The practice of music in volleyball players’ physical recovery: incorporating data-driven analysis

Athletes’ physical recovery refers to the process of making athletes’ body functions recover quickly and reach the optimal state through a series of scientific training measures and methods after athletic training or competition. Physical recovery training is very important for athletes, which can help to recover muscle fatigue, improve physical adaptability, and prevent sports injuries [1-4]. Through reasonable relaxation training, aerobic recovery training and anaerobic recovery training, combined with reasonable nutritional supplementation and sufficient rest and sleep to help athletes quickly recover physical fitness, improve physical adaptability, of course, in addition to these common ways of physical recovery, music is also an effective means of relieving stress and relaxing the body and mind [5-8].

Music can directly touch people’s emotions and regulate the emotional state. Cheerful melodies can bring pleasure and excitement, while soothing rhythms can make people relax and calm. At the same time, athletes can mobilize all the senses and motor functions of the body by listening to music after the game, which can help improve the coordination and flexibility of the body [9-11]. In addition, music also has certain social attributes, athletes in the process of physical recovery through music can also communicate and interact with others to enhance social self-confidence [12-13]. Volleyball itself is a very high physical requirements of a sports program, which involves strength, speed, explosive force, flexibility, endurance and other aspects of physical fitness. In order to improve the overall competitive level of volleyball players, it is very necessary and effective to adjust their physical recovery through music [14-17].

Literature [18] explored the effects of motivational music on activity patterns and blood lactate concentration during post-exercise recovery, measured heart rate and lactate concentration at different time periods after running in 20 men, and compared the differences in performance with and without music during the recovery period, and the results pointed out that listening to music can remove lactic acid from the body faster, reduce perceived motor rate, and have a significant effect on athletes’ recovery of physical performance. Literature [19] described the current development of music in the recovery of athletes in terms of sports performance and physical fitness through a literature review, emphasizing that the motivational effect of music has a relationship with the individual athlete’s perception of self-esteem and self-confidence, motor coordination, etc., in addition to the ability of music to increase the self-efficacy of the athlete, and have an encouraging effect on them. Literature [20] conducted a comparative experiment on 12 athletes with the aim of understanding the effect of music on their physical recovery, practicing measurements on athletes’ heart rate, thigh circumference, reaction time, etc., at different times, and the results showed that music helps to improve and recover athletes’ physical fitness, and the effect is obvious. Literature [21] examined the psychophysiological effects of music on acute recovery and popular recreation between high-intensity sports. Experiments with 13 athletes yielded that fast-paced and positive music resulted in a more pleasurable experience during recovery, while music accelerated cardiorespiratory recovery during high-intensity exercise. Literature [22] explored the effect of “detached rhythm” on recovery after high-intensity exercise, and a comparative trial with and without music during exercise showed that music enhanced recovery, with the tempo of the music having the greatest effect on recovery. Literature [23] describes the application of different types of music to alleviate the problem of decreased pleasure caused by high-intensity interval training, which was investigated through the three conditions of “pause music”, “continuous music” and “no music”. “The results indicated that music had no effect on affective performance during high-intensity interval training, but sustained music listening elicited powerful post-task enjoyment. Literature [24], based on a meta-analysis to quantify the effects of music in exercise and sport, experimentally demonstrated that music has the ability to promote more positive affective valence, enhance physical performance, and reduce fatigue. Literature [25] assessed endurance and high-intensity exercise in 19 women under randomized conditions, using repeated analyses of variance measures to detect differences between different music rhythms during high-intensity and low-intensity exercise, and the results of the study showed that the role of music is prominent and evident in endurance exercise, and that music can be used as a powerful tool to stimulate physical activity.

This paper utilizes meta-analysis methods, using Chinese and English databases as search platforms, to conduct in-depth mining of literature related to music and physical performance, screening the searched literature and eliminating the relevant literature that does not meet the conditions, in order to ensure the accuracy and validity of the data results. Mean effect sizes, publication bias, and moderating effect tests were utilized to explore in depth the implied effects or relationships between music and physical performance. By analyzing the degree of recovery of volleyball players’ sense of accomplishment, exercise fatigue, and emotional/physical exhaustion after the music conditioning method, we explored whether the music conditioning method is effective in relieving athletes’ physical performance, and examined the practical application value of the music conditioning method for physical performance.

2

Mechanisms of action of music regulation

Regarding the mechanism of action of the music conditioning method, it can be divided into three perspectives: neurophysiological regulation, resonance principle theory, and psychological perspective.The first one is neurophysiological regulation. Research shows that music mainly regulates human psychology and physiology through the physiological activities of the complex nervous system of the human brain. When we are listening to music, the sound waves of the music stimulate the human auditory system, resulting in visual and emotional experiences. Physiologically, music produces stimulation in the auditory receptors in the cochlea, and then the vestibulocochlear nerve transmits this stimulation to the dorsal and ventral cochlear nuclei of the medulla oblongata, and then to the medial geniculate nucleus from a hypothalamic location, before finally reaching the auditory areas of the temporal lobe of the cerebral cortex. With the joint participation of the central nerves of the brain related to emotional mood, attention and cognition, the music will be integrated and processed to promote the regulation of the functional activity of the vegetative nervous system, which in turn triggers physiological and psychological effects, such as balancing the changes in endorphin hormones, so that the body remains sedated, or to reduce the secretion of proadrenocorticotropic hormone and reduce the functional activity of the adrenergic nerves, which promotes the Lowering of blood pressure, slowing of pulse and respiratory rhythms, and reduction of anxiety symptoms, and so on.

Then there is the resonance principle. The resonance principle is a hypothesis put forward by relevant scholars in their research on the mechanism of action of music training at the physical level. According to the resonance principle, music is a kind of sound wave with energy, which can transmit information through elements such as rhythm, melody, and tune. In the human body, there are nearly a hundred physiological activities, all of which have similar rhythms to music. When the body’s internal movement frequency and physiological rhythms and music frequency, rhythm and other aspects of the same tone, the body’s internal organs, muscles and brain waves will appear resonance phenomenon, in the resonance of the role of the body’s organs will produce a harmonious and consistent rhythm of the movement, the rhythm of the cellular massage has a similar role, which is able to regulate the human nerves, muscles, and other physiological activities, as well as soothing people’s Emotions. Finally, there is the psychological perspective. There are many schools of theory related to the mechanism of the physical and mental effects of music regulation. They can be summarized into two main ones. The first theory holds that music first affects emotions through elements such as rhythm, beat, and melody, then triggers human emotions, and finally produces effects on human physiology.According to the second theory, music has an effect on human physiology first through the elements of rhythm, beat, and melody, and then on the mind. It can be seen that although the two theories have their own viewpoints, they are closely related to each other and the two mechanisms interact. In conclusion, music has a significant impact on human emotions and can affect human cognition while also transforming emotions.It can release undesirable emotions by awakening, connecting, and combining the power of personality.

3

Data-driven meta-analysis-based approach

3.1

Data-driven mechanisms

The concept of “data-driven” has been noticed and frequently used with the rise of big data. In the era of big data, data-driven has become a major trend. The continuous collection and accumulation of data on the process and outcomes of music therapy for volleyball players’ physical recovery has led to the construction of a rich music therapy dataset. Through the deep mining and meta-analysis of these big data, the meaning and value behind the data can be revealed, thus providing powerful data support for athletes’ physical recovery.

3.2

Meta-analysis steps and methods

Performing a meta-analysis typically involves the following steps, which are shown in the step flow diagram in Figure 1.

3.2.1

Selection of topics

The determination of the research topic is the beginning of the meta-analysis, which is related to the quality of the research, and a suitable topic selection is very important for the research. First of all, adhere to the problem-oriented and demand-oriented, determine the purpose of the research, what kind of problems to solve? What kind of theoretical and social significance does it bring? Secondly, through the literature database search, first determine the amount of literature on the research topic and whether it meets the basic requirements of meta-analysis to avoid the problem of too much or too little literature. Finally, grasp the feasibility of the research as a whole, determine the purpose, value, innovation, etc., and formulate a research plan.

3.2.2

Searching and evaluating literature

First of all, according to the research plan, limit the literature publication time search interval, determine the database for conducting literature search, including Chinese and foreign language databases, and carry out supplementary search for the literature that is difficult to be searched, which can be interlibrary loan, etc., to ensure the inclusion of the number of literature as much as possible. Precise Chinese and English search subject terms are concerned with the scope of the retrieved literature. Secondly, the initial access to the literature screening, according to the literature data access standards, the literature that does not meet the standards to be eliminated, the quality of the literature does not meet the standards to be eliminated. Finally, the target data was extracted from the qualified literature obtained from screening and processed and coded to organize the data and meet the requirements for meta-analysis data analysis.The extracted information includes external characteristics of the literature: first author, publication time, and source of the literature.Content features of the literature include the network topology characteristic parameters of the study, link prediction algorithm, and AUC value.Data coding includes: study number, network dataset number, link prediction algorithm, AUC value, sample size, effect value, standard error, and network structure characterization parameters.

3.2.3

Data analysis

The complete data was imported into the meta-analysis software, and the relevant table header variables, data types, and other contents were set. Publication bias test, the first step of data analysis, is a test to determine whether there is a bias in the included literature, to clarify the distribution of the included literature, which is the basis for the next step of the main effects test; main effects test, the core of the meta-analysis, provides two kinds of fixed-effects model and random-effects model, determines the effect model according to the literature, and obtains the estimated effect value of the key variables after the analysis, and effect value Sensitivity analysis, to exclude the impact of a single data value on the overall data, to determine the range of confidence intervals, to determine the sensitivity level. Heterogeneity tests were conducted to determine whether the findings of the included literature were “lopsided”; if the findings of the included literature were the same or similar, then there was no point in conducting a meta-analysis. The level of significance is needed to determine the overall heterogeneity of the included literature. Moderated effects test, through the effect value and significance level, to determine the impact of moderating variables on the relevant variables, can reveal the reasons for the differences between the conclusions of a large number of similar studies, moderated effects analysis, explaining “how to regulate?” “What is the strength of the moderating effect?” and explain “how to regulate?” and “how strong is the effect of regulation?”.

Calculation of effect sizes: the AUC values of the outcome metrics for the included studies were extracted, and if the study results reported AUC values for the test set under multiple proportional divisions, the mean value was taken for the calculation. The AUC values were converted to standardized effect sizes for the way the studies were conducted, etc., with cohen's d as the final effect size: (1) $E S_{t} = l o g [\frac{p}{1 - p}] = l n [\frac{p}{1 - p}]$ \[E{{S}_{t}}=log\left[ \frac{p}{1-p} \right]=ln\left[ \frac{p}{1-p} \right]\] (2) $S E_{t} = \sqrt{\frac{1}{n p} + \frac{1}{n (1 - p)}}$ \[S{{E}_{t}}=\sqrt{\frac{1}{np}+\frac{1}{n(1-p)}}\] (3) $V_{l o g} = S E_{t}^{2}$ \[{{V}_{log}}=SE_{t}^{2}\] (4) $c o h e n^{'} s \cdot d = E S_{t} \times \frac{\sqrt{3}}{π}$ \[cohe{n}'s\cdot d=E{{S}_{t}}\times \frac{\sqrt{3}}{\pi }\] (5) $V_{d} = V_{l o g} \times \frac{3}{π^{2}}$ \[{{V}_{d}}={{V}_{log}}\times \frac{3}{{{\pi }^{2}}}\]

Publication bias assessment: The funnel plot directly presents the publication bias of the meta-analysis from a subjective point of view, and it is generally considered that a symmetrical and uniform distribution of included studies in the funnel plot indicates that the meta-analysis does not suffer from publication bias; otherwise, it indicates that the meta-analysis has publication bias. Based on the traditional meta-analysis funnel plot, the contour-enhanced funnel plot adds the identification of statistical differences in the included studies, which is usually combined with the cut-and-patch method to determine whether the skewed distribution of the funnel plot is caused by publication bias.

Moderated tests: multifactor meta-regression framework models were constructed in three-level meta-analysis for moderated effects tests with subgroup analyses. Variables that may affect heterogeneity were set in Rstudio through the input parameter mods in the ram.mv() function, which in turn identified moderating variables through statistical tests.

3.2.4

Conclusion and discussion

Based on the results of the data analysis, the results are interpreted and reported accordingly at the theoretical and societal levels. In particular, the interpretation of the results of the main effects test and moderated effects test is of particular importance, as it is crucial for the study to meet the expectations of the research. Based on all the results of the meta-analysis, the final conclusions of the study will be drawn, and all the processes of this meta-analysis will be reviewed to determine whether there are any mistakes and shortcomings, to write about the limitations of this inquiry and the areas that can be improved, and to explore new research points.

4

A study of the effects of musical conditioning on the physical recovery of athletes

4.1

Application of music conditioning in competitive sports

Music tune, rhythm, melody, volume of different, on the human body can produce different degrees of regulation, so music to be screened, music not only need to feel the fatigue of the person according to the selection and screening of music, the organization of appropriate musical activities, but also consider the feeler’s age, living habits, ethnic areas, as well as the work environment, personality, cultural level, personal preferences and artistic cultivation and other factors. Music regulation method focuses on the overall regulation of people rather than the regulation of a certain part of them, which is to eliminate mental and physical fatigue through the adjustment of people and their living environment so as to make them achieve coherence and coordination.

The average age of active athletes in China’s Volleyball League A is 22.7 years old, and the vast majority of athletes come from provincial, municipal, or regional gymnasiums.That is to say, the age and education level of active athletes are roughly the same, and the music they like is also dominated by pop, rock, and light music. The results are shown in Figure 2: Through the analysis of the athletes’ questionnaires, it is found that 85.5% of the athletes like to listen to slow and soothing music when they are fatigued after sports training and competitions. 69% of athletes prefer different music genres for different types of fatigue.69.9% of the athletes believe that during the fatigue recovery process, the recovery area is given with blue, green, or light blue, light green, or light blue. Green, blue, or light blue or light green lighting or scenery was the most pleasant.

4.2

Meta-analysis in conjunction with big data on music literature

The study utilized a deep mining method with the keyword combination “music* (physical education + sports + physical performance)” to search the Chinese database CNKI by literature title. The English database was searched using the keyword combination “music and (Sports + exercise + physical performance)” with the title of the article. Among them, the English databases include Web of Science, EBSCO Host, ScienceDirect, and so on.Based on the above principles, the search period for literature was 2003-2023, and a total of 75 documents were obtained for meta-analysis, including 22 documents in Chinese and 53 documents in English. At the same time, literature supplementation was conducted through the literature retrospective method. Including the way of music application, music rhythm, and the degree of specialization in movement, etc.Due to the space limitation, only a few representative literatures are listed in the table.

The funnel plot is shown in Figure 3. The additional contour line funnel plot of physical performance after clipping and patching is basically symmetrical, with most of the studies symmetrically distributed in statistically significant areas (i.e., the white area in the figure), whereas a small number of asymmetrical studies are dispersed in statistically insignificant areas (i.e., the light-green area in the figure), which suggests that there is a small likelihood that there was a publication bias in the study, and that the present study did not suffer from a publication bias.

The mean effect sizes are shown in Table 1, and the random effects model was chosen for the main effects test because the literature included in this study varied in many aspects such as type of sport, music characteristics, and sportsperson characteristics. For physical performance, the mean effect size of music was significant, a medium effect (g = 0.422, P < 0.001), which means that music can significantly enhance the physical performance of athletes. From domestic and international studies: for Chinese literature, the mean effect size of music on physical performance was (g = 1.053, P<0.001). For English literature, the mean effect size of music on physical performance was (g = 0.267, P<0.001). Although both studies have statistically significant effects, the mean effect size of music on physical performance in domestic studies is much larger than that of foreign studies.

Table 1.

Master effect test

Result variable	K	g	95%CI
Physical performance	112	0.422***	{0.321;0.534}
Physical performance(Chinese)	20	1.053***	{0.695;1.411}
Physical performance(English)	94	0.267***	{0.194;0.322}

The results of the meta-regression analysis analysis are shown in Figure 4, with physical performance as the outcome variable and age and percentage of females among exercisers as continuous type moderator variables, respectively. Since only 2 studies were conducted for exercisers over 30 years of age, only studies under 30 years of age were included in the meta-regression analysis analysis in order to avoid large bias of discrete points on the meta-regression analysis analysis. The results showed that the regression result of age with the effect size of music was significant (B=0.014, p=0.040), i.e., within the age of 30 years, there was a trend towards an increase in the effect of music on the promotion of physical performance as the age of the exercisers increased. However, the results of the regression of the percentage of females on the effect size of music were not significant (B=0.002, P=0.234), indicating that gender did not significantly modulate the effect of music on physical performance.

4.3

Practical effects of music on physical recovery

This study required the selection of some male volleyball players as research subjects, this intervention study was set for 8 weeks, 6 days a week of music relaxation training, daily relaxation training time was placed at the end of the afternoon training, data collection was conducted on the subject athletes before the beginning of the experiment. After locking and completing massage and stretching on the Achilles tendon. Adjustment started 5 minutes after the end of training each day.

Music tracks as shown in Table 2, for the experimental group of athletes, on the basis of the traditional relaxation training, 6 pieces of music will be provided each week for athletes to choose, the volume of the music will be adjusted to the appropriate score, select the rhythm of the soothing, emotional with empathy, soft, calm, and able to touch the feelings of the subjects, subjects in the experimental group of about 20-30 minutes of music relaxation, according to the actual training intensity of the situation of flexible The relaxation time was adjusted according to the actual training intensity, but not too long or too short, and the athletes could choose any song from it every day to start the relaxation music training, to ensure that the relaxation music was different every day. Comparison can be seen, this relaxation training process, the experimental group selected the music, for the tone of the soothing, rhythm change is not big music, suitable for soothing athletes’ emotions, treatment of mental fatigue. The control group chose the traditional treatment method.

Table 2.

Music therapy

Week	Music name	Music type	Music time	Cycle number
1	Blue Danube	Classical music	9.54	3
2	Beethoven respects Alice	World classical piano	3.22	8
3	The wedding of a dream	Classical piano	2.48	9
4	triad	Classical Chinese music	4.30	6
5	Flower and moon (guzheng)	Classical Chinese music	3.41	7
6	Holiday beach	China light music	3.22	9

A two-factor repeated measures ANOVA test was performed on the data of the two groups after the intervention through the 8-week music conditioning method. In terms of achievement reduction, the results of the two-factor repeated measures ANOVA test are shown in Table 3, with a significant group main effect F=(2, 48)=12.5, P=0.001**<0.01, indicating that there is a highly significant difference in achievement reduction between the different groups. The time effect was not significant F(2, 48) = 1.547, P = 0.224 > 0.05,The decrease in sense of accomplishment over time was not significant. Group and time main effects were not significant F(2, 48) = 1.554, P = 0.223 > 0.05, indicating that different groups were not significant for fatigue recovery levels at different time points. A post-hoc comparative test analysis of the main effects revealed that the experimental group showed a decreasing trend in the meso-test data compared to the baseline data, while the control group did not decrease. In the post-test data compared to the baseline data, the experimental group showed a significant decrease with a significant difference (P < 0.05), and the control group showed no significant difference.

Table 3.

Psychological fatigue score comparison analysis

Index	Group		Time		Time group
Index	F	P	F	P	F	P
Achievement	12.5	0.001**	1.547	0.224	1.554	0.223
Emotions and physical strength	10.63	0.003**	2.353	0.114	4.782	0.014*
Motion negative evaluation	18.43	0.001**	5.022	0.002**	8.054	0.002**

In terms of emotional/physical exhaustion, the results of the two-factor repeated measures ANOVA test showed that the two groups were significant in terms of emotional/physical exhaustion, with a significant main effect for their group, F=(1, 48)=10.63, P=0.003**<0.01), and highly significant differences in emotional/physical exhaustion between the different groups. The time effect was not significant F (2, 48) = 2.353, P = 0.114 > 0.05, indicating no significant change in emotional/physical exhaustion over time. The group and time interaction effect was significant F(2, 48) = 4.782, p = 0.014* < 0.05, indicating that different groups were significant on the level of fatigue recovery at different time points. A post hoc comparative analysis of the group and time interaction effects revealed that in the meso-test data compared to the baseline data, the experimental group showed a decreasing trend and the control group showed an increase in fatigue scores after 8 weeks of music intervention.

In the posttest data, compared to the midtest data, the experimental group showed a highly significant decrease in fatigue scores. In the comparison of the posttest data with the baseline data, the fatigue score of the experimental group showed a highly significant decrease, while the fatigue phenomenon of the control group increased; in terms of the negative evaluation of exercise, the results of the two-factor repeated-measures ANOVA test showed a significant group effect, F (1, 48) = 18.43, P = 0.001** < 0.01, indicating that the improvement of the negative evaluation of exercise in the different groups had a highly significant difference. The time effect was significant F(2, 48) = 5.022, p = 0.002* < 0.05, indicating a significant change in negative motor evaluation over time. The time and group interaction effect was significant F(2, 48) = 8.054, p = 0.002** < 0.01, indicating that different groups were significant for fatigue recovery levels at different time points. The results of the post hoc comparative analysis test of the group and time interaction effects showed that in the mid-test data compared to the baseline data, there was a significant difference in the gradual decrease of the fatigue scores of the experimental group and a significant increase in the fatigue scores of the control group.

In the post-test data compared with the baseline data, the fatigue scores of the experimental group showed a significant decrease, with a highly significant difference, and the fatigue scores of the control group increased significantly, and there was a highly significant difference between the experimental group in the post-test and mid-test comparison. In summary, the data of the two groups in the three dimensions of the fatigue questionnaire showed that the intervention effect of the experimental group was significant, and the fatigue scores showed a significant decrease, while there was no significant difference in the control group, and the comparison of the data of the two groups showed the improvement effect of the different training methods on fatigue.

5

Conclusion

This paper meta-analyzes the results of an 8-week experiment conducted on volleyball players from the perspectives of their sense of accomplishment, athletic fatigue, and emotional/physical exhaustion with the use of music conditioning as an adjunctive treatment for physical recovery, and compares and generalizes the results of the experimental group with those of the control group. The main effects of achievement, exercise fatigue, and emotional/physical exhaustion were significantly different between the experimental and control groups.Thus, the music conditioning method is also a reform and innovation compared to the existing recovery methods.

Langue:: Anglais

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Sujets de la revue:: Sciences de la vie, Sciences de la vie, autres, Mathématiques, Mathématiques appliquées, Mathématiques générales, Physique, Physique, autres

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The practice of music in volleyball players’ physical recovery: incorporating data-driven analysis

Dan Lv

Li Zhou

Wei Li

Yan Wang

Publié en ligne: 17 mars 2025

Reçu: 13 oct. 2024

Accepté: 02 févr. 2025

DOI: https://doi.org/10.2478/amns-2025-0261

Mots clésMeta-analysis, Data-driven, Mean effect size, Physical performance, Volleyball players

© 2025 Dan Lv et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Meta-analysis, Data-driven, Mean effect size, Physical performance, Volleyball players