Interaction between the leg recovery test and subjective measures of fatigue in handball players: short-, mid-, and long-term assessment

Abstract

Background: The physical and mental demands of handball during training or competition often lead to fatigue which can impair performance. Many attempts have been made to assess the level of fatigue in athletes either by objective (neuromuscular performance) or subjective (questionnaires) measures, however, their interplay over short-, mid-, and long-term periods is currently unknown. Knowledge about both types of assessments is important as load management by coaches is traditionally based on direct adjustments following a training session, adjustments of content structure of training weeks between games, as well as adjustments of load management over the entire competitive season. Thus, this study aimed to investigate the interplay between objective and subjective fatigue measures at multiple test times throughout a handball season.

Methods: A total of 100 highly trained (Tier level 3) adolescent or young adult team handball players (23 females) took part in the study. The parameters tested were the Leg Recovery Test (LRT score) which is based on the countermovement jump height (CMJ) and was assessed by a commercial wristwatch (Polar Vantage V2) as an objective measure of neuromuscular fatigue. Additionally, on a subjective level, questionnaire-based athlete self-report measures, specifically the Perceived Recovery Status Scale (PRSS) and the Short Scale of Recovery and Strain (KEB) were assessed. We used non-parametric tests to detect differences between relevant test time points (short-term: immediately following one handball-specific training session, i.e., from T₀ to T₁; mid-term: over the course of three consecutive training days, i.e., from T₀ to T₂; long-term: over the course of 8 months of training, i.e., from T₀ to T₁₂) and linear mixed models to evaluate the interplay between objective (LRT score) and subjective (KEB score and PRSS score) measures of fatigue across one season.

Results: Non-parametric tests showed that CMJ height (p = .012) and the KEB (p < .001) were higher at T₁ compared to T₀ for the short-term assessment. Over the course of three consecutive training days (i.e., mid-term assessment), the CMJ height score decreased (T₀ to T₂: p < .001; T₁ to T₂: p = .018) and the PRSS score (T₀ to T₂: p < .001; T₁ to T₂: p = .003) increased. Linear mixed models revealed no significant effects of KEB or PRSS score on LRT score (i.e., CMJ height) for the short- and mid-term assessments. In terms of the long-term assessments, we detected no general direct or interaction effects of PRSS score, workload, and test time point on LRT score, except for an interaction between PRSS score and workload on LRT score (p = .032), which indicates a workload-dependent association between PRSS and the objective fatigue measure (LRT score).

Conclusion: Athlete self-reported measures of fatigue indicated significantly higher cumulative fatigue after both short- and mid-term periods, whereas this increase was observed in the LRT score only during the mid-term period. Furthermore, the absence of a relationship between the objective and subjective measures of fatigue during short- and mid-term periods suggests that these measures assess distinct types of fatigue. In the long-term assessments, the significant interaction between the PRSS score and workload on the LRT score suggests that higher workloads are associated with an increased correlation between subjective (PRSS score) and objective (LRT score) measures of fatigue. This indicates that perceived fatigue may be a more sensitive indicator of fatigue, which can be managed to maintain high levels of neuromuscular performance (LRT score). However, with higher workloads (>10 h per week), associations between the objective and subjective measures become apparent, suggesting that workload serves as a common factor influencing overall fatigue.

Keywords: athlete self-report measures; exhaustion; monitoring; regeneration; team sports; workload.

Figure 1

Schematic diagram of the study designs. CMJ, countermovement jump; KEB, short scale of recovery and strain; PRSS, perceived recovery status scale.

Figure 2

Interaction between athlete self-report measures (ASRM) and leg recovery test (LRT) performance. (a) Jitterplot of perceived recovery status scale (PRSS) score (0–10) and workload (hours/week) on countermovement jump (CMJ) height. Dots represents jitters of individual data points in terms of the relationship between the LRT score and the PRSS score. The lines represent marginal effects from the mixed linear models reported above. The three exemplary marginal slopes illustrate the interaction between workload and PRSS on LRT score during 4, 9, and 13 h per week. The points do not model the raw data, but the mixed linear model. The marginal slopes are selected as examples for better visualization. (b) Association between the slope of the relationship between the PRSS and workload on LRT scores with Johnson-Neyman intervals. Significant deviances from a slope of 0 occur around a workload of 10 h per week. This interaction might be interpreted by assuming that at lower workload there is apparently no relationship between PRSS and the LRT. With higher workload we see an increasingly pronounced negative relationship between PRSS and the LRT.