BACKGROUND: The relationship between acute:chronic workload ratios (ACWR) and the incidence of injury, as well as the relationship between subjective wellness scores and training load, is unclear in hockey players. OBJECTIVES: This study investigated these relationships to determine if the ACWR is a useful workload management tool for field hockey players. In addition, the study investigated the association between specific subjective wellness scores (fatigue, sleep quality, general muscle soreness, mood and stress level) and the acute:chronic workload ratio and training load. The study also assessed the association between individual players' training loads with the self-reported wellness scores. METHODS: Fourteen male field hockey players from the men's first team at the University of the Witwatersrand participated in the study over ten weeks during a premier league competition phase. A Google form was completed within thirty minutes of every training session/match. This form consisted of questions that allowed for the calculation of the ACWR. RESULTS: No incidence of injury was reported during the ten-week period. ACWR scores ranged between 0.67 and 1.87. The ACWR was associated with general muscle soreness (p = 0.010) and training load was associated with fatigue (p = 0.002), sleep quality (p = 0.05), general muscle soreness (p = 0.004), and mood (p=0.025). CONCLUSION: There may be some merit in the use of subjective wellness measures as workload management tools in field hockey. Further research is required to determine if there is an optimal ACWR for this sport.

É apresentado o sistema PDNET, um conjunto de subrotinas em Fortran para a otimização de fluxos lineares em redes utilizando um algoritmo de pontos interiores. O algoritmo e a sua implementação são descritos com algum detalhe. A utilização do sistema é explicada e são apresentados alguns resultados computacionais. O código fonte está disponível em http://www.research.att.com/~mgcr/pdnet.

We describe Fortran subroutines for network flow optimization using an interior point network flow algorithm, that, together with a Fortran language driver, make up PDNET. The algorithm is described in detail and its implementation is outlined. Usage of the package is described and some computational experiments are reported. Source code for the software can be downloaded at http://www.research.att.com/~mgcr/pdnet.