Towards a More Efficient Training Process in High-Level Female Volleyball From a Match Analysis Intervention Program Based on the Constraint-Led Approach: The Voice of the Players

The Perception of Volleyball Student-Athletes: Evaluation of Well-Being, Sport Workload, Players' Response, and Academic Demands

Physical activity has been shown to improve the health and well-being of students, athletes and the general population, especially when it is properly monitored and responses are evaluated. However, data are mostly gathered without considering a valuable element, participants' perceptions. Therefore, the objective was to know the perception of volleyball student-athletes when using different monitoring and response tools that assess well-being, workloads, responses to workloads, and academic demands. A qualitative study using semi-structured interviews with female volleyball student-athletes (n = 22) was used to know players' perceptions when using a wellness/well-being questionnaire, session ratings of perceived exertion (sRPE), and countermovement jumps (CMJ), and consider academic demands. Results show that the wellness questionnaire and sRPE increased student-athletes' awareness of well-being and readiness to perform, improved self-evaluation, self-regulation, and self-demand. However, motivation and overcoming challenges were based on the CMJ. Academic demands affected 82% of student-athletes, altering stress, fatigue, and sleep quality. Nonetheless, sport was seen as an activity that helped with academic commitments. Therefore, the wellness questionnaires and the sRPE facilitated self-awareness and positive dispositions toward self-regulation. Simultaneous intensive academic demands and training can produce mutual positive effects if the variables of physical and mental loads are harmonized in the critical academic and sports periods.

Immediate faculty feedback using debriefing timing data and conversational diagrams

BACKGROUND

Debriefing is an essential skill for simulation educators and feedback for debriefers is recognised as important in progression to mastery. Existing assessment tools, such as the Debriefing Assessment for Simulation in Healthcare (DASH), may assist in rating performance but their utility is limited by subjectivity and complexity. Use of quantitative data measurements for feedback has been shown to improve performance of clinicians but has not been studied as a focus for debriefer feedback.

METHODS

A multi-centre sample of interdisciplinary debriefings was observed. Total debriefing time, length of individual contributions and demographics were recorded. DASH scores from simulation participants, debriefers and supervising faculty were collected after each event. Conversational diagrams were drawn in real-time by supervising faculty using an approach described by Dieckmann. For each debriefing, the data points listed above were compiled on a single page and then used as a focus for feedback to the debriefer.

RESULTS

Twelve debriefings were included (µ = 6.5 simulation participants per event). Debriefers receiving feedback from supervising faculty were physicians or nurses with a range of experience (n = 7). In 9/12 cases the ratio of debriefer to simulation participant contribution length was ≧ 1:1. The diagrams for these debriefings typically resembled a fan-shape. Debriefings (n = 3) with a ratio < 1:1 received higher DASH ratings compared with the ≧ 1:1 group (p = 0.038). These debriefings generated star-shaped diagrams. Debriefer self-rated DASH scores (µ = 5.08/7.0) were lower than simulation participant scores (µ = 6.50/7.0). The differences reached statistical significance for all 6 DASH elements. Debriefers evaluated the 'usefulness' of feedback and rated it 'highly' (µ= 4.6/5).

CONCLUSION

Basic quantitative data measures collected during debriefings may represent a useful focus for immediate debriefer feedback in a healthcare simulation setting.

Analysis of Volleyball Video Intelligent Description Technology Based on Computer Memory Network and Attention Mechanism

There are some problems in the process of video intelligent description and analysis of volleyball, such as poor effective information extraction rate and poor dynamic tracking effect. Based on this, combined with long-term and short-term memory network and attention mechanism, this paper designs an intelligent description model of volleyball video based on deep learning algorithm and studies how to improve the extraction rate of volleyball video information through intelligent detection hardware and image recognition technology. This paper first introduces the application of image recognition technology and deep learning algorithm in the intelligent description of volleyball video, then designs the volleyball video and image recognition model based on deep learning algorithm according to the requirements of volleyball video intelligent description, and selects three correlation factors related to the impact indicators of volleyball skills. This study selects three characteristic parameters associated with volleyball video analysis indexes, namely, take-off, bounce, and hand movement, combined with image sensing hardware assisted sensor network to realize real-time monitoring of action state in volleyball video analysis system. The experimental results show that, compared with the current mainstream sports video intelligent analysis and image recognition methods with data analysis as the core, the intelligent volleyball sports video intelligent description and image recognition system based on the integration of deep learning algorithm and sensor hardware assistance has the advantages of good detection effect, high data effectiveness, low cost, and high efficiency of volleyball sports video analysis. It can effectively improve the efficiency of volleyball video intelligent description.