Effects of intensified training with insufficient recovery on joint level and single muscle fibre mechanical function: the role of myofibrillar Ca2+ sensitivity

Intense exercise training with insufficient recovery time is associated with reductions in neuromuscular performance. However, it is unclear how single muscle fibre mechanical function and myofibrillar Ca2+ sensitivity contribute to these impairments. We investigated the effects of overload training on joint-level neuromuscular performance and cellular-level mechanical function. Fourteen athletes (4 female and 10 male) underwent a 3-week intensified training protocol consisting of up to 150% of their regular training hours with three additional high-intensity training sessions per week. Neuromuscular performance of the knee extensors was assessed via maximal voluntary contraction (MVC) force, electrically evoked twitch contractions, and a force-frequency relationship. Muscle biopsies were taken from the vastus lateralis to assess single fibre mechanical function. Neither MVC force nor twitch parameters were altered following training (all p > 0.05), but a rightward shift in the force-frequency curve was observed with average reduction in force of 6%-27% across frequencies 5-20 Hz (all p < 0.05). In single fibres, maximal force output was not reduced following training, but there was a rightward shift in the force-pCa curve driven by a 6% reduction in Ca2+ sensitivity (p < 0.05). These data indicate intensified training leads to impaired Ca2+ sensitivity at the single fibre level, which in part explains impaired neuromuscular function at the joint level during lower frequencies of activation. This is an important consideration for athletes, as performance is often assessed at maximal levels of activation, and these underlying impairments in force generation may be less obvious.

The Determinants of Maximal Speed Adaptation During Preseason in Subelite Female Australian Rules Footballers

PURPOSE

Maximal speed is an important physical-fitness attribute for female Australian footballers. The effects of sprint training, maximal strength, and technical training have been reported in laboratory studies. However, no study has determined the combined effect and relative contribution of these training modalities on maximal speed adaptation in situ. Therefore, the aim of this study was to determine the training factors affecting maximal speed adaptation during a preseason in subelite female Australian Rules footballers.

METHODS

Maximal speed during field training, predicted 1-repetition maximum (1RM) for box squat and hip thrust, and sprint biomechanics were assessed during early and late preseason (∼9 wk apart) in 15 female subelite Australian Rules Football players (age 20 [3] y). On-field training volume and intensity (total distance, high-speed running, very-high-speed running, and maximal speed) were determined using a Global Positioning System. A multivariate regression model was used to determine the factors associated with changes in maximal speed across the preseason.

RESULTS

The preseason training program had a small effect on maximal speed and large to very large effects on strength and sprint biomechanics. The multivariate regression with the greatest fit (P < .001, R2 = .939) included change in estimated 1RM box squat (β = -0.63), total distance per week (β = -0.55), and change in hip projection (β = 0.16) as factors. Multivariate regression of biomechanical factors (P = .044, R2 = .717) and maximal strength factors (P = .003, R2 = .676) were also significant.

CONCLUSION

The development of maximal speed across a preseason is dependent on (1) total distance per week, (2) maximal strength adaptation, and (3) sprint technique adaptation in female subelite Australian rules football players.

Menstrual Cycle and Situational Match Variables: Effects on Well-Being in Professional Female Soccer Players

Purpose: We examined whether the situational match factors (location, result, and opposition quality), as well as playing position and menstrual cycle phase, influenced self-reported well-being in female soccer players. Methods: Twenty-two professional players (25 ± 5 years) from a squad in the Women's Super League in England self-reported their menstrual cycle phase via an online survey, and their soreness, sleep, stress, mood, and fatigue on a 1 (positive) to 5 (negative) scale on the morning of match day (MD) and for the next 3 days (referred to as MD + 1, MD + 2, MD + 3, respectively). Data was collected for 26 matches across the 2021-2022 season. Session rate of perceived exertion (sRPE) and total distance covered (measured with GPS) were measured on match and training days. Results: Menstrual cycle phase and the situational match factors had no influence on distance covered during matches and sRPE (p > .05). Sleep was worse on MD + 1 (p < .001; g = 1.59) and MD + 2 (p = .005; g = 0.98) after away matches. Stress and mood were > 28% worse after playing top vs. mid and bottom table opposition at MD + 1, and significantly worse when losing compared to winning and drawing (p > .05). Playing position and menstrual cycle phase has no influence on self-reported well-being (p > .05). Conclusion: Sleep quality was worse after away matches and losing matches negatively affects stress and mood for 2 days post-match, but menstrual cycle phase does not influence post-match well-being.

The Psychology of Athletic Tapering in Sport: A Scoping Review

Taper is a common training strategy used to reduce fatigue and enhance athletic performance. However, currently, no review has summarised what psychological research has been conducted examining taper, what this research shows and what future research needs to be undertaken to extend the field. Consequently, a scoping review was conducted with three aims: (a) to determine the characteristics of psychological research examining taper, (b) to summarise psychological research collected during taper with adult athletes and coaches, and (c) to identify gaps in psychological research examining taper. Forty-eight articles were identified following an exhaustive search strategy and charted following scoping review guidelines. Results showed most research was quantitative, used a longitudinal design, was conducted in swimming, triathlon, cycling or across multiple sports, and used a university-, regional- or national-level male athlete sample. Eight themes were developed to summarise the research: Mood, Perception of Effort, Perceived Fatigue and Wellness, Recovery-Stress, Taper as a Stressor, Stress Tolerance, Psychological Preparation and Cognitive Functioning. Additionally, four research recommendations were identified: (a) conducting exploratory research that examines the impact taper has on athletes' and coaches' competition preparation and stress experience, (b) asking more advanced psychological questions and conducting multi-disciplinary research, (c) including a more diverse participant sample in studies and (d) examining the impact of psychological interventions during taper. Overall, this scoping review has highlighted the limited research examining the psychology of taper and the need for focused research that asks more complex questions across diverse populations.

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

This study aimed to identify the predictive capacity of wellness questionnaires on measures of training load using machine learning methods. The distributions of, and dose-response between, wellness and other load measures were also examined, offering insights into response patterns. Data (n= 14,109) were collated from an athlete management systems platform (Catapult Sports, Melbourne, Australia) and were split across three sports (cricket, rugby league and football) with data analysis conducted in R (Version 3.4.3). Wellness (sleep quality, readiness to train, general muscular soreness, fatigue, stress, mood, recovery rating and motivation) as the dependent variable, and sRPE, sRPE-TL and markers of external load (total distance and m.min^-1) as independent variables were included for analysis. Classification and regression tree models showed high cross-validated error rates across all sports (i.e., > 0.89) and low model accuracy (i.e., < 5% of variance explained by each model) with similar results demonstrated using random forest models. These results suggest wellness items have limited predictive capacity in relation to internal and external load measures. This result was consistent despite varying statistical approaches (regression, classification and random forest models) and transformation of wellness scores. These findings indicate practitioners should exercise caution when interpreting and applying wellness responses.

Relationship between Perceived Training Load, Well-Being Indices, Recovery State and Physical Enjoyment during Judo-Specific Training

This study investigated the relationship between well-being indices and the session rating of perceived exertion (session-RPE), recovery (TQR), and physical enjoyment (PE) during intensified, tapering phases of judo training. Sixty-one judo athletes (37 males, ranges 14-17 years, 159-172 cm, 51-67 kg) were randomly assigned to three experimental (i.e., randori, uchi-komi, running) and control groups (regular training). Experimental groups trained four times per week for 4 weeks of intensified training followed by 12 days of tapering. Session-RPE, well-being indices (i.e., sleep, stress, fatigue, delayed onset of muscle soreness (DOMS), Hooper index (HI)), and TQR were measured every session, whereas PE was recorded after intensified, tapering periods. Recovery (TQR) was negatively correlated with sleep, stress, fatigue, DOMS, HI, session-RPE in intensified period and was negatively correlated with sleep, stress, fatigue, DOMS, HI in tapering. Session-RPE was positively correlated with sleep, fatigue, DOMS, HI in intensified period and positively correlated with fatigue, DOMS in tapering. PE was negatively correlated with stress in intensified training. Enjoyment could be partially predicted by sleep only in intensified periods. Session-RPE could be partially predicted by TQR, fatigue during intensified periods and by sleep, and HI during tapering. Sleep, recovery state, pre-fatigue states, and HI are signals contributing to the enjoyment and internal intensity variability during training. Coaches can use these simple tools to monitor judo training.