Dietary behaviour, well-being and training load of female student field hockey players

Abstract

Athlete monitoring has become an established practice in high performance environments. The purpose of monitoring is to balance training load (TL) and recovery toward athlete readiness to perform. Research has shown inverse associations between total pretraining well-being and internal load (IL). However, findings on the associations between pretraining well-being subscales and IL are not consistent or conclusive. The current dearth of literature on female field hockey players’ pretraining well-being, IL and dietary behaviour, and the relationships between these variables within different training weeks (i.e. micro cycles) during the pre-season motivated this investigation.

Nineteen female student field hockey players (age 20 ± 1 y, stature 165 ± 7 cm, body mass 62.4 ± 7.0 kg) participated in this observational study. Data collection occurred during three different training periods: general preparation week (GPW), reduced load week (RLW) and practice match week (PMW). Pretraining well-being (total well-being, energy levels, fatigue, motivation, sleep quality, soreness and stress), and IL (training duration and session rating of perceived exertion - sRPE) were captured daily, and dietary behaviour (diet quality, health-, dairy-, meat-, dessert-, and fat pattern) was completed at the end of each training period, all three variables were recorded using a smartphone application. For each training period, descriptive statistics, expressed as a mean and standard deviation, were calculated for pretraining well-being, IL and dietary behaviour. This was followed by determining the significance of the inter-period differences for each variable, as well as the nature, strength and significance of the relationships between the variables within each training period using Spearman’s correlation coefficient.

Total pretraining well-being remained similar (15.08 arbitrary units [AU], 16.00 AU and 15.89 AU) for consecutive training periods. Weekly IL ranged from 1715 AU to 1957 AU. Diet quality proved fairly adequate and constant between 70% - 73%. Only one statistically significant inter-period difference was found, namely for sRPE (p = 0.045). However, due to the nature of the data and the statistical analysis (Wilcoxon rank sum test), it could not be determined between which two training periods. All associations between diet quality and dietary patterns with IL and pretraining well-being proved non-significant and trivial to small in magnitude. However, three moderate and statistically significant associations were identified between pretraining well-being and IL. For the GPW, statistically significant inverse associations were identified between sleep quality with sRPE (r = -0.52, p = 0.027) and training duration (r = -0.51, p = 0.029). For the RLW, a statistically significant association was found between fatigue and sRPE (r = 0.49, p = 0.046).

Compared to existing data for different training periods in team sports, IL was similar for the PMW but higher during the GPW and RLW. Significant inter-period differences for pretraining well-being have been found in studies reporting on training periods during in-season or a macrocycle. However, the current study, which is the first to investigate differences in pretraining well-being between pre-season micro cycles, found none. Despite popular opinion, this study found no statistically significant differences for dietary behaviour between training periods. Current findings corroborate previous research demonstrating statistically significant associations between pretraining fatigue and sRPE.

Future research exploring differences between training periods should ensure greater distinction between training periods. Due to COVID-19 restrictions disrupting the pre-season and in-season structure the training periods were not as distinct as planned.