Analysing the predictive capacity and dose-response of wellness in load monitoring

Stress Drives Soccer Athletes' Wellness and Movement: Using Convergent Cross-Mapping to Identify Causal Relationships in a Dynamic Environment

PURPOSE

Prediction of athlete wellness is difficult-or, many sports-medicine practitioners and scientists would argue, impossible. Instead, one settles for correlational relationships of variables gathered at fixed moments in time. The issue may be an inherent mismatch between usual methods of data collection and analysis and the complex nature of the variables governing athlete wellness. Variables such as external load, stress, muscle soreness, and sleep quality may affect each other and wellness in a dynamic, nonlinear, way over time. In such an environment, traditional data-collection methods and statistics will fail to capture causal effects. If we are to move this area of sport science forward, a different approach is required.

METHODS

We analyzed data from 2 different soccer teams that showed no significance between player load and wellness or among individual measures of wellness. Our analysis used methods of attractor reconstruction to examine possible causal relationships between GPS/accelerometer-measured external training load and wellness variables.

RESULTS

Our analysis showed that player self-rated stress, a component of wellness, seems a fundamental driving variable. The influence of stress is so great that stress can predict other components of athlete wellness, and, in turn, self-rated stress can be predicted by observing a player's load data.

CONCLUSION

We demonstrate the ability of nonlinear methods to identify interactions between and among variables to predict future athlete stress. These relationships are indicative of the causal relationships playing out in athlete wellness over the course of a soccer season.

Are non-starters accumulating enough load compared with starters? Examining load, wellness, and training/match ratios of a European professional soccer team

BACKGROUND

The aims of the study were to: (i) compare accumulated load and wellness between starters and non-starters of a European professional soccer team; (ii) analyze the relationships between wellness and load measures and; (iii) compare training/match ratio (TMr) of external and internal load between starters and non-starters.

METHODS

Ten players were considered starters while seven were classified as non-starters over a 16-week period in which six training sessions and match day (MD) were considered in each weekly micro-cycle. The following measures were used: wellness (fatigue, quality of sleep, muscle soreness, stress, and mood); load (rated of perceived exertion (RPE), session-RPE (s-RPE), high-speed running (HSR), sprinting, accelerations (ACC) and decelerations (DEC)). Accumulated wellness/load were calculated by summing all training and match sessions, while TMr was calculated by dividing accumulated training load by match data for all load measures and each player. Mann-Whitney U test was used for wellness variables, while independent T-test was used for the remaining variables to compare groups. Moreover, relationships among variables were explored using the Spearman's Rho correlation coefficient.

RESULTS

The main results showed that non-starters presented higher significant values for fatigue (p < 0.019; g = 0.24) and lower significant values for duration (p < 0.006; ES = 1.81) and s-RPE (p < 0.001; ES = 2.69) when compared to starters. Moreover, positive and very large correlation was found between quality of sleep and RPE, while negative and very large correlation were found between stress and deceleration, and mood and deceleration (all, p < 0.05). Finally, non-starters presented higher values in all TMr than starters, namely, RPE (p = 0.001; g = 1.96), s-RPE (p = 0.002; g = 1.77), HSR (p = 0.001; g = 2.02), sprinting (p = 0.002; g = 4.23), accelerations (p = 0.001; g = 2.72), decelerations (p < 0.001; g = 3.44), and duration (p = 0.003; g = 2.27).

CONCLUSIONS

In conclusion, this study showed that non-starters produced higher TMr in all examined variables despite the lower match and training durations when compared with starters, suggesting that physical load was adjusted appropriately. Additionally, higher RPE was associated with improved sleep while higher number of decelerations were associated with decreased wellness, namely, stress and mood for non-starters.

Evaluation of Athlete Monitoring Tools across 10 Weeks of Elite Youth Basketball Training: An Explorative Study

The growth of sport science technology is enabling more sporting teams to implement athlete monitoring practices related to performance testing and load monitoring. Despite the increased emphasis on youth athlete development, the lack of longitudinal athlete monitoring literature in youth athletes is concerning, especially for indoor sports such as basketball. The aim of this study was to evaluate the effectiveness of six different athlete monitoring methods over 10 weeks of youth basketball training. Fourteen state-level youth basketball players (5 males and 9 females; 15.1 ± 1.0 years) completed this study during their pre-competition phase prior to their national basketball tournament. Daily wellness and activity surveys were completed using the OwnUrGoal mobile application, along with heart rate (HR) and inertial measurement unit (IMU) recordings at each state training session, and weekly performance testing (3x countermovement jumps [CMJs], and 3x isometric mid-thigh pulls [IMTPs]). All of the athlete monitoring methods demonstrated the coaching staff's training intent to maintain performance and avoid spikes in workload. Monitoring IMU data combined with PlayerLoad™ data analysis demonstrated more effectiveness for monitoring accumulated load (AL) compared to HR analysis. All six methods of athlete monitoring detected similar trends for all sessions despite small-trivial correlations between each method (Pearson's correlation: -0.24 < r < 0.28). The use of subjective monitoring questionnaire applications, such as OwnUrGoal, is recommended for youth sporting clubs, given its practicability and low-cost. Regular athlete education from coaches and support staff regarding the use of these questionnaires is required to gain the best data.

Monitorización y periodización del rendimiento desde la fisioterapia deportiva ¿Hacia dónde vamos?

RESUMEN Introducción El concepto de monitorización y periodización es un visión que se ha desarrollado en la última década, donde se busca que se pueda tener un control de la carga interna y externa en los deportistas de distintas disciplinas deportivas producidas por el proceso de interacción con variables de prescripción de ejercicio que causan modificaciones a nivel fisiológico, bioquímico, biomecanico, muscular, neuromuscular  ocasionando la tensión en los subsistemas del movimiento corporal humano generando adaptaciones especificas ante una carga determinada la cual puede ser medida mediante el uso de tecnología especifica o test indirectos Metodología Revisión de la literatura con la combinación de palabras clave como Monitoring, Performance, sports training load, Periodization en bases de datos como Pubmed, Ebsco, Medline, Scopus, Science Direct Resultados se pudo identificar 65 artículos que referencian la existencia  de herramientas tecnológicas para realizar un proceso de monitorización y periodización desde la rehabilitación , prevención, control de carga, recuperación y readaptación deportiva que permiten la generación de datos estadísticos y crear perfiles desde cada área de actuación del fisioterapeuta deportivo. Conclusión la fisioterapia es una profesión encargada de muchos procesos deportivos que deben ser monitorizados y generen datos que permitan estandarizar procesos, crear perfiles de seguimiento específicos para facilitar la toma de decisiones desde el equipo biomédico e investigaciones en el deporte de alto rendimiento fusionando la practica con la evidencia científica.

Training Load Monitoring Considerations for Female Gaelic Team Sports: From Theory to Practice

Athlete monitoring enables sports science practitioners to collect information to determine how athletes respond to training loads (TL) and the demands of competition. To date, recommendations for females are often adapted from their male counterparts. There is currently limited information available on TL monitoring in female Gaelic team sports in Ireland. The collection and analysis of female athlete monitoring data can provide valuable information to support the development of female team sports. Athletic monitoring can also support practitioners to help minimize risk of excessive TL and optimize potential athletic performance. The aims of this narrative review are to provide: (i) an overview of TL athlete monitoring in female team sports, (ii) a discussion of the potential metrics and tools used to monitor external TL and internal TL, (iii) the advantages and disadvantages of TL modalities for use in Gaelic team sports, and (iv) practical considerations on how to monitor TL to aid in the determination of meaningful change with female Gaelic team sports athletes.