The chain mediating effect of emotional regulation ability and exercise persistence on college students’ sleep and cardiopulmonary endurance

Abstract

Cardiopulmonary endurance is a crucial factor affecting cardiovascular health. In recent years, the incidence of metabolic syndrome among university students has been on the rise due to poor cardiopulmonary endurance. Existing studies have shown that high-quality sleep is an important means of improving cardiopulmonary health; however, the mechanism by which sleep influences the cardiopulmonary endurance of university students remains unclear. This study introduces emotional regulation ability and exercise adherence as mediating variables. Using a combination of testing and questionnaire surveys, it explores the relationship between sleep quality and cardiopulmonary endurance in university students and conducts path analysis. The 20-meter shuttle run test (20mSRT) was used to assess cardiopulmonary endurance, and effective scales such as the Pittsburgh Sleep Quality Index (PSQI), the Emotional Intelligence Scale (EIS), and the Exercise Adherence Scale (EAS) were employed in the survey. A total of 266 valid questionnaires were collected (validity rate of 96.4%), and the Bootstrap method was applied to conduct chain mediation effect analysis. The results indicate that the proportion of students with good and poor sleep quality were 30.5% and 0.7%, respectively, while 98.9% of students were at high risk in terms of maximum oxygen uptake (VO2 max) for cardiopulmonary endurance. Sleep quality was found to have a low negative correlation with cardiopulmonary endurance (r = −0.033), a significant negative correlation with emotional self-regulation ability (r = −0.281), and a significant low negative correlation with exercise adherence (r = −0.143). Emotional self-regulation ability was moderately positively correlated with exercise adherence (r = 0.499). Mediation effect testing revealed that emotional self-regulation ability and exercise adherence fully mediated the relationship between sleep quality and cardiopulmonary endurance. The total indirect effect was significant, with a total effect of −0.412, a direct effect of −0.184, and an indirect effect of −0.228. In conclusion, the overall sleep quality of university students is relatively good. Higher sleep quality can predict stronger cardiopulmonary endurance, and emotional self-regulation ability and exercise adherence fully mediate the relationship between sleep quality and cardiopulmonary endurance. In other words, sleep quality indirectly enhances cardiopulmonary endurance by improving emotional regulation and exercise adherence, with no direct effect between the two.

Introduction

Cardiorespiratory endurance is a crucial component of the body’s capacity for activity and represents a composite of the functions of various organ systems and psychological qualities¹. Aerobic endurance refers to the body’s ability to perform prolonged exercise predominantly through aerobic metabolism. High levels of aerobic endurance aid in the efficient, long-term delivery of oxygen and nutrients to working muscles by the heart, lungs, and vascular system and prevent various chronic diseases, including cardiovascular diseases and type 2 diabetes. It also better equips individuals to handle stress and negative emotion^2–4. Aerobic endurance enhancement reflects the health status and functional level of the entire cardiopulmonary system. Additional research has shown that a decrease in cardiorespiratory endurance can impact university students’ enthusiasm for regular physical exercise and their experience of flow, prolong the time it takes for the heart rate to return to resting levels, and may lead to a decrease in immune system function, thereby affecting the quality of life⁵. Nationally, the overall level of cardiorespiratory endurance among university students is experiencing a “funnel-shaped” decline, prompting reflection on what is causing the decline in cardiopulmonary function levels and insufficient physical activity among students despite national efforts to promote the “National Student Physical Health Standards” and other motivational measures. The Eighth National Student Physical Health Survey Report highlights performance in endurance and speed among university students. Declines in the cardiorespiratory endurance levels of university students are likely to cause complex changes in their psychological and physiological states⁶.

Sleep is one of the fundamental physiological needs of humans, with sleep quality affecting the comfort and depth of an individual’s sleep^7,8. Sleep has garnered support from numerous classic theories in the development of circadian rhythm theory. Notably, Borbély’s two-process model and the Exceeding Compensation Theory are models and theories based on sleep to explain and predict cardiopulmonary performance under high-intensity exercise^9,10. The two-process model is established based on the sleep homeostasis mechanism and controlled by the circadian pacemaker. It has been refined into a model to predict fatigue and recovery and regulate neurological and health functions. The Exceeding Compensation Theory is the General Adaptation Syndrome (GAS) proposed by Hans Selye to restore homeostasis in the body after stress¹¹. Scholars internationally have found that the two-process model and Exceeding Compensation Theory correspond in attributing cardiorespiratory endurance. They relate to short-term recovery and long-term adaptation in high-level cardiorespiratory aerobic exercises, connected to the steady-state (Process S) of the two-process model, with a significant regulatory role of the sleep–wake cycle in the accumulation and release of pre-exercise stress¹². The sleep–wake cycle is an essential factor influencing cardiorespiratory endurance. It is closely related to energy consumption and supply and neural regulation. Further research indicates that sleep deprivation, which disrupts this cycle, not only interferes with these physiological processes but also significantly affects overall endurance performance and negatively impacts cardiovascular health and the body’s circulatory system¹³. Multiple comparative studies have shown that sleep quality significantly impacts cardiovascular endurance and other health indicators ¹⁴. In adolescents, there is a close association between sleep duration and cardiometabolic factors. Those who sleep less than six hours per night have higher systolic blood pressure¹⁵. The increased pressure on the blood vessel walls during heart contractions weakens the heart’s ability to oxygenate the blood. Consequently, the heart’s pumping capacity and efficiency affect cardiorespiratory endurance performance. Moreover, recent studies suggest that solely exploring the direct impact of sleep on cardiorespiratory endurance cannot fully reveal the complex mechanisms involved. There is still a lack of robust empirical evidence on the underlying mechanisms explaining why sleep can affect the cardiorespiratory fitness of university students¹⁶. This study is based on the basic requirements and standards for sleep quality outlined in the “China Sleep Research Report 2024” and adopts the definition of sleep quality by Professor Charles Czeisler of Harvard Medical School. It considers the role of certain psychological variables, such as self-regulation, goal attainment, and positive mindset, in this association. Therefore, this study will further analyse the mediating role of self-emotion management and exercise adherence in the relationship between sleep and cardiorespiratory endurance in university students from a psychological and cognitive perspective^17–20.

Emotional management ability refers to an individual’s capacity to recognise, express, and regulate emotions, as well as to identify and appropriately respond to the surrounding context²¹. According to Goleman’s Emotional Intelligence Five-Factor Model, emotional management is not only the ability to perceive, control, and evaluate one’s own emotions but also the ability to understand, interpret, and respond to the emotions of others^22. When university students engage in physical fitness exercises, individuals can establish empathy with others through emotional management, thereby enhancing their intrinsic motivation and psychological resilience when facing difficulties during exercise²³. By effectively managing their emotions, students can maintain a positive mindset and sustain continuity and enthusiasm in their exercise routines. Specifically, individuals can regulate emotional responses during aerobic exercise, thus improving their exercise performance and adherence.

Furthermore, the frequency and depth of emotional regulation strategies used can reflect an individual’s cognitive and emotional response to their environment during exercise²⁴. Especially when facing challenges and setbacks, good emotional regulation can help individuals enhance their sense of agency.

Other studies suggest that when individuals face intense negative emotional experiences, an imbalance between the brain’s emotional system and cognitive control system may lead to ineffective emotional management, resulting in anxiety and irritability²⁵. This can cause psychological discomfort during exercise, hindering improvements in cardiorespiratory endurance²⁶.

According to the emotional schema theory, individuals with varying levels of cardiorespiratory endurance form different cognitive schemas through interaction with their environment and accumulated experiences, helping them understand and process new information^27,28. During this process, a person’s self-efficacy contributes to the formation of a consistent self-concept and worldview, thereby maintaining consistent emotional responses and behaviour across different situations. Moreover, the emotional stability brought about by this consistency may help reduce the negative impact of emotions on physiological functions during intensive physical tasks, allowing individuals to conserve cognitive resources in maintaining their psychological state, thereby positively affecting their cardiorespiratory endurance.

Exercise adherence can significantly impact the physical fitness of university students, representing the process by which individuals maintain perseverance and problem-solving abilities when facing challenges^29. With the development of Self-Determination Theory (SDT), scholars have increasingly clarified the structure of self-determination³⁰. When an individual’s three basic psychological needs for autonomy, competence, and relatedness are satisfied, they exhibit higher intrinsic motivation and are more likely to persistently complete set tasks³¹. Research indicates that exercise adherence is a crucial factor in achieving challenging goals. Individuals with high exercise adherence experience increased psychological benefits during and after exercise (perceived exercise plasticity), which in turn raises their physical activity intention and exercise intention, leading to longer participation in such sports activities³². Conversely, individuals with poor exercise adherence experience decreased feelings of competence and control during physical exercise.

According to Harter’s Competence Motivation Theory, the sense of competence gained during activities forms the basis of intrinsic motivation for behavior³³. Successful experiences and exercise satisfaction result from exercise adherence and active activity participation. Erikson’s psychosocial development theory posits that the development of self-identity and a sense of belonging are critical factors in predicting future social adaptability³⁴. University students with strong self-identity and belonging often exhibit higher self-efficacy. They are more confident in their ability to persist in achieving exercise goals, and this confidence and positive self-perception enhance their initiative, thereby increasing exercise adherence. Previous research has found a significant correlation between exercise adherence and frequency, intensity, and motivation³⁵. In an appropriate sports environment, an internal drive is generated when individuals feel the need or obligation to participate in a specific physical activity³⁶. The more robust this internal drive, the longer the duration of the exercise behavior, leading to more substantial positive effects on cardiorespiratory fitness and endurance.

There is a bidirectional relationship between emotional management ability and exercise adherence. Exercise adherence largely depends on the activation of intrinsic motivation; that is, when individuals engage in aerobic endurance exercises for intrinsic values such as health and enjoyment, they are more likely to sustain this behavior. The three basic psychological needs in Self-Determination Theory (SDT)—autonomy, competence, and relatedness—are crucial in supporting intrinsic motivation³⁷. When individuals feel they can control their goals and plans (autonomy), believe they can effectively complete the exercise behavior (competence), and establish connections with others (relatedness), their exercise adherence is strengthened. According to Self-Regulation Theory, good emotional management ability helps individuals better cope with potential negative emotions, such as frustration, anxiety, and irritability, by maintaining the motivation to complete set tasks and overcoming physiological “threshold” barriers³⁸. Erikson’s theory examines how individuals face and resolve various social and psychological challenges through the psychosocial development stages of the lifecycle, particularly in adolescence and early adulthood³⁹. During these stages, exercise can be a tool for self-assessment and social interaction, helping individuals build identity and confidence. Integrating SDT and Erikson’s theory, continuous exercise improves overall psychological health and well-being by satisfying autonomy, competence, and relatedness needs. Enhanced cardiac and pulmonary energy supply and circulation efficiency during exercise subsequently improve cardiorespiratory endurance. In summary, this study attempts to establish a pathway for the impact of sleep status on cardiorespiratory endurance in university students, based on the psychological and physiological mechanism frameworks from existing literature on sleep quality affecting endurance fitness. Based on this, the study proposes Hypothesis H1: There is a correlation between sleep, emotional self-regulation, exercise persistence, and cardiorespiratory endurance. It aims to explore how sleep quality, emotional management ability, and exercise persistence influence the cardiorespiratory endurance performance of university students. Further exploring the mechanisms through which sleep quality affects university students’ endurance, and based on the Dual Process Model and Self-Determination Theory principles, this study proposes Hypothesis H2: Both emotional management ability and exercise persistence partially mediate university students’ cardiorespiratory endurance. This hypothesis aims to reveal how emotional regulation and exercise persistence, as mediating variables, indirectly influence students’ cardiorespiratory endurance by affecting their exercise behavior and psychological state. Facing challenges posed by irritability and anxiety among university students, individuals with higher levels of exercise adherence believe they have the ability to overcome setbacks encountered in various sports activities. They adopt a series of positive mental health behaviors to rationally regulate their emotional changes. Therefore, Hypothesis H3 is proposed: Emotional management ability and exercise persistence play a chain-mediated role in the impact of sleep on university students’ cardiorespiratory endurance. This hypothesis aims to analyze how emotional management and exercise persistence interact and serve as the key mechanisms through which sleep quality influences cardiorespiratory endurance.

Methods

Data sources

Survey participants and sampling method

The survey participants were students enrolled at Nantong University in Nantong City, Jiangsu Province, China. The sample size was estimated using Shao Zhiqiang’s maximum sample size calculator (see Formula 1), with a Type I error (α) set at 0.05, allowable error (δ) at 0.03, sample rate (P) at 0.05, and a finite population size (N) of 42,000. The results indicated a minimum required sample size of 203⁴⁰. A stratified random sampling method was used, dividing the population into two groups based on gender. Freshmen and sophomore students from different majors at Nantong University were selected as participants. Questionnaires were distributed and completed collectively in administrative classes, with a total of 276 paper questionnaires distributed. After excluding 10 invalid questionnaires (those with patterned responses or incomplete answers), 266 valid questionnaires were recovered (age: M ± SD = 18.93 ± 0.72). The effective recovery rate was 96.4%, and the sample size met the standards. The sample size calculation is shown in Fig. 1.

Fig. 1.

The formula for calculating sample size.

Survey quality control

The quality control of the study includes the following aspects: First, standardization of the research plan and the implementation of the questionnaire survey. Prior to the formal survey, special training was provided to the surveyors, standardized introductory statements were formulated, and familiarity with the questionnaire content and filling precautions were emphasized. The surveyors were the students’ counselors or instructors. Second, data cleaning rules were established to ensure the external validity of the analyzed data. During data preprocessing, data with logical errors, omissions, mistakes, or indistinguishable entries were retested or removed to ensure the authenticity and validity of the data. Third, a common method bias test was conducted before applying data analysis. During the survey administration to college students, the principal investigator emphasized the anonymity and confidentiality of the questionnaire, explaining that the data were solely for scientific research purposes to control the sources of common method bias as much as possible. Additionally, Harman’s single-factor test was used to test for common method bias. The results showed that there were seven factors with eigenvalues greater than 1, and the variance explained by the first common factor was 37.69%, which is below the critical standard of 40%, indicating that there was no serious common method bias in this study⁴¹.

Inclusion and exclusion criteria

The inclusion and exclusion criteria are as follows. By ensuring participants are aged 18 or older, have regular communication and expression abilities, and have not engaged in regular aerobic exercise in the past 3 months (with exercise intensity close to 80% of maximum heart rate), the study ensures sample consistency and comparability, avoiding the influence of individuals with higher baseline cardiorespiratory endurance. Furthermore, ensuring that participants understand the purpose and process of the study and voluntarily agree to participate ensures compliance with ethical standards, safeguarding both the study’s legitimacy and participants’ autonomy.

Excluding individuals with known severe mental health issues or those currently taking medication that affects exercise performance or emotions (such as antidepressants or anxiolytics) helps ensure the independence of emotional management ability. It eliminates potential confounding factors that could influence emotional regulation and exercise performance. Additionally, excluding individuals with severe respiratory diseases, chronic exercise injuries, or underlying and hereditary conditions ensures that the study sample is relatively homogenous in terms of health status, reducing the interference of these factors on cardiorespiratory endurance, thereby enhancing the reliability and accuracy of the results.

Testing methods

Meter incremental shuttle run test

1. Feasibility of the evaluation

   The 20mSRT (20-Meter Shuttle Run Test) is a commonly used method to predict maximal oxygen uptake (VO2 max). It is simple to administer, requiring only an audio device for instruction playback, making it very suitable for evaluating cardiorespiratory fitness in large populations. This test is highly adaptable and does not require specialized sports facilities; it can be conducted in any sufficiently large flat area. According to the research by Liu Haiyun and colleagues, the 800/1000 m run test can effectively evaluate the cardiorespiratory endurance of 16–18 year-old high school students, predict VO2 max regression models, and be applied to the physical health assessment of high school students. However, this study did not compare the 800/1000 m performance of 16–18 year-old high school students with that of 18–20 year-old college students. According to the research by Cai Qiu, Wang Bubiao, and Gong Zhengwei, although Cooper’s 12-min run is also a simple and effective method for predicting VO2 max, it is more suitable for athletes with some training background⁴². In contrast, the 20mSRT developed by Léger shows high predictive accuracy for both trained athletes and untrained individuals⁴³. The research by Wang Xiang and colleagues indicates that the 20mSRT is an effective tool for evaluating VO2 max in Chinese college students. Léger and colleagues established regression equations for different age groups based on the relationship between VO2 max, maximal running speed (S), and age (A). For individuals aged 8 to 18, the prediction formula for VO2 max is: 8–18 years: VO2 max (ml/kg/min) = 31.025 + 3.238 × S − 3.248 × A + 0.1536 × A × S; for those over 18: VO2 max (ml/kg/min) = − 27.4 + 6.0 × S (R2 = 0.80, SEE = 4.7 ml/kg/min)^44,45.

2. Preparation

   The test is divided into two stages: the adaptation stage and the experimental stage. Before the test, a one-week adaptation training is conducted, where two professional coaches instruct the subjects. They explain the preparation, precautions, rules, and other behavioral details of the 20-Meter Music Shuttle Run Test to the recruited subjects, ensuring that all subjects fully understand the entire testing process. The formal experiment will be conducted from November 30, 2023, to January 20, 2024 (lasting 2 months), testing two administrative classes per week, each session lasting 55 min (including 10 min of explanation and preparation activities, 40 min of testing, and 5 min of stretching). Considering the feasibility of the study, the researchers also informed the class counselors about the test conditions, research objectives, and procedures, ensuring the voluntariness, confidentiality, and safety of the participants. Additionally, full support and cooperation were obtained from the parents of the subjects. During the study, if any subject feels unwell, they can withdraw unconditionally at any time, and their results will not be recorded by the researchers. All subjects signed informed consent forms.

3. Testing procedure

   Grouping: Each class is divided into two large groups based on gender, with each group consisting of 18–20 individuals. Each group is tested once in total. Testing Equipment: Sound system for playing the test audio; 20-Meter Shuttle Run Tester for recording test time, level, laps, and speed. Test Venue: East Playground of Nantong University in Nantong City. Measure a 20-m distance with a tape measure, mark the start and finish lines with chalk, place marker cones, and ensure there is a 5-m buffer zone on both sides of the start and finish lines. Test Explanation: First, explain the rules and precautions of the 20-Meter Shuttle Run Test and listen to the audio to ensure that the subjects understand the rules. Before the test begins, the test personnel lead the subjects in warm-up activities to prevent injuries. Five minutes after the warm-up activities, the subjects are equipped with heart rate monitors. Once ready, the first group of college students stands at the starting line, waiting for the test to begin. During the test, record each subject’s maximum speed and laps (one-way 20 m is counted as 1 lap), the total number of completed laps, and the speed at the final level. After the test, the test personnel lead the subjects in cool-down activities .According to Léger’s load scheme, the 20-Meter Shuttle Run is performed from slow to fast. The music rhythm increases by one level (stage) per minute, with an initial speed of 8.5 km/h, and each subsequent stage increases the speed by 0.5 km/h. The test is terminated when the subject can no longer maintain the speed set by the music and stops running midway, or fails to reach the end line twice in a row before the music sounds. Each one-way 20 m is recorded as 1 lap, and the maximum number of laps completed by the subject is recorded.

4. Cardiorespiratory fitness evaluation

   To effectively promote the improvement of college students’ cardiorespiratory endurance, the maximum oxygen uptake (VO2 max) was calculated using Léger’s regression model in conjunction with the 20-Meter Shuttle Run Test results. The Fitnessgram® Healthy Fitness Zone Standards, developed by the Cooper Institute, were used to evaluate the cardiorespiratory endurance of college students (ml/kg/min).The Fitnessgram testing standards have been widely applied in 12 countries and over 8,500 schools across all 50 states in the United States. The American Fitnessgram uses school-based norms to determine three zones for each indicator: “Healthy Zone,” “Needs Improvement Zone,” and “Health Risk Zone.” These zones provide clear reference boundaries for the test takers, guiding them towards relevant health goals. According to Fitnessgram 10.0, the healthy range for males aged 17 and above is 72–106 laps, and for females, it is 41–72 laps, corresponding to maximum oxygen uptake values of 41.3–44.2 ml/kg/min for males and 35.4–38.5 ml/kg/min for females^42,46.

Measurement tools

General information questionnaire

Includes demographic data such as gender, age, grade, ethnicity, and student number.

Pittsburgh Sleep Quality Index (PSQI)

A revised questionnaire based on Buysse’s Pittsburgh Sleep Quality Index, adapted by Liu Xianchen, used to assess the sleep quality of college students.The scale covers 7 dimensions with a total of 19 items, scored from 0 to 3. The overall scale uses reverse scoring, with higher total scores indicating more severe sleep quality problems. The general score ranges from 0 to 21.In past research, the sleep quality questionnaire has been commonly used among medical staff, high-stress groups, the elderly, and individuals with special sleep disorders, demonstrating high reliability and validity levels (0.77 ~ 0.88), within the range defined by social science research⁴⁷.

Exercise Adherence Scale (EAS)

Using the “Amateur Sports Exercise Adherence Questionnaire” developed by researchers Wang Shen and Gu Chunqiang, this questionnaire has 3 dimensions: effort investment, emotional experience, and behavioral habits. It contains 14 items, scored on a 5-point scale, used to measure students’ exercise adherence. Content validity was ensured through expert evaluation, and homogeneity reliability was tested using Cronbach’s α coefficient. The α coefficients for the total questionnaire and the three dimensions were 0.923, 0.791, 0.860, and 0.882, respectively. The fit indices were X2/df = 2.896 < 5, CFI = 0.945 > 0.9, GFI = 0.901 > 0.8, and RESEA = 0.069 < 0.08^48,49.

Emotional Intelligence Scale (EIS)

The EIS was developed by Scott and colleagues based on the theory by MEYER (1990). The Chinese version of the EIS was translated by Wang Caikang from South China Normal University, and its construct validity was verified (a = 0.83). This study decided to use this scale for investigation and analysis. This scale can be used to assess individuals’ ability to perceive, understand, express, control, and manage emotions in themselves and others. Higher total scores indicate higher levels of emotional management ability⁵⁰.

Statistical processing

Data processing in this study was conducted using SPSS 26.0, EXCEL, GraphPad Prism 10.2.2, and the Forestploter package in R for statistical analysis and graphical plotting. The specific steps are as follows: (1) Use Excel software for preliminary processing of the questionnaire data, re-testing or deleting missing or problematic data. (2) Perform a common method bias test to avoid common method bias issues. (3) Use one-way ANOVA and chi-square tests to analyze core variables, with Cramer’s V coefficient ranging from [0–1]. The greater the difference, the stronger the influence and correlation between variables. (4) Use Pearson correlation analysis to examine the relationships between sleep quality, endurance quality, emotional management ability, and exercise adherence, with values ranging from [− 1, + 1], where 1 indicates a perfect correlation, -1 indicates a perfect negative correlation, and 0 indicates no correlation. (5) Use linear regression analysis to explore the joint effects between independent and dependent variables. (6) Conduct mediation analysis using model 6 in the Process 4.0 plugin and use the Bootstrap method to test the mediation model for relevant core variables.

Results

Descriptive results analysis

Before examining the mechanism of the impact of sleep quality on college students’ endurance quality, a description of the basic situation of each variable is necessary, including sample size, percentage, chi-square value, and Cramer’s V coefficient. The results in Table 1 show that college students’ overall sleep quality is relatively optimistic, with female college students having better sleep quality than their male counterparts (χ2 = 2.611, p > 0.001, Cramer’s V = 0.099). From the “gold standard” of cardiorespiratory endurance, maximum oxygen uptake, 98.9% of the student population is in the unhealthy-high risk state, with no students in the healthy cardiorespiratory endurance range (χ2 = 169.296, p < 0.001, Cramer’s V = 0.796). Male college students generally have better exercise adherence than females, but only a tiny portion of the college student population has high exercise adherence. Sophomore students generally have better exercise adherence than first-year students (χ2 = 2.143, p < 0.001, Cramer’s V = 0.09). Differences in emotional management ability are not significant across different genders (F = 0.019, p > 0.05, η² < 0.001) and grades (F = 0.038, p > 0.05, η² < 0.001).

Table 1.

Descriptive statistic variables.

Norm	Assemble		Gender				Grade
			Male (n = 174)		Female (= 92)		Freshman (n = 101)		Sophomore (n = 165)
	n	%	n	%	n	%	n	%	n	%
Sleep quality
Excellent	81	77.6	50	28.7	31	33.7	31	30.7	50	30.3
Great	139	13.1	90	51.7	49	53.3	53	52.5	86	52.1
Not so good	44	9.3	32	18.4	12	13	16	15.8	28	16.9
Very bad	2	0.7	2	1.2	0	0	1	1	1	0.7
χ2			2.611				0.176
p			> 0.001				> 0.001
Cramer’s V			0.099				0.026
VO2max
Unhealthy—High Risk	263	98.9	171	98.3	100	100	99	98	164	99.4
Unhealthy—Low Risk	3	1.1	3	1.7	0	0	2	2	1	0.6
Healthy	0	0	0	0	0	0	0	0	0	0
χ2			169.296				195.405
p			< 0.001				< 0.001
Cramer’s V			0.796				0.605
Exercise adherence
Lower exercise adherence group	46	17.3	26	14.9	20	21.7	13	12.8	33	20
General exercise adherence group	167	62.8	111	63.8	56	60.9	63	62.4	104	63
Higher exercise adherence group	53	19.9	37	21.3	16	17.4	25	24.8	28	17
χ2			3.75				2.143
p			< 0.001				< 0.001
Cramer’s V			0.119				0.09
Emotional self-regulation ability
	M	SD	M	SD	M	SD	M	SD	M	SD
	30.229	4.606	30.201	4.777	30.283	4.287	30.158	4.8943	30.273	4.434
F			0.019				0.038
p			> 0.05				> 0.05
η2			< 0.001				< 0.001

Note: In the text, ** indicates P < 0.01, and *** indicates P < 0.001.

Correlation analysis

The correlation analysis of each variable is shown in Fig. 2. The data presented in the figure show the correlations between sleep quality, emotional self-regulation ability, exercise persistence, and cardiorespiratory endurance (maximum oxygen uptake). Significant relationships are marked with an asterisk in the image, and the depth of color reflects the size and direction of the correlation coefficient (red for positive correlations, blue for negative correlations). Sleep quality is negatively correlated with maximum oxygen uptake (VO2 max), with a low negative correlation. The improvement in VO2 max indirectly affects the enhancement of sleep quality in college students. Sleep quality is significantly negatively correlated with emotional management ability (r = − 0.28). Sleep quality is significantly negatively correlated with exercise adherence (r = − 0.20). Emotional management ability is positively correlated with VO2 max (r = 0.065) with a low positive correlation. The gradual improvement in VO2 max may accompany the increase in emotional management ability. Exercise adherence is significantly positively correlated with VO2 max (r = 0.22). Emotional management ability is significantly positively correlated with exercise adherence, with a moderate positive correlation (r = 0.53).College students’ exercise adherence increases with the rise in emotional management ability, confirming the original hypothesis H1.

Fig. 2.

Correlation matrix of variables.

Multiple stepwise regression analysis

According to the results in Fig. 3, compared to male college students, the VO2 max of female college students is significantly lower (β = − 4.84, 95% CI = − 5.83 to − 3.85, P < 0.001). This indicates that, all else being equal, females have lower average cardiorespiratory endurance than males. Sophomore students compared to freshman students show no significant difference in cardiorespiratory endurance (β = − 0.11, 95% CI = − 1.30 to 1.09, P > 0.05). Grade level does not have a significant impact on cardiorespiratory endurance, but age has a significant negative effect (β = − 2.15, 95% CI = − 2.88 to − 1.42, P < 0.001), indicating that increasing age may contribute to a decline in cardiorespiratory endurance. Sleep quality (β = − 0.19, 95% CI = − 0.78 to 0.41, P > 0.05) and emotional management ability (β = − 0.01, 95% CI = − 0.11 to 0.10, P > 0.05) do not have a significant impact on cardiorespiratory endurance. Exercise adherence has a significant positive effect on cardiorespiratory endurance (β = 1.17, 95% CI = 0.46 to 1.88, P < 0.001). The higher the exercise adherence, the faster the potential increase in VO2 max values. According to the results of Model 2 in Table 2, with grade, gender, and age as control variables, the adjusted R-squared of the model is 0.532, indicating that all independent variables in the model explain 53.2% of the variance in the dependent variable. According to the results of the analysis of variance, the F value is 51.231, P < 0.001, indicating that the established linear regression model is appropriate. Additionally, comprehensive analysis suggests that gender and age are also important factors affecting cardiorespiratory endurance. The impact of exercise adherence on the dependent variable is greater than that of emotional management ability. Further research may be needed to explore the effects of these two factors on college students in different physical conditions.

Fig. 3.

Forest plot for multiple linear regression analysis.

Table 2.

Hierarchical regression analysis for chained mediation.

Regression equation		Overall fit index			Significance of regression coefficients
Outcome variable	Predictor variable	R	R2	F	β	SE	t
Cardiorespiratory Endurance		0.720	0.518	70.105***
	Sleep quality				− 0.060	0.296	− 1.392
	Gender				− 0.502	0.514	− 9.554***
	Grade				0.027	0.620	− 0.410
	Age				− 0.328	0.376	− 5.650***
Emotional self-regulation ability		0.293	0.086	6.146***
	Sleep quality				− 0.285	0.403	− 4.787***
	Gender				− 0.036	0.699	0.500
	Grade				− 0.037	0.845	− 0.411
	Age				0.107	0.512	1.335
Exercise adherence		0.553	0.306	22.951***
	Sleep quality				− 0.061	0.052	− 1.132
	Emotional self-regulation ability				0.524	0.008	9.692***
	Gender				− 0.016	0.086	− 0.251
	Grade				− 0.074	0.104	− 0.955
	Age				− 0.045	0.064	− 0.649
Cardiorespiratory endurance		0.737	0.543	51.231***
	Sleep quality				− 0.027	0.303	− 0.609
	Emotional self-regulation ability				− 0.005	0.052	− 0.093
	Exercise adherence				0.164	0.361	3.258**
	Gender				− 0.497	0.503	− 9.658***
	Grade				− 0.011	0.608	− 0.178
	Age				− 0.330	0.369	− 5.775***

Note: In the text, ** indicates P < 0.01, and *** indicates P < 0.001.

Chain mediation effect analysis

Based on the hierarchical regression analysis method, the following model is established: sleep quality, emotional management ability, and exercise adherence are used as independent variables; gender, grade, and age are used as control variables; and cardiorespiratory endurance (with VO2 max as the gold standard) is used as the dependent variable. The model tests the main effect, direct effect, and indirect effect. The results of the hierarchical regression analysis for chain mediation effects are shown in Table 2. As can be seen from the table, under the control of gender, grade, and age, sleep quality does not significantly predict cardiorespiratory endurance, β = − 0.060, SE = 0.296, t = − 1.392; Sleep quality significantly negatively predicts emotional management ability, β = − 0.285, SE = 0.403, t = − 4.787, P < 0.001; and significantly negatively predicts exercise adherence, β = − 0.061, SE = 0.052, t = − 1.132, P < 0.001; Emotional management ability significantly positively predicts exercise adherence, β = 0.524, SE = 0.008, t = 9.692, P < 0.001; but does not significantly negatively predict cardiorespiratory endurance, β = − 0.005, SE = 0.052, t = − 0.093, P < 0.001; Exercise adherence significantly positively predicts cardiorespiratory endurance, β = 0.164, SE = 0.361, t = 3.258, P < 0.01.

Using the bias-corrected percentile Bootstrap method (drawing 5000 Bootstrap samples) and model 6 from the SPSS macro program PROCESS 4.0 developed by Hayes (2013), a chain mediation effect analysis was conducted under the control of gender, age, and grade. The Bootstrap method is a resampling and non-parametric statistical method, particularly suitable for estimating statistics whose sampling distribution shape is unknown or difficult to derive, such as chain-mediated effects. Unlike traditional parametric testing methods (e.g., the Sobel test), the Bootstrap method generates a larger sample through resampling with replacement, thereby improving the accuracy of the estimates. In mediation analysis, this method involves resampling many subsamples (e.g., 5000 times) with replacement from the original sample, each subsample being the same size as the original. The mediation model parameters are then re-estimated for each subsample, and the mediation effect (i.e., the product of paths a and b) is calculated. The mediation effect values obtained from all subsamples are then sorted, and the upper and lower bounds of the confidence interval are determined based on the required confidence level (e.g., 95%). The lower bound of the 95% confidence interval is the 2.5th percentile, and the upper bound is the 97.5th percentile. This method provides an effective way to test the significance of the mediation effect. In the application of the Bootstrap method to examine the impact of sleep on university students’ cardiorespiratory endurance through the chain-mediated effects of emotional management ability and exercise persistence, the direct and indirect effect tests are explained as follows:

Direct Effect Test: First, regression analysis is performed to test the direct effect (coefficient c’) of the independent variable (sleep quality) on the dependent variable (cardiorespiratory endurance). After controlling for the mediation variables (emotional management ability and exercise persistence), if c’ is significant, it indicates the presence of a direct effect (cardiorespiratory endurance = c' * sleep quality + e). Bootstrap is then used to generate the confidence interval for c’. If the confidence interval does not include 0, the direct effect is considered significant, where e represents the residual.

Indirect Effect Test: The product of the path coefficients is calculated: a1 * b1 * b2, where a1 is the effect of sleep quality on emotional management ability, b1 is the effect of emotional management ability on exercise persistence, and b2 is the effect of exercise persistence on cardiorespiratory endurance. Bootstrap generates the confidence interval for a1 * b1 * b2. The indirect effect is considered significant if the confidence interval does not include 0. A Forest Plot examined the mediating effects of self-rated health and sleep quality between physical exercise and negative emotions in college students⁵¹. The Bootstrap chain mediation effect analysis results are shown in Table 3. It can be seen that the primary effect value of sleep quality on cardiorespiratory endurance is − 0.412, with a 95% confidence interval of [− 0.995, 0.171], which crosses zero, indicating that the total effect is not significant. The total indirect effect value is − 0.228, with a 95% confidence interval of [− 0.465, − 0.015], which does not cross zero, indicating that the total indirect effect is significant, accounting for 55.34% of the total effect. The total direct effect value is − 0.184, with a 95% confidence interval of [− 0.780, 0.412], which crosses zero, indicating that the total direct effect is insignificant. Thus, the model is a fully mediated chain. Specifically, the indirect effect value from sleep quality → , emotional management ability → , exercise adherence, → cardiorespiratory endurance is − 0.168, with a 95% confidence interval of [− 0.322, − 0.047], accounting for 40.78% of the total effect. The original hypothesis H2 is not supported, while H3 is supported.

Table 3.

Chained mediation effect analysis of university students’ cardiopulmonary endurance using the bootstrap method.

Effect	Effect size	BootSE	95%CI		Effect proportion
			LLCI	ULCI
Total effect	− 0.412	0.296	− 0.995	0.171
Total direct effect	− 0.184	0.303	− 0.78	0.412	44.66%
Total indirect effect	− 0.228	0.115	− 0.465	− 0.015	55.34%
Sleep Quality → Emotional Self-Regulation Ability → Cardiorespiratory Endurance	0.009	0.104	− 0.204	0.207	2.19%
Sleep Quality → Exercise Adherence → Cardiorespiratory Endurance	0.069	0.070	− 0.224	0.055	16.75%
Sleep Quality → Emotional Self-Regulation Ability → Exercise Adherence → Cardiorespiratory Endurance	− 0.168	0.071	− 0.322	− 0.047	40.78%

Figure Caption: Forest plot illustrating the results of chain mediation effects. The horizontal lines represent the 95% confidence intervals for each estimate, with the red dots indicating the point estimates for the effect sizes. The values next to each line show the effect size, 95% confidence interval, and p-values. Effect Size: Represents the magnitude of the estimated effect. 95% Confidence Interval: The horizontal lines show the range within which the true effect size is likely to fall with 95% confidence. Red Dots: Indicate the point estimates for the effect sizes of each mediation pathway. P-Values: Indicate the statistical significance of the estimates. According to the results in Fig. 3, when comparing the indirect effects of different mediation paths, the effect coefficient for C1 is 0.078, for C2 is 0.177, and for C3 is 0.099. The confidence intervals for C1, C2, and C3 all cross zero, indicating no significant differences in the sizes of the mediation effects. Overall, this indicates that sleep quality has no direct effect on the cardiorespiratory endurance of college students but does have an indirect effect. Among the indirect effects, there is no mediation effect of emotional management ability, and no mediation effect of exercise adherence alone. However, there is a chain mediation effect of emotional management ability and exercise adherence (as shown in Fig. 4). The chain mediation effect (Ind3, i.e., sleep quality → emotional self-regulation ability → exercise persistence → cardiorespiratory endurance) is significant, with an effect size of − 0.168, a 95% confidence interval of (− 0.323, − 0.048), and a bootstrap standard error of 0.0701.

Fig. 4.

Forest plot of chain mediation effects.

In summary, the mediation analysis indicates that emotional self-regulation ability and exercise persistence play a chain-mediated role in the relationship between sleep quality and cardiorespiratory endurance, with no significant direct effect of sleep quality. This suggests that sleep quality indirectly promotes improvements in cardiorespiratory endurance by enhancing individuals’ emotional management ability, which in turn influences exercise persistence. Specifically, emotional self-regulation ability serves as the first mechanism through which sleep quality impacts cardiorespiratory endurance, by reducing negative emotions, improving emotional control and stability, and enhancing students’ psychological motivation and focus during exercise, thereby making exercise behaviour more sustainable and regular. Exercise persistence, as the second mechanism, strengthens self-efficacy and positive expectations regarding exercise outcomes, further promoting students’ resilience during exercise, which in turn affects cardiorespiratory endurance. The lack of a significant direct effect of sleep quality on cardiorespiratory endurance may be because sleep quality primarily influences cardiorespiratory endurance through psychological and behavioural mechanisms, rather than directly improving physiological indicators. The significance of the chain mediation model indicates that the effect of sleep quality is transmitted step-by-step through emotional regulation ability and exercise persistence, ultimately promoting improvements in cardiorespiratory endurance (As shown in Fig. 5).

Fig. 5.

MuIti-mediating model.

Discussion

This study simultaneously examines the roles of emotional self-regulation and exercise adherence in the impact of sleep quality on the cardiorespiratory endurance of Chinese college students. Using scales, it explores the relationship between sleep quality and cardiorespiratory endurance in college students, and both Hypothesis 1 and Hypothesis 3 are validated. Sleep quality can influence cardiorespiratory endurance through the chain mediation of emotional self-regulation and exercise adherence. In other words, the impact of sleep quality on the cardiorespiratory function of college students is mediated by self-regulation and goal-directed interference. Additionally, this study suggests that there are potential benefits for college students in actively participating in physical activities to improve their cardiorespiratory health. It is also found that emotional self-regulation ability and exercise adherence play key mediating roles in regulating cardiorespiratory endurance. This highlights the importance for college students to monitor their emotional states and develop exercise adherence. By comprehensively considering the relationships among sleep quality, cardiorespiratory endurance, emotional self-regulation ability, and exercise adherence, this study provides a scientific basis for designing comprehensive intervention programs to enhance the cardiorespiratory function of college students.

The correlation between sleep quality, cardiorespiratory endurance, exercise adherence, and emotional management ability

The results of this study found that sleep quality has a low negative correlation with cardiorespiratory endurance in college students, is negatively correlated with sleep quality, and has a significant low negative correlation with emotional self-regulation ability and exercise adherence. After including the mediating variables, the positive predictive effect of sleep quality on cardiorespiratory endurance in college students remains significant, validating Hypothesis H1. Studying occupies a key position in college students’ campus life, and good sleep provides individuals with sufficient rest and recovery, enhances cognitive function, and helps the immune system function properly. Sleep is the foundation for maintaining normal physiological functions and social activities for college students. Poor sleep habits, such as staying up late, frequent napping, and over-reliance on sleeping pills, can affect college students’ lives, studies, and peer relationships. A cross-sectional study shows that sleep duration, sleep quality, and bedtime have varying degrees of impact on physical fitness. Gender is an important factor affecting the impact of daily sleep status on physical performance, and the sleep duration of both males and females is correlated with BMI and sit-up indicators, which is not consistent with the results of this study^52,53. Other studies have shown that sleep deprivation (PSD) significantly affects physical performance and psychophysiological responses during a 12-min self-paced running exercise, with deterioration in cardiorespiratory responses and physiological reactions post-exercise⁵⁴. Won’s research demonstrates that poor sleep quality is associated with impaired cognitive function, cortical atrophy, and reduced cortical thickness. Higher cardiovascular endurance has neuroprotective effects and may mitigate the relationship between poor subjective sleep quality and reduced entorhinal cortex thickness⁵⁵. The essence of emotional self-regulation ability in improving cardiorespiratory endurance for college students lies in maintaining motivation. By maintaining positive emotions and high motivation levels during exercise and identifying and understanding their emotions during it, students can better cope with temporary fatigue and discomfort, thereby maintaining motivation and enthusiasm for continuous exercise⁵⁶. This emotional self-regulation ability enables college students to view physical exercise as a positive life value experience. The results of this study show a positive moderate correlation between emotional self-regulation ability and exercise adherence. Internationally, based on the Executive Function Theory, researchers have examined the effects of different stress management programs on the cardiorespiratory efficiency of male athletes. The results of a 20-min submaximal running test showed significant differences in cardiorespiratory efficiency between the experimental and control groups^57,58. Other researchers have found that combining imaging and feedback in emotional management strategies can significantly improve individuals’ self-efficacy and actual performance⁵⁹. Similarly, emotional management strategies targeting college students’ cardiorespiratory endurance could consider incorporating various methods, such as emotional regulation training and immediate feedback, to maximize effectiveness. Previous research has often focused on the factors influencing exercise adherence in elderly populations and postoperative patients. Exercise adherence, as a psychological factor influencing the cardiorespiratory endurance of college students, can be enhanced by setting clear and challenging goals. Conversely, individuals with high exercise adherence are more likely to improve their sense of accomplishment and responsibility, thereby effectively achieving specific predetermined goals^60,61.

Direct effect of sleep quality on cardiorespiratory endurance in college students

The results of this study indicate that sleep quality does not significantly affect the cardiorespiratory endurance of college students. The direct effect is not significant, which does not directly align with previous research conclusions on the impact of sleep quality on the physical fitness of different populations. The neuroprogression hypothesis suggests that long-term regular physical exercise can significantly counteract the progression of mood disorders, enhance the expression of neurotrophic factors in the brain, and reduce neuroinflammatory responses caused by stress⁶². However, due to the gradual changes in the nervous system under stress and disease conditions, this hypothesis can also explain why the impact of sleep quality on the body may vary among different populations. This change may manifest as a more vital stress adaptation ability in young people rather than an immediate decline in neural performance. The cardiorespiratory endurance of college students may not be significantly affected by short-term declines in sleep quality. Although the direct impact of sleep quality on cardiorespiratory endurance in college students is insignificant, it may have indirect effects through other pathways. Poor sleep quality may affect college students’ emotions, cognitive function, and overall health, indirectly influencing their exercise habits and cardiorespiratory endurance⁶³. Other studies have shown significant physiological differences between college students and other populations (such as older people and athletes). Young people have a more remarkable ability to recover and better adapt to short-term sleep deprivation, so the impact on cardiorespiratory endurance is less noticeable than in older or less physically fit populations⁶⁴. The lifestyle and stress sources of college students (such as academic pressure and social activities) differ from other populations, and these factors might partially obscure the direct impact of sleep quality on cardiorespiratory endurance.

The mediating role of emotional self-regulation ability in the relationship between sleep quality and cardiorespiratory endurance

This study found that sleep quality does not affect cardiorespiratory endurance in college students through the mediation of emotional self-regulation, meaning that sleep quality cannot significantly predict the cardiorespiratory endurance level of college students⁶⁵. Previous research has shown that high-intensity physical exercise is beneficial for emotions and cognition, and the level of exercise intensity typically directly affects the extent and direction of its impact on emotional and cognitive control. Phased interventions targeting cardiorespiratory and psychological aspects can effectively enhance participants’ implicit emotional regulation abilities. Strengthening adaptive responses or recovery from negative emotional states is an important pathway to promote better emotional health^23,66. The aforementioned research found that cardiorespiratory endurance levels in exercise inversely affect emotional management, but evidence on the impact of emotional management on cardiorespiratory endurance is uncommon⁶⁷. In one study, participants who engaged in three minutes of effortful self-regulation before exercises requiring high levels of cardiorespiratory endurance showed lower average power output and affected rhythm and performance during the physical task⁶⁸. Other related research supports that self-regulation theory and the emotional regulation process model can guide the endurance performance of exercise participants. From a physiological perspective, the mechanisms through which sleep quality affects cardiorespiratory fitness and cardiovascular health often involve the body’s metabolic capacity and potential biochemical markers⁶⁹. Emotional states, as psychological manifestations of sleep disorders and sleep deprivation, as well as means of regulation and intervention before exercising cardiorespiratory endurance, play a partially insignificant mediating role in the direct effect mechanism of sleep quality on cardiorespiratory endurance. Therefore, further exploration of the specific mechanisms by which sleep quality affects cardiorespiratory function is needed. Although there is a relationship between sleep quality and cardiorespiratory endurance, this impact may be more evident in improving bodily metabolic capacity, immune function, and cardiovascular health. In this reciprocal process, emotional management ability may not be the direct mediator; its influence is more likely to be reflected in exercise motivation and psychological resilience rather than directly improving physiological conditions or cardiorespiratory adaptation, thereby influencing overall exercise performance. Universities should promote sleep health education, help students adjust their routines scientifically to enhance sleep quality and provide professional sleep counseling services. Additionally, offering courses on emotional management and psychological resilience, teaching students emotional regulation techniques and stress coping methods, can enhance their motivation and psychological resilience during exercise, helping to prevent emotional fluctuations from negatively affecting their performance.

The mediating role of exercise adherence in the relationship between sleep quality and cardiorespiratory endurance

The results of this study indicate that exercise adherence does not significantly mediate the impact of sleep quality on cardiorespiratory endurance. This suggests that while sleep quality influences exercise motivation and individuals’ beliefs and attitudes towards health, the effect is not significant. On the one hand, the direct impact of sleep quality on cardiorespiratory endurance may be so strong that the mediating role of exercise adherence becomes insignificant. Previous research largely supports the notion that improvements in exercise intensity and frequency, as well as enhanced metabolic and recovery abilities from consistent exercise, improve cardiorespiratory fitness and athletic performance⁷⁰. Additionally, high self-efficacy helps overcome challenges in exercise, and outcome expectations, which are individuals’ anticipations of exercise benefits, enhance the motivation for continued exercise. Together, these factors improve exercise adherence and overall exercise behavior⁷¹. On the other hand, studies have shown that significant differences in physiological and psychological conditions among individuals mean that some college students can maintain high exercise adherence even with poor sleep quality, while others may not adhere to exercise due to other factors (e.g., academic pressure, social activities), leading to an insignificant mediation effect in the overall sample. Exercise persistence may depend more on an individual’s self-efficacy and expectations of exercise outcomes. Therefore, psychological and motivational factors likely influence exercise continuity more than sleep quality alone. Due to the significant individual differences within the university student population, some students may maintain high exercise persistence even with poor sleep quality. In contrast, others may struggle to continue exercising due to academic pressure and social activities. As a result, exercise persistence has not been a significant mediating factor.

Chain mediation effect analysis

The mediation effect test results show that the model’s indirect effect (− 0.228), accounting for 55.34% of the total effect, indicates that the mediation effect plays a major role. The chain mediation effect of emotional self-regulation ability and exercise adherence is significantly greater than the total direct effect. This suggests that the emotional self-regulation ability and exercise adherence of college students are the main mechanisms by which sleep quality affects cardiorespiratory endurance. This study proposed and validated the “sleep quality → emotional self-regulation ability → exercise adherence → cardiorespiratory endurance” influence pathway, indicating the fundamental role of sleep in individual health and physical performance. It also highlights the potential pathway for optimizing exercise and enhancing physical function through emotional self-regulation.

This study shows that emotional self-regulation ability and exercise adherence have become important bridges connecting sleep and cardiorespiratory endurance. Good sleep quality is an important foundation for maintaining emotional stability and management abilities. Adequate sleep helps the brain recover timely and consolidate memory, promoting neural function transmission and recovery, thereby reducing the occurrence of negative emotions such as anxiety and depression⁷². Conversely, lack of sleep may lead to emotional fluctuations, irritability, and increased feelings of stress. On the one hand, cognitive appraisal theory suggests that the generation of emotions depends on individuals’ cognitive appraisal of events. How individuals assess the relationship between situations and personal interests depends on their sense of control over the situation and their reflection on emotional responses⁷³. A longitudinal study found that from the start to the end of exercise, regardless of whether participants knew the duration and process of the exercise, their emotional experience increased within 10 min after starting the exercise⁷⁴. This increase came from a strong sense of self-efficacy. After the exercise, regardless of their lack of exercise experience, participants felt more positive emotions compared to before the exercise⁷⁵. Some studies suggest that when exercise intensity approaches the ventilatory threshold or lactate threshold, there may be a decrease in pleasure or even unpleasantness. As the exercise nears its end, emotional experience may gradually change negatively with emotional management ability. This change often comes from individuals’ cognitive insight and complex reflective understanding of exercise, which is inconsistent with the results of this study⁷⁶. Experimental results from the psychology research team at Tufts University suggest that running enhances emotions and arousal more than walking. At low to moderate intensities, cognitive factors such as exercise self-efficacy, emotional management, and goal assessment determine positive emotional responses. Even if exercise reduces activation of the prefrontal cortex (PFC), individuals’ general cognitive control remains stable, which is similar to the results of this study^65,75. Other studies suggest that there may be differences in the emotional experiences expressed by exercise participants between continuous and intermittent exercise⁷⁷. Overall, emotional fluctuations and the amplitude of emotional value show a slight upward trend before and after exercis. Furthermore, emotional regulation strategies do not significantly affect participants’ perceived emotional arousal levels and physical exertion during physical activities. The positive emotional changes in college students’ daily physical activities primarily come from self-efficacy. Regardless of the preparation before exercise, post-exercise emotions are generally more positive than pre-exercise emotions. This positive emotional change not only enhances exercise adherence but also promotes the enhancement of cardiorespiratory function through good emotional management.

On the other hand, based on self-determination theory, when college students exercise out of intrinsic interest, enjoyment, or to achieve predetermined goals, they can better stimulate intrinsic and extrinsic motivation⁷⁸. This has a significant impact on achieving goals, allowing individuals to better exert their subjective initiative. Previous research has often considered exercise adherence as an independent variable⁷⁹. On the one hand, good exercise adherence helps college students establish regular exercise habits, potentially leading to increased self-confidence and better coupling in coping with stress, resulting in positive sustained exercise effects⁸⁰. On the other hand, moderate exercise adherence is more likely to cause psychological changes in college students, positively stimulating and supporting social interactions among students, thereby promoting improved exercise performance. When considering exercise adherence as a mediating variable to explore the mechanism by which sleep quality affects cardiorespiratory endurance in college students, the results show that exercise adherence has a relatively positive impact on cardiorespiratory endurance. This is similar to the findings of European scholars⁸¹. The physiological changes brought about by exercise adherence in college students often lead to increased peer support and self-efficacy, which in turn promotes emotional control and emotional intimacy and stability⁸². Furthermore, there are relatively close path connections between subjective exercise experience, exercise commitment, and exercise behavior. Effective emotional management ability has a significant impact on the exercise adherence of college students⁸³. The results of this study provide a breakthrough in understanding the mediating role of emotional self-regulation and exercise persistence in the relationship between sleep and the enhancement of endurance levels in university students. Specifically, sleep quality affects exercise performance through physiological recovery and improves an individual’s emotional management ability, which influences exercise persistence and indirectly promotes improvements in cardiorespiratory endurance. Adequate sleep aids in releasing brain-derived neurotrophic factor (BDNF), the clearance of cerebrospinal fluid and metabolic waste, and the regulation of stress hormones such as cortisol. This helps reduce emotional fluctuations and stress, thereby improving emotional management ability and promoting continued participation in exercise the following day. The positive expectations and higher self-efficacy among university students further motivate them to persist in exercise, ultimately improving their cardiorespiratory endurance. Based on the above, it is recommended that universities develop reasonable student schedules (such as setting appropriate dormitory check times) to reduce the internal strain caused by excessive socializing. On the other hand, universities should offer courses on emotional management training, sports psychology, and sports health to help students develop a positive attitude towards exercise and emotional regulation skills. Additionally, psychological support before and during exercise (such as enhancing arousal levels) should be strengthened to improve overall exercise performance further. Furthermore, social organizations can create exercise plans suitable for different groups, considering the specific environmental characteristics of communities or workplaces and offering flexible exercise options to help more individuals establish healthy exercise habits.

Conclusions

By exploring the chain-mediated role of emotional management ability and exercise persistence and analyzing the intrinsic connections between these factors, this study helps to expand the explanation of the mechanisms influencing university students’ endurance quality. Additionally, the study has a positive societal impact on promoting healthy lifestyles and enhancing university students’ physical and mental well-being. It provides a feasible foundation for designing more comprehensive intervention programs to improve students’ endurance. The contributions of this study are as follows: (1) It reveals the complex relationships among sleep quality, emotional management ability, exercise persistence, and cardiorespiratory endurance, proposing a new chain mediation model; (2) It provides empirical evidence for psychological interventions and health management to improve university students’ cardiorespiratory endurance; (3) It offers an innovative and contemporary practical pathway for psychological education, mental health promotion, and exercise interventions in universities.

Recommendations

This study clarifies the significant role of emotional regulation ability and exercise adherence in the pathway through which sleep impacts the cardiovascular endurance of university students.

1. To improve sleep quality, university students should maintain a regular sleep schedule, going to bed and waking up at the same time each day, even on weekends. This helps regulate the individual’s biological clock. Additionally, creating a quiet, dark, and calm sleep environment is essential, and using electronic devices before bed should be avoided, as the blue light emitted by screens can interfere with melatonin secretion. Furthermore, engaging in appropriate relaxation activities before sleep, such as reading, meditation, or deep breathing exercises, can help with psychological adjustment.

2. Enhancing emotional management ability is crucial. University students should learn to identify and express their emotions, releasing emotional stress through journaling and talking to friends, close companions, or family. Learning and applying emotional regulation techniques, such as mindfulness meditation and progressive muscle relaxation, can help alleviate anxiety and depressive emotions.

3. Universities can establish peer support groups, regularly distribute relevant questionnaires, and organize mental health lectures to provide emotional support and a platform for communication. For students with more serious emotional issues, it is recommended that they seek professional psychological counseling. The university’s counseling center should provide free consultation services.、

4. Strengthening exercise persistence is key to improving cardiorespiratory endurance. University students can develop a realistic aerobic exercise plan, engaging in at least three weekly aerobic sessions, such as jogging, swimming, or cycling, for 30–60 min each time. Breaking down larger goals into smaller, manageable targets can help achieve them step by step, boosting the sense of accomplishment and self-confidence. Finding exercise partners, such as friends or classmates, to work out together can provide mutual supervision and support, making exercise more enjoyable and sustainable. Additionally, using fitness tracking tools to record the time, distance, and heart rate during each session and reviewing and summarising periodically can motivate consistency. Moreover, by trying different forms of exercise, such as yoga, dance, or group fitness classes, students can avoid monotony and increase their enjoyment of working out.

5. Universities can adopt comprehensive intervention measures to encourage and provide more avenues for exercise (such as regular physical health tests, on-campus sports competitions, and a wide variety of fitness club activities and sports societies). These measures can help identify and address potential physical and psychological issues promptly. By fostering a positive campus atmosphere through campus culture and various practical activities and strengthening family and social support, parents, communities, and internship units can also be involved in students’ health education, creating a collaborative educational environment involving the school, family, and society.

Limitation

The study’s limitations are as follows: Firstly, the research sample is primarily concentrated in Nantong City, Jiangsu Province. Future studies could consider diversifying the sample sources to make the findings more generalizable. Secondly, this study mainly utilized testing and surveys to examine how sleep quality affects university students’ cardiorespiratory endurance. Future research could consider case studies to focus on specific indicators for more precise discussions and analyses, which may yield more valuable insights. Additionally, while self-reports provide participants’ subjective experiences and opinions and allow for quick and convenient data collection, the reported data may be subject to response bias, potentially influencing individual students’ cognitive levels and emotional states. Finally, cross-sectional studies cannot establish definitive causal relationships. Future research could select more representative samples for longitudinal intervention studies while also considering the impact of potential external environmental factors on the variables being studied, making the results more meaningful.

Abbreviations

PSQI

The Pittsburgh Sleep Quality Index

20mSRT

20-Meter shuttle run test

EIS

Emotional Intelligence Scale

EAS

Exercise Adherence Scale

SD

Standardized deviation

Boot SE

The standard error of 95% Bootstrap confidence interval

Boot LLCI

Lower limits of 95% Bootstrap confidence interval

Boot ULCI

Upper limits of 95% Bootstrap confidence interval

Author contributions

F-zM: Data curation, Formal analysis, Investigation, Methodology, Writing original draft, Writing—review & editing. B-wZ: Data curation, Formal analysis, Investigation, Methodology. BL:Data curation, Funding acquisition, Methodology, Resources, Writing—review & editing. HL: Funding acquisition, Resources, Writing—review & editing. W-dZ: Data curation, Validation, Writing—review & editing. X-hC: Writing—Review & Editing. MC: Offer a proposal. Q-cW: Check the methodology section. L-lZ: Methodology, Writing—review & editing. JL: Methodology, Resources, Writing—review & editing.

Funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. 2022 Jiangsu Province Education Science Planning Project. (B/2022/01/173). 2024 Jiangsu Higher Education Society Labour Education Research Committee: Research on the University Curriculum System of "Five Educations Simultaneously"—Moral, Intellectual, Physical, Aesthetic, and Labour Education.(2024GZJX049).

Data availability

The raw data supporting the conclusions of this article will be available from Jun Liu (13515203919@163.com) on reasonable requests.

Declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

This study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of Nantong University (No 70/2022). Informed consent was obtained from all participants involved in this study.