] = 0.04-0.15 Hz, and HF/LF ratio) of HRV. Logistic mixed models described the relationship between the explanatory variables and the risk of upper respiratory tract and pulmonary infections, muscular affections, and all-type pathologies. RESULTS: The risk of all-type pathologies was higher for national swimmers and in winter (P < 0.01). An increase in the parasympathetic indexes (HF, SD1) in the supine position assessed 1 wk earlier was linked to a higher risk of upper respiratory tract and pulmonary infections (P < 0.05) and to a higher risk of muscular affections (increase in HF, P < 0.05). Multivariate analyses showed (1) a higher all-type pathologies risk in winter and for an increase in the total power of HRV associated with a decline SD1 in supine position, (2) a higher all-type pathologies risk in winter associated with a decline in HF assessed 1 wk earlier in orthostatic position, and (3) a higher risk of muscular affections in winter associated with a decrease SD1 and an increase LF in orthostatic position. CONCLUSIONS: Swimmers' health maintenance requires particular attention when autonomic balance shows a sudden increase in parasympathetic indices in the supine position assessed 1 wk earlier evolving toward sympathetic predominance in supine and orthostatic positions.

Assessing the limitations of the Banister model in monitoring training.

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: t(a) = 38 (17, 59), t(f) = 19 (6, 32), t(n) = 19 (7, 35), t(g) = 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.

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 w(LIT), high-intensity w(HIT), and strength training w(ST)), 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 w(HIT) and w(LIT), 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.