Immersion in a relaxing virtual reality environment is associated with similar effects on stress and anxiety as heart rate variability biofeedback

doi:10.21203/rs.3.rs-3221200/v1

Download PDF

Research Article

Immersion in a relaxing virtual reality environment is associated with similar effects on stress and anxiety as heart rate variability biofeedback

https://doi.org/10.21203/rs.3.rs-3221200/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Practicing guided breathing at 0.1 Hz in virtual reality yields psychological and physiological benefits. Nonetheless, it remains uncertain whether these effects surpass those induced in a real-world setting. Indeed, the potential influence of the virtual environment on perceived stress and anxiety is not yet fully understood. In this experiment, we aimed to compare the effects of heart rate variability biofeedback combining both haptic and visual cues in real and virtual reality settings within the same population. Additionally, to decipher whether psychological benefits are caused by viewing an environment in virtual reality or performing guided breathing in this setting, a “control” immersion condition was added.

36 healthy sport students (9 females) participated in this study, performing both the real and virtual reality protocols in a randomized order. At different points in each protocol, modulations of anxiety and stress were measured using the STAI-Y questionnaire and a visual analogic scale, respectively. Physiological effects were assessed with measures of heart rate variability, and the performance of cardiac coherence was compared between both real and virtual implementations of guided breathing.

As expected, both real and virtual reality heart rate variability biofeedback resulted in similar physiological changes and cardiac coherence performances. A reduction in stress and anxiety was observed in both protocols, particularly for participants who were already stressed or anxious at the beginning. However, no additional changes in psychological states were observed when performing guided breathing alongside being immersed in the virtual environment.

cardiac coherence

virtual reality

anxiety

stress

1.1 - Relaxation and stress management in virtual reality

Due to its ability to easily display worlds with particular visual characteristics, virtual reality (VR) is a promising tool to cope with stress and anxiety, which have been labeled as a major determinant of chronic diseases in our current society (Egger and Dixon, 2014). In particular, virtual nature scenery have demonstrated beneficial effects for relaxation and stress management (Anderson et al., 2017; de Kort et al., 2006; Liszio et al., 2018; Wang et al., 2019; White et al., 2018; Yin et al., 2020), on both psychological and physiological aspects (Spano et al., 2023), which is on par with results observed in a real setting (Corazon et al., 2019; Park et al., 2017). These effects may be explained by the Attention Restoration Theory (Kaplan, 1995), which states that spending time in environments with ‘restorative’ qualities, such as nature settings, allows replenishing attentional resources and thus relieving mental fatigue and stress. Therefore, as even a reproduction of nature environment yields psychological advantages, virtual reality has obvious benefits in the sense that the relaxing environment can be brought directly to the patient, thus allowing therapies for people with reduced mobility or living in mostly urban locations.

1.2 – Heart rate variability biofeedback

Cardiac coherence is a breathing technique that involves breathing at the resonance frequency of the baroreflex (Lehrer et al., 2000). This frequency is close to 0.1 Hz, but may vary slightly from one individual to another (Vaschillo et al., 2006).

A method of guided breathing at 6 breaths per minute (one every 10 seconds) involves shifting the respiratory sinus arrhythmia, usually found around a respiratory frequency of 0.2–0.3 Hz, toward the frequency of 0.1 Hz. When these two oscillators (respiratory sinus arrhythmia and baroreflex) share the same frequency, a phenomenon of resonance occurs, considerably increasing the power of the autonomic nervous system. Descriptively, in this state of cardiac coherence, heart rate oscillations follow a quasi-sinusoidal rhythm with a period of 10 seconds.

To help participants achieve this physiological state, various cues can be given to help following the imposed rhythm. It has recently been demonstrated that combining visual and haptic sensory inputs seems to be the best combination to potentiate cardiac coherence (Bouny et al., 2023). Additionally, feedback based on cardiac data can be given to the participants in real time, allowing them to adapt their breathing pattern for best results. This method, labeled heart rate variability (HRV) biofeedback, has proved effective in helping participants achieve cardiac coherence (Lehrer, 2013; Vaschillo et al., 2006).

1.3 - Effects of HRV biofeedback on stress management

Cardiac coherence has been linked with various psychological and physiological benefits. In particular, it helps reduce stress and anxiety (Deschodt-Arsac et al., 2020, 2018; Goessl et al., 2017; Prinsloo et al., 2013), and could improve cognitive performance (Blum et al., 2019; Prinsloo et al., 2011; Sutarto et al., 2013). These benefits are often attributed to the increased power in bottom-up heart-brain interactions, helping practitioners cope with potential stressors (Mather and Thayer, 2018; Vanderhasselt and Ottaviani, 2022).

1.4 - Implementations of HRV biofeedback in virtual reality

Virtual reality (VR) devices are becoming more accessible and used in many industries. Based on this finding, head-mounted display (HMD) induction of cardiac coherence has recently attracted increasing interest. In particular, the advantage of using a virtual environment lies in the possibility of modulating and reacting in real time according to the variations of the physiological state of the user over time, thus providing integrated feedback. At the same time, as virtual environments induce a sense of presence in the user, they can be used to influence emotional states more than real environments (Riva et al., 2007). As biofeedback mechanisms are engaging and generate a sense of agency (Houzangbe et al., 2020), they can also enhance presence and therefore induce restorative effects.

The comparative implementation of HMD and desktop applications to induce cardiac coherence has already been studied in several experiments, but the conclusions are currently inconsistent. Implementation of HMD sometimes yields better results than 2D visualization (Blum et al., 2019), improving resilience to a stressful task following HRV biofeedback. However, most studies do not find significant differences in stress and anxiety reduction between virtual and 2D interventions (Rockstroh et al., 2020, 2019; Weibel et al., 2023). Likewise, even if a VR system may lead to a greater concentration of autonomous power below the resonant frequency (Weibel et al., 2023), no other differences in HRV indices were found. As reviewed by Lüddecke & Felnhofer (2022), VR HRV biofeedback is not more effective than traditional implementation. Nonetheless, virtual reality often leads to better motivation, user experience, attentional focus, and engagement, which can help practitioners feel engaged in using the device and limit therapy dropouts. At last, it has been previously shown that guided breathing interventions performed with and without an additional relaxing underwater environment in VR are equally effective on the physiological and psychological levels (Soyka et al., 2016). However, we don’t know yet if the same virtual environment, seen with and without performing HRV biofeedback, leads to similar results.

To the best of our knowledge, there is still a question about the rationale of using HRV biofeedback in virtual reality. Since seeing relaxing environments can already reduce stress and anxiety, does an additional guided breathing exercise improves psychological states even more?

To answer this question, we carried out an experiment composed of two distinct protocols in which all the participants took part: the Real protocol, during which they performed HRV biofeedback using a handheld device, and the HMD protocol in virtual reality, where subjects were immersed twice in a relatively relaxing virtual environment, first without any stimulation, and then performing guided breathing with the same visual and haptic cues as in the Real protocol.

1.5 - Hypotheses

We hypothesized that:

H_1A – Performing guided breathing using the real device and in virtual reality should lead to decreased stress and anxiety values.

H_1B – Viewing a “relaxing” environment in virtual reality while breathing at the resonance frequency should reduce even more stress and anxiety levels.

H₂ – Guided breathing in Real and HMD conditions should help achieve the same level of cardiac coherence, as assessed by heart rate variability calculations.

H₃ – Improvements in psychological states should be linked with improved physiological values.

2.1 - Participants

36 healthy sport students (9 females, 19.2 ± 1.3 years old) gave their informed consent to participate in this experiment for which they received credits as part of their academic curriculum. The institutional review board “Faculté des STAPS” approved the procedure that respected all ethical recommendations and followed the declaration of Helsinki. All the participants had normal or corrected-to-normal vision. Participants were instructed not to consume caffeine or alcohol at least 24 h before the experiment. None of them had any experience with the VR task used here, but some had previously participated in protocols including HRV biofeedback.

2.2 - Protocol

The experimental setup consisted of two distinct protocols (Real and HMD protocol). Upon arrival, the experimenter explained the protocol to the participants, and equipped them with electrodes for electrocardiogram (ECG) recording. Both the Real and HMD protocol started with 5 minutes of questionnaires, acting as a baseline for heart rate variability (HRV) measurements (Fig. 1). After that, during the Real protocol, participants performed 5 minutes of guided breathing at 0.1 Hz, using the device presented in section 2.3, and then answered a second set of questionnaires. In the HMD protocol, participants were equipped with an HTC Vive Pro headset (HTC America, Inc., Seattle, WA, United Stated), and were immersed in the virtual environment. They were instructed to look roughly in front of them, without making sudden movements. After 5 minutes of immersion, they completed a questionnaire outside of the virtual environment. They were then immersed again, performing 5 minutes of guided breathing. At the end of the protocol, they completed the last iteration of the questionnaire.

The protocol order (Real or HMD condition) was randomly determined.

2.3 - Guided breathing device

To perform guided breathing, participants used a tool held in their dominant hand giving visual and haptic cues, already used in a previous study (Bouny et al., 2023). The breathing pace was set to 0.1 Hz, with 5 seconds of inspiration and 5 seconds of expiration. During the inspiration phase, the vertical bar is progressively filled with a light, before emptying during expiration. The haptic cue was given in the form of a vibration with increasing and decreasing intensities during inspiration and expiration, respectively. During the Real protocol, the participants were asked to keep their eyes on the device and focus on the visual and haptic cues. In the HMD protocol, the device was still handheld, but the visual feedback was presented in the virtual environment, as described in section 2.5.

2.4 - Measure of cardiac coherence score

A coherence score is calculated in real time to give feedback to the user. For that, a photoplethysmography (PPG) sensor, located below the thumb, detects the user’s heartbeat and compares its variation to the 0.1 Hz sinus wave expected in a cardiac coherence state. This calculation takes place after each detected heartbeat, and updates a coherence score. Heart-rate-variability biofeedback is represented as a color change on the vertical bar of the device. The participants are aware of the meanings of the different colors, with blue, purple and orange representing good, medium and bad coherence, respectively.

2.5 - Virtual reality environment

The virtual environment was developed with Unity (Unity Technologies, San Francisco, CA, United States), and represents an island with nature elements (rocks, grass, trees) floating in a sea of clouds (Fig. 2). Some of the elements in the scene are moving, but none of them with a frequency that could be used as a respiratory guide by the user. During the guided breathing phase of the HMD protocol, a white sphere placed in the middle of the user’s view was added; the size of this element increased and decreased for 5-s in a sigmoidal pattern during the inspiration and expiration phases, respectively. Using the same palette as the real device, HRV biofeedback was provided as the color of the sphere.

2.6 - Measures of stress and anxiety

Levels of stress and anxiety were measured twice during the Real protocol and three times in the HMD protocol (Fig. 1). Participants were asked to provide their level of stress by moving a slider on a visual analogic scale (VAS), labeled from “No Stress” (0) to “Extreme Stress” (100). The definition of stress given to participants was the following: Stress is a normal reaction of the body to an external event or to something perceived as threatening.

The level of anxiety was calculated using the Spielberger’s State-Trait Anxiety Inventory (STAI-Y, Spielberger, 1983) questionnaire, consisting of 20 questions and providing scores ranging from 20 to 80.

2.7 - Presence and cybersickness

The sense of presence in the virtual environment was measured using the Igroup Presence Questionnaire (IPQ, Schubert et al., 2001), and cybersickness was assessed with the Simulator Sickness Questionnaire (SSQ, French version, Bouchard et al., 2009). Both questionnaires were completed in the HMD protocol following the 5-minutes immersion and guided-breathing phases.

2.8 - ECG measurements

For the entire duration of the protocol, the participants’ electrocardiogram was recorded with three electrodes using a PowerLab 8/35 device (ADInstruments, Dunedin, New Zealand) and a dedicated bioamplifier (FE132, ADInstruments, Dunedin, New Zealand) at a 1 kHz sampling frequency.

ECG recordings were then cut into 5-minutes sequences corresponding to the different phases of both protocols: Baseline and Cardiac coherence in the Real protocol, and Baseline, Immersion and Cardiac coherence in the HMD protocol (Fig. 1).

In the HMD protocol, cardiac interbeat intervals (R-to-R peaks interval duration) were computed in real time and transmitted to the virtual environment software to calculate the cardiac coherence score using the same algorithm as the one implemented in the real device (see section 2.4).

2.9 Physiological Characteristics

The RR intervals time series were exported to Matlab (Matlab 2021b, Matworks, Natick, MA, USA), and several heart rate variability (HRV) indicators were computed using custom algorithms.

Artefacts were identified from differences between two successive intervals larger than 250 ms. Artefacted RR were replaced by the average value of the nearby values. Indices of HRV in time and frequency domains were then calculated. The root mean square of successive differences between heartbeats (RMSSD) for an RR time series of size N was calculated as follows:

$$RMSSD= \sqrt{\frac{1}{N-1}\sum _{i=1}^{N}(R{R}_{i+1}-R{R}_{i})²}$$

RR times series were analyzed in the frequency domain to compute autonomous power. Each 5-minutes individual time series were first resampled at 4 Hz using a cubic spline interpolation, and a fast Fourier transform was used to compute the Power Spectral Density (PSD). From the PSD, we computed the total power in the signal within the range 0.04 Hz – 0.4 Hz, the height of the resonance peak located around 0.1 Hz, and the P_0.1 index, which corresponds to the ratio between the power of the highest peak (resonance) during cardiac coherence and the total power in Baseline. This ratio is used as a cardiac coherence performance metric, and measures to which extent the total autonomous power is concentrated under the resonant frequency (Bouny et al., 2023).

$${P}_{0.1}=\text{m}\text{a}\text{x}\left({Power}_{coherence}\right)/{\sum }_{f=0.04 Hz}^{f=0.4 Hz}{Power}_{baseline}$$

2.10 - Statistical analysis

Normal distribution for datasets was assessed using a Shapiro-wilk test. Datasets from questionnaires were analyzed for the Real and HMD conditions independently.

As there are only two measuring points in the Real condition, statistical analysis concerning this part of the protocol are performed using either a Wilcoxon signed rank or a paired samples t-test. In the HMD protocol, one-way repeated measures ANOVAs or Friedman tests were first performed, followed by a post-hoc Tukey if differences were found. All statistical analyses were performed using Matlab with the Statistical and Machine Learning Toolbox.

3.1 - Presence and cybersickness

Considering answers to the presence questionnaire in virtual reality, no difference was observed for values of IPQ (p = 0.15, t = -1.46) measured after immersion (52.0 ± 7.4) and after guided breathing (54.0 ± 9.3). Considering cybersickness, a significant difference (p = 0.004, z = -2.88) was described between values of SSQ measured after immersion (5.5 ± 3.8) and after guided breathing in virtual reality (7.1 ± 4.5) (Table 1).

Table 1 – Scores recorded from questionnaires in both the Real and HMD protocols. Values are displayed in mean ± sd. * indicates a significant difference from baseline. ^† indicates a significant difference between conditions Immersion and Cardiac Coherence.

	Real protocol		HMD protocol
Item	Baseline	Cardiac coherence	Baseline	Immersion	Cardiac coherence
STAI-Y	27.6 ± 9.0	24.9 ± 6.8*	27.6 ± 7.1	25.8 ± 6.3*	26.1 ± 7.0
VAS Stress	23.4 ± 27.5	16.9 ± 21.3*	19.5 ± 21.7	10.9 ± 14.0*	8.9 ± 12.5*
IPQ				52.0 ± 7.4	54 ± 9.3
SSQ				5.5 ± 3.8	7.0 ± 4.5^†

3.2 - Stress and anxiety

During the Real protocol, STAI-Y as well as VAS Stress values significantly decreased between Baseline and Cardiac coherence conditions (respectively p = 0.001, Z = 3.68 and p = 0.01, Z = 2.48).

In the HMD protocol, a decrease of STAI-Y values (Friedman: χ²(2) = 7.72, p = 0.02) and VAS Stress values (Friedman: χ²(2) = 25.62, p = 2.7 × 10^− 6) was found as well. Considering VAS Stress values, a post-hoc Tukey showed significant differences between conditions Baseline and Immersion (p = 1.6 × 10^− 4) and Baseline and Cardiac coherence (p = 9.1 × 10^− 6), but no differences between Immersion and Cardiac coherence (p = 0.80). For STAI-Y values, a significant decrease was found between Baseline and Immersion (p = 0.03), but no differences were found between Baseline and Cardiac coherence (p = 0.06) and between Immersion and Cardiac coherence (p = 0.98).

To further explore the effects of the experimental conditions (Real and HMD) on psychological values, we divided the population into two groups (High and Low) according to the baseline values of the STAI-Y and the VAS Stress values in each protocol. Participants with STAI-Y scores above and below the median of the baseline condition for the Real and HMD protocols (both medians = 25) were placed in the High Anxiety (n = 17) and Low Anxiety (n = 19) groups, respectively. For the Low Anxiety group, there were no differences in STAI-Y values in both protocols (Real: Wilcoxon signed rank test, p = 0.16, Z = 1.40; HMD: Friedman, χ²(2) = 0.24, p = 0.89). STAI-Y scores significantly lowered for the High Anxiety group in both protocols (Real: Wilcoxon signed rank test, p = 0.004, Z = 2.90; HMD: Friedman, χ²(2) = 17.37, p = 1.6 × 10^− 4). In the HMD protocol, a post-hoc Tukey showed differences between the Baseline and Cardiac coherence conditions (p = 0.001) and between Baseline and Immersion (p = 0.001), but no differences between Immersion and Cardiac coherence (p = 1.00) (Fig. 4).

Participants with VAS Stress scores above and below the median of the baseline condition for the Real (median = 9.5) and HMD (median = 10) protocols were placed in the High Stress (n = 18) and Low Stress (n = 18) groups, respectively. For the Low Stress group, there were no differences in VAS Stress values in both protocols (Real: Wilcoxon signed rank test, p = 0.86, Z = 0.18; HMD: Friedman, χ²(2) = 2.32, p = 0.31) (Fig. 4). Conversely, VAS Stress scores significantly lowered for the High Stress group in both protocols (Real: Wilcoxon signed rank test, p = 0.005, Z = 2.79; HMD: one-way ANOVA, F(2,34) = 19.88, p = 1.9 × 10^− 6). In the HMD protocol, a post-hoc Tukey showed significant decreases between the Baseline and Immersion conditions (p = 0.01) and between Baseline and Cardiac coherence (p = 2.0 × 10^− 4), but no differences between Immersion and Cardiac coherence (p = 0.22) (Fig. 4).

3.3 – RMSSD and Total cardiac autonomic Power

In the Real protocol, RMSSD increased between Baseline and Cardiac coherence (Wilcoxon signed rank test: p = 1.2 × 10^− 4, Z = -3.85).

RMSSD increased during the HMD protocol (Friedman: χ²(2) = 41.06, p = 1.2 × 10^− 9), with significant differences between Baseline and Cardiac coherence (p = 1.2 × 10^− 9), between Cardiac coherence and Immersion (p = 3.9 × 10^− 5). No differences appeared between Baseline and Immersion conditions (p = 0.14).

The same results were shown for total power. A significant increase was found in both the Real protocol (Wilcoxon signed rank test: p = 3.3 × 10^− 7, Z = -5.10) and the HMD protocol (Friedman: χ²(2) = 54.89, p = 1.2 × 10^− 12). In the latter, total power increased between conditions Baseline and Cardiac Coherence (p = 1.2 × 10^− 9) and between Immersion and Cardiac coherence (p = 1.2 × 10^− 9), but no differences were found between Baseline and Immersion (p = 0.61).

3.4 - Cardiac coherence score

Cardiac coherence scores during the guided breathing phase in Real and HMD protocols were not different (Wilcoxon signed-rank test: p = 0.80, Z = 0.25). A significant correlation was shown between the score obtained in Real condition (77.6 ± 10.4%) and the HMD condition (76.3 ± 10.7%) (Pearson correlation: r² = 0.43, p = 1.4 × 10^− 5). The higher the participants' score in one condition, the higher their score in the other.

A two-way ANOVA performed on Protocol (Real or HMD) and Anxiety Level (Low or High) as independent factors showed that the cardiac coherence score was neither affected by the display (F (1,71) = 0.27, p = 0.61) nor starting anxiety levels (F (1,71) = 0.58, p = 0.45). There was no interaction effect (F (1,71) = 0.01, p = 0.91).

3.5 - Cardiac coherence assessed by P_0.1

There were no differences concerning maximum peak height in the PSD at the resonance frequency around 0.1 Hz in Real and HMD protocols during cardiac coherence (paired samples t-test: p = 0.89, t = -0.12; Real: 54.7 ± 22.3 ms²/Hz; HMD: 55.0 ± 20.5 ms²/Hz), and a significant correlation between peak heights in both protocols was observed (Pearson correlation: r² = 0.64, p = 4.8 × 10^− 9). Likewise, there were no differences in total power computed within the range 0.04 Hz – 0.4 Hz at baseline between both protocols (Wilcoxon signed rank test: p = 0.27, Z = 1.10; Real: 1.19 ± 0.48 ms²; HMD: 1.11 ± 0.51 ms²)

The analysis of the P_0.1 index showed that cardiac coherence was identically achieved during the guided breathing phase in the Real and HMD protocols (Wilcoxon signed rank test: p = 0.19, Z = -1.32; Real: 51.3 ± 25.3 Hz^− 1; HMD: 53.3 ± 18.8 Hz^− 1), with a significant correlation between P_0.1 values computed in the Real and HMD protocols (Pearson correlation: r² = 0.17, p = 0.01).

A two-way ANOVA performed on Protocol (Real or HMD) and Anxiety Level (Low or High) as independent factors showed that P_0.1 was not affected by the display (F (1,71) = 0.10, p = 0.76) nor starting anxiety levels (F (1,71) = 1.80, p = 0.18). There was no interaction effect (F (1,71) = 1.13, p = 0.29).

3.6 – Relationship between Cardiac coherence score and P_0.1 values

There was no correlation between cardiac coherence scores and P0.1 values, neither in the Real (Pearson correlation: r² < 0.01, p = 0.57) nor the HMD protocol (Pearson correlation: r² = 0.02, p = 0.43).

The aim of the present study was to explore the relevance of incorporating HRV biofeedback within virtual reality. HRV biofeedback is a well-established technique for enhancing cognitive activities and managing stress by guiding the modulation of the respiratory rate, leading to reduced stress and anxiety. Moreover, nature environments in virtual reality have shown beneficial effects for stress management. Our hypotheses were therefore grounded in the possibility of an additional benefit provided by performing guided breathing in virtual reality to further optimize cognitive activities and improve stress management.

Under our experimental conditions, we confirmed that cardiac coherence significantly improved participants' psychological state by reducing signs of stress and anxiety, thus confirming H_1A. Notably, the participants who reported the highest levels of anxiety at baseline experienced the most significant improvements in their psychological state after the cardiac coherence session. This finding aligns with existing literature, which suggests the possible existence of different anxiety patterns (Blons et al., 2019; Dickerson and Kemeny, 2004; Dimitriev et al., 2016).However, the improvement in psychological state was no different in the Real and HMD protocols.

Initially, one might have assumed that the two tested methods (HMD and Real) would have different effects on the subjects. Some studies suggested that conducting HRV biofeedback in VR may help reduce mental wandering (Lüddecke and Felnhofer, 2022), enabling the subjects to maintain better focus during the HMD protocol, thus resulting in higher cardiac coherence scores and P0.1 values compared to real-life situations. Moreover, incorporating HRV biofeedback guidance with haptic stimuli in virtual reality has been demonstrated to enhance coherence performance (Bouny et al., 2023). As sensory inputs are not processed in the same way in virtual reality, partly due to visuo-vestibular conflicts, certain participants may have been more responsive to either the Real or HMD modalities.

However, our findings show similar average results between HMD and Real protocols, with a correct correlation between P0.1 and cardiac coherence scores. In fact, it appears that both induction methods yield comparable effects, providing support for H₂.

Experiencing a relaxing virtual reality environment with natural elements for five minutes proves sufficient in our study to induce significant psychological benefits, which were not further enhanced with the use of HRV biofeedback. As a result, our hypothesis H_1B could not be confirmed. It's essential to note that these results were obtained from young, healthy participants with low levels of anxiety at the beginning of the protocol (Real: 27.6 ± 9.0; HMD: 27.6 ± 7.1, measured with a scale ranging from 20 to 80). This suggests that the potential for improving stress and anxiety management through methods like HRV biofeedback may be limited in this specific group. An intriguing direction for future research could involve applying this method to pathological and/or stressed populations.

Despite the significant reduction in stress and anxiety achieved within 5 minutes of immersion in the pleasant VR environment without performing guided breathing, these changes are not accompanied by physiological alterations. In fact, the cardiac indices (RMSSD and total power, as shown in Fig. 5) remained unchanged. Consequently, our findings suggest that immersion alone, without an optimization of autonomous regulation, might have only a transient effect, lacking a lasting impact over time, unlike the longer-term effects observed with cardiac coherence training (Bögge et al., 2022; de Souza et al., 2022; Deschodt-Arsac et al., 2018). Therefore, H₃ is not supported in the control immersion condition.

There are several limitations to this study that should be addressed. Firstly, we employed a simple fixed breathing rate of 0.1 Hz for all participants. However, as demonstrated in previous works by Lehrer (2003) and Vaschillo (2006), the resonant frequency can vary between individuals, depending on their individual baroreflex frequency. It would be valuable to assess each individual's resonance frequency and apply it to the breathing rate in both the Real and HMD protocols. Although this procedure introduces design complexities - and does not seem to induce significant physiological benefits in many cases (Tabor et al., 2022), it is worth considering due to the heterogeneity of some of our results, particularly among subjects with different levels of anxiety. If the HRV biofeedback method is optimized for everyone, its effects could potentially complement the benefits already observed in this study during virtual reality immersion in a natural environment.

Furthermore, a recent study on meditation in virtual reality, where participants performed exercises at a controlled respiratory rate (10 breaths per minute) higher than the resonant frequency, demonstrated significantly better anxiety reduction when virtual scene elements moved independently of the participant's breathing (Tinga et al., 2019). This highlights the need for continued experimentation to better understand the relationship between virtual reality and physiological induction, especially when biofeedback is involved.

Lastly, our primary focus was to examine the effects of an immersion in a virtual reality environment, with or without cardiac coherence. However, it remains uncertain whether similar effects could be observed outside the virtual environment, such as when observing a pleasant nature sequence on a 2D screen or with a 360° view (Li et al., 2021).

This study has demonstrated, for a young and healthy population, that a session of virtual reality in an environment with nature elements, with or without HRV biofeedback, leads to similar psychological benefits as an HRV biofeedback session performed in real-life situations.

Our results confirm the findings of previous studies describing HRV biofeedback practice with guided breathing at a fixed frequency of 0.1 Hz (resonance frequency) as a method that brings about such psychological and physiological advantages. The present study provides an additional result by showing that a combination of visual and haptic stimuli to guide breathing produces similar or even complementary effects, using both real-life and virtual reality devices.

As expected, a visually pleasant virtual environment with nature elements already brings significant reductions in stress and anxiety levels. Based on the results of our study, the additional use of HRV biofeedback applied in this VR environment does not bring any additional effects regarding the psychological state of the subjects, which was already optimized by the initial immersion. It is important to note that the tested subjects were all young, healthy adults, and despite the results not being significant, individual data showed considerable heterogeneity, suggesting the possibility of individual response profiles to the effect of HRV biofeedback in VR.

Therefore, is there a rationale of using HRV biofeedback in VR, since it doesn’t seem to provide any additional psychological benefits compared to simple immersion and the use of a real device?

First of all, it must be kept in mind that, even though it led to reduced states of stress and anxiety, the baseline immersion did not result in physiological modulations, which means that these effects may not last over time, and may not improve cognitive performances. Then, when comparing HRV biofeedback effects in real and virtual conditions, it is essential to keep in mind that VR offers various advantages, particularly concerning participant engagement and subjective appreciation. Therefore, since the adherence of subjects to perform cardiac coherence sessions (or similar practices) over time is a challenge that industry professionals are trying to address, better adherence could be achieved through the use of VR (due to its more enjoyable and engaging nature, and the possibility to simulate infinite environments). The type of stimulation and their induction methods should be tailored to the target audience.

Author contributions

Conceptualization, Y.P., P.B. and V.D.-A.; methodology, Y.P., P.B. and V.D.-A.; software, Y.P.; formal analysis, Y.P.; investigation, Y.P. and P.B.; data curation, Y.P.; writing—original draft preparation, Y.P., P.B., and V.D.-A.; writing—review and editing, Y.P., P.B., and V.D.-A. All authors have read and agreed to the published version of the manuscript.

Funding
No funds, grants, or other support was received.

Data availability statement

The dataset generated and analyzed during the current study is available from the corresponding author on a reasonable request.

Conflict of interests

The company URGOTECH which develops the HRV-biofeedback device, supports the thesis work of PB by the CIFRE grant No. 2020/0649.

Anderson, A.P., Mayer, M.D., Fellows, A.M., Cowan, D.R., Hegel, M.T., Buckey, J.C., 2017. Relaxation with Immersive Natural Scenes Presented Using Virtual Reality. Aerosp. Med. Hum. Perform. 88, 520–526. https://doi.org/10.3357/AMHP.4747.2017
Blons, E., Arsac, L.M., Gilfriche, P., McLeod, H., Lespinet-Najib, V., Grivel, E., Deschodt-Arsac, V., 2019. Alterations in heart-brain interactions under mild stress during a cognitive task are reflected in entropy of heart rate dynamics. Sci. Rep. 9, 18190. https://doi.org/10.1038/s41598-019-54547-7
Blum, J., Rockstroh, C., Göritz, A.S., 2019. Heart Rate Variability Biofeedback Based on Slow-Paced Breathing With Immersive Virtual Reality Nature Scenery. Front. Psychol. 10.
Bögge, L., Colás-Blanco, I., Piolino, P., 2022. Respiratory sinus arrhythmia during biofeedback is linked to persistent improvements in attention, short-term memory, and positive self-referential episodic memory. Front. Neurosci. 16.
Bouchard, S., St-Jacques, J., Renaud, P., Wiederhold, B.K., 2009. Side effects of immersions in virtual reality for people suffering from anxiety disorders. J. CyberTherapy Rehabil. 2, 127–138.
Bouny, P., Arsac, L.M., Guérin, A., Nerincx, G., Deschodt-Arsac, V., 2023. Guiding Breathing at the Resonance Frequency with Haptic Sensors Potentiates Cardiac Coherence. Sensors 23, 4494. https://doi.org/10.3390/s23094494
Corazon, S.S., Sidenius, U., Poulsen, D.V., Gramkow, M.C., Stigsdotter, U.K., 2019. Psycho-Physiological Stress Recovery in Outdoor Nature-Based Interventions: A Systematic Review of the Past Eight Years of Research. Int. J. Environ. Res. Public. Health 16, 1711. https://doi.org/10.3390/ijerph16101711
de Kort, Y.A.W., Meijnders, A.L., Sponselee, A.A.G., IJsselsteijn, W.A., 2006. What’s wrong with virtual trees? Restoring from stress in a mediated environment. J. Environ. Psychol. 26, 309–320. https://doi.org/10.1016/j.jenvp.2006.09.001
de Souza, P.M., de Cássia Souza, M., Diniz, L.A., Araújo, C.R.V., Lopez, M., Volchan, E., Fernandes, O., Sanchez, T.A., Souza, G.G.L., 2022. Long-term benefits of heart rate variability biofeedback training in older adults with different levels of social interaction: a pilot study. Sci. Rep. 12, 18795. https://doi.org/10.1038/s41598-022-22303-z
Deschodt-Arsac, V., Blons, E., Gilfriche, P., Spiluttini, B., Arsac, L.M., 2020. Entropy in Heart Rate Dynamics Reflects How HRV-Biofeedback Training Improves Neurovisceral Complexity during Stress-Cognition Interactions. Entropy 22, 317. https://doi.org/10.3390/e22030317
Deschodt-Arsac, V., Lalanne, R., Spiluttini, B., Bertin, C., Arsac, L.M., 2018. Effects of heart rate variability biofeedback training in athletes exposed to stress of university examinations. PloS One 13, e0201388. https://doi.org/10.1371/journal.pone.0201388
Dickerson, S.S., Kemeny, M.E., 2004. Acute Stressors and Cortisol Responses: A Theoretical Integration and Synthesis of Laboratory Research. Psychol. Bull. 130, 355–391. https://doi.org/10.1037/0033-2909.130.3.355
Dimitriev, D.A., Saperova, E.V., Dimitriev, A.D., 2016. State Anxiety and Nonlinear Dynamics of Heart Rate Variability in Students. PLOS ONE 11, e0146131. https://doi.org/10.1371/journal.pone.0146131
Egger, G., Dixon, J., 2014. Beyond obesity and lifestyle: a review of 21st century chronic disease determinants. BioMed Res. Int. 2014, 731685. https://doi.org/10.1155/2014/731685
Goessl, V.C., Curtiss, J.E., Hofmann, S.G., 2017. The effect of heart rate variability biofeedback training on stress and anxiety: a meta-analysis. Psychol. Med. 47, 2578–2586. https://doi.org/10.1017/S0033291717001003
Houzangbe, S., Christmann, O., Gorisse, G., Richir, S., 2020. Effects of voluntary heart rate control on user engagement and agency in a virtual reality game. Virtual Real. 24, 665–681. https://doi.org/10.1007/s10055-020-00429-7
Kaplan, S., 1995. The restorative benefits of nature: Toward an integrative framework. J. Environ. Psychol., Green Psychology 15, 169–182. https://doi.org/10.1016/0272-4944(95)90001-2
Lehrer, P., 2013. How Does Heart Rate Variability Biofeedback Work? Resonance, the Baroreflex, and Other Mechanisms. Biofeedback 41, 26–31. https://doi.org/10.5298/1081-5937-41.1.02
Lehrer, P.M., Vaschillo, E., Vaschillo, B., 2000. Resonant frequency biofeedback training to increase cardiac variability: rationale and manual for training. Appl. Psychophysiol. Biofeedback 25, 177–191. https://doi.org/10.1023/a:1009554825745
Lehrer, P.M., Vaschillo, E., Vaschillo, B., Lu, S.-E., Eckberg, D.L., Edelberg, R., Shih, W.J., Lin, Y., Kuusela, T.A., Tahvanainen, K.U.O., Hamer, R.M., 2003. Heart rate variability biofeedback increases baroreflex gain and peak expiratory flow. Psychosom. Med. 65, 796–805. https://doi.org/10.1097/01.psy.0000089200.81962.19
Li, H., Zhang, X., Wang, H., Yang, Z., Liu, H., Cao, Y., Zhang, G., 2021. Access to Nature via Virtual Reality: A Mini-Review. Front. Psychol. 12.
Liszio, S., Graf, L., Masuch, M., 2018. The relaxing effect of virtual nature: Immersive technology provides relief in acute stress situations. Annu. Rev. CyberTherapy Telemed. 16, 87–93.
Lüddecke, R., Felnhofer, A., 2022. Virtual Reality Biofeedback in Health: A Scoping Review. Appl. Psychophysiol. Biofeedback 47, 1–15. https://doi.org/10.1007/s10484-021-09529-9
Mather, M., Thayer, J.F., 2018. How heart rate variability affects emotion regulation brain networks. Curr. Opin. Behav. Sci., Emotion-cognition interactions 19, 98–104. https://doi.org/10.1016/j.cobeha.2017.12.017
Park, S.-A., Song, C., Oh, Y.-A., Miyazaki, Y., Son, K.-C., 2017. Comparison of Physiological and Psychological Relaxation Using Measurements of Heart Rate Variability, Prefrontal Cortex Activity, and Subjective Indexes after Completing Tasks with and without Foliage Plants. Int. J. Environ. Res. Public. Health 14, 1087. https://doi.org/10.3390/ijerph14091087
Prinsloo, G.E., Derman, W.E., Lambert, M.I., Rauch, H.G.L., 2013. The effect of a single episode of short duration heart rate variability biofeedback on measures of anxiety and relaxation states. Int. J. Stress Manag. 20, 391–411. https://doi.org/10.1037/a0034777
Prinsloo, G.E., Rauch, H.G.L., Lambert, M.I., Muench, F., Noakes, T.D., Derman, W.E., 2011. The effect of short duration heart rate variability (HRV) biofeedback on cognitive performance during laboratory induced cognitive stress. Appl. Cogn. Psychol. 25, 792–801. https://doi.org/10.1002/acp.1750
Riva, G., Mantovani, F., Capideville, C.S., Preziosa, A., Morganti, F., Villani, D., Gaggioli, A., Botella, C., Alcañiz, M., 2007. Affective Interactions Using Virtual Reality: The Link between Presence and Emotions. Cyberpsychol. Behav. 10, 45–56. https://doi.org/10.1089/cpb.2006.9993
Rockstroh, C., Blum, J., Göritz, A.S., 2020. Combining VR and Biofeedback. J. Media Psychol. 32, 176–186. https://doi.org/10.1027/1864-1105/a000270
Rockstroh, C., Blum, J., Göritz, A.S., 2019. Virtual reality in the application of heart rate variability biofeedback. Int. J. Hum.-Comput. Stud. 130, 209–220. https://doi.org/10.1016/j.ijhcs.2019.06.011
Schubert, T., Friedmann, F., Regenbrecht, H., 2001. The Experience of Presence: Factor Analytic Insights. Presence Teleoperators Virtual Environ. 10, 266–281. https://doi.org/10.1162/105474601300343603
Soyka, F., Leyrer, M., Smallwood, J., Ferguson, C., Riecke, B.E., Mohler, B.J., 2016. Enhancing stress management techniques using virtual reality, in: Proceedings of the ACM Symposium on Applied Perception, SAP ’16. Association for Computing Machinery, New York, NY, USA, pp. 85–88. https://doi.org/10.1145/2931002.2931017
Spano, G., Theodorou, A., Reese, G., Carrus, G., Sanesi, G., Panno, A., 2023. Virtual nature and psychological and psychophysiological outcomes: A systematic review. J. Environ. Psychol. 89, 102044. https://doi.org/10.1016/j.jenvp.2023.102044
Spielberger, C.D., 1983. State-trait anxiety inventory for adults.
Sutarto, A.P., Wahab, M.N.A., Zin, N.M., 2013. Effect of biofeedback training on operator’s cognitive performance. Work 44, 231–243. https://doi.org/10.3233/WOR-121499
Tabor, A., Bateman, S., Scheme, E.J., schraefel, m. c., 2022. Comparing heart rate variability biofeedback and simple paced breathing to inform the design of guided breathing technologies. Front. Comput. Sci. 4.
Tinga, A.M., Nyklíček, I., Jansen, M.P., de Back, T.T., Louwerse, M.M., 2019. Respiratory Biofeedback Does Not Facilitate Lowering Arousal in Meditation Through Virtual Reality. Appl. Psychophysiol. Biofeedback 44, 51–59. https://doi.org/10.1007/s10484-018-9421-5
Vanderhasselt, M.-A., Ottaviani, C., 2022. Combining top-down and bottom-up interventions targeting the vagus nerve to increase resilience. Neurosci. Biobehav. Rev. 132, 725–729.
Vaschillo, E.G., Vaschillo, B., Lehrer, P.M., 2006. Characteristics of Resonance in Heart Rate Variability Stimulated by Biofeedback. Appl. Psychophysiol. Biofeedback 31, 129–142. https://doi.org/10.1007/s10484-006-9009-3
Wang, X., Shi, Y., Zhang, B., Chiang, Y., 2019. The Influence of Forest Resting Environments on Stress Using Virtual Reality. Int. J. Environ. Res. Public. Health 16, 3263. https://doi.org/10.3390/ijerph16183263
Weibel, R.P., Kerr, J.I., Naegelin, M., Ferrario, A., Schinazi, V.R., La Marca, R., Hoelscher, C., Nater, U.M., von Wangenheim, F., 2023. Virtual reality-supported biofeedback for stress management: Beneficial effects on heart rate variability and user experience. Comput. Hum. Behav. 141, 107607. https://doi.org/10.1016/j.chb.2022.107607
White, M.P., Yeo, N.L., Vassiljev, P., Lundstedt, R., Wallergård, M., Albin, M., Lõhmus, M., 2018. A prescription for “nature” – the potential of using virtual nature in therapeutics. Neuropsychiatr. Dis. Treat. 14, 3001–3013. https://doi.org/10.2147/NDT.S179038
Yin, J., Yuan, J., Arfaei, N., Catalano, P.J., Allen, J.G., Spengler, J.D., 2020. Effects of biophilic indoor environment on stress and anxiety recovery: A between-subjects experiment in virtual reality. Environ. Int. 136, 105427. https://doi.org/10.1016/j.envint.2019.105427

Competing interest reported. The company URGOTECH which develops the HRV-biofeedback device, supports the thesis work of PB by the CIFRE grant No. 2020/0649.

Download PDF

Version 1

posted

You are reading this latest preprint version

Immersion in a relaxing virtual reality environment is associated with similar effects on stress and anxiety as heart rate variability biofeedback

Status:

Version 1

Abstract

Figures

1 – Introduction

1.1 - Relaxation and stress management in virtual reality

1.2 – Heart rate variability biofeedback

1.3 - Effects of HRV biofeedback on stress management

1.4 - Implementations of HRV biofeedback in virtual reality

1.5 - Hypotheses

2 - Material and Methods

2.1 - Participants

2.2 - Protocol

2.3 - Guided breathing device

2.4 - Measure of cardiac coherence score

2.5 - Virtual reality environment

2.6 - Measures of stress and anxiety

2.7 - Presence and cybersickness

2.8 - ECG measurements

2.9 Physiological Characteristics

2.10 - Statistical analysis

3 – Results

3.1 - Presence and cybersickness

3.2 - Stress and anxiety

3.3 – RMSSD and Total cardiac autonomic Power

3.4 - Cardiac coherence score

3.5 - Cardiac coherence assessed by P_0.1

3.6 – Relationship between Cardiac coherence score and P_0.1 values

Discussion

Conclusion

Declarations

References

Additional Declarations

Status:

Version 1

Immersion in a relaxing virtual reality environment is associated with similar effects on stress and anxiety as heart rate variability biofeedback

Status:

Version 1

Abstract

Figures

1 – Introduction

1.1 - Relaxation and stress management in virtual reality

1.2 – Heart rate variability biofeedback

1.3 - Effects of HRV biofeedback on stress management

1.4 - Implementations of HRV biofeedback in virtual reality

1.5 - Hypotheses

2 - Material and Methods

2.1 - Participants

2.2 - Protocol

2.3 - Guided breathing device

2.4 - Measure of cardiac coherence score

2.5 - Virtual reality environment

2.6 - Measures of stress and anxiety

2.7 - Presence and cybersickness

2.8 - ECG measurements

2.9 Physiological Characteristics

2.10 - Statistical analysis

3 – Results

3.1 - Presence and cybersickness

3.2 - Stress and anxiety

3.3 – RMSSD and Total cardiac autonomic Power

3.4 - Cardiac coherence score

3.5 - Cardiac coherence assessed by P0.1

3.6 – Relationship between Cardiac coherence score and P0.1 values

Discussion

Conclusion

Declarations

References

Additional Declarations

Status:

Version 1

3.5 - Cardiac coherence assessed by P_0.1

3.6 – Relationship between Cardiac coherence score and P_0.1 values