The Effects of Combining Visual-Auditory Stimuli with Exercise on Short-Term Affect Improvement: A Randomized Controlled Trial

The Effects of Combining Visual-Auditory Stimuli with Exercise on Short-Term Affect Improvement: A Randomized Controlled Trial | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The Effects of Combining Visual-Auditory Stimuli with Exercise on Short-Term Affect Improvement: A Randomized Controlled Trial Meng Tao, Jingchuan Gao, Haiquan Huang, Yuanyuan Cao, Jie Zhuang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4345575/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Aug, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Objectives: Prior research has explored the effects of engaging with real or virtual natural landscapes and listening to music during aerobic exercise on short-term affect, However, the specific differences in the improvement of short-term affect by different combinations of VR and music rhythm require further investigation. This study aims to explore the differential impact of distinct VR and music integration strategies on short-term affect, thereby informing future research directions and optimizing public fitness practices. Methods: This study recruited 132 valid subjects (mean age 24.0±0.9 years), with a gender distribution of 68 males and 64 females. Participants were randomly assigned to one of four groups: Visual-Music (V-M), Music-Visual (M-V), Visual-only (V), and Music-only (M). The exercise mode was 15 minutes of aerobic power cycling with 2 minutes of low-intensity power cycling intervals in the middle. After the exercise, the subjects were asked to sit and then performed either a VR intervention or a music intervention for 15 minutes. The collected indicators included blood pressure, positive/negative affect, and heart rate variability indicators (RMSSD, SDNN, LF/HF). Data analysis included descriptive statistics, repeated measures ANOVA, and multifactor ANOVA. The effect of different VR and Music combined with exercise interventions on the improvement of short-term affect was analyzed based on the effect size (ɳp 2 ) and combined with the significance p-value. Results: Intra-group differences showed that DBP, positive affect, negative affect, SDNN, RMSSD indicators in V-M group were significantly different before and after the experiment (p<0.05), while SBP, positive affect, negative affect, SDNN, RMSSD, LF/HF indicators in M-V group were significantly different before and after the intervention (all p<0.05). Only SDNN and RMSSD indicators in group M had significant differences before and after the experiment (p<0.05), and only SBP and RMSSD indicators in group V had significant differences before and after the experiment (p<0.05). The difference between groups showed that compared with other short-term affect response indicators, only SDNN and LH/HF groups had a significant difference (p0.05). In general, the improvement effect of the visual-auditory combined exercise on short-term affect was due to the single visual or auditory activity. Conclusion: Aerobic exercise with consistent intensity and the combined visual-auditory interventions (M-V and V-M) significantly improved blood pressure, and the short-term affect of physiological responses (LF/HF, SDNN, RMSSD), along with subjective affect measures, compared to other intervention groups.These findings suggest that incorporating VR and music with exercise can effectively enhance short-term affect, recommending an integrated approach to aerobic exercise and relaxation through music and visual exposure to natural environments. Biological sciences/Physiology Biological sciences/Psychology Earth and environmental sciences/Environmental sciences Health sciences/Health care Virtual Reality Music Heart Rate Variability Aerobic Exercise Short-term Affect Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 Introduction Music therapy utilizes different music-based techniques to improve physical and mental well-being through therapeutic interventions and musical activities. It supports individuals in enhancing their mental health and managing illnesses through the music. The field has gained recognition with medical technology advancements, highlighting the integration of music into exercise routines as a common practice in music therapy. Such practices have been shown to reduce exercise-induced fatigue and elevate mood post-exercise (Juslin and Västfjäll, 2008 ; Heneghan et al., 2020). Music is not just a form of auditory art; specific musical compositions can impact individual behavior, affects, and physiological responses, contributing to the regulation of both psychological and physiological functions in the human body(Koelsch, 2014 ; Rönnberg et al., 2022 ). Recent advances in cognitive neuroscience and technology have deepened our understanding of how music affects the brain mechanisms, offering insights into the regulatory effects of music interventions on human emotions (Peretz, 2016 ; Koelsch, 2018 ). Simultaneously, the development of virtual reality (VR) technology has introduced immersive tool for experiencing natural environments. VR create a profound sense of mental presence within the virtual environment, by immersing individuals in interactive computer-generated simulations, serving as a valuable complement to real-world nature experiences (Litleskare et al., 2020 ). Ongoing research suggests that integrated visual and auditory interventions, combining physical activities with exposure to natural environment videos and green spaces, can further contribute to the regulation and enhancement of short-term affect (Campillo et al., 2018 ; Li et al., 2021 ). Moreover, beyond offering the public innovative ways to engage with nature videos, VR applications have demonstrated significant utility in tailoring personalized intervention programs. Studies have indicated that utilizing VR technology to present virtual natural settings can enhance individuals’ ability to sustain focus over extended periods and facilitate short-term affect for well-being enhancements (Anderson et al., 2017 ). Numerous controlled trial studies have demonstrated a synergistic effect when combining music or viewing videos of natural environments during exercise to further enhance or maintain the body’s short-term affect state. Listening to music during physical activity has been shown to reduce negative effects associated with fatigue, trigger sports-related memories, and reduce feelings of exhaustion induced by exercise (Barreto-Silva et al., 2018 ; Almeida et al., 2021 ). The advantages of integrating movement with music extend beyond training sessions, as music used as a therapeutic tool also plays a positive role in post-exercise fatigue recovery (Karageorghis et al., 2009 ). When participants watched natural environment videos during exercise via VR technology, they reported higher levels of positive effects during physical activity, increased enjoyment, and reduced negative effects. With the growing popularity of VR technology among the general population, there is a noticeable trend towards incorporating VR into physical activity and health promotion initiatives(Mestre et al., 2011 ; Murray et al., 2016 ). Despite the wealth of existing literature, focusing on natural environment videos or music interventions during exercise, there remains a scarcity of studies examining the impact of these interventions during the post-exercise recovery period. Particularly lacking are comparative studies investigating the short-term affect benefits of different combined interventions, such as natural environment videos or music during and after exercise. Therefore, this study compared four groups: VR-Music (VR intervention during exercise and music intervention after exercise, V-M group), Music-VR (music intervention during exercise and VR intervention after exercise, M-V group), VR (all VR interventions during and after exercise, V group), and Music (all music interventions during and after exercise, M group), to investigate the most effective approach to enhancing short-term affect during aerobic exercise. The findings will contribute to a better understanding of how visual-auditory interventions combined with exercise can optimize human affective states. 2 Methods 2.1 Subjects The study was conducted with the approval of the Ethics Committee of Zhejiang Normal University (Approval No. ZSRT2023003) and was registered with the China Clinical Trial Registry (Approval No. ChiCTR2300073580), and the first registration date is 2023/7/14. The recruited subjects volunteered to participate in the study, and all provided informed consent by signing the experimental informed consent form. Prior to the test, subjects were informed about the study’s content and requirements. They were also advised to adhere to certain guidelines: (1) avoid engaging in strenuous physical activities such as ball games, calisthenics, and hiking in the first three days of the test; (2) refrain from consuming coffee, alcohol, tea, and spicy foods in the initial three days of the test. The study utilized G*Power sample size estimation software to determine the required sample size for the repeated measures ANOVA test. The selected parameters included an F-test, ANOVA: Repeated Measures Within-Between Interaction, effect size of 0.125, alpha error of 0.05, and power of 0.80. This analysis determined that 33 participants were needed for each of the four groups, resulting in a total of 132 participants to ensure statistical validity. To account potential sample loss, 140 university students (72 males and 68 females) were initially recruited. The participants were randomly assigned to the V-M group, M-V group, V group, and M group. However, due to equipment failure and issues with data reception during the experiment, the final number of valid subjects included in the analysis was 132, with 68 boys and 64 girls. The basic characteristics of the subjects are detailed in Table 1 . Table 1 Basic characteristics of study subjects. V-M Group M-V Group M Group V Group P-Value n 33 33 33 33 Age 24.09 ± 0.81 24.09 ± 0.91 24.09 ± 1.10 23.82 ± 0.73 0.423 Height /cm Male 176.76 ± 4.52 177.71 ± 8.45 175.78 ± 4.81 173.76 ± 5.19 0.517 Female 165.31 ± 3.57 164.38 ± 5.07 164.80 ± 5.48 165.44 ± 4.56 0.257 Weight /Kg Male 71.29 ± 9.08 76.82 ± 6.40 71.89 ± 7.91 70.82 ± 7.66 0.267 Female 55.75 ± 2.82 55.69 ± 5.98 54.33 ± 9.21 54.31 ± 3.89 0.986 BMI /(kg·m − 2 ) Male 21.71 ± 2.10 22.55 ± 2.68 21.76 ± 3.03 21.68 ± 2.44 0.821 Female 21.47 ± 3.10 21.77 ± 2.45 21.30 ± 2.23 21.57 ± 2.30 0.355 An independent team member, not involved in other stages of the research project, was responsible for randomly assigning participants. Each recruited participant received a code, and after the baseline data collection, they were randomly allocated to either the experimental or control conditions. The individuals conducting the randomization were unaware of the participant’s circumstances, ensuring a blind assignment process. Furthermore, the data collectors remained unaware of the participants’ groupings throughout the study period. The subject assignment process is illustrated in Fig. 1 . 2.2 Music and VR programs In this study, two types of music were chosen for the music intervention based on their tempo. The selection process involved using the Mix Meister BPM Analyzer software to determine the number of beats per minute (bpm) of the music. Following the tempo classification by Lee and Kimmerly ( 2014 ), music with a fast tempo ranging from 120 to 150 bpm and music with a slow tempo ranging from 55 to 90 bpm were selected(Lee and Kimmerly, 2014 ). The fast-tempo music was used during the music intervention phase while the subjects were engaged in the MONARK power cycling exercise from Sweden. On the other hand, slow-tempo music was employed during the music intervention phase when the subjects were in the post-exercise recovery period. Given the individual differences in the subjects’ music preferences, the subjects could choose the music according to their needs. To align with the public’s preferences for fast and slow-tempo music, network data analysis was conducted to select music tracks with high audience appeal (shown in Table 2 ). During the music intervention in the experiment, subjects were instructed to wear sports noise-canceling headphones, specifically the Beats Solo3 Wireless model. This measure aimed to reduce any potential interference in the experimental results caused by the noise generated by the power car. The volume of the headphones was set to a maximum of 75 dB, and subjects had the flexibility to adjust the volume within a range of 10 dB according to their preference. The VR intervention in the experiment involved subjects immersing themselves in natural environment videos while wearing Pico Neo VR glasses all-in-one. The selected video, sourced from the Internet, featured a green natural environment, and had a duration of at least 15 minutes. Throughout the video playback, the audio was muted. The content of the video showcased various elements of natural green vegetation such as green grass, trees, shrubs, jungles, rivers, waterfalls, and more, as illustrated in Fig. 2 . Table 2 Characteristics of Music Music Duration Rhythm(beats/min) Catch My Breath 4min10s 125(fast) Remember Our Summer 2min43s 128 Stronger (What Doesn’t Kill You) 3min41s 130 Lost in the Discotheque (Radio Edit) 3min31s 143 River Flows In You (Original Mix) 4min58s 128 Wake 4min31s 130 I Love You 4min22s 69(slow) To Me 4min17s 78 Silver City 3min52s 75 Love Is Gone 2min56s 70 Critical 3min10s 83 So Far Away 2min51s 74 2.3 Experimental site and time This study was carried out in the Exercise Science Laboratory of Zhejiang Normal University, located in China. The laboratory’s environment and soundproofing were deemed satisfactory after inspection and comparison, contributing to the smooth execution of the experiment. The research was conducted between September and November 2023, with testing taking place on the same day from Monday to Friday, spanning the hours of 8:00 a.m. to 11:00 a.m. and 3:00 p.m. to 5:00 p.m. The study site’s climate during the experimental period was favorable, with minimal temperature variation from the beginning to the end of the experiment. This stability helped reduce the impact of natural environmental factors such as air temperature and humidity on the study outcomes. 2.4 Experimental process (1) Preparation stage: The experimenter ensured that the relevant experimental equipment was set up for each test group before the subjects arrived at the experimental test site. Upon the subjects’ arrival, the experimenter provided an introduction to the test instructions, briefing them on the procedures before commencing the formal testing. This preparation phase typically lasted approximately 3 minutes, allowing the subjects to familiarize themselves with the upcoming tasks and ensuring a smooth transition into the formal testing phase. (2) Pre-test stage: The subjects’ individual demographic and social variables, daily physical activity level, and other relevant information were collected. Following this, the dependent variables were collected in the following order: ①Measurement of the systolic and diastolic blood pressure of the subjects using an Omron electronic sphygmomanometer; ②Collection of heart rate variability data using the First Beat wearable wireless physiological device, specifically 5 minutes before the experiment; ③Completion of real-time assessments using the “Positive Affect Scale” and “Negative Affect Scale” by the subjects. This comprehensive data collection process lasted approximately 8 minutes and aimed to establish baseline measurements and assess the subjects’ physiological and affective states before the formal testing procedures commenced. (3) Intervention stage: Subjects were directed to wear either Pico Neo VR glasses or sports noise-canceling headphones with the volume set at 75 ± 5 noise level, ensuring that the ambient sound level in the laboratory remained below 40 dB. The subjects commenced a 15-minute session of moderate-intensity aerobic power cycling. The exercise load was adjusted to 60–69% of each individual’s maximal heart rate, with heart rate monitoring to maintain a range of 120–150 beats/min. Following the aerobic cycling phase, subjects proceeded with 2-minute power cycling intervals at an intensity of 20%-30% of their maximal heart rate. This interval period allowed the subjects’ heart rates to gradually return to their resting rate without any VR or music interference. Subsequently, the subjects were instructed to maintain a sedentary position while continuing to wear VR glasses or sports noise-canceling headphones for an additional 15 minutes. The specific experimental flow chart detailing these instructions and activities can be found in Fig. 3 , illustrating the sequence of events during the experimental procedure. V-M group: Subjects watched a natural environment video during exercise and experienced music intervention after exercise. This group engaged in 15 minutes of aerobic power cycling with a natural environment video, followed by 2 minutes of interval exercise, and concluded with 15 minutes of sedentary rest accompanied by slow music. M-V group: Subjects listened to music during exercise and viewed a natural environment video after exercise. They participated in 15 minutes of aerobic power cycling with fast-paced music, followed by 2 minutes of interval exercise, and ended with 15 minutes of sedentary rest combined with a video of the natural environment. M group: This group received music interventions during and after exercise. They performed 15 minutes of aerobic power cycling with fast-paced music, followed by 2 minutes of interval exercise, and concluded with 15 minutes of sedentary rest accompanied by slow-paced music. V Group: Subjects watched videos of the natural environment during and after exercise. They engaged in a 15-minute aerobic power cycling session with natural environment videos, followed by a 2-minute interval session, and ended with a 15-minute meditation break with videos of the natural environment. (4) Post-test stage: At the end of the intervention, the heart rate belt was kept on until the subject’s heart rate had returned to the baseline quiet state prior to the start of the experiment. This return to the quiet state was maintained for 30 seconds or longer compared to the heart rate prior to the experiment initiation. The post-test phase included the following assessments:①Systolic and diastolic blood pressure measurements were taken using an Omron electronic sphygmomanometer; ②Heart rate variability data was collected using the First Beat wearable wireless physiological device, specifically 5 minutes before the conclusion of the experiment; ③Subjects were required to complete real-time assessments using the “Positive Affect Scale” and “Negative Affect Scale” to gauge their affective states following the intervention. These post-test measures aimed to evaluate the physiological and affective responses of the subjects after the completion of the intervention. The photos of the experiment site are shown in Fig. 4 . 2.5 Dependent variable (1) Heart rate variability In the controlled combined exercise–music intervention experiments, the low frequency to high frequency (LF/HF) ratio is often used as the frequency domain analysis indicator. The LF/HF ratio can determine the equilibrium of sympathetic and vagal nerves or the modulation degree of the sympathetic nerves. The time domain analysis indicator primarily uses the root mean square of the difference between adjacent full RR intervals (RMSSD) and the standard deviation of continuous regular RR intervals (SDNN) as comprehensive reflective markers of the effect of short-intervention control experiment on affect improvement(Koelsch, 2018 ; Jacquet et al., 2021 ). Therefore, LF/HF, RMSSD, and SDNN data were collected in this experimental study as the metrics for processing and analysis. For the acquisition of the above heart rate variability (HRV) indices, the First Beat Sports wireless physiological data collection system with the ECG module device was selected for the experiment. The apparatus can detect and capture changes in the subject’s heart rate in real-time and during the activity. Simultaneously, the changing signal of the subject’s heart rate can be automatically converted into time- and frequency-domain data in the background for recording and storage. The selected device has been used in many controlled experiments involving green fitness and gardening activities. The real-time accuracy and reliability of the data recorded by the aforementioned device system have been verified in many controlled experiments (Light et al., 2012 ; Beauchaine and Thayer, 2015 ) (2) BP Blood pressure includes systolic blood pressure (SBP) and diastolic blood pressure (DBP), the reduction of blood pressure to a certain extent can reflect the reduction of the negative effect on the subjects, and the improvement effect of the blood pressure indicator can be reflected from the side of the human body to improve the effect of short-term affect (Pretty, 2004 ; Zijlema et al., 2018 ). In this experimental study, the systolic and diastolic blood pressure of the subjects will be measured using an Omron upper arm electronic sphygmomanometer. After one measurement is completed and the readings are stabilized, the data will be recorded and the procedure will be repeated after 1–2 minutes, and the final value will be the average of the two measurements. To avoid the possibility of differences in blood pressure measurements between the pre-test and post-test due to different arms, the arms of the subjects should be kept in the same position during the pre-test and post-test blood pressure measurements. (3) Short-term affect The "affect" indicator can be a good way to record and assess the real-time mental health status of the subjects promptly. For the real-time affect condition of the subjects, the experiment used the International Positive/Negative Affect Scale (short version), which has been proven to be widely used and reliable in many controlled experiments(Thompson, 2007 ; Liu et al., 2012 ). During the pre-test and post-test phases, the subjects will rate the positive and negative questions according to their real-time affective state. Finally, the affective state of the subjects in the pre-test and post-test phases will be reflected by calculating the total score values of the positive and negative scores respectively. 2.6 Statistical methods (1)The collected data were initially entered into Excel 2010 for storage and later imported into SPSS 22.0 software for statistical analysis. Descriptive statistics were utilized to analyze the demographic and sociological variables such as gender and age within the sample, presenting the results in terms of percentages or mean standard deviation. To compare the effect of improvement between groups in the pre-test and post-test phases, a multifactor ANOVA was conducted. The analysis considered the F-value, p-value, and effect size (ɳp 2 ) to assess the significance and magnitude of the observed differences. Furthermore, differences in the effects of improvement between groups were evaluated using repeated measures ANOVA. This analysis also took into account the F-values, p-values, and effect sizes (ɳp 2 ) to determine the significance and impact of these differences over time. Each statistical model was adjusted for various covariates including gender, age, height, weight, and BMI to control for potential confounding variables and ensure a more accurate assessment of the impact of the interventions on affect improvement. (2)The HRV data were imported into an Excel sheet to process calculations, and the export was divided into a time domain analysis table and a frequency domain analysis table. The calculations used were all corrected RR values, and the specific calculations for RMSSD, SDNN, and LF/HF were as follows: Time domain indicators 1) SDNN Defined as: the standard deviation of the continuous normal RR period. Excel calculations were made by applying the standard deviation formula to the RR values. Take cell A1:A4 data as an example: =STDEVP (A1:A4). 2) RMSSD Defined as: the root mean square of the difference between adjacent RR intervals. Excel calculations were as follows: (1) The RR interval value column was copied and then staggered before and after the “—” in front of the value, and then “+” dropped down, resulting in the adjacent interval difference. (2) The root mean square was then calculated. Cell A1:A4 data were used as an example: = SQRT (SUMSQ (A1:A4)/ COUNTA (A1:A4)). Note SQRT calculates the square root of an arithmetic number; SUMSQ calculates the sum of squares of several numbers; and COUNTA calculates the total number of numbers. Frequency Domain Indicators 3) LF/HF Defined as: the ratio of LF to HF. Excel calculates it as = LF /HF, then “+” drops down to obtain the average value of LF/HF for that time duration. 3 Results 3.1 Comparison of pre-test and post-test variability of dependent variable indicators (1) BP The results of descriptive statistics and tests for differences in blood pressure indicators are presented in Table 3 and Fig. 5 , indicating a decrease in systolic and diastolic blood pressure in the post-test data compared to the pre-test data across all four groups. Specifically, for the SBP indicator, a significant within-group difference was observed between the post-test and pre-test data in the VR group (F = 4.578; ɳp 2 = 0.067; p = 0.036). The within-group difference between the post-test and pre-test data for the systolic blood pressure indicator in the M-V group was also found to be significant. (F = 5.105; ɳp 2 = 0.074; p = 0.027). In contrast, the within-group difference between the pre-test and post-test data for the M group and the V-M group was not found to be significant. However, for the DBP indicator, the within-group difference between the post-test and pre-test data in the V-M group was significant (F = 6.015; ɳp 2 = 0.086; p = 0.017), the within-group difference between the post-test and pre-test data in the other three groups was not found to be significant. The study results indicated that the intervention modes involving VR combined with music exercise and VR alone had a positive promotional effect on improving the blood pressure indicator. However, the music-only intervention mode did not demonstrate a significant positive impact on the improvement of the blood pressure indicator. Table 3 Differential results of blood pressure indicator V-M group M-V group M Group V Group Comparison between Groups SBP [mmHg] pre-test 104.97 ± 12.19 111.33 ± 14.07 105.88 ± 13.73 107.88 ± 16.37 F = 1.357; ɳp 2 = 0.031; p = 0.259 post-test 99.70 ± 9.89 104.30 ± 11.03 104.94 ± 13.03 100.00 ± 13.39 Comparison within groups (time main effect) F = 3.725; ɳp 2 = 0.055; p = 0.058 F = 5.105; ɳp 2 = 0.074; p = 0.027* F = 0.081; ɳp 2 = 0.001; p = 0.777 F = 4.578; ɳp 2 = 0.067; p = 0.036* DBP [mmHg] pre-test 65.82 ± 9.38 67.39 ± 10.37 66.88 ± 8.59 66.45 ± 10.90 F = 1.320; ɳp 2 = 0.030; p = 0.271 post-test 60.79 ± 7.14 64.48 ± 7.66 67.00 ± 8.08 63.91 ± 9.76 Comparison within groups (time main effect) F = 6.015; ɳp 2 = 0.086; p = 0.017* F = 1.682; ɳp 2 = 0.026; p = 0.199 F = 0.003; ɳp 2 = 0.000; p = 0.953 F = 0.991; ɳp 2 = 0.015; p = 0.321 Note: The above models are designed as follows: intercept + gender + age + BMI level. (*) indicates a significant difference between the mid-test and post-test, respectively, and the pre-test, were *: p < 0.05; **: p < 0.01; ***: p < 0.001. (2) Short-term affect The descriptive statistics and differential results of the short-term affect indicators are presented in Table 4 and Fig. 6 . These findings reveal a decreasing trend in the post-test stage scores for negative affect among subjects in the four groups compared to the pre-test stage. Additionally, the subjects’ scores for positive affect in the post-test stage demonstrated an overall increasing trend when compared to the pre-test stage scores. For positive affect, there was a significant within-group difference between the post-test and pre-test data for positive affect in the V-M group (F = 6.558; ɳp 2 = 0.093; p = 0.013) and a significant within-group difference between the post-test and pre-test data for positive affect in the M-V group. (F = 4.315; ɳp 2 = 0.063; p = 0.042). There was no significant within-group difference between the post-test and pre-test data for positive affect in the M group and V group. However, for negative affect, there was a significant within-group difference between the post-test and pre-test data for negative affect in the V-M group (F = 13.832; ɳp 2 = 0.178; p = 0.001). There was a significant within-group difference between the post-test and pre-test data for negative affect in the M-V group as well. (F = 8.637; ɳp 2 = 0.119; p = 0.005). The within-group difference in negative affect between the post-test and pre-test for the M and V groups was not statistically significant. The results of the between-group analyses indicated that there were no significant differences in positive and negative affect between the groups. In response to the short-term affect change situation, among the four groups, exercise combined with visual and auditory stimuli improved both the positive and negative effects of the subjects. The improvement was greater in the V-M group and M-V group, with a significant within-group difference, while the between-group difference was not statistically significant. While the single music and VR groups also enhanced the subjects’ affect. Table 4 Differential results of short-term affect indicator V-M Group M-V Group M Group V Group Comparison between Groups Positive affect pre-test 13.15 ± 3.16 12.91 ± 2.79 13.91 ± 2.95 12.76 ± 3.94 F = 0.897; ɳp 2 = 0.021; p = 0.445 post-test 15.21 ± 3.37 14.67 ± 3.98 14.82 ± 4.15 13.55 ± 4.15 Comparison within groups (time main effect) F = 6.558; ɳp 2 = 0.093; p = 0.013* F = 4.315; ɳp 2 = 0.063; p = 0.042* F = 1.052; ɳp 2 = 0.016; p = 0.309 F = 0.625; ɳp 2 = 0.010; p = 0.432 Negative affect pre-test 7.67 ± 3.16 6.76 ± 1.90 7.39 ± 2.77 6.79 ± 2.41 F = 1.915; ɳp 2 = 0.043; p = 0.130 post-test 5.55 ± 0.87 5.64 ± 1.08 7.03 ± 2.26 5.91 ± 1.59 Comparison within groups (time main effect) F = 13.832; ɳp 2 = 0.178; p = 0.001*** F = 8.637; ɳp 2 = 0.119; p = 0.005** F = 0.342; ɳp 2 = 0.005; p = 0.561 F = 3.063; ɳp 2 = 0.046; p = 0.085 Note: The above models are designed as follows: intercept + gender + age + BMI level. (*) indicates a significant difference between the mid-test and post-test, respectively, and the pre-test, were *: p < 0.05; **: p < 0.01; *** : p < 0.001. (3) Heart rate variability The descriptive statistics and difference test results of the heart rate variability indicator are shown in Table 5 and Fig. 7 . Significant differences were observed in the SDNN indicator between the post-test and pre-test data for the M group (F = 12.678; ɳp 2 = 0.165; p = 0.001) and the V group (F = 5.685; ɳp 2 = 0.082; p = 0.020). Both the V-M group (F = 18.091; ɳp2 = 0.220; p = 0.001) and the M-V group (F = 5.467; ɳp 2 = 0.079; p = 0.023) showed significant differences. Significant differences were found in the RMSSD indicator between the post-test and pre-test data for the M group (F = 7.567; ɳp 2 = 0.107; p = 0.007) and the V group (F = 5.977; ɳp 2 = 0.085; p = 0.017). Additionally, there were significant differences within the V-M group (F = 7.786; ɳp 2 = 0.108; p = 0.007) and the M-V group (F = 16.568; ɳp 2 = 0.206; p = 0.001). For the LF/HF indicator, the within-group difference was significant only for the M-V group (F = 8.158; ɳp 2 = 0.113; p = 0.006), while it was not significant for the other three groups. The results of the between-group analysis for the HRV indicators revealed that, except for the RMSSD indicator where the between-group differences were not significant, significant differences were found between the groups for the SDNN indicator (F = 3.088; ɳp 2 = 0.067; p = 0.030), as well as for the LF/HF indicator (F = 4.204; ɳp 2 = 0.090; p = 0.007). Table 5 Differential results of heart rate variability indicator V-M Group M-V Group M Group V Group Comparison between Groups SDNN pre-test 34.60 ± 24.46 30.15 ± 22.17 22.55 ± 18.49 33.55 ± 21.72 F = 3.088; ɳp 2 = 0.067; p = 0.030* post-test 59.25 ± 22.58 43.16 ± 22.10 45.25 ± 17.30 44.12 ± 13.29 Comparison within groups (time main effect) F = 18.091; ɳp 2 = 0.220; p = 0.001*** F = 5.467; ɳp 2 = 0.079; p = 0.023* F = 12.678; ɳp 2 = 0.165; p = 0.001*** F = 5.685; ɳp 2 = 0.082; p = 0.020* RMSSD pre-test 49.55 ± 32.63 39.94 ± 17.44 44.59 ± 24.87 48.17 ± 30.39 F = 1.770; ɳp 2 = 0.040; p = 0.156 post-test 68.20 ± 20.24 59.90 ± 22.12 58.54 ± 14.86 62.52 ± 14.64* Comparison within groups (time main effect) F = 7.786; ɳp 2 = 0.108; p = 0.007** F = 16.568; ɳp 2 = 0.206; p = 0.001*** F = 7.567; ɳp 2 = 0.107; p = 0.007** F = 5.977; ɳp 2 = 0.085; p = 0.017* LF/HF pre-test 4.78 ± 3.42 5.83 ± 4.00 3.67 ± 2.62 3.17 ± 2.39 F = 4.204; ɳp 2 = 0.090; p = 0.007** post-test 3.91 ± 3.60 3.33 ± 3.02 3.21 ± 2.53 2.13 ± 1.98 Comparison within groups (time main effect) F = 1.008; ɳp 2 = 0.016; p = 0.319 F = 8.158; ɳp 2 = 0.113; p = 0.006** F = 0.529; ɳp 2 = 0.08; p = 0.470 F = 3.704; ɳp 2 = 0.055; p = 0.059 Note: The above models are designed as follows: intercept + gender + age + BMI level. (*) indicates a significant difference between the mid-test and post-test, respectively, and the pre-test, were *: p < 0.05; **: p < 0.01; *** : p < 0.001. 3.2 Multiple comparisons of the effects of different combined visual-auditory exercises on dependent variables Further analysis using repeated measures ANOVA to compare the variability of the effects of different combined visual and auditory exercises on the dependent variables in the four groups, all with the same exercise duration and intensity, revealed significant between-group differences only for the SDNN indicator (F = 3.088; ɳp 2 = 0.067; p = 0.030) and the LF/HF indicator (F = 4.204; ɳp 2 = 0.090; p = 0.007). The remaining indicators showed non-significant between-group differences (p > 0.05). Multiple comparisons were conducted to compare the effects of different combined visual and auditory exercises on the dependent variable indicators among the four groups, and the results are presented in Table 6 . The results indicate that the M-V group showed the most significant improvement in the SDNN indicator, outperforming the V group (p = 0.036), the M group (p = 0.014), and the V-M group (p = 0.008). Regarding the LF/HF indicator, the V-M group exhibited the most significant improvement, surpassing the V group (p = 0.006), while the M-V group also showed significant improvement compared to the V group (p = 0.002) for the LF/HF indicator. The RMSSD indicator showed that the M-V group had the most significant improvement compared to the V-M group (p = 0.037), while the M group demonstrated the greatest improvement in the negative affect indicator, significantly outperforming the V-M group (p = 0.028). In summary, the analysis of between-group variability in the effects of various combined visual and auditory exercises on the dependent variables indicated that the M-V group showed greater effectiveness in enhancing the heart rate variability indicator, whereas the V group demonstrated better improvements in the short-term affect and blood pressure indicators. Table 6. Comparative results of all indicators in different groups (Post hoc test) Marginal mean difference Indicator 1-2 1-3 1-4 2-3 2-4 3-4 SDNN 10.2723 (p=0.008) 9.5262 (p=0.014) 8.0905 (p=0.036) -7461 -2.1818 -1.4358 LF/HF -0.2311 0.9065 1.6953 (p=0.006) 1.1376 1.9264 (p=0.002) 0.7888 RMSSD 8.9494 (p=0.037) 7.3044 3.5283 -1.6450 -5.4211 -3.5283 SBP -5.4848 -3.0758 -1.6061 2.4090 3.8788 1.4697 DBP -2.6364 -3.6364 -1.8788 -1.0000 0.7576 1.7576 Positive affect 0.4091 -0.6061 0.2576 -1.0152 (p=0.028) -0.1515 0.8636 Negative affect 0.3939 -0.1818 1.0303 -0.5758 0.6364 1.2121 Note: 1:V-M group；2:M-V group；3:M group；4:V group. 4 Discussion The results indicated a significant difference in the improvement of short-term affect before and after the intervention experiment between the M-V group and the V-M group. While there was no significant difference between the single V group and the single M group before and after the intervention, both groups still showed some improvement in short-term affect or a positive trend of enhancement. The analysis based on physiological indicators revealed that the M-V group demonstrated significantly greater improvement in SDNN and LF/HF indicators compared to the single V group and single M group, with the V-M group showing the most significant improvement. Additionally, for the RMSSD indicator, the M-V group exhibited the most notable enhancement, surpassing the other groups significantly. Similar trends were observed in subjective affect and blood pressure indicators. In conclusion, the comparison of various visual-auditory combined movements on short-term affect indicated that the M-V group generally had a more positive impact on short-term affect. Previous studies have consistently demonstrated that music intervention can enhance short-term affect during and after exercise, a finding corroborated by the results of this study. The combination of music with exercise, extensively explored in combined exercise interventions, has been shown to assist individuals in recovering from exercise-induced fatigue and boosting their positive affect (Chair et al., 2021 ). Listening to fast-paced music during aerobic exercise has been found to induce feelings of cheerfulness, relaxation, enhanced cognitive agility, and fatigue reduction (Edwards et al., 2018 ). When individuals listen to music during exercise, they tend to synchronize their movement pace with the rhythm of the music unconsciously. This synchronization can lead to positive effects on both the psychological and physiological functions of the participants (Paluska and Schwenk, 2000 ). Listening to music during aerobic jogging has become a popular exercise practice among many runners. Fast-paced music can help runners maintain a consistent rhythm, thereby saving energy that would have been expended in adjusting their pace. This practice enhances the overall exercise experience and pleasure for participants, while also boosting post-exercise excitement and perceptual abilities (Karageorghis et al., 2013 ; Karageorghis and Jones, 2014 ). In the post-exercise recovery period, individuals often prefer slow-paced music. Slow rhythm music has been shown to significantly relax individuals during this recovery phase, effectively aiding in the reduction of exercise-induced fatigue (North and Hargreaves, 2000 ; Scheufele, 2000 ). Music is a highly complex auditory medium that engages various aspects of the human body and mind, including hearing, attention, perception, sound pattern analysis, memory, and emotional experience. It is considered a powerful stimulus that can evoke profound personal experiences due to its multifaceted nature (Koelsch, 2010 ). The integrated cognitive processing characteristics of music play a crucial role in human life, particularly in five key areas: emotion regulation, language, communication, motor skills, and cognitive regulation (Harikumar et al., 2006 ; Burrai et al., 2016 ). Music has been shown to reduce individual stress by lowering physiological arousal, as evidenced by decreased levels of cortisol, heart rate, and mean arterial pressure (Kreutz et al., 2012 ; Linnemann et al., 2015 ). In this context, the integration of music with movement can be viewed as a more suitable intervention to enhance an individual’s athletic performance (Zoormand, 2023 ). In this study, subjects who underwent music intervention during aerobic power bicycle exercise followed by VR green natural environment intervention during the recovery period exhibited a more pronounced improvement in short-term affect compared to other groups. The brain relies on various senses to gather information from the internal and external environments, with vision and hearing being crucial channels for human perception. The synergistic interaction of vision and hearing can enhance the overall impact at times. While multiple studies have demonstrated the positive effects of both natural environment VR and music interventions on short-term affect improvement, the combination of intervention methods can yield varying experimental outcomes (Jiang et al., 2019 ; Mendes et al., 2021 ). The researchers employed random assignment to allocate participants into the music intervention group, VR group, audio-visual combination group, and non-intervention control group. The study results revealed that individuals in the audio-visual combination group reported the lowest self-perceived fatigue levels during bicycle pedaling. Moreover, the music intervention group and the VR group exhibited enhanced self-perception, whereas the control group demonstrated the least favorable outcomes (Chow and Etnier, 2017 ). Research on task completion duration under different conditions has indicated that subjects tend to perform better when exposed to music and video stimuli (Razon et al., 2009 ). The positive impact observed in task completion under conditions with music and video stimuli may be attributed to factors such as the spatial experience facilitated by VR environments and the rhythmic stimulation from music. These factors could help mitigate feelings of burnout induced by fatigue, consequently extending task completion duration. Furthermore, research supports the notion that engaging with VR in natural green environments during post-exercise recovery can positively influence short-term affect. Watching videos of natural environments post-exercise has been shown to promote physical and mental relaxation, potentially enhancing blood pressure regulation (Cho et al., 2002 ; Ojala et al., 2019 ). The Attention Restoration Theory (ART) suggests that exposure to green spaces not only sustains short-term affect levels but also fosters the cultivation of positive effects, hastens the alleviation of fatigue-related factors, and enhances individual feelings of enjoyment (Kaplan, 1995 ; Wells, 2000 ). The continuous advancement of scientific research has demonstrated that exposure to virtual reality (VR) green natural landscapes can indeed enhance short-term affect, a finding that has been increasingly supported and acknowledged by a growing body of studies (Legrand et al., 2011 ; Dadvand et al., 2015 ). Both physical spaces with actual green plants and virtual reality environments featuring green landscapes are inclined to cultivate a relaxed and pleasant atmosphere, with the effectiveness being influenced by the extent of green plant coverage. A higher coverage rate of green plants is associated with a greater capacity for individuals to alleviate work or study-related pressures, facilitating the recovery from physical and mental fatigue (Bringslimark et al., 2007 ; Thomsen et al., 2011 ). Numerous scientific studies have increasingly supported the effectiveness of VR green natural environments and music interventions in aerobic exercise for enhancing short-term affect. People are now more inclined to engage in exercise using their preferred intervention methods. However, there remains a lack of novel intervention strategies combining natural environment videos and music in aerobic exercise. Additionally, research on optimizing the combination of VR and music interventions to enhance short-term affect is relatively scarce. The findings of this study demonstrate the significant benefits of combining VR green natural environments and music interventions in aerobic power cycling on both physiological and psychological levels, offering practical value for enhancing short-term affect. Individuals can now select different intervention methods based on their preferences during aerobic exercise to elevate their short-term affect levels and improve physical fitness. 5 Limitations (1) Given that the study participants were exclusively college students, the generalizability of the findings to other populations may be limited. College students typically exhibit higher cognitive levels compared to other demographic groups, potentially influencing the study outcomes. As mental health comparisons were not included in this pilot study, future research with this population should consider incorporating such assessments for a more comprehensive understanding. (2) While this study utilizes virtual reality technology to immerse participants in a green natural environment, factors such as video clarity, spatial enclosure, and individual perception of the 3D effect may vary, potentially influencing the experimental outcomes. It is important to note that virtual reality technology is still evolving, and discrepancies exist between the virtual effects produced by VR equipment and real-life settings, introducing limitations to the study. (3) Popular songs were selected for the experiment. Previous literature has indicated that classical music enhances students’ cognitive abilities, while jazz music improves their physical performance. Therefore, it is valuable to investigate the impact of combining various types of music on enhancing mental states during combined aerobic exercise. 6 Conclusion Analysis was conducted to compare subjective evaluation indicators and objective physiological indicators of individuals undergoing visual-auditory combined exercise. The study confirmed that the combined exercise intervention involving music and VR exposure to a green environment had a positive impact on short-term affect. Under the same intensity of aerobic exercise, the M-V group demonstrated greater improvements in physiological response indicators including SBP, heart rate variability (LF/HF, SDNN, RMSSD), as well as subjective positive/negative affect indicators, followed by the V-M group which showed significantly better results compared to the other groups. Declarations Data availability The datasets generated and/or analysed during the current study are not publicly available due the data contained some of the subjects' private information, they wanted to make it public after the article was published but are available from the corresponding author on reasonable request. When the paper is successfully published, this information can be accessed. Author Contribution The authors confirm contribution to the paper as follows: Conceptualization:Meng Tao, Jie Zhuang; Data curation: Meng Tao, Jingchuan Gao; Formal Analysis:Meng Tao; Writing-original draft、Writing- review & editing: Meng Tao, Jingchuan Gao, Haiquan Huang, Yuanyuan Cao. All authors reviewed the results and approved the final version of the manuscript. References Almeida, C.M.M., Araujo, F.D.S., Lima, E.V.D.C., De Souza, M.F., Sales, M.M., De Moraes, J.F.V.N., 2021. 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2","display":"","copyAsset":false,"role":"figure","size":1071304,"visible":true,"origin":"","legend":"\u003cp\u003eVR intervention with the natural video scene\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4345575/v1/a4d719dacced04434e005ea5.png"},{"id":56477521,"identity":"ed5f75a5-d3c8-4b7d-a529-a8c61a561dba","added_by":"auto","created_at":"2024-05-14 17:47:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":189914,"visible":true,"origin":"","legend":"\u003cp\u003eExperiment-specific flow chart.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4345575/v1/876c0e718d3c79c6eb5e9a89.png"},{"id":56477527,"identity":"193663ba-7cfb-47dd-815b-9c92192a193b","added_by":"auto","created_at":"2024-05-14 17:47:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":439593,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental site photos\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4345575/v1/81fad628822a1fad4575290f.png"},{"id":56477515,"identity":"4ba68b8b-44d5-4ef6-a66c-a9849d619e90","added_by":"auto","created_at":"2024-05-14 17:47:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":63399,"visible":true,"origin":"","legend":"\u003cp\u003eThe trend of pre-test and post-test changes in systolic and diastolic blood pressure\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4345575/v1/841c2c59da0e930c7ca6c81d.png"},{"id":56477519,"identity":"7a1979a9-aa16-4f69-9716-1ca85618f844","added_by":"auto","created_at":"2024-05-14 17:47:49","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":38429,"visible":true,"origin":"","legend":"\u003cp\u003eThe trend of pre-test and post-test changes in short-term affect\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4345575/v1/3a048cda686921bda28b30ef.png"},{"id":56477526,"identity":"0a67d242-d25c-4304-9679-5f07e2b163b5","added_by":"auto","created_at":"2024-05-14 17:47:49","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":101609,"visible":true,"origin":"","legend":"\u003cp\u003eThe trend of pre-test and post-test changes in SDNN, RMSSD, and LF/HF\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4345575/v1/f9b902b58f5053fb0550db5e.png"},{"id":63071351,"identity":"71a40b4b-5e88-4a17-b3f5-09d5ebc76ac6","added_by":"auto","created_at":"2024-08-22 20:06:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3020583,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4345575/v1/e2167c50-4251-4722-a8fd-1c7e232c8695.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Effects of Combining Visual-Auditory Stimuli with Exercise on Short-Term Affect Improvement: A Randomized Controlled Trial","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eMusic therapy utilizes different music-based techniques to improve physical and mental well-being through therapeutic interventions and musical activities. It supports individuals in enhancing their mental health and managing illnesses through the music. The field has gained recognition with medical technology advancements, highlighting the integration of music into exercise routines as a common practice in music therapy. Such practices have been shown to reduce exercise-induced fatigue and elevate mood post-exercise (Juslin and V\u0026auml;stfj\u0026auml;ll, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Heneghan et al., 2020). Music is not just a form of auditory art; specific musical compositions can impact individual behavior, affects, and physiological responses, contributing to the regulation of both psychological and physiological functions in the human body(Koelsch, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; R\u0026ouml;nnberg et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent advances in cognitive neuroscience and technology have deepened our understanding of how music affects the brain mechanisms, offering insights into the regulatory effects of music interventions on human emotions (Peretz, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Koelsch, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Simultaneously, the development of virtual reality (VR) technology has introduced immersive tool for experiencing natural environments. VR create a profound sense of mental presence within the virtual environment, by immersing individuals in interactive computer-generated simulations, serving as a valuable complement to real-world nature experiences (Litleskare et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Ongoing research suggests that integrated visual and auditory interventions, combining physical activities with exposure to natural environment videos and green spaces, can further contribute to the regulation and enhancement of short-term affect (Campillo et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, beyond offering the public innovative ways to engage with nature videos, VR applications have demonstrated significant utility in tailoring personalized intervention programs. Studies have indicated that utilizing VR technology to present virtual natural settings can enhance individuals\u0026rsquo; ability to sustain focus over extended periods and facilitate short-term affect for well-being enhancements (Anderson et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNumerous controlled trial studies have demonstrated a synergistic effect when combining music or viewing videos of natural environments during exercise to further enhance or maintain the body\u0026rsquo;s short-term affect state. Listening to music during physical activity has been shown to reduce negative effects associated with fatigue, trigger sports-related memories, and reduce feelings of exhaustion induced by exercise (Barreto-Silva et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Almeida et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The advantages of integrating movement with music extend beyond training sessions, as music used as a therapeutic tool also plays a positive role in post-exercise fatigue recovery (Karageorghis et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). When participants watched natural environment videos during exercise via VR technology, they reported higher levels of positive effects during physical activity, increased enjoyment, and reduced negative effects. With the growing popularity of VR technology among the general population, there is a noticeable trend towards incorporating VR into physical activity and health promotion initiatives(Mestre et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Murray et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Despite the wealth of existing literature, focusing on natural environment videos or music interventions during exercise, there remains a scarcity of studies examining the impact of these interventions during the post-exercise recovery period. Particularly lacking are comparative studies investigating the short-term affect benefits of different combined interventions, such as natural environment videos or music during and after exercise.\u003c/p\u003e \u003cp\u003eTherefore, this study compared four groups: VR-Music (VR intervention during exercise and music intervention after exercise, V-M group), Music-VR (music intervention during exercise and VR intervention after exercise, M-V group), VR (all VR interventions during and after exercise, V group), and Music (all music interventions during and after exercise, M group), to investigate the most effective approach to enhancing short-term affect during aerobic exercise. The findings will contribute to a better understanding of how visual-auditory interventions combined with exercise can optimize human affective states.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Subjects\u003c/h2\u003e \u003cp\u003eThe study was conducted with the approval of the Ethics Committee of Zhejiang Normal University (Approval No. ZSRT2023003) and was registered with the China Clinical Trial Registry (Approval No. ChiCTR2300073580), and the first registration date is 2023/7/14. The recruited subjects volunteered to participate in the study, and all provided informed consent by signing the experimental informed consent form. Prior to the test, subjects were informed about the study\u0026rsquo;s content and requirements. They were also advised to adhere to certain guidelines: (1) avoid engaging in strenuous physical activities such as ball games, calisthenics, and hiking in the first three days of the test; (2) refrain from consuming coffee, alcohol, tea, and spicy foods in the initial three days of the test.\u003c/p\u003e \u003cp\u003eThe study utilized G*Power sample size estimation software to determine the required sample size for the repeated measures ANOVA test. The selected parameters included an F-test, ANOVA: Repeated Measures Within-Between Interaction, effect size of 0.125, alpha error of 0.05, and power of 0.80. This analysis determined that 33 participants were needed for each of the four groups, resulting in a total of 132 participants to ensure statistical validity. To account potential sample loss, 140 university students (72 males and 68 females) were initially recruited. The participants were randomly assigned to the V-M group, M-V group, V group, and M group. However, due to equipment failure and issues with data reception during the experiment, the final number of valid subjects included in the analysis was 132, with 68 boys and 64 girls. The basic characteristics of the subjects are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBasic characteristics of study subjects.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eV-M Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM-V Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eV Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e 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\u003cp\u003e175.78\u0026thinsp;\u0026plusmn;\u0026thinsp;4.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e173.76\u0026thinsp;\u0026plusmn;\u0026thinsp;5.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e165.31\u0026thinsp;\u0026plusmn;\u0026thinsp;3.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e164.38\u0026thinsp;\u0026plusmn;\u0026thinsp;5.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e164.80\u0026thinsp;\u0026plusmn;\u0026thinsp;5.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e 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\u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003cp\u003e/(kg\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.71\u0026thinsp;\u0026plusmn;\u0026thinsp;2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.55\u0026thinsp;\u0026plusmn;\u0026thinsp;2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.76\u0026thinsp;\u0026plusmn;\u0026thinsp;3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.68\u0026thinsp;\u0026plusmn;\u0026thinsp;2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.47\u0026thinsp;\u0026plusmn;\u0026thinsp;3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.77\u0026thinsp;\u0026plusmn;\u0026thinsp;2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.30\u0026thinsp;\u0026plusmn;\u0026thinsp;2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.57\u0026thinsp;\u0026plusmn;\u0026thinsp;2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.355\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAn independent team member, not involved in other stages of the research project, was responsible for randomly assigning participants. Each recruited participant received a code, and after the baseline data collection, they were randomly allocated to either the experimental or control conditions. The individuals conducting the randomization were unaware of the participant\u0026rsquo;s circumstances, ensuring a blind assignment process. Furthermore, the data collectors remained unaware of the participants\u0026rsquo; groupings throughout the study period. The subject assignment process is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Music and VR programs\u003c/h2\u003e \u003cp\u003eIn this study, two types of music were chosen for the music intervention based on their tempo. The selection process involved using the Mix Meister BPM Analyzer software to determine the number of beats per minute (bpm) of the music. Following the tempo classification by Lee and Kimmerly (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), music with a fast tempo ranging from 120 to 150 bpm and music with a slow tempo ranging from 55 to 90 bpm were selected(Lee and Kimmerly, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The fast-tempo music was used during the music intervention phase while the subjects were engaged in the MONARK power cycling exercise from Sweden. On the other hand, slow-tempo music was employed during the music intervention phase when the subjects were in the post-exercise recovery period. Given the individual differences in the subjects\u0026rsquo; music preferences, the subjects could choose the music according to their needs. To align with the public\u0026rsquo;s preferences for fast and slow-tempo music, network data analysis was conducted to select music tracks with high audience appeal (shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDuring the music intervention in the experiment, subjects were instructed to wear sports noise-canceling headphones, specifically the Beats Solo3 Wireless model. This measure aimed to reduce any potential interference in the experimental results caused by the noise generated by the power car. The volume of the headphones was set to a maximum of 75 dB, and subjects had the flexibility to adjust the volume within a range of 10 dB according to their preference. The VR intervention in the experiment involved subjects immersing themselves in natural environment videos while wearing Pico Neo VR glasses all-in-one. The selected video, sourced from the Internet, featured a green natural environment, and had a duration of at least 15 minutes. Throughout the video playback, the audio was muted. The content of the video showcased various elements of natural green vegetation such as green grass, trees, shrubs, jungles, rivers, waterfalls, and more, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of Music\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMusic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDuration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRhythm(beats/min)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCatch My Breath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4min10s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125(fast)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemember Our Summer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2min43s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStronger (What Doesn\u0026rsquo;t Kill You)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3min41s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLost in the Discotheque (Radio Edit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3min31s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRiver Flows In You (Original Mix)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4min58s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4min31s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI Love You\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4min22s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69(slow)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTo Me\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4min17s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSilver City\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3min52s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLove Is Gone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2min56s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCritical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3min10s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSo Far Away\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2min51s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Experimental site and time\u003c/h2\u003e \u003cp\u003eThis study was carried out in the Exercise Science Laboratory of Zhejiang Normal University, located in China. The laboratory\u0026rsquo;s environment and soundproofing were deemed satisfactory after inspection and comparison, contributing to the smooth execution of the experiment. The research was conducted between September and November 2023, with testing taking place on the same day from Monday to Friday, spanning the hours of 8:00 a.m. to 11:00 a.m. and 3:00 p.m. to 5:00 p.m.\u003c/p\u003e \u003cp\u003eThe study site\u0026rsquo;s climate during the experimental period was favorable, with minimal temperature variation from the beginning to the end of the experiment. This stability helped reduce the impact of natural environmental factors such as air temperature and humidity on the study outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Experimental process\u003c/h2\u003e \u003cp\u003e(1) Preparation stage: The experimenter ensured that the relevant experimental equipment was set up for each test group before the subjects arrived at the experimental test site. Upon the subjects\u0026rsquo; arrival, the experimenter provided an introduction to the test instructions, briefing them on the procedures before commencing the formal testing. This preparation phase typically lasted approximately 3 minutes, allowing the subjects to familiarize themselves with the upcoming tasks and ensuring a smooth transition into the formal testing phase.\u003c/p\u003e \u003cp\u003e(2) Pre-test stage: The subjects\u0026rsquo; individual demographic and social variables, daily physical activity level, and other relevant information were collected. Following this, the dependent variables were collected in the following order: ①Measurement of the systolic and diastolic blood pressure of the subjects using an Omron electronic sphygmomanometer; ②Collection of heart rate variability data using the First Beat wearable wireless physiological device, specifically 5 minutes before the experiment; ③Completion of real-time assessments using the \u0026ldquo;Positive Affect Scale\u0026rdquo; and \u0026ldquo;Negative Affect Scale\u0026rdquo; by the subjects. This comprehensive data collection process lasted approximately 8 minutes and aimed to establish baseline measurements and assess the subjects\u0026rsquo; physiological and affective states before the formal testing procedures commenced.\u003c/p\u003e \u003cp\u003e(3) Intervention stage: Subjects were directed to wear either Pico Neo VR glasses or sports noise-canceling headphones with the volume set at 75\u0026thinsp;\u0026plusmn;\u0026thinsp;5 noise level, ensuring that the ambient sound level in the laboratory remained below 40 dB. The subjects commenced a 15-minute session of moderate-intensity aerobic power cycling. The exercise load was adjusted to 60\u0026ndash;69% of each individual\u0026rsquo;s maximal heart rate, with heart rate monitoring to maintain a range of 120\u0026ndash;150 beats/min. Following the aerobic cycling phase, subjects proceeded with 2-minute power cycling intervals at an intensity of 20%-30% of their maximal heart rate. This interval period allowed the subjects\u0026rsquo; heart rates to gradually return to their resting rate without any VR or music interference. Subsequently, the subjects were instructed to maintain a sedentary position while continuing to wear VR glasses or sports noise-canceling headphones for an additional 15 minutes. The specific experimental flow chart detailing these instructions and activities can be found in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, illustrating the sequence of events during the experimental procedure.\u003c/p\u003e \u003cp\u003eV-M group: Subjects watched a natural environment video during exercise and experienced music intervention after exercise. This group engaged in 15 minutes of aerobic power cycling with a natural environment video, followed by 2 minutes of interval exercise, and concluded with 15 minutes of sedentary rest accompanied by slow music. M-V group: Subjects listened to music during exercise and viewed a natural environment video after exercise. They participated in 15 minutes of aerobic power cycling with fast-paced music, followed by 2 minutes of interval exercise, and ended with 15 minutes of sedentary rest combined with a video of the natural environment. M group: This group received music interventions during and after exercise. They performed 15 minutes of aerobic power cycling with fast-paced music, followed by 2 minutes of interval exercise, and concluded with 15 minutes of sedentary rest accompanied by slow-paced music. V Group: Subjects watched videos of the natural environment during and after exercise. They engaged in a 15-minute aerobic power cycling session with natural environment videos, followed by a 2-minute interval session, and ended with a 15-minute meditation break with videos of the natural environment.\u003c/p\u003e \u003cp\u003e(4) Post-test stage: At the end of the intervention, the heart rate belt was kept on until the subject\u0026rsquo;s heart rate had returned to the baseline quiet state prior to the start of the experiment. This return to the quiet state was maintained for 30 seconds or longer compared to the heart rate prior to the experiment initiation. The post-test phase included the following assessments:①Systolic and diastolic blood pressure measurements were taken using an Omron electronic sphygmomanometer; ②Heart rate variability data was collected using the First Beat wearable wireless physiological device, specifically 5 minutes before the conclusion of the experiment; ③Subjects were required to complete real-time assessments using the \u0026ldquo;Positive Affect Scale\u0026rdquo; and \u0026ldquo;Negative Affect Scale\u0026rdquo; to gauge their affective states following the intervention. These post-test measures aimed to evaluate the physiological and affective responses of the subjects after the completion of the intervention. The photos of the experiment site are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Dependent variable\u003c/h2\u003e \u003cp\u003e(1) Heart rate variability\u003c/p\u003e \u003cp\u003eIn the controlled combined exercise\u0026ndash;music intervention experiments, the low frequency to high frequency (LF/HF) ratio is often used as the frequency domain analysis indicator. The LF/HF ratio can determine the equilibrium of sympathetic and vagal nerves or the modulation degree of the sympathetic nerves. The time domain analysis indicator primarily uses the root mean square of the difference between adjacent full RR intervals (RMSSD) and the standard deviation of continuous regular RR intervals (SDNN) as comprehensive reflective markers of the effect of short-intervention control experiment on affect improvement(Koelsch, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Jacquet et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, LF/HF, RMSSD, and SDNN data were collected in this experimental study as the metrics for processing and analysis.\u003c/p\u003e \u003cp\u003eFor the acquisition of the above heart rate variability (HRV) indices, the First Beat Sports wireless physiological data collection system with the ECG module device was selected for the experiment. The apparatus can detect and capture changes in the subject\u0026rsquo;s heart rate in real-time and during the activity. Simultaneously, the changing signal of the subject\u0026rsquo;s heart rate can be automatically converted into time- and frequency-domain data in the background for recording and storage. The selected device has been used in many controlled experiments involving green fitness and gardening activities. The real-time accuracy and reliability of the data recorded by the aforementioned device system have been verified in many controlled experiments (Light et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Beauchaine and Thayer, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e(2) BP\u003c/p\u003e \u003cp\u003eBlood pressure includes systolic blood pressure (SBP) and diastolic blood pressure (DBP), the reduction of blood pressure to a certain extent can reflect the reduction of the negative effect on the subjects, and the improvement effect of the blood pressure indicator can be reflected from the side of the human body to improve the effect of short-term affect (Pretty, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Zijlema et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In this experimental study, the systolic and diastolic blood pressure of the subjects will be measured using an Omron upper arm electronic sphygmomanometer. After one measurement is completed and the readings are stabilized, the data will be recorded and the procedure will be repeated after 1\u0026ndash;2 minutes, and the final value will be the average of the two measurements. To avoid the possibility of differences in blood pressure measurements between the pre-test and post-test due to different arms, the arms of the subjects should be kept in the same position during the pre-test and post-test blood pressure measurements.\u003c/p\u003e \u003cp\u003e(3) Short-term affect\u003c/p\u003e \u003cp\u003eThe \"affect\" indicator can be a good way to record and assess the real-time mental health status of the subjects promptly. For the real-time affect condition of the subjects, the experiment used the International Positive/Negative Affect Scale (short version), which has been proven to be widely used and reliable in many controlled experiments(Thompson, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). During the pre-test and post-test phases, the subjects will rate the positive and negative questions according to their real-time affective state. Finally, the affective state of the subjects in the pre-test and post-test phases will be reflected by calculating the total score values of the positive and negative scores respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical methods\u003c/h2\u003e \u003cp\u003e(1)The collected data were initially entered into Excel 2010 for storage and later imported into SPSS 22.0 software for statistical analysis. Descriptive statistics were utilized to analyze the demographic and sociological variables such as gender and age within the sample, presenting the results in terms of percentages or mean standard deviation. To compare the effect of improvement between groups in the pre-test and post-test phases, a multifactor ANOVA was conducted. The analysis considered the F-value, p-value, and effect size (ɳp\u003csup\u003e2\u003c/sup\u003e) to assess the significance and magnitude of the observed differences. Furthermore, differences in the effects of improvement between groups were evaluated using repeated measures ANOVA. This analysis also took into account the F-values, p-values, and effect sizes (ɳp\u003csup\u003e2\u003c/sup\u003e) to determine the significance and impact of these differences over time. Each statistical model was adjusted for various covariates including gender, age, height, weight, and BMI to control for potential confounding variables and ensure a more accurate assessment of the impact of the interventions on affect improvement.\u003c/p\u003e \u003cp\u003e(2)The HRV data were imported into an Excel sheet to process calculations, and the export was divided into a time domain analysis table and a frequency domain analysis table. The calculations used were all corrected RR values, and the specific calculations for RMSSD, SDNN, and LF/HF were as follows:\u003c/p\u003e \u003cp\u003eTime domain indicators\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e1) SDNN\u003c/h3\u003e\n\u003cp\u003eDefined as: the standard deviation of the continuous normal RR period.\u003c/p\u003e \u003cp\u003eExcel calculations were made by applying the standard deviation formula to the RR values. Take cell A1:A4 data as an example: =STDEVP (A1:A4).\u003c/p\u003e\n\u003ch3\u003e2) RMSSD\u003c/h3\u003e\n\u003cp\u003eDefined as: the root mean square of the difference between adjacent RR intervals.\u003c/p\u003e \u003cp\u003eExcel calculations were as follows: \u003cb\u003e(1)\u003c/b\u003e The RR interval value column was copied and then staggered before and after the \u0026ldquo;\u0026mdash;\u0026rdquo; in front of the value, and then \u0026ldquo;+\u0026rdquo; dropped down, resulting in the adjacent interval difference. \u003cb\u003e(2)\u003c/b\u003e The root mean square was then calculated. Cell A1:A4 data were used as an example:\u003c/p\u003e \u003cp\u003e= SQRT (SUMSQ (A1:A4)/ COUNTA (A1:A4)).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eSQRT calculates the square root of an arithmetic number; SUMSQ calculates the sum of squares of several numbers; and COUNTA calculates the total number of numbers.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eFrequency Domain Indicators\u003c/p\u003e\n\u003ch3\u003e3) LF/HF\u003c/h3\u003e\n\u003cp\u003eDefined as: the ratio of LF to HF.\u003c/p\u003e \u003cp\u003eExcel calculates it as =\u0026thinsp;LF /HF, then \u0026ldquo;+\u0026rdquo; drops down to obtain the average value of LF/HF for that time duration.\u003c/p\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Comparison of pre-test and post-test variability of dependent variable indicators\u003c/h2\u003e\n \u003cp\u003e(1) BP\u003c/p\u003e\n \u003cp\u003eThe results of descriptive statistics and tests for differences in blood pressure indicators are presented in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, indicating a decrease in systolic and diastolic blood pressure in the post-test data compared to the pre-test data across all four groups. Specifically, for the SBP indicator, a significant within-group difference was observed between the post-test and pre-test data in the VR group (F\u0026thinsp;=\u0026thinsp;4.578; ɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.067; p\u0026thinsp;=\u0026thinsp;0.036). The within-group difference between the post-test and pre-test data for the systolic blood pressure indicator in the M-V group was also found to be significant. (F\u0026thinsp;=\u0026thinsp;5.105; ɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.074; p\u0026thinsp;=\u0026thinsp;0.027). In contrast, the within-group difference between the pre-test and post-test data for the M group and the V-M group was not found to be significant. However, for the DBP indicator, the within-group difference between the post-test and pre-test data in the V-M group was significant (F\u0026thinsp;=\u0026thinsp;6.015; ɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.086; p\u0026thinsp;=\u0026thinsp;0.017), the within-group difference between the post-test and pre-test data in the other three groups was not found to be significant. The study results indicated that the intervention modes involving VR combined with music exercise and VR alone had a positive promotional effect on improving the blood pressure indicator. However, the music-only intervention mode did not demonstrate a significant positive impact on the improvement of the blood pressure indicator.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDifferential results of blood pressure indicator\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eV-M group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eM-V group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eM Group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eV Group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComparison\u003c/p\u003e\n \u003cp\u003ebetween Groups\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSBP [mmHg]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epre-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104.97\u0026thinsp;\u0026plusmn;\u0026thinsp;12.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111.33\u0026thinsp;\u0026plusmn;\u0026thinsp;14.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105.88\u0026thinsp;\u0026plusmn;\u0026thinsp;13.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107.88\u0026thinsp;\u0026plusmn;\u0026thinsp;16.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;1.357;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.031;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epost-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.70\u0026thinsp;\u0026plusmn;\u0026thinsp;9.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104.30\u0026thinsp;\u0026plusmn;\u0026thinsp;11.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104.94\u0026thinsp;\u0026plusmn;\u0026thinsp;13.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u0026thinsp;\u0026plusmn;\u0026thinsp;13.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComparison\u003c/p\u003e\n \u003cp\u003ewithin groups\u003c/p\u003e\n \u003cp\u003e(time main effect)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;3.725;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.055;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;5.105;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.074;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.027*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.081;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.001;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;4.578;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.067;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.036*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDBP [mmHg]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epre-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.82\u0026thinsp;\u0026plusmn;\u0026thinsp;9.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.39\u0026thinsp;\u0026plusmn;\u0026thinsp;10.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.88\u0026thinsp;\u0026plusmn;\u0026thinsp;8.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.45\u0026thinsp;\u0026plusmn;\u0026thinsp;10.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;1.320;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.030;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.271\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epost-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.79\u0026thinsp;\u0026plusmn;\u0026thinsp;7.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.48\u0026thinsp;\u0026plusmn;\u0026thinsp;7.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.00\u0026thinsp;\u0026plusmn;\u0026thinsp;8.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.91\u0026thinsp;\u0026plusmn;\u0026thinsp;9.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComparison\u003c/p\u003e\n \u003cp\u003ewithin groups\u003c/p\u003e\n \u003cp\u003e(time main effect)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;6.015;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.086;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.017*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;1.682;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.026;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.003;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.000;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.991;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.015;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.321\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eNote: The above models are designed as follows: intercept\u0026thinsp;+\u0026thinsp;gender\u0026thinsp;+\u0026thinsp;age\u0026thinsp;+\u0026thinsp;BMI level. (*) indicates a significant difference between the mid-test and post-test, respectively, and the pre-test, were *: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **: p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e(2) Short-term affect\u003c/p\u003e\n \u003cp\u003eThe descriptive statistics and differential results of the short-term affect indicators are presented in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. These findings reveal a decreasing trend in the post-test stage scores for negative affect among subjects in the four groups compared to the pre-test stage. Additionally, the subjects\u0026rsquo; scores for positive affect in the post-test stage demonstrated an overall increasing trend when compared to the pre-test stage scores. For positive affect, there was a significant within-group difference between the post-test and pre-test data for positive affect in the V-M group (F\u0026thinsp;=\u0026thinsp;6.558; ɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.093; p\u0026thinsp;=\u0026thinsp;0.013) and a significant within-group difference between the post-test and pre-test data for positive affect in the M-V group. (F\u0026thinsp;=\u0026thinsp;4.315; ɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.063; p\u0026thinsp;=\u0026thinsp;0.042). There was no significant within-group difference between the post-test and pre-test data for positive affect in the M group and V group. However, for negative affect, there was a significant within-group difference between the post-test and pre-test data for negative affect in the V-M group (F\u0026thinsp;=\u0026thinsp;13.832; ɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.178; p\u0026thinsp;=\u0026thinsp;0.001). There was a significant within-group difference between the post-test and pre-test data for negative affect in the M-V group as well. (F\u0026thinsp;=\u0026thinsp;8.637; ɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.119; p\u0026thinsp;=\u0026thinsp;0.005). The within-group difference in negative affect between the post-test and pre-test for the M and V groups was not statistically significant. The results of the between-group analyses indicated that there were no significant differences in positive and negative affect between the groups. In response to the short-term affect change situation, among the four groups, exercise combined with visual and auditory stimuli improved both the positive and negative effects of the subjects. The improvement was greater in the V-M group and M-V group, with a significant within-group difference, while the between-group difference was not statistically significant. While the single music and VR groups also enhanced the subjects\u0026rsquo; affect.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDifferential results of short-term affect indicator\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eV-M Group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eM-V Group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eM Group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eV Group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComparison\u003c/p\u003e\n \u003cp\u003ebetween Groups\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive affect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epre-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.15\u0026thinsp;\u0026plusmn;\u0026thinsp;3.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.91\u0026thinsp;\u0026plusmn;\u0026thinsp;2.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.91\u0026thinsp;\u0026plusmn;\u0026thinsp;2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.76\u0026thinsp;\u0026plusmn;\u0026thinsp;3.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.897;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.021;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epost-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.21\u0026thinsp;\u0026plusmn;\u0026thinsp;3.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.67\u0026thinsp;\u0026plusmn;\u0026thinsp;3.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.82\u0026thinsp;\u0026plusmn;\u0026thinsp;4.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.55\u0026thinsp;\u0026plusmn;\u0026thinsp;4.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComparison\u003c/p\u003e\n \u003cp\u003ewithin groups\u003c/p\u003e\n \u003cp\u003e(time main effect)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;6.558;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.093;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.013*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;4.315;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.063;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.042*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;1.052;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.016;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.625;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.010;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.432\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative affect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epre-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.67\u0026thinsp;\u0026plusmn;\u0026thinsp;3.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.39\u0026thinsp;\u0026plusmn;\u0026thinsp;2.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.79\u0026thinsp;\u0026plusmn;\u0026thinsp;2.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;1.915;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.043;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epost-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.64\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.03\u0026thinsp;\u0026plusmn;\u0026thinsp;2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComparison\u003c/p\u003e\n \u003cp\u003ewithin groups\u003c/p\u003e\n \u003cp\u003e(time main effect)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;13.832;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.178;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;8.637;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.119;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.005**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.342;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.005;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;3.063;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.046;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eNote: The above models are designed as follows: intercept\u0026thinsp;+\u0026thinsp;gender\u0026thinsp;+\u0026thinsp;age\u0026thinsp;+\u0026thinsp;BMI level. (*) indicates a significant difference between the mid-test and post-test, respectively, and the pre-test, were *: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **: p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; *** : p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e(3) Heart rate variability\u003c/p\u003e\n \u003cp\u003eThe descriptive statistics and difference test results of the heart rate variability indicator are shown in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. Significant differences were observed in the SDNN indicator between the post-test and pre-test data for the M group (F\u0026thinsp;=\u0026thinsp;12.678; ɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.165; p\u0026thinsp;=\u0026thinsp;0.001) and the V group (F\u0026thinsp;=\u0026thinsp;5.685; ɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.082; p\u0026thinsp;=\u0026thinsp;0.020). Both the V-M group (F\u0026thinsp;=\u0026thinsp;18.091; ɳp2\u0026thinsp;=\u0026thinsp;0.220; p\u0026thinsp;=\u0026thinsp;0.001) and the M-V group (F\u0026thinsp;=\u0026thinsp;5.467; ɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.079; p\u0026thinsp;=\u0026thinsp;0.023) showed significant differences. Significant differences were found in the RMSSD indicator between the post-test and pre-test data for the M group (F\u0026thinsp;=\u0026thinsp;7.567; ɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.107; p\u0026thinsp;=\u0026thinsp;0.007) and the V group (F\u0026thinsp;=\u0026thinsp;5.977; ɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.085; p\u0026thinsp;=\u0026thinsp;0.017). Additionally, there were significant differences within the V-M group (F\u0026thinsp;=\u0026thinsp;7.786; ɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.108; p\u0026thinsp;=\u0026thinsp;0.007) and the M-V group (F\u0026thinsp;=\u0026thinsp;16.568; ɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.206; p\u0026thinsp;=\u0026thinsp;0.001). For the LF/HF indicator, the within-group difference was significant only for the M-V group (F\u0026thinsp;=\u0026thinsp;8.158; ɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.113; p\u0026thinsp;=\u0026thinsp;0.006), while it was not significant for the other three groups. The results of the between-group analysis for the HRV indicators revealed that, except for the RMSSD indicator where the between-group differences were not significant, significant differences were found between the groups for the SDNN indicator (F\u0026thinsp;=\u0026thinsp;3.088; ɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.067; p\u0026thinsp;=\u0026thinsp;0.030), as well as for the LF/HF indicator (F\u0026thinsp;=\u0026thinsp;4.204; ɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.090; p\u0026thinsp;=\u0026thinsp;0.007).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDifferential results of heart rate variability indicator\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eV-M Group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eM-V Group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eM Group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eV Group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComparison\u003c/p\u003e\n \u003cp\u003ebetween Groups\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSDNN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epre-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.60\u0026thinsp;\u0026plusmn;\u0026thinsp;24.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.15\u0026thinsp;\u0026plusmn;\u0026thinsp;22.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.55\u0026thinsp;\u0026plusmn;\u0026thinsp;18.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.55\u0026thinsp;\u0026plusmn;\u0026thinsp;21.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;3.088;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.067;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.030*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epost-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.25\u0026thinsp;\u0026plusmn;\u0026thinsp;22.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.16\u0026thinsp;\u0026plusmn;\u0026thinsp;22.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.25\u0026thinsp;\u0026plusmn;\u0026thinsp;17.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.12\u0026thinsp;\u0026plusmn;\u0026thinsp;13.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComparison\u003c/p\u003e\n \u003cp\u003ewithin groups\u003c/p\u003e\n \u003cp\u003e(time main effect)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;18.091;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.220;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;5.467;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.079;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.023*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;12.678;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.165;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;5.685;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.082;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.020*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRMSSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epre-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.55\u0026thinsp;\u0026plusmn;\u0026thinsp;32.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.94\u0026thinsp;\u0026plusmn;\u0026thinsp;17.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.59\u0026thinsp;\u0026plusmn;\u0026thinsp;24.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.17\u0026thinsp;\u0026plusmn;\u0026thinsp;30.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;1.770;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.040;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epost-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.20\u0026thinsp;\u0026plusmn;\u0026thinsp;20.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.90\u0026thinsp;\u0026plusmn;\u0026thinsp;22.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.54\u0026thinsp;\u0026plusmn;\u0026thinsp;14.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.52\u0026thinsp;\u0026plusmn;\u0026thinsp;14.64*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComparison\u003c/p\u003e\n \u003cp\u003ewithin groups\u003c/p\u003e\n \u003cp\u003e(time main effect)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;7.786;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.108;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.007**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;16.568;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.206;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;7.567;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.107;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.007**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;5.977;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.085;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.017*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLF/HF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epre-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.78\u0026thinsp;\u0026plusmn;\u0026thinsp;3.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.83\u0026thinsp;\u0026plusmn;\u0026thinsp;4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.17\u0026thinsp;\u0026plusmn;\u0026thinsp;2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;4.204;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.090;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.007**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epost-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.91\u0026thinsp;\u0026plusmn;\u0026thinsp;3.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.33\u0026thinsp;\u0026plusmn;\u0026thinsp;3.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.21\u0026thinsp;\u0026plusmn;\u0026thinsp;2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComparison\u003c/p\u003e\n \u003cp\u003ewithin groups\u003c/p\u003e\n \u003cp\u003e(time main effect)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;1.008;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.016;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;8.158;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.113;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.006**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.529;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.08;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;3.704;\u003c/p\u003e\n \u003cp\u003eɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.055;\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eNote: The above models are designed as follows: intercept\u0026thinsp;+\u0026thinsp;gender\u0026thinsp;+\u0026thinsp;age\u0026thinsp;+\u0026thinsp;BMI level. (*) indicates a significant difference between the mid-test and post-test, respectively, and the pre-test, were *: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **: p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; *** : p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Multiple comparisons of the effects of different combined visual-auditory exercises on dependent variables\u003c/h2\u003e\n \u003cp\u003eFurther analysis using repeated measures ANOVA to compare the variability of the effects of different combined visual and auditory exercises on the dependent variables in the four groups, all with the same exercise duration and intensity, revealed significant between-group differences only for the SDNN indicator (F\u0026thinsp;=\u0026thinsp;3.088; ɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.067; p\u0026thinsp;=\u0026thinsp;0.030) and the LF/HF indicator (F\u0026thinsp;=\u0026thinsp;4.204; ɳp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.090; p\u0026thinsp;=\u0026thinsp;0.007). The remaining indicators showed non-significant between-group differences (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Multiple comparisons were conducted to compare the effects of different combined visual and auditory exercises on the dependent variable indicators among the four groups, and the results are presented in Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. The results indicate that the M-V group showed the most significant improvement in the SDNN indicator, outperforming the V group (p\u0026thinsp;=\u0026thinsp;0.036), the M group (p\u0026thinsp;=\u0026thinsp;0.014), and the V-M group (p\u0026thinsp;=\u0026thinsp;0.008). Regarding the LF/HF indicator, the V-M group exhibited the most significant improvement, surpassing the V group (p\u0026thinsp;=\u0026thinsp;0.006), while the M-V group also showed significant improvement compared to the V group (p\u0026thinsp;=\u0026thinsp;0.002) for the LF/HF indicator. The RMSSD indicator showed that the M-V group had the most significant improvement compared to the V-M group (p\u0026thinsp;=\u0026thinsp;0.037), while the M group demonstrated the greatest improvement in the negative affect indicator, significantly outperforming the V-M group (p\u0026thinsp;=\u0026thinsp;0.028). In summary, the analysis of between-group variability in the effects of various combined visual and auditory exercises on the dependent variables indicated that the M-V group showed greater effectiveness in enhancing the heart rate variability indicator, whereas the V group demonstrated better improvements in the short-term affect and blood pressure indicators.\u003c/p\u003e\n \u003cp\u003eTable 6. Comparative results of all indicators in different groups (Post hoc test)\u003c/p\u003e\n \u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"554\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003eMarginal mean difference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.710669077757686%\"\u003e\n \u003cp\u003eIndicator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.658227848101266%\"\u003e\n \u003cp\u003e1-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.658227848101266%\"\u003e\n \u003cp\u003e1-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.189873417721518%\"\u003e\n \u003cp\u003e1-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.743218806509946%\"\u003e\n \u003cp\u003e2-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.924050632911392%\"\u003e\n \u003cp\u003e2-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.115732368896927%\"\u003e\n \u003cp\u003e3-4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.710669077757686%\"\u003e\n \u003cp\u003eSDNN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.658227848101266%\"\u003e\n \u003cp\u003e10.2723\u003c/p\u003e\n \u003cp\u003e(p=0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.658227848101266%\"\u003e\n \u003cp\u003e9.5262\u003c/p\u003e\n \u003cp\u003e(p=0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.189873417721518%\"\u003e\n \u003cp\u003e8.0905\u003c/p\u003e\n \u003cp\u003e(p=0.036)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.743218806509946%\"\u003e\n \u003cp\u003e-7461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.924050632911392%\"\u003e\n \u003cp\u003e-2.1818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.115732368896927%\"\u003e\n \u003cp\u003e-1.4358\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.710669077757686%\"\u003e\n \u003cp\u003eLF/HF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.658227848101266%\"\u003e\n \u003cp\u003e-0.2311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.658227848101266%\"\u003e\n \u003cp\u003e0.9065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.189873417721518%\"\u003e\n \u003cp\u003e1.6953\u003c/p\u003e\n \u003cp\u003e(p=0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.743218806509946%\"\u003e\n \u003cp\u003e1.1376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.924050632911392%\"\u003e\n \u003cp\u003e1.9264\u003c/p\u003e\n \u003cp\u003e(p=0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.115732368896927%\"\u003e\n \u003cp\u003e0.7888\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.710669077757686%\"\u003e\n \u003cp\u003eRMSSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.658227848101266%\"\u003e\n \u003cp\u003e8.9494\u003c/p\u003e\n \u003cp\u003e(p=0.037)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.658227848101266%\"\u003e\n \u003cp\u003e7.3044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.189873417721518%\"\u003e\n \u003cp\u003e3.5283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.743218806509946%\"\u003e\n \u003cp\u003e-1.6450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.924050632911392%\"\u003e\n \u003cp\u003e-5.4211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.115732368896927%\"\u003e\n \u003cp\u003e-3.5283\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.710669077757686%\"\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.658227848101266%\"\u003e\n \u003cp\u003e-5.4848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.658227848101266%\"\u003e\n \u003cp\u003e-3.0758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.189873417721518%\"\u003e\n \u003cp\u003e-1.6061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.743218806509946%\"\u003e\n \u003cp\u003e2.4090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.924050632911392%\"\u003e\n \u003cp\u003e3.8788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.115732368896927%\"\u003e\n \u003cp\u003e1.4697\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.710669077757686%\"\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.658227848101266%\"\u003e\n \u003cp\u003e-2.6364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.658227848101266%\"\u003e\n \u003cp\u003e-3.6364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.189873417721518%\"\u003e\n \u003cp\u003e-1.8788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.743218806509946%\"\u003e\n \u003cp\u003e-1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.924050632911392%\"\u003e\n \u003cp\u003e0.7576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.115732368896927%\"\u003e\n \u003cp\u003e1.7576\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.710669077757686%\"\u003e\n \u003cp\u003ePositive affect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.658227848101266%\"\u003e\n \u003cp\u003e0.4091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.658227848101266%\"\u003e\n \u003cp\u003e-0.6061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.189873417721518%\"\u003e\n \u003cp\u003e0.2576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.743218806509946%\"\u003e\n \u003cp\u003e-1.0152\u003c/p\u003e\n \u003cp\u003e(p=0.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.924050632911392%\"\u003e\n \u003cp\u003e-0.1515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.115732368896927%\"\u003e\n \u003cp\u003e0.8636\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.710669077757686%\"\u003e\n \u003cp\u003eNegative affect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.658227848101266%\"\u003e\n \u003cp\u003e0.3939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.658227848101266%\"\u003e\n \u003cp\u003e-0.1818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.189873417721518%\"\u003e\n \u003cp\u003e1.0303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.743218806509946%\"\u003e\n \u003cp\u003e-0.5758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.924050632911392%\"\u003e\n \u003cp\u003e0.6364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.115732368896927%\"\u003e\n \u003cp\u003e1.2121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eNote: 1:V-M group；2:M-V group；3:M group；4:V group.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe results indicated a significant difference in the improvement of short-term affect before and after the intervention experiment between the M-V group and the V-M group. While there was no significant difference between the single V group and the single M group before and after the intervention, both groups still showed some improvement in short-term affect or a positive trend of enhancement. The analysis based on physiological indicators revealed that the M-V group demonstrated significantly greater improvement in SDNN and LF/HF indicators compared to the single V group and single M group, with the V-M group showing the most significant improvement. Additionally, for the RMSSD indicator, the M-V group exhibited the most notable enhancement, surpassing the other groups significantly. Similar trends were observed in subjective affect and blood pressure indicators. In conclusion, the comparison of various visual-auditory combined movements on short-term affect indicated that the M-V group generally had a more positive impact on short-term affect.\u003c/p\u003e \u003cp\u003ePrevious studies have consistently demonstrated that music intervention can enhance short-term affect during and after exercise, a finding corroborated by the results of this study. The combination of music with exercise, extensively explored in combined exercise interventions, has been shown to assist individuals in recovering from exercise-induced fatigue and boosting their positive affect (Chair et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Listening to fast-paced music during aerobic exercise has been found to induce feelings of cheerfulness, relaxation, enhanced cognitive agility, and fatigue reduction (Edwards et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). When individuals listen to music during exercise, they tend to synchronize their movement pace with the rhythm of the music unconsciously. This synchronization can lead to positive effects on both the psychological and physiological functions of the participants (Paluska and Schwenk, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Listening to music during aerobic jogging has become a popular exercise practice among many runners. Fast-paced music can help runners maintain a consistent rhythm, thereby saving energy that would have been expended in adjusting their pace. This practice enhances the overall exercise experience and pleasure for participants, while also boosting post-exercise excitement and perceptual abilities (Karageorghis et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Karageorghis and Jones, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the post-exercise recovery period, individuals often prefer slow-paced music. Slow rhythm music has been shown to significantly relax individuals during this recovery phase, effectively aiding in the reduction of exercise-induced fatigue (North and Hargreaves, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Scheufele, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Music is a highly complex auditory medium that engages various aspects of the human body and mind, including hearing, attention, perception, sound pattern analysis, memory, and emotional experience. It is considered a powerful stimulus that can evoke profound personal experiences due to its multifaceted nature (Koelsch, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The integrated cognitive processing characteristics of music play a crucial role in human life, particularly in five key areas: emotion regulation, language, communication, motor skills, and cognitive regulation (Harikumar et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Burrai et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Music has been shown to reduce individual stress by lowering physiological arousal, as evidenced by decreased levels of cortisol, heart rate, and mean arterial pressure (Kreutz et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Linnemann et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In this context, the integration of music with movement can be viewed as a more suitable intervention to enhance an individual\u0026rsquo;s athletic performance (Zoormand, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, subjects who underwent music intervention during aerobic power bicycle exercise followed by VR green natural environment intervention during the recovery period exhibited a more pronounced improvement in short-term affect compared to other groups. The brain relies on various senses to gather information from the internal and external environments, with vision and hearing being crucial channels for human perception. The synergistic interaction of vision and hearing can enhance the overall impact at times. While multiple studies have demonstrated the positive effects of both natural environment VR and music interventions on short-term affect improvement, the combination of intervention methods can yield varying experimental outcomes (Jiang et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mendes et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe researchers employed random assignment to allocate participants into the music intervention group, VR group, audio-visual combination group, and non-intervention control group. The study results revealed that individuals in the audio-visual combination group reported the lowest self-perceived fatigue levels during bicycle pedaling. Moreover, the music intervention group and the VR group exhibited enhanced self-perception, whereas the control group demonstrated the least favorable outcomes (Chow and Etnier, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Research on task completion duration under different conditions has indicated that subjects tend to perform better when exposed to music and video stimuli (Razon et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The positive impact observed in task completion under conditions with music and video stimuli may be attributed to factors such as the spatial experience facilitated by VR environments and the rhythmic stimulation from music. These factors could help mitigate feelings of burnout induced by fatigue, consequently extending task completion duration.\u003c/p\u003e \u003cp\u003eFurthermore, research supports the notion that engaging with VR in natural green environments during post-exercise recovery can positively influence short-term affect. Watching videos of natural environments post-exercise has been shown to promote physical and mental relaxation, potentially enhancing blood pressure regulation (Cho et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Ojala et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The Attention Restoration Theory (ART) suggests that exposure to green spaces not only sustains short-term affect levels but also fosters the cultivation of positive effects, hastens the alleviation of fatigue-related factors, and enhances individual feelings of enjoyment (Kaplan, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Wells, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The continuous advancement of scientific research has demonstrated that exposure to virtual reality (VR) green natural landscapes can indeed enhance short-term affect, a finding that has been increasingly supported and acknowledged by a growing body of studies (Legrand et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Dadvand et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Both physical spaces with actual green plants and virtual reality environments featuring green landscapes are inclined to cultivate a relaxed and pleasant atmosphere, with the effectiveness being influenced by the extent of green plant coverage. A higher coverage rate of green plants is associated with a greater capacity for individuals to alleviate work or study-related pressures, facilitating the recovery from physical and mental fatigue (Bringslimark et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Thomsen et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNumerous scientific studies have increasingly supported the effectiveness of VR green natural environments and music interventions in aerobic exercise for enhancing short-term affect. People are now more inclined to engage in exercise using their preferred intervention methods. However, there remains a lack of novel intervention strategies combining natural environment videos and music in aerobic exercise. Additionally, research on optimizing the combination of VR and music interventions to enhance short-term affect is relatively scarce. The findings of this study demonstrate the significant benefits of combining VR green natural environments and music interventions in aerobic power cycling on both physiological and psychological levels, offering practical value for enhancing short-term affect. Individuals can now select different intervention methods based on their preferences during aerobic exercise to elevate their short-term affect levels and improve physical fitness.\u003c/p\u003e"},{"header":"5 Limitations","content":"\u003cp\u003e(1) Given that the study participants were exclusively college students, the generalizability of the findings to other populations may be limited. College students typically exhibit higher cognitive levels compared to other demographic groups, potentially influencing the study outcomes. As mental health comparisons were not included in this pilot study, future research with this population should consider incorporating such assessments for a more comprehensive understanding.\u003c/p\u003e \u003cp\u003e(2) While this study utilizes virtual reality technology to immerse participants in a green natural environment, factors such as video clarity, spatial enclosure, and individual perception of the 3D effect may vary, potentially influencing the experimental outcomes. It is important to note that virtual reality technology is still evolving, and discrepancies exist between the virtual effects produced by VR equipment and real-life settings, introducing limitations to the study.\u003c/p\u003e \u003cp\u003e(3) Popular songs were selected for the experiment. Previous literature has indicated that classical music enhances students\u0026rsquo; cognitive abilities, while jazz music improves their physical performance. Therefore, it is valuable to investigate the impact of combining various types of music on enhancing mental states during combined aerobic exercise.\u003c/p\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eAnalysis was conducted to compare subjective evaluation indicators and objective physiological indicators of individuals undergoing visual-auditory combined exercise. The study confirmed that the combined exercise intervention involving music and VR exposure to a green environment had a positive impact on short-term affect. Under the same intensity of aerobic exercise, the M-V group demonstrated greater improvements in physiological response indicators including SBP, heart rate variability (LF/HF, SDNN, RMSSD), as well as subjective positive/negative affect indicators, followed by the V-M group which showed significantly better results compared to the other groups.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due the data contained some of the subjects\u0026apos; private information, they wanted to make it public after the article was published but are available from the corresponding author on reasonable request. When the paper is successfully published, this information can be accessed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm contribution to the paper as follows: Conceptualization:Meng Tao, Jie Zhuang; Data curation: Meng Tao, Jingchuan Gao; Formal Analysis:Meng Tao; Writing-original draft、Writing- review \u0026amp; editing: Meng Tao, Jingchuan Gao, Haiquan Huang, Yuanyuan Cao. All authors reviewed the results and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlmeida, C.M.M., Araujo, F.D.S., Lima, E.V.D.C., De Souza, M.F., Sales, M.M., De Moraes, J.F.V.N., 2021. Acute effects of listening to music and/or watching video clips on perceptual variables and performance during a high-intensity exercise session. 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Annual Review of Cybertherapy and Telemedicine 2011, 122\u0026ndash;127.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurray, E.G., Neumann, D.L., Moffitt, R.L., Thomas, P.R., 2016. The effects of the presence of others during a rowing exercise in a virtual reality environment. Psychology of Sport and Exercise 22, 328\u0026ndash;336.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNorth, A.C., Hargreaves, D.J., 2000. Musical preferences during and after relaxation and exercise. American journal of psychology 113, 43\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOjala, A., Korpela, K., Tyrv\u0026auml;inen, L., Tiittanen, P., Lanki, T., 2019. Restorative effects of urban green environments and the role of urban-nature orientedness and noise sensitivity: A field experiment. Health \u0026amp; place 55, 59\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaluska, S.A., Schwenk, T.L., 2000. 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HortScience 46, 744\u0026ndash;752.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWells, N.M., 2000. At home with nature: Effects of \u0026ldquo;greenness\u0026rdquo; on children\u0026rsquo;s cognitive functioning. Environment and behavior 32, 775\u0026ndash;795.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZijlema, W.L., Avila-Palencia, I., Triguero-Mas, M., Gidlow, C., Maas, J., Kruize, H., Andrusaityte, S., Grazuleviciene, R., Nieuwenhuijsen, M.J., 2018. Active commuting through natural environments is associated with better mental health: Results from the PHENOTYPE project. Environment International 121, 721\u0026ndash;727%@ 0160\u0026ndash;4120.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZoormand, G., 2023. The Effect of Classical Music on Two Sports Skills, Agility and Quick Penalty Shot, in Female Basketball Players. International Journal of Sport Studies for Health 6.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Virtual Reality, Music, Heart Rate Variability, Aerobic Exercise, Short-term Affect","lastPublishedDoi":"10.21203/rs.3.rs-4345575/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4345575/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives: \u003c/strong\u003ePrior research has explored the effects of engaging with real or virtual natural landscapes and listening to music during aerobic exercise on short-term affect, However, the specific differences in the improvement of short-term affect by different combinations of VR and music rhythm require further investigation. This study aims to explore the differential impact of distinct VR and music integration strategies on short-term affect, thereby informing future research directions and optimizing public fitness practices.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis study recruited 132 valid subjects (mean age 24.0±0.9 years), with a gender distribution of 68 males and 64 females. Participants were randomly assigned to one of four groups: Visual-Music (V-M), Music-Visual (M-V), Visual-only (V), and Music-only (M). The exercise mode was 15 minutes of aerobic power cycling with 2 minutes of low-intensity power cycling intervals in the middle. After the exercise, the subjects were asked to sit and then performed either a VR intervention or a music intervention for 15 minutes. The collected indicators included blood pressure, positive/negative affect, and heart rate variability indicators (RMSSD, SDNN, LF/HF). Data analysis included descriptive statistics, repeated measures ANOVA, and multifactor ANOVA. The effect of different VR and Music combined with exercise interventions on the improvement of short-term affect was analyzed based on the effect size (ɳp\u003csup\u003e2\u003c/sup\u003e) and combined with the significance p-value.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eIntra-group differences showed that DBP, positive affect, negative affect, SDNN, RMSSD indicators in V-M group were significantly different before and after the experiment (p\u0026lt;0.05), while SBP, positive affect, negative affect, SDNN, RMSSD, LF/HF indicators in M-V group were significantly different before and after the intervention (all p\u0026lt;0.05). Only SDNN and RMSSD indicators in group M had significant differences before and after the experiment (p\u0026lt;0.05), and only SBP and RMSSD indicators in group V had significant differences before and after the experiment (p\u0026lt;0.05). The difference between groups showed that compared with other short-term affect response indicators, only SDNN and LH/HF groups had a significant difference (p\u0026lt;0.05), and other indicators had a trend of improvement or positive promotion to a certain extent, but the statistical difference was not significant (p\u0026gt;0.05). In general, the improvement effect of the visual-auditory combined exercise on short-term affect was due to the single visual or auditory activity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eAerobic exercise with consistent intensity and the combined visual-auditory interventions (M-V and V-M) significantly improved blood pressure, and the short-term affect of physiological responses (LF/HF, SDNN, RMSSD), along with subjective affect measures, compared to other intervention groups.These findings suggest that incorporating VR and music with exercise can effectively enhance short-term affect, recommending an integrated approach to aerobic exercise and relaxation through music and visual exposure to natural environments.\u003c/p\u003e","manuscriptTitle":"The Effects of Combining Visual-Auditory Stimuli with Exercise on Short-Term Affect Improvement: A Randomized Controlled Trial","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-14 17:47:44","doi":"10.21203/rs.3.rs-4345575/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-01T10:43:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-29T07:48:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"112252059588855352531119534067472872011","date":"2024-06-18T20:00:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-17T17:15:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"337484895168632167262115826593850844598","date":"2024-06-14T17:58:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-13T14:28:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-06T10:42:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-07T06:56:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-06T10:54:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-04-30T01:49:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1893f5c2-5f40-40f6-b5fc-677ec61e86a6","owner":[],"postedDate":"May 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":31803663,"name":"Biological sciences/Physiology"},{"id":31803664,"name":"Biological sciences/Psychology"},{"id":31803665,"name":"Earth and environmental sciences/Environmental sciences"},{"id":31803666,"name":"Health sciences/Health care"}],"tags":[],"updatedAt":"2024-08-22T19:35:36+00:00","versionOfRecord":{"articleIdentity":"rs-4345575","link":"https://doi.org/10.1038/s41598-024-69578-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-08-16 15:57:33","publishedOnDateReadable":"August 16th, 2024"},"versionCreatedAt":"2024-05-14 17:47:44","video":"","vorDoi":"10.1038/s41598-024-69578-y","vorDoiUrl":"https://doi.org/10.1038/s41598-024-69578-y","workflowStages":[]},"version":"v1","identity":"rs-4345575","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4345575","identity":"rs-4345575","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}