The Effect of Mixed Natural Sounds on Stress Recovery: Insights into Physiological Benefits and Temporal Dynamics

doi:10.21203/rs.3.rs-4979692/v1

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The Effect of Mixed Natural Sounds on Stress Recovery: Insights into Physiological Benefits and Temporal Dynamics

https://doi.org/10.21203/rs.3.rs-4979692/v1

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The effect of natural sound on stress recovery is heterogeneous. The combination mode of natural sounds and sound duration may affect stress recovery. We conducted a study with 104 participants, randomly assigning them to one of three groups. Each group underwent an adapted version of the Montreal Imaging Stress Test (MIST) to induce stress, followed by a rest stage where they were exposed to either a single natural sound, a mixed sound environment, or no sound at all. Physiological data and self-reported stress levels were recorded at baseline, during the MIST test and the rest stages, and were analyzed using a Generalized Additive Model (GAM). Our findings revealed that the mixed sound environment was more effective in promoting physiological stress recovery, with a consistent recovery time course observed across all participant groups. Our study provides valuable insights and practical guidelines for stress management.

Earth and environmental sciences/Environmental social sciences/Psychology and behaviour

Health sciences/Health care/Health services

Biological sciences/Psychology/Human behaviour

Time Course of Stress Recovery

Natural sounds

Generalized Additive Model

Montreal Imaging Stress Test (MIST)

Stress is an inevitable part of daily life. If not managed effectively, acute stress can develop into chronic stress, increasing the risk of health issues such as hypertension and heart disease (Geurts & Sonnentag, 2006; Ulrich, 1983). This has led to a growing focus on strategies for effective stress recovery. Research suggests that natural environments can facilitate stress recovery from the bottom up, without the additional need for cognitive effort (Kardan et al., 2015; Li, H. et al., 2023; Stigsdotter et al., 2017; Stobbe et al., 2022). This allows for a subtle reduction in daily stress and places fewer demands on individuals' self-control (Schutte, Torquati, & Stevens, 2021). Furthermore, combining natural stimuli with emerging technologies can enhance accessibility and adaptability of nature, providing a valuable mental health resource in today’s fast-paced world (Riches et al., 2021).

While much research has focused on the effects of natural visual stimuli on stress recovery, the role of acoustic stimuli is also significant. People under stress often seek tranquil environments (Van & Bird, 2018), and studies have shown that the sense of tranquility is linked to activation in the auditory cortex (Hunter et al., 2010). "Tranquility" typically involves a sound environment enriched with natural sounds rather than complete silence (Ratcliffe, 2021). Natural sounds possessed unique characteristics, which may be linked to evolutionary aspects of human survival, providing a sense of lightness, brightness and security (Ratcliffe, Gatersleben, & Sowden, 2018; Van Hedger et al., 2019). Additionally, specific acoustic vibrations in natural sounds may resonate with physiological activities like breathing and heartbeats This resonance could enhance heart rate variability, potentially leading to further stress reduction (Sonnenschein, 2001). These findings underscore the importance of natural sounds in creating a calming and relaxing environment.

Laboratory studies using stress-inducing protocols, such as the Trier Social Stress Test (TSST) and the Montreal Imaging Stress Test (MIST), have explored the recovery effects of various stimuli (Dedovic et al., 2005; Kirschbaum, Pirke, & Hellhammer, 1993). These studies measure stress through physical indicators and self-reports, noting that stress activates the sympathetic nervous system and suppresses the parasympathetic system, leading to changes in heart rate, blood pressure, and skin conductance (Villarejo, Zapirain, & Zorrilla, 2012). Recent research has introduced the Sympathetic Activity Index (SAI) and Parasympathetic Activity Index (PAI) to separately evaluate the roles of the sympathetic and parasympathetic systems. These indicators offer a more precise evaluation of autonomic system responses, outperforming traditional heart rate variability measures (Valenza et al., 2018). By combining these indicators, researchers can more accurately quantify the stress-recovery effects of natural sounds.

Research on the effects of natural sounds on stress recovery has yielded varied results. While some studies report significant stress reduction from natural sounds, others find minimal differences compared to urban sound environments (Benfield et al., 2014; Jo et al., 2019; Payne & Guastavino, 2018; Hedblom et al., 2019; Jahncke et al., 2011). Factors contributing to these mixed results include the specific types of natural sounds used and their combination modes. Previous studies have used two main sound combination modes: looping a single sound source, such as a raining sound, for a period of time, or mixing multiple sound sources, such as wind and rain in the background with birdsong in the foreground, to create a sound environment. Some researchers have found that human brain automatically prioritizes processing and recognition of certain features, such as repetitive rhythms, and certain cortical areas are particularly sensitive to specific features (Zatorre, Bouffard, & Belin, 2004). They believe that these unique features may be crucial for stress recovery, and mixed sound environments could potentially obscure them. On the other hand, other researchers suggest that mixed sound environments, which contain more auditory content, are more likely to induce immersion and imagination (Schafer, 1993). Immersion could enhance a sense of individual presence and agency (Makransky & Petersen, 2021, Nilsson et al., 2017), and imagination could help distract attention from stressful events to foster positive emotions (Chen & Spence, 2017), both of which can contribute to more effective stress recovery. However, there is currently no direct evidence proving which combination mode is more effective for stress recovery.

Additionally, the duration of sound exposure is also critical for stress recovery. Previous studies suggested that, in the absence of any restorative stimulus, stress recovery can occur rapidly at first and then slows down, reaching baseline levels within about 15 minutes (de Boer et al., 2007; Healey & Picard, 2005). However, the specific relationship between sound exposure duration and stress recovery remains underexplored.

Therefore, our study aims to address this gap by investigating (a) the effects of different combination modes of natural sounds on stress recovery, and (b) the relationship between the duration of natural sound exposure and stress recovery (i.e. the time course of stress recovery induced by natural sounds). Our findings seek to provide insights into the mechanisms of stress recovery and the practical application of natural sounds.

Baseline Level Difference Test

ANOVA results revealed no significant differences in the five physiological indicators or subjective stress levels among the three groups, indicating comparable baseline conditions (Heart rate: F = 1.796, p = 0.171; SAI: F = 0.867, p = 0.423; PAI: F = 0.876, p = 0.420; Respiratory rate: F = 0.295; p = 0.745; SCL: F = 1.739, p = 0.181; Subjective stress level: F = 2.605, p = 0.079).

Effectiveness of Stress Induction

Repeated measures t-tests confirmed significant increases in stress-related measures during the MIST test compared to baseline across all groups, except for PAI, which showed a significant decrease, indicating increased parasympathetic activity (see Appendix 1.2). Analysis of change values showed no significant differences in these indicators' change values among the three groups (Heart rate: F = 0.066, p = 0.936; SAI: F = 0.189, p = 0.828; PAI: F = 0.189, p = 0.828; Respiratory rate: F = 0.240, p = 0.787; SCL: F = 0.637, p = 0.531; Subjective stress levels: F = 0.906, p = 0.407), indicating uniform stress induction across groups.

Stress Recovery

The results showed that, in groups 1 and 2, all stress-related measures were significantly lower during the rest stage compared to the MIST test stage, except for PAI, which was significantly higher. In group 3, all indicators except for SCL were significantly lower during the rest stage (see Appendix 1.3). These results suggest effective stress recovery in each group.

Analysis of changes between the rest stage and MIST test stage showed no significant differences in changes in heart rate (F = 0.091, p = 0.913), SAI (F = 0.042, p = 0.959), PAI (F = 0.041, p = 0.959), and respiratory rate (F = 0.388, p = 0.679) among the groups. However, significant differences were found in changes in SCL (F = 4.474, p = 0.014) and marginal differences were found in subjective stress level (F = 2.828, p = 0.064). Post hoc tests revealed that for SCL, group 2 had significantly lower levels than group 3 (p < 0.05), and for subjective stress levels, group 1 was significantly lower than group 3 (p < 0.05), with no significant differences between groups 1 and 2.

Generalized additive model (GAM) results

Heart rate

The GAM analysis for heart rate (Table 1; Fig. 1a) revealed a significant effect of time, with an edf of 8.132, suggesting a non-linear relationship between time and heart rate recovery. The effects of group and the interaction between time and group were not significant. All curves had a k-index of 1.01, indicating reliable smoothing function nodes.

Table 1

GAM model indicators of Heart Rate
Parametric coefficients:
	Estimate	Std. Error	t value	p-value
(Intercept)	-11.7518	0.3532	-33.276	< 0.001^***
group2	0.1657	0.4994	0.332	0.74
group3	-0.2711	0.5031	-0.539	0.59
Approximate significance of smooth terms:
	edf	Ref.df	F	p-value
s(time21)	8.132	8.789	14.154	< 0.001^***
s(time21): group2	1.004	1.007	0.03	0.871
s(time21): group3	1.004	1.009	0.011	0.933
Note: * means p < 0.05, means p < 0.01, * means p < 0.001.

SAI

For SAI (Table 2; Fig. 1b), GAM results indicated a significant effect of time, with group 2 exhibiting significantly lower SAI values compared to the other groups. No significant interaction between time and group was observed. The edf was 8.132, pointing to a non-linear relationship between time and SAI recovery, with a k-index of 1.02 for all curves, indicating reliable node numbers.

Table 2

GAM model indicators of SAI
Parametric coefficients:
	Estimate	Std. Error	t value	p-value
(Intercept)	-531.0528	15.9013	-33.397	< 0.001^***
group2	-47.689	22.4878	-2.121	0.034^*
group3	0.3806	22.6526	0.017	0.9866
Approximate significance of smooth terms:
	edf	Ref.df	F	p-value
s(time21)	8.299	8.859	14.6	< 0.001^***
s(time21): group2	1.005	1.01	0.916	0.338
s(time21): group3	1.005	1.009	0.255	0.615
Note: * means p < 0.05, means p < 0.01, * means p < 0.001.

PAI

The GAM results for PAI (Table 3; Fig. 1c) revealed a significant time effect, with group 2 exhibiting notably higher PAI values compared to the other groups. No significant interaction was identified. The edf for time was 8.267, suggesting a non-linear pattern. The k-index for all curves was 1.02, indicating a reliable and appropriate nodes number.

Table 3

GAM model indicators of PAI
Parametric coefficients:
	Estimate	Std. Error	t value	p-value
(Intercept)	2042.153	34.247	59.629	< 0.001^***
group2	-140.056	59.174	-2.367	0.018^*
group3	-2.218	59.462	-0.037	0.97
Approximate significance of smooth terms:
	edf	Ref.df	F	p-value
s(time21)	8.267	8.846	15.155	< 0.001^***
s(time21): group2	1.005	1.011	0.853	0.355
s(time21): group3	1.005	1.01	0.208	0.65
Note: * means p < 0.05, means p < 0.01, * means p < 0.001.

SCL

The GAM results for SCL (Table 4; Fig. 1d) revealed a significant effect of time, with group 2 exhibiting significantly lower SCL values than the other groups, and group 3 showing significantly lower values than group 1. No significant interaction effect was found. The edf of time was 4.584, suggesting a non-linear pattern. The k-index for all curves was 0.96.

Table 4

GAM model indicators of SCL
Parametric coefficients:
	Estimate	Std. Error	t value	p-value
(Intercept)	-1.40919	0.08295	-16.988	< 0.001^***
group2	1.24724	0.14333	8.702	< 0.001^***
group3	0.31247	0.14403	2.169	0.030^*
Approximate significance of smooth terms:
	edf	Ref.df	F	p-value
s(time21)	4.584	5.599	3.989	0.0011^**
s(time21): group2	1.186	1.336	1.484	0.2758
s(time21): group3	1.626	2.008	0.792	0.4549
Note: * means p < 0.05, means p < 0.01, * means p < 0.001

Residual Normality Test and Autocorrelation Test

Residual normality and autocorrelation tests for each GAM indicated that the residuals of all models conformed to a normal distribution and exhibited no autocorrelation or partial autocorrelation (see Appendices 1.4 to 1.7 for more details).

Curve Analysis

Curve analysis identified that heart rate, SAI, and PAI indicators exhibited similar trends, allowing us to divide the recovery process into four stages (See Fig. 2a).

Rapid Decline Stage (t0-t3, 0-90s)

All curves showed a sharp decline in stress, with inflection points at t2 and lowest points at t3, where PAI peaked.

Fluctuation Stage (t3-t7, 90-210s)

In this stage, the curves exhibited fluctuations—first rising and then declining (with the PAI curve first declining and then rising), forming inflection points at t5 and t7.

Slow Decline Stage (t7-t15, 210-450s)

The curves demonstrated a slow decline (with the PAI curve rising), culminating in an inflection point at t15.

Plateau stage (t15-t20, 450-600s)

In this final stage, the curves leveled off, with slight rebound trends observed in SAI and PAI for groups 1 and 3.

The SCL curve followed a different trend, showing a rapid decline followed by a gradual rise, leading to its division into two stages: Decline (0-150s) and Rising (150-600s)

This study aimed to investigate how different combinations of natural sounds affect stress recovery and the relationship between sound duration and stress recovery. Our findings suggest that mixed sound environments are more effective in promoting physiological stress recovery, with a consistent recovery time course observed across all groups. Below, we delve into these results, discussing the potential mechanisms underlying these recovery effects and their implications for stress management.

Baseline measures showed no significant differences, confirming that individual differences did not affect internal validity. Stress induction was effective across all groups, as evidenced by significant increases in stress indicators.

During the rest stage, all groups experienced significant reductions in both physiological and subjective stress levels, and participants exposed to natural sounds (group1 and 2) reported significantly lower subjective stress than the control group. When examining the amount of physiological stress recovery using ANOVA, only SCL showed significant differences among the groups. However, the GAM analysis, which accounts for group and time variables, revealed inter-group differences in most indicators (SAI, PAI, and SCL). This discrepancy may arise because ANOVA might not adequately address low-frequency physiological oscillations present in ECG indicators. In contrast, GAM’s smooth curve fitting effectively balances out these oscillations, offering a more precise reflection of stress recovery trends.

GAM results indicated that participants in the mixed sound environment (group 2) had significantly lower SAI and SCL levels and higher PAI levels compared to the other groups. In contrast, those in the single sound group (group 1) did not differ significantly from the control group in SAI and PAI changes. This finding may partly explain inconsistencies in previous research, where single natural sound did not show additional stress recovery benefits (Hedblom et al., 2019; Jahncke et al., 2011). Furthermore, our finding also demonstrated that the SAI and PAI indicators are particularly sensitive to changes in autonomic nervous activity compared to heart rate.

Our analysis revealed a nonlinear trend in stress recovery over time. While the mixed sound environment enhanced overall physiological stress recovery, the time course of recovery remained consistent across all groups. The heart rate, SAI, and PAI indicators followed a similar pattern, with four distinct stages: rapid decline, fluctuation, slow decline, and plateau. Our study revealed a fluctuation stage between the rapid decline and slow decline stages, which previous research had not identified (de Boer et al., 2007; Healey & Picard, 2005). This finding could be attributable to the more refined timescales and the GAM model used in our research.

Given the different mental processes at each stage, the mixed sound environment may have unique recovery mechanisms for each stage.

Rapid Decline Stage

At this stage, with the removal of stressors, participants reported a sudden sense of relaxation and mental emptiness, aligning with previous research (de Boer et al., 2007). This effect may be due to a rapid shift in attention away from stress, as suggested by Ulrich (1983). Participants exposed to mixed sound environment (group 2) began to show greater stress recovery, possibly due to the mixed sound environment's ability to stimulate a rapid imagination process automatically, allowing participants to vividly visualize natural landscapes in their minds as the sounds change dynamically (Chen & Spence, 2017). This imaginative engagement helped to stick participants' attention on the relaxing stimuli, thereby reducing rumination on stressful events and contributing to sustained stress recovery (Kaufman & Beghetto, 2009). Conversely, those listening to a single sound loop reported simpler memory associations, which likely hindered deeper and richer imaginative engagement and led to more rumination, limiting stress recovery.

Fluctuation stage: During this stage, stress levels fluctuated and eventually approached the baseline. The possible reason is that participants may experience thinking rebound after the "mental emptying", and try to control their thinking. Research has demonstrated that when thinking activities are temporarily suppressed, a rebound can occur, reflected by stronger connections in brain networks associated with thinking (Berkovich-Ohana et al., 2013). In this stage, we identified two primary forms of spontaneous thought: rumination on previous stressful events and aimless mind-wandering. The rumination tends to become automatically entrenched and is difficult to shift, while mind-wandering is relatively easier to manage (Christoff et al., 2016). Therefore, participants exposed to the mixed natural sound environment exerted less effort in controlling thoughts due to the automatic imagination process and less rumination, which led to lower physiological stress.

Slow decline stage

In the slow decline stage, participants' directed attention and cognitive resources gradually recovered, leading to a further reduction in stress (Kaplan, 1995).

Plateau stage

The stress recovery process reached a plateau, where the influence of sounds on recovery diminished. Participants in the single sound loop and control groups reported increased impatience and boredom, which may have counteracted the stress recovery effects, leading to a slight rebound in physiological stress. In contrast, participants exposed to the mixed sound environment reported significantly less impatience, likely due to the variety of sounds and ongoing imaginative engagement, which sustained the recovery effect and minimized stress rebound.

The SCL indicator followed a different time course from ECG-related indicators, with an initial decrease followed by an increase. This inconsistency may be attributed to different autonomic nerve branches and different sensitivities (Boucsein, 2012). SCL is more sensitive to cognitive load and attention, while ECG indicators are more responsive to stress, leading to observed variations (Nourbakhsh et al., 2012). During the fluctuation stage, an increase in cognitive load due to thought rebound may create an inflection point in the SCL curves. Therefore, despite the SCL indicator being inconsistent with other indicators, it may still offer insights into the time course of stress recovery.

Our study is the first to demonstrate that exposure to a mixed natural sound environment leads to greater physiological stress recovery compared to listening to a single natural sound or no sound. Notably, the mixed sound environment not only enhanced recovery but also reduced stress levels below baseline. Just 3–4 minutes of listening was sufficient to reduce sympathetic activation and enhance parasympathetic activation beyond baseline levels.

Research suggests that achieving stress recovery below baseline levels through training can strengthen the parasympathetic nervous system's ability to regulate stress, which enables individuals to recover more effectively from future stress, thereby enhancing their adaptability and mental resilience (Southwick, Vythilingam, & Charney, 2005). Presenting this environment during stress may help strengthen the parasympathetic nervous system and subtly improve an individual’s adaptability and resilience. In contrast, a single natural sound, while beneficial for stress recovery, may have a limited role in training autonomic regulatory function. Therefore, Effective use of natural sound environments could transform stress-inducing situations into opportunities for improving mental resilience.

Finally, we provided a time-based framework of stress recovery that could guide future research in examining and validating the mechanisms and influencing factors of stress recovery. Additionally, our findings indicated that stress levels could return to baseline within approximately 3–4 minutes and the recovery reached its peak around 7-7.5 minutes, after which a rebound effect may occur. These insights offer valuable time references for future intervention studies and enhanced the practical application of stress recovery strategies.

However, this study had several limitations. First, our sample consisted solely of college students between 18–28 years old, and future research should include a more diverse population to validate and expand upon our results. Second, while our study tries to simulate a real stress environment, it may not fully replicate real-world conditions. Future research should consider field experiments and include additional mental health and job performance indicators. Finally, this study only focused on the immediate effects of sound on stress recovery, without exploring the short-term and long-term impacts. Future studies should incorporate additional measures, such as cortisol levels and life satisfaction, to explore the enduring effects of sound interventions.

Overall, this study examines the effect of different combinations of natural sounds on stress recovery and identifies a nonlinear time course of recovery. Our findings suggest that mixed natural sound environments may have unique recovery mechanisms at each stage, providing an effective intervention strategy for enhancing physiological stress recovery and reducing rumination. This study provides valuable insights into stress recovery processes and offers practical guidelines for stress management.

Participants

Based on a G*power analysis indicating that 112 participants are sufficient to detect a large effect size (f = 0.4) for physiological data, we recruited 113 undergraduate and graduate students aged 18-28 through convenience sampling. To ensure the validity of the stress-related experiment, we excluded two participants with severe depression and two with severe anxiety based on the simplified Chinese version of the Depression-Anxiety-Stress Scale (DASS-21). Due to equipment issues and invalid responses, five more participants were excluded, resulting in a final sample of 104 participants. All participants were right-handed and had no cognitive or hearing impairments, psychiatric illnesses, or recent use of psychotropic drugs. The final sample included 54 males (51.9%) and 50 females (48.1%), with a mean age of 24.04 years (SD = 2.259, range: 19-29 years).

Materials

Restorative Sounds

We created the restorative sounds in two steps. First, we sampled 171 undergraduate students to evaluate the valence and arousal of 167 sounds from the International Affective Digitized Sounds-2 (IADS-2). We selected natural sounds with high valence and low arousal, which could evoke calm emotions and stress recovery, to use as the sound materials. The chosen sounds included rain, brook, cicada, village pond, robin birdsong, and cow.

Next, we synthesized these selected sounds with different combination modes to create two types of restorative sounds. In the single sound looping mode, we looped a single stimulus for a set duration, creating a rain sound loop, a birdsong loop and a brook sound loop. In the mixed sound environment mode, we set 1-2 sounds as the foreground and 1-2 sounds as the background. We distinguished between foreground and background sounds by using different soundtracks, volumes (with the foreground sounds being louder), and reverb levels (with the background sounds having more reverb). We present various combinations of foreground and background sounds over time, incorporating all the natural sound stimuli to create a dynamically changing sound environment. See appendix 2 for all audio materials.

Measures and Instruments

Depression-Anxiety-Stress Scale (DASS-21): We used the Simplified Chinese version of the DASS-21 to assess participants' depression, anxiety, and stress levels over the past week. This scale consists of 21 items, each scored on a 4-point scale, with 0 being completely inconsistent and 3 being always consistent. The threshold score was 11 for major depression, 8 for severe anxiety, and 13 for severe stress. The overall Cronbach's α coefficient of the scale was 0.891, indicating good reliability and validity (Gong et al., 2010). This scale was used to screen out participants with abnormal psychological conditions.

Self-Report Stress: Participants rated their current stress level on a five-point scale, ranging from not stressed at all to very stressed.

Biopac and Acknowledge: We used a multichannel physiograph (Biopac) and Acknowledge 5.0 software to collect physiological indicators, including electrocardiograph (ECG), respiratory metrics, and skin conductance levels, sampled at 2000 Hz.

Experiment Coding and Presentation: The experiment was coded using Python, and presented by PsychoPy.

Procedure

Participants and Experimental Design

Participants were randomly assigned to one of three groups: Group 1 (single sound looping), Group 2 (mixed sound environment), and Group 3 (control, no sound). Each group had a similar sample size with a 1:1 male-to-female ratio.

The experiment was conducted in a soundproof laboratory, with participants wearing soundproof headphones. Each participant sat approximately 70 cm away from the computer, signed an informed consent form, and then began the experiment.

Experimental Stages:

The experiment consisted of four stages.

Baseline Stage: Participants sat still for two minutes while their physiological data were continuously recorded to establish baseline measurements. Then they reported their subjective stress level to establish their baseline stress level.

Stress Induction: Next, to induce stress, participants completed an adapted version of the Montreal Imaging Stress Test (ad-MIST) tailored for the Chinese population (see Appendix 1.1). The ad-MIST included a five-minute training stage followed by a five-minute test stage. Physiological data and subjective stress levels were collected during and after the test stage.

Rest Stage: After the stress induction, participants rested for 10 minutes. They were informed that the test would resume following the rest period. This procedure was designed to simulate a real work environment, where rest period typically follows work sessions. During this stage, Group 1 listened to a single sound loop. To control the impact of the sound materials, some participants listened to a rain sound loop, some listened to a brook sound loop, and others listened to a birdsong loop. Group 2 listened to a mixed sound environment, and Group 3 had no sound. All sounds were presented at approximately 60 decibels. Participants focused on the sound while staring at a "+" sign on the screen. Physiological data and subjective stress levels were recorded during and after this stage.

Debriefing and Interviews

Participants were initially told the experiment was a math ability test to mask the study's true purpose and prevent biased responses. Afterward, the true purpose was explained, and participants were assured their performance on the MIST task was not indicative of their math ability. This debriefing aimed to prevent psychological distress. Finally, interviews were conducted to gather participants' perceptions of stress and their experiences during the rest stage.

The full experimental process is outlined in Appendix 1.8.

Data Analysis

We used Python 3.11 for the preprocessing and feature extraction of physiological data, and SPSS 18.0 for preliminary analysis. For modeling and visualization of the physiological indicators, we utilized R 4.4.1.

Preprocessing and Feature Extraction

ECG data were filtered using a Butterworth bandpass filter to remove noise below 1 Hz and above 20 Hz and R-waves were detected with the NeuroKit2 package. Using these intervals and the algorithm proposed by Valenza et al. (2018), we computed the heart rate, sympathetic activity Index (SAI), and parasympathetic activity Index (PAI) for the baseline, MIST test and rest stages, respectively. The rest stage was divided into 20 segments of 30 seconds each to analyze stress recovery over time. The choice of 30-second intervals was based on the oscillation period of heart rate. The vascular tension-blood pressure baroreflex causes significant fluctuations in heart rate every 12-15 seconds, with an oscillation period of 24-30 seconds (Lehrer et al., 2020). By averaging heart rate, SAI and PAI for every 30s, we aimed to balance the fluctuations caused by this oscillation, providing a more accurate reflection of the stress recovery trend. Respiratory metrics and skin conductance level (SCL) were pre-processed similarly, though we did not calculate the average respiration rate for every 30 seconds due to missing data.

Finally, we eliminated outliers using a ±3 standard deviation criterion and addressed missing values by applying mean interpolation.

Formal Analysis

Baseline Level Differences: A one-way ANOVA was used to compare baseline physiological stress indicators and subjective stress levels among the three groups.

Effectiveness of Stress Induction: To assess the effectiveness of stress induction, we conducted repeated measures t-tests to compare stress indicators during the test stage against baseline measures. Changes in each indicator (e.g., MIST test heart rate - baseline heart rate) were used as dependent variables in an independent samples ANOVA to assess differences in stress induction levels among the three groups.

Effectiveness of Stress Recovery: To assess the effectiveness of stress recovery, repeated measures t-tests were conducted to evaluate differences in physiological indicators and subjective stress levels between the MIST test stage and the rest stage for each group. Then, changes between the rest stage and the MIST test stage were calculated (e.g., rest stage heart rate - MIST test heart rate) and used as dependent variables in an independent samples ANOVA to assess differences in stress recovery levels among the three groups.

Generalized Additive Models (GAM): To investigate the time course of stress recovery in three groups, we defined time points as the first independent variable, with the beginning of the rest stage marked as t0. The rest stage was divided into 20 segments, each lasting 30 seconds, and each segment represented a time point from t1 to t20. The second independent variable was group assignment: Group 1 (single sound looping), Group 2 (mixed sound environment), Group 3 (control, no sound stimuli).

For each physiological indicator (Heart Rate, SAI, PAI, and SCL), we calculated the change between each time point and t0 (e.g., t1 heart rate – t0 heart rate) to represent the amount of stress recovery at each 30-second interval. These changes served as the dependent variables.

Then, we constructed a Generalized Additive Model (GAM) using R to fit the data to a smooth curve, allowing us to capture the non-linear patterns in the time course of stress recovery and test the effects of each independent variable. The model function was defined as:

g(yij) = β0 groupj + s(timei) + s(timei * groupj) + εij

Here, yij represents the amount of stress recovery for each physiological indicator, with j indicating the group assignment of participants and i representing time points from t0 to t20. The term timei * groupj denotes the interaction between time and group. The smoothing function s () captures the non-linear effects of the variables.

Additionally, we tested the basic assumptions of the model. First, a curve fitting test was performed to determine if the model followed a curve. A significant effective degrees of freedom (edf) value greater than 1 indicates that the model is indeed a curve. Next, we conducted a node test to ensure the appropriateness of the number of nodes used in the smoothing function; a k-index greater than 1 signified a reasonable number of nodes. Normality of the residuals was tested using Q-Q plots, and autocorrelation was checked. If autocorrelation was detected, we adjusted the model by establishing a Generalized Additive Mixed Model (GAMM), incorporating individual subjects as a random effect to account for this issue.

Finally, we used R to visualize the model, and identify curve inflection points and extreme value points. providing a clearer understanding of the stress recovery time course for each group.

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There is NO Competing Interest.

Ethical standards statement:
All authors complied with APA ethical standards in the treatment of their participants and that the work was approved by the ethics review committee of the Institute of Psychology, Chinese Academy of Sciences (H24085).

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The Effect of Mixed Natural Sounds on Stress Recovery: Insights into Physiological Benefits and Temporal Dynamics

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Abstract

Figures

Introduction

Results

Baseline Level Difference Test

Effectiveness of Stress Induction

Stress Recovery

Generalized additive model (GAM) results

Heart rate

SAI

PAI

SCL

Residual Normality Test and Autocorrelation Test

Curve Analysis

Discussion

Conclusion

Methods

Participants

Materials

Restorative Sounds

Measures and Instruments

Procedure

Participants and Experimental Design

Experimental Stages:

Debriefing and Interviews

Data Analysis

Preprocessing and Feature Extraction

Formal Analysis

References

Additional Declarations

Supplementary Files

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