Amélie Apinis-Deshaies

Objective: Medio-lateral trunk dynamics during walking may be sensitive to subtle changes in locomotor regulation and can be measured with trunk-worn inertial sensors in natural sport environments. This study examined whether head impact exposure (HIE) sustained during Canadian varsity football games was associated with magnitude-dependent differences in medio-lateral trunk dynamics.Methods: Sixty-nine male collegiate football athletes were monitored across multiple competitive seasons using instrumented mouthguards for head impact detection and chest-worn inertial measurement units for walking analysis. HIE was quantified at play-level and game-level temporal scales. Linear mixed-effects models assessed associations between HIE and root mean square lateral trunk acceleration (ACC) and trunk roll angular velocity (ROLL) extracted from linear walking bouts, controlling for baseline dynamics, movement speed, physical workload, and game exposure.Results: Across 10,156 linear walking bouts and 3,403 head impacts, HIE was associated with magnitude-dependent differences in medio-lateral trunk dynamics. Play-level HIE was associated with increased ROLL (β = 0.06, p < 0.001), independent of concurrent physical workload. Game-level HIE was associated with post-game increases in ACC (β = 0.09, p = 0.041) and ROLL (β = 0.15, p < 0.001), with larger estimates than play-level models. Across temporal scales, associations were more consistent for ROLL than ACC.Conclusion: HIE was associated with measurable differences in medio-lateral trunk dynamics during field-based walking, particularly trunk roll angular velocity. Continuous trunk-worn inertial sensing may provide a scalable field-based approach for characterizing locomotor-dynamics changes associated with head impact exposure in contact sport athletes

Sleep is particularly important for student-athletes in the pursuit of both academic success and athletic performance. The primary aim of this study was to investigate the relationship between training load (TL), perceived cognitive workload, and sleep in varsity male volleyball athletes. The secondary aim was to understand the physiological and cognitive mechanisms leading to sleep disturbances following a match. Twelve varsity players on a men’s university volleyball team were examined for 12 weeks of the competitive season. Sleep, internal TL and cognitive load were assessed using daily surveys. External TL was assessed using wearable jump monitors during training and matches. Participants filled the Pre-Sleep Arousal Scale to assess pre-sleep arousal and the Ottawa Mental Skills Assessment Tool to assess mental skills after eight matches. Our results show that the perceived cognitive load had a negative impact on total sleep time (p = 0.003) and sleep quality (p = 0.048). Moreover, internal TL had detrimental effects on sleep quality (p = 0.04). We also observed a negative association between cognitive pre-sleep arousal and sleep efficiency (p = 0.004). and sleep onset latency (p = 0.03). Finally, no effect was found between mental skills and post-match sleep. Our results suggest that assessing perceived cognitive workload is essential when monitoring student-athletes. Moreover, higher sleep onset latency and lower sleep efficiency were associated with elevated levels of cognitive arousal supporting the idea that post-match sleep difficulties are due to cognitive arousal rather than physiological arousal.

This study aimed to use a data-driven approach to identify individualized speed thresholds to characterize running demands and athlete workload during games and practices in skill and linemen football players. Data were recorded from wearable sensors over 28 sessions from 30 male Canadian varsity football athletes, resulting in a total of 287 performances analyzed, including 137 games and 150 practices, using a global positioning system. Speed zones were identified for each performance by fitting a 5-dimensional Gaussian mixture model (GMM) corresponding to 5 running intensity zones from minimal (zone 1) to maximal (zone 5). Skill players had significantly higher (p < 0.001) speed thresholds, percentage of time spent, and distance covered in maximal intensity zones compared to linemen. The distance covered in game settings was significantly higher (p < 0.001) compared to practices. This study highlighted the use of individualized speed thresholds to determine running intensity and athlete workloads for American and Canadian football athletes, as well as compare running performances between practice and game scenarios. This approach can be used to monitor physical workload in athletes with respect to their tactical positions during practices and games, and to ensure that athletes are adequately trained to meet in-game physical demands.

Consolidation has been associated with performance gains without additional practice (i.e., off-line learning). However, the movement characteristics improving off-line remain poorly understood. To investigate this question, participants were trained to produce a sequence of planar reaching movements toward four different visual targets. The training session with feedback required them to learn the relative time of the movements, the total movement time and aim accurately at each target. The retention test was performed either 10-min or 24-h after. Results revealed that a 24-h consolidation interval did not result in better temporal or spatial accuracy. This finding suggests that off-line learning may be restricted to sequence production tasks in which the different segments must be regrouped ("chunked") together to accelerate their execution.

Purpose: To evaluate whether sleep quantity and quality of professional hockey players is affected by external training load (TL), their perception of well-being, and contextual factors associated with match participation. Methods: Fifty male athletes were monitored daily during the 28 weeks of the regular season using well-being and sleep surveys. On-ice external TL was monitored using portable inertial measurement units during practices and matches. Linear mixed-effects models were applied to evaluate whether well-being perception (ie, pain, nutrition, stress, and rest) and external TL may affect sleep quality and quantity. Results: High levels of well-being positively affected sleep duration and quality (P < .001), whereas high-intensity TL had a detrimental impact on sleep duration (P = .007). Moreover, away and evening matches had a detrimental effect on sleep quantity and quality (P < .001). Finally, a high match on-ice load per minute had a negative association with sleep quality (P = .04). Conclusions: Findings indicate that well-being and high-intensity trainings can impair sleep duration and quality. In addition, high-intensity match load, away matches, and evening matches can impair postmatch sleep. Therefore, monitoring well-being in conjunction with TL is essential to understand sleep disturbances in athletes. Practitioners should also implement sleep hygiene strategies that facilitate longer time in bed after high-intensity, away, and evening matches to help athletes recover.