The effects of measurement error and testing frequency on the fitness-fatigue model applied to resistance training: a simulation approach.

This study investigated the effects of measurement error and testing frequency on prediction accuracy of the standard fitness-fatigue model. A simulation-based approach was used to systematically assess measurement error and frequency inputs commonly used when monitoring the training of athletes. Two hypothetical athletes (intermediate and advanced) were developed and realistic training loads and daily ‘true’ power values were generated using the fitness-fatigue model across 16 weeks. Simulations were then completed by adding Gaussian measurement errors to true values with mean 0 and set standard deviations to recreate more and less reliable measurement practices used in real-world settings. Errors were added to the model training phase (weeks 1–8) and sampling of data was used to recreate different testing frequencies (every day to once per week) when obtaining parameter estimates. In total, 210 sets of simulations (N = 104 iterations) were completed using an iterative hill-climbing optimisation technique. Parameter estimates were then combined with training loads in the model testing phase (weeks 9–16) to quantify prediction errors. Regression analyses identified positive associations between prediction errors and the linear combination of measurement error and testing frequency (R2adj =0.87–0.94). Significant model improvements (P < 0.001) were obtained across all scenarios by including an interaction term demonstrating greater deleterious effects of measurement error at low testing frequencies.The findings of this simulation study represent a lower-bound case and indicate that in real-world settings, where a fitness-fatigue model is used to predict training response, measurement practices that generate coefficients of variation greater than ≈4% will not provide satisfactory results

A systematic risk assessment and meta-analysis on the use of oral β-alanine supplementation.

β-Alanine supplementation is one of the world's most commonly used sports supplements, and its use as a nutritional strategy in other populations is ever-increasing, due to evidence of pleiotropic ergogenic and therapeutic benefits. Despite its widespread use, there is only limited understanding of potential adverse effects. To address this, a systematic risk assessment and meta-analysis was undertaken. Four databases were searched using keywords and Medical Subject Headings. All human and animal studies that investigated an isolated, oral, β-alanine supplementation strategy were included. Data were extracted according to 5 main outcomes, including 1) side effects reported during longitudinal trials, 2) side effects reported during acute trials, 3) effect of supplementation on circulating health-related biomarkers, 4) effect of supplementation on skeletal muscle taurine and histidine concentration, and 5) outcomes from animal trials. Quality of evidence for outcomes was ascertained using the Grading of Recommendations Assessment Development and Evaluation (GRADE) framework, and all quantitative data were meta-analyzed using multilevel models grounded in Bayesian principles. In total, 101 human and 50 animal studies were included. Paraesthesia was the only reported side effect and had an estimated OR of 8.9 [95% credible interval (CrI): 2.2, 32.6] with supplementation relative to placebo. Participants in active treatment groups experienced similar dropout rates to those receiving the placebo treatment. β-Alanine supplementation caused a small increase in circulating alanine aminotransferase concentration (effect size, ES: 0.274, CrI: 0.04, 0.527), although mean data remained well within clinical reference ranges. Meta-analysis of human data showed no main effect of β-alanine supplementation on skeletal muscle taurine (ES: 0.156; 95% CrI: −0.38, 0.72) or histidine (ES: −0.15; 95% CrI: −0.64, 0.33) concentration. A main effect of β-alanine supplementation on taurine concentration was reported for murine models, but only when the daily dose was ≥3% β-alanine in drinking water. The results of this review indicate that β-alanine supplementation within the doses used in the available research designs, does not adversely affect those consuming it

The utility of mathematical fitness-fatigue models for assisting with the planning of physical training for sport: from in silico experiments employing synthetic data, lower-bound operational conditions and model estimation, to the development of software resources for future research.

The greatest potential application of mathematical models in sport science is to predict future performance of individual athletes in response to training, with sufficient accuracy to assist with planning of training programs and short tapering periods. The most widely known and investigated set of mathematical models include the fitness-fatigue models (FFMs). However, despite over 45 years of FFM study, problems remaining within the research base and gaps in existing knowledge have limited interpretation of prior research, and prevented progression toward practical implementation. These limitations include: 1) inadequate study of model validity in prior experimental study as a result of unsatisfactory model testing; 2) a disorganised literature body without a connective narrative linking previous research and providing consistent recommendations for the requirements and direction of future study; 3) limitations in the structure of basic FFMs matched by little awareness of extensions that have been proposed to address them; 4) no consideration of experimental factors and methods that may interact with model accuracy (e.g. measurement error, testing frequency, parameter estimation); 5) limited practical resources elucidating key concepts, and no tools available to facilitate processes required to fit and evaluate more promising FFMs. Subsequently the aims of this PhD were to: 1) systematise the FFM literature body, providing sufficient detail and structure to the point where there exists a consistent narrative threading the historical literature, pertinent concepts, and contemporary work to address limitations in basic FFM structure; 2) conduct original study of key experimental factors (measurement error and testing-frequency) and methods of model estimation that may affect model accuracy or utility; 3) identify and raise awareness of alternative FFMs - beyond the standard model and advanced methods - that reflect more promising avenues for future research; 4) develop flexible code tools that facilitate future study and address the current gap in available resources. The first aim was achieved by comprehensive review, balancing mathematical rigor with clarity in the communication of concepts and methods, ranging from the standard model to the most advanced FFMs. The second aim was achieved by two novel studies that developed in silico experimental designs, and which represented prerequisite work prior to any further study of model validity. Study 1 quantified the effects of key experimental factors (measurement noise and testing-frequency) on lower-bound model accuracy in the standard FFM, demonstrating that testing practices comprising high error will provide unsatisfactory results and that greater deleterious effects of error exist at lower testing frequencies. Study 2 focused on suitability of a traditional quasi-Newton algorithm for fitting FFMs, by assessing starting point sensitivity and existence/implications of local extrema. Study 2 demonstrated that the model-fitting problem is more challenging than researchers have previously acknowledged and that the presence of many local extrema even in the standard FFM may now necessitate global optimisation approaches. The third and fourth aims were achieved by development of extensive code resources in the R programming language for fitting and evaluating FFMs, facilitating future study under the most promising models/methods. The original research and systematised literature body provides clearer direction for future FFM research, guidance with respect to key experimental factors/methods (e.g. measurement practices, estimation, model testing), and reflects the most up-to-date resource available for researchers interested in FFMs. The developed code tools meet the need for flexible practical resources for researchers, and the novel experimental designs developed for the two studies provide a unique and cost-effective approach to study FFMs and potentially other phenomena in sport science