Neurophysiological and subjective responses to a virtual downhill cycling exercise

doi:10.21203/rs.3.rs-4960350/v1

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Neurophysiological and subjective responses to a virtual downhill cycling exercise

https://doi.org/10.21203/rs.3.rs-4960350/v1

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Purpose ̶ Downhill parts of a cycling race are among the most complex and therefore the most stressful elements for cyclists. Virtual Reality (VR) is thus an interesting tool to monitor reactions to such situations, in safe environment. The present exploratory study aimed to measure physiological and subjective stress during a virtually-simulated downhill race, according to the degree of realism of the simulation.

Methods ̶ Eighteen young, healthy participants were divided into two groups according to their experience in cycling: high-level amateur cyclists (Cyclists; 9.7 ± 2.5 weekly hours of training) and non-cyclists (Controls). They randomly performed 4 different conditions of passive immersion, by manipulating the position (sitting, SIT; on reclined Cycle Ergometer, ERG), with (VR) or without immersion (SCREEN), each time visualizing a virtual 5-minute downhill cycling race from a personal point of view. Physiological stress responses were monitored with upper limb muscle activity (Electromyographic, EMG), electrodermal (Galvanic Skin Response, GSR) and heart activity (Electro-CardioGraphy, ECG). Subjective sensations of fear and cybersickness were assessed using visual analog scales.

Results ̶ VR had higher effects than SCREEN on all markers, with more pronounced effects in CONTROLS. Despite overall lower EMG activity than CONTROLS, CYCLISTS exhibited more muscle tension under VR on the trapezius. ERG conditions showed higher stress responses than SIT.

Conclusion ̶ These results demonstrate that VR induces psychophysiological stress which increases with the simulation’s realism (from incongruent to congruent posture, for instance). CYCLISTS were only sensitive to VR with the highest level of realism, i.e. during VR-ERG

virtual reality

stress

galvanic skin response

cycling

Virtual Reality (VR) is a computerized simulation of a three-dimensional environment that reproduces reality, usually displayed by means of a helmet equipped with two small screens (one per eye). The immersion of oneself in such a virtual environment provides artificial sensation to the individual (Fuchs, 2016). This technology has been extensively used in clinical setups, for instance to train future surgeons to virtually practice challenging interventions (Alaker et al., 2016; Nagendran et al., 2013). Over the past decades, VR has also garnered increasing interest in the field of sports training, with many trainers and athletes being attracted by the additive effect it could produce during exercise. For instance, it helps team sports players to experience complex game situations during training that are difficult to reproduce in a real setup (Faure et al., 2020). Furthermore, this method can also help track an athlete's training progress and load (Neumann et al., 2018).

VR has also shown benefits for the management of fears and phobias, post-traumatic stress, or general anxiety disorder. Indeed, VR may help to decrease stress and apprehension, associated with a reduction in subjective feelings of fatigue, and more generally leading to improved quality of life (Maples-Keller et al., 2017; Wang et al., 2019). According to Hans Selye, stress is defined as a reflex reaction of our organism to any event, as a general adaptation syndrome (Selye, 1956). This reaction is therefore the opposite of a state of rest, and can be expressed by both physiological and subjective markers. The physiological manifestations of stress are mediated by the autonomic nervous system (ANS) and more particularly by modulations of its two branches, the parasympathetic and sympathetic nervous systems. Usual physiological markers of stress are related to an activation of the sympathetic branch, such as an increase in heart rate (HR) or skin conductance / galvanic skin response (GSR). Subjective markers are usually assessed by self-report questionnaires or scales, such as Visual Analog Scales (VAS).

VR has been shown to elicit the ANS. For instance, during a stressful situation experienced in VR, such as a ride on a rollercoaster, activation of the sympathetic system has been reported, manifesting as an increase in heart and ventilatory rates, as well as an increase in GSR, concomitantly with a decrease in parasympathetic activity, as monitored by heart rate variability (Fadeev et al., 2020). In contrast, when VR is used to treat anxiety and phobias, immersing participants in a relaxing environment reduces pathological symptoms such as negative emotions (Parsons & Rizzo, 2008). However, there is still a paucity of evidence about the effects of VR on physiological and subjective markers of stress in the motor and sport context. Existing evidence in the literature is sparse and relates to very specific situations. For instance, VR has been used to induce stress in a shooter training drill, and to assess the effect of caffeine intake on stress remediation (McAllister et al., 2024). In soccer, VR sport-orientated displays were shown to increase both physiological and subjective markers of stress (Stinson & Bowman, 2014). Conversely, VR can also be used to reduce stress reactions, as it has also proven its efficiency in reducing anxiety and improving self confidence in female soccer players (Harrison et al., 2021).

Overall, the known effects of VR on physiological and psychological reactions to stress in general, and the sparse evidence in the literature specifically in the sports context, make VR of major interest for sports training science. VR can help to simulate different stressful conditions, such as those experienced in competition, by immersing participants in situations that represent a risk, but in a safe and controlled environment. In this regard, downhill cycling is the perfect example of a situation that puts participants at risk and generates a great amount of stress and anxiety (Sperlich et al., 2012). In professional road cycling races, athletes can reach over 90 km.h^− 1 during downhill parts, and falling at such a speed presents a very high risk of severe injuries, or even death. However, during training it remains difficult to work on such a situation in a real-life context without putting the athlete in danger. Yet, in other domains, such as reducing stress in elderly people, in cases where exposing them to the real situation was not possible, VR has proven its efficiency in reproducing the sensations of real-world scenarios (Kalantari et al., 2021). The application of VR to managing stressful sport situations in athletes, and more particularly in cyclists participating in fast downhill races, has never been investigated.

The aim of the present study was thus to assess the effects of virtual simulation of a downhill race on psychological and physiological markers of stress. Since VR of stressful situations may also affect the motor neuromuscular system (Grosprêtre et al., 2023), muscle activity modulation was also assessed. To test the effects of different levels of immersion, several sensory contexts were tested from an incongruent (seated on a chair) to a congruent position (seated on a reclined cycle ergometer), and in several types of virtual setups (2D screen versus immersive helmets). Different levels of experience were also tested, by recruiting high-level amateur cyclists and athletes from other non-related disciplines. We hypothesized that the condition that would provide the best modulation of all stress markers would be the use of a VR helmet in a congruent position (i.e. on the ergometer), and the condition that would provide the least modulation would be the incongruent position with the 2D screen display of the downhill race. Finally, greater physiological and psychological stress was expected in the non-cyclists than in the cyclists, due to the experience of these latter in managing such situations.

2.1. Participants

Eighteen athletes from various disciplines volunteered to participate in the study (age: 22.6 ± 4.7 years old, height: 174.3 ± 9.3 cm, weight: 67.4 ± 12.1 Kg, 8 females and 10 males). They all provided written informed consent to participate in the present experiment and were asked not to engage in any unusual physical or cognitive activity before the protocol. None reported any neurological or musculoskeletal disorders. The study protocol was approved by the local ethics committee and conducted in accordance with the Declaration of Helsinki. None of the participants had prior experience of regular VR practice.

Participants were divided into two groups according to their discipline. The first group was composed of amateurs cyclists (CYCLISTS; n = 8; age: 23.6 ± 4.9 years old, height: 171.3 ± 8.6 cm, weight: 62.2 ± 8.3 Kg, 4 females, 4 males, mean training frequency: 9.7 ± 2.5 hours.week^− 1), and the second group was composed of non-cyclists who were active participants in various other sports, such as swimming or team sports (CONTROL; n = 10; age: 21.7 ± 4.5 years old, height: 176.8 ± 9.5 cm, weight: 72.7 ± 12.4 Kg, 4 females, 6 males, mean training frequency: 5.1 ± 2.0 hours.week^− 1).

2.2. Study protocol

Participants made two visits to the laboratory. The first was a familiarization session to detect potential intolerance to VR and to ask participants about their sports practice and past experiences. The second visit consisted of the full experiment and lasted about an hour.

First, participants were seated on a comfortable chair to carry out skin preparation and electrode placement, and to complete the preliminary questionnaires. Second, a 15-minutes rest period was observed to ensure body homeostasis, for baseline measurement of all physiological markers. Participants were asked to stay relaxed and not to move.

Subsequently, 6 experimental conditions were performed in a random order. Each condition lasted 5 minutes (the duration of the downhill cycling race) and conditions were interspaced by a minimum 5-minutes rest period. The 6 conditions, displayed in Fig. 1, are presented hereafter, from the least to the most immersive:

Seated at rest (SIT-REST)
Seated with a 2D screen display (SIT-S)
Seated with VR (SIT-VR)
Cycle Ergometer rest measurement (ERG-REST)
Cycle Ergometer with a 2D screen display (ERG-S)
Cycle Ergometer with VR (ERG-VR)

All physiological parameters (muscle activity of upper limb, cardiovascular and skin impedance markers) were continuously recorded during each condition. After each condition, participants were asked to complete 4 VAS to assess the following: Fear of falling, fear of speed, perceived stress, and nausea. Each condition was separated by 5 minutes of rest. To prevent exercise effects, all conditions were performed in passive mode, meaning that participants were asked to relax and remain motionless (i.e. not to pedal or to tilt during turns in ERG conditions).

Except for REST conditions, each condition consisted of watching a video clip of a downhill race lasting 5 minutes. This videoclip was previously recorded by a professional cyclist (French Groupama-FDJ continental team) on a real downhill race, by means of a 360-degree 4k camera (VIRB 360, Garmin, Olathe, KS, USA) placed on the handlebars of the bike. The videoclip was recorded at 60 frames per second, and the file could then be displayed in either 360 mode for VR or 2D for screen display (S condition).

For VR conditions, participants wore a Fit Immersion Pro-2 Edition helmet (Fit Immersion, Montpellier, France) which has two 2K screens and a refresh rate of 120 Hz. The headsets are specifically designed for cycling, since the weight is distributed between the back and the front of the head (battery located on the back strap).

The 2D screen display conditions (S condition) were performed by watching the downhill race on a 17’’ screen (37,6 x 21,2 cm) placed at eye level to the participant, i.e. in front of their head for SIT conditions or aligned with the Cycle Ergometer tilt for ERG conditions. The screen position was set to be at 1 meter distant from the participant’s head.

For ERG conditions, participants sat on a cycle ergometer (SmartBike Neo+, TACX company, Oegstgeest, Nederland). To provide the downhill slope sensation, the Cycle Ergometer was tilted 15° forward using a 25cm wooden structure placed below the rear support. Configuration of the saddle and handlebars was set individually for each participant to fit their standard setup (for cyclists), or the most comfortable position (for non-cyclists).

2.3. Electrodermal activity

The first physiological variable related to stress that we measured was galvanic skin response (GSR). This parameter is an electrophysiological marker sympathetic nervous system activity (Grapperon et al., 2012). Two stainless steel finger plates (GSR finger electrodes, AdInstruments, Sydney, Australia) were strapped to the index and middle fingers of the left hand to record a bipolar response in micro Siemens (µS). The signal was amplified by an amplifier device (GSR Amp, AdInstruments, Sydney, Australia). For data processing, analysis of tonic phases was performed and averaged over the 5-minute period of each condition.

2.4. Electrocardiography

Cardiovascular markers of stress were measured by monitoring heart rate (HR). Heart rate variability (HRV) was also determined as the variation in the time between two heartbeats (R-R intervals; (Marsac, 2013). Temporal parameters of HR variation were also determined by the Root Mean Square of Successive R-R Interval Differences (RMSSD).

Electrocardiography signals were recorded by the derivation of the signal between two silver chloride surface electrodes (8 mm diameter, Contrôle Graphique S.A., Brie-Comte-Robert, France). One electrode was placed under the collarbone above the right pectoralis minor, and the other at the level of the left iliac crest. The skin was first shaved and rubbed with alcohol to ensure low impedance (< 5 kΩ). ECG signals were amplified (gain = 1000) with a sampling frequency of 4 kHz using the Powerlab system (Powerlab 16/35, AdInstruments, Sydney, Australia).

The average value of the parameters recorded continuously during each 5-minute condition was calculated and used for analysis. This duration has been recommended as the minimum time required for such analysis (Nunan et al., 2010).

2.5. Muscle activity

To account for possible muscle tension induced by stress, electromyographic (EMG) activity of the upper limb muscles related to the cycling posture was continuously recorded during each condition. EMG activity of four muscles of the right side were recorded, namely: the Palmaris Longus (PL), the Trapezius (TRAP), the Biceps Brachii (BB) and the Triceps Brachii (TB). The skin was first shaved and cleaned with alcohol to ensure low impedance (< 5 kΩ). Wireless sensors were used to provide the least inconvenience for the participants (Delsys, Natick, MA), and placed according to SENIAM recommendations (Freriks & Hermens, 2000). EMG signals were amplified with a bandwidth frequency ranging from 0.3 Hz to 1 kHz and recorded at a sampling frequency of 2 kHz (Powerlab, LabChart 8, ADInstruments, Sydney, Australia).

For EMG data analysis, the root mean square (RMS) of the signal was calculated along each condition by a 200 ms moving window, and the average value over the 5-minutes of each condition was considered.

2.6. Subjective markers of stress

Visual Analog Scales (VAS) were used to assess the subjective sensations of participants after each condition. VAS enable a simple, quantitative approach to the level of a perceived, subjective feeling of stress (Lazarus & Folkman, 1984). Four scales were used, namely to assess the following: perceived stress (0: no stress at all; 10: maximum stress), fear of falling (0: no fear at all; 10: extreme fear), fear of speed (0: no fear at all; 10: extreme fear) and feeling of nausea (0: no nausea at all; 10: extreme nausea). On a graduated piece of paper, participants were asked to put a mark on a 10 cm line with unipolar axes, with the left extremity representing the minimum and the right extremity the maximum. The result of each scale was given by measuring the distance from the left extremity in cm.

2.7. Statistical analysis

The normality and sphericity of the data were tested and confirmed by the Shapiro–Wilk and Mauchly’s tests respectively. For ANS variables (GSR, HR, RR, RMSSD) and EMG data (RMS of LP, TRAP, BB, TB), a 3-way repeated measure ANOVA was performed with the factors position (SIT vs. ERG), group (CONTROL vs. CYCLIST) and level of immersion (REST vs. SCREEN vs. VIRTUAL REALITY). For subjective markers (Perceived stress, fear of speed, fear of falling, nausea), a 2-way repeated measure ANOVA was performed with the factors group (CONTROL vs. CYCLIST) and condition (SIT-S vs. SIT-VR vs. ERG-S vs. ERG-VR). In case of a significant effect, a post hoc test with Bonferroni correction was performed. P < 0.05 was taken as the level of statistical significance for all comparisons. Absolute values are expressed as mean ± SD or mean difference with 95% confidence intervals (95%CI). Statistics were performed using JASP Software (version 0.13, JASP Team (2020), University of Amsterdam).

3.1. Autonomic Nervous System (ANS)

A significant main effect of position was found on mean GSR (F_1,16=16.508, P < 0.001), with values being higher in SIT than in ERG (Fig. 2a). Moreover, a significant main effect of group was found (F_1,16=6.958, P = 0.018), with CONTROL exhibiting higher values (+ 110.6%) than CYCLISTS, regardless of the condition. A significant main effect of level of immersion (F_2,32=17.661, P < 0.001) was also found. Significant interactions were found for position x group (F_1,16=8.794, P = 0.009) and level of immersion x group (F_2,32=6.310, P = 0.004). Mean values of GSR were significantly higher (+ 270.2 ± 133.4%) in VR as compared to REST in both SIT and ERG conditions for CONTROL (Fig. 2a).

A significant main effect of position was found on mean HR (F_1,16=19.160, P < 0.001), with values being higher in SIT than in ERG (Fig. 2b). A significant main effect of level of immersion (F_2,32=19.227, P < 0.001) was also found, with higher HR in Screen than in REST (P = 0.008), in VR than in REST (P < 0.001), and in VR than in Screen (P = 0.018). A significant relationship across the different conditions was found for CONTROL and for CYCLIST groups (Fig. 2b), with no inter-group differences in slope. However, HR was on average 7.9% lower in CYCLIST compared to CONTROL groups across all conditions.

Regarding mean R-R intervals, a significant main effect of position was found (F_1,16=17.676, P < 0.001), with lower values in SIT than in ERG (Fig. 2c). A significant main effect of level of immersion (F_2,32=44.151, P < 0.001) was also found, with higher HR in Screen than in REST (P < 0.001), in VR than in REST (P < 0.001), and in VR than in Screen (P = 0.043).

RMSSD values exhibited a significant main effect of level of immersion (F_2,32=5.533, P = 0.023), with HR higher in VR than in REST (P = 0.007)(Fig. 2d). A significant relationship across the different conditions was found for CONTROL and CYCLIST groups, but only in ERG conditions (Fig. 2d), with no inter-group differences in slope.

3.2. Myoelectrical activity

For the PL muscle, a significant main effect of position was found on mean EMG RMS activity (F_1,16=388.008, P < 0.001), with higher values in SIT than in ERG (Fig. 3a). A significant main effect of group was also found (F_1,16=10.180, P = 0.006), with CONTROL exhibiting higher values than CYCLISTS whatever the condition. A significant main effect of level of immersion (F_2,32=12.394, P = 0.002) was also found. Significant interactions were found for position x group (F_1,16=13.748, P = 0.009) and level of immersion x position (F_2,32=13.601, P < 0.001).

For the TRAP muscle, a significant main effect of position was found on mean EMG RMS activity (F_1,16=47.903, P < 0.001), with higher values in SIT than in ERG (Fig. 3b). A significant main effect of group was found (F_1,16=4.785, P = 0.044), with CONTROL exhibiting higher values than CYCLISTS whatever the condition. A significant main effect of level of immersion (F_2,32=6.998, P = 0.014) was also found. A significant interaction was observed for level of immersion x position (F_2,32=4.369, P = 0.040).

For the BB muscle, a significant main effect of position was found on mean EMG RMS activity (F_1,16=5.485, P = 0.032), with higher values in SIT than in ERG (Fig. 3c). A significant main effect of group was found (F_1,16=5.349, P = 0.034), with CONTROL exhibiting higher values than CYCLIST whatever the condition.

For the TB muscle, a significant main effect of position was found on mean EMG RMS activity (F_1,16=63.414, P < 0.001), with values being higher in SIT than in ERG (Fig. 3d). A significant interaction was found for level of immersion x group (F_2,32=6.457, P = 0.011).

3.3. Subjective markers

Perceived stress showed a significant main effect of condition (F_3,48=9.721, P < 0.001). Values were higher in ERG-VR than in SIT-S (P < 0.001) and than in ERG-S (P < 0.001)(Fig. 4a). A significant condition x group interaction was found (F_3,48=3.199, P = 0.031).

Fear of speed showed a significant main effect of condition (F_3,48=12.505, P < 0.001). Values were higher in SIT-VR than in SIT-S (P < 0.001), in ERG-S than in SIT-VR (P = 0.012), in ERG-VR than in SIT-S (P < 0.001), and in ERG-VR than in SIT-S (P < 0.001)(Fig. 4b). A significant condition x group interaction was found (F_3,48=3.199, P = 0.031).

Fear of falling showed a significant main effect of condition (F_3,48=7.010, P < 0.001). Values were higher in ERG-VR than in SIT-S (P < 0.001), SIT-VR (P = 0.031) and ERG-S (P = 0.002) (Fig. 4c). A significant condition x group interaction was found (F_3,48=3.682, P = 0.018).

Nausea showed a significant main effect of condition (F_3,48=8.660, P < 0.001). Values were higher in ERG-VR than in SIT-S (P < 0.001) and ERG-S (P < 0.001)(Fig. 4d).

The aim of the present study was to assess the effect of a stressful sports situation (a downhill cycling race) on physiological and subjective markers of stress, according to different levels of immersion and the varying expertise of the participants. Overall, VR immersion yielded greater effects than SCREEN, both in terms of physiological and subjective markers. Moreover, greater effects of immersion were observed in control participants (CONTROL) than in cyclists (CYCLISTS). Regardless of the group, the greatest effect on objective and subjective markers of stress was found when VR was combined with the ergometer (ERG-VR).

4.1. Autonomic Nervous System

Cardiovascular dynamics and skin conductance were affected by VR immersion in both positions (Fig. 2). Although posture (from SIT to ERG) greatly affected cardiovascular markers, adding VR still led to an increase in HR in each condition, ranging from 2 to 9% over resting, proportional to the degree of immersion (SIT-S > SIT-VR > ERG-S > ERG-VR) (Fig. 2b). The RMSSD was also influenced, with a significant drop in both groups, but only in the ERG position (Fig. 2d). On the other hand, the RR intervals did not seem to be affected by VR, but rather by position (Fig. 2c), and GSR seemed to be influenced by the degree of immersion only in CONTROL, with a gradual increase in GSR (Fig. 2a). This dynamic of ANS cardiovascular markers has previously been observed in the literature. When used for relaxation and/or stress remediation purposes, VR can modulate anxiety and stress, although to a lesser extent than other methods such as biofeedback (Blum et al., 2019; Kim et al., 2021). Previous studies have noted similar kinetics on GSR when VR was applied during exercise, although this was explained more by physical activity than by the immersion itself (Peterson et al., 2018). However, previous works found lower levels of skin conductance in passive immersion (Simeonov et al., 2005). Given that our experiment was performed in a passive mode, to prevent contamination by exercise effects, an increase in sympathetic activity may have resulted from increased physiological stress.

However, this stress induction appeared to be different across groups and conditions. Overall, CYCLISTS reacted less to immersion than CONTROLS. Moreover, RR intervals were greater in both VR conditions, than in REST (Fig. 2c). RMSSD was greater or equal in SIT-VR than in SIT-REST and SIT-S (Fig. 2d). These controversial results might be explained by the quality of immersion, i.e. the conditions that most help participants to feel immersed, and required to induce any emotional state (Aganov et al., 2020). Quality will influence the degree of immersion, representing the degree of realism of the VR (visual display, position, etc.). The degree of immersion has an impact on the physiological and psychological responses expected in VR (Felnhofer et al., 2015; Riva et al., 2007). The greater the degree of immersion, the closer the response is to reality. When VR is used as a relaxation tool, the enhanced immersion achieved by wearing a headset leads to higher HRV, lower HR, and higher RMSSD, all reflecting an increase in parasympathetic activity, inferring a state of relaxation (Aganov et al., 2020). One of our indicators of quality immersion is HR (Fig. 2b), whose increasing linear relationship with the degree of immersion is evidence of stress induced by VR, and by extension showed the degree and quality of the present immersion. The degree of immersion remains an important factor for the effectiveness of the intervention, mainly because it increases the sense of presence. The sense of presence, defined by the feeling of being inside the virtual environment, is necessary for the emergence of the emotions sought in immersion (Riva et al., 2007). Previous studies have demonstrated the importance of this factor (Blum et al., 2019; Pallavicini et al., 2018). Finally, the time spent in the immersive environment also impacts the induction of stress. Indeed, cardiovascular markers were shown to be more affected by VR for longer durations of immersion, of up to one hour (Malińska et al., 2015). This may explain the lack of difference between conditions in the present study for some markers, such as RR intervals, which may need a longer exposure to VR.

4.2. Muscular activity

As can be expected, greater muscle activity was found in ERG than in SIT conditions for the four muscles measured. Indeed, maintaining the posture on the bike obviously required a certain level of activity of those muscles, chosen for their involvement in the cycling posture. However, there was a level-of-immersion effect on LP and TRAP, ranging from a + 2.6% to a + 119.15% increase in activity compared to rest, and a group effect on BB activity.

Although there is very little literature about upper limb activation during actual cycling, it is known that cycling vibrations impact upper limb kinematics (Viellehner & Potthast, 2022), and pedaling intensity modulates upper body muscle responses (Yoon et al., 2023).

In the present study, the increase in EMG activity from SIT to ERG condition thus originated from the change of position rather than the immersion effect. Muscular stress induced by ERG conditions (Fig. 3) can be explained by the common neural control of upper and lower body muscles (Laine et al., 2021).

In ERG conditions, the degree of immersion seemed to affect only the CONTROL group on the LP, and TB (Fig. 3a, b). In CYCLISTS, only the TRAP muscle was affected by VR in the ERG position, as compared to ERG-REST. Overall, stress may then activate the motor system, as a result of increased muscle tension. An increase in TRAP muscle activity by 15% as compared to rest has been shown to reflect mental activity, i.e. by practicing repeated Stroop test (Wijsman et al., 2013). Therefore, the present study raised the hypothesis that in cyclists, the increase in TRAP activity may reflect increased mental work in the ERG-VR condition, caused by mental simulation of the action. On the contrary, the greater TRAP muscle activity in CONTROL, as well as in the BB muscle, may result more from a lack of habituation to the cycling position.

4.3. Subjective markers

At a subjective level, the position did not affect perceived stress, fear of speed or nausea. Only the fear of falling was influenced by the position, with a score in VAS ERG-VR that was higher than in the SIT-VR condition (Fig. 4a, b, d). The CYCLIST group was affected by the most immersive situation, i.e. Erg-VR. The group x condition interaction demonstrated that CONTROL participants were more affected by condition, with a maximal score in ERG-VR in all VAS, while CYCLISTS seemed less affected, except for nausea (Fig. 4c). Overall, it should be noted that all VAS scores were quite low, since the maximum was 3 out of 10.

Usually, it is acknowledged that subjective emotions in VR are related to the sense of presence (Riva et al., 2007). This sense increases as soon as participants are able to see their body (a virtual representation of it), whether it is in the first-person point of view (POV) or the third person. A previous study demonstrated that performances (grasping and throwing a ball) were not different when performed under a virtual third-person POV than under real conditions (Pastel et al., 2020). However, first person POV creates a greater sense of presence by bringing the participant closer to reality (Burin et al., 2019, 2020). Indeed, it increases the sense of self-ownership and self-agency in immersion (Gallagher, 2000). Therefore, contrary to motor performance, when it comes to recreating a stressful environment, first-person POV could be recommended. In the present study, the use of first-person POV may have increased the realism of the situation, despite the passive aspect of watching a pre-recorded race.

The lower effects of immersion in CYCLISTS as compared to CONTROL can be explained mainly by prior experience of the situation. In 2021, Hu et al. demonstrated that VR training increases energy expenditure, while also increasing perceived pleasure and, above all, lowering perceived fatigue (Hu et al., 2021). The reduction in fatigue is thought to be a consequence of the increase in pleasure induced by VR. Stress perceived in VR can affect cognitive functions, as shown by an increase in cognitive flexibility when VR provokes mild stress (Delahaye et al., 2015). Cognitive flexibility is the ability to adapt to a situation (Dajani & Uddin, 2015). VR would thus provide optimal arousal enabling improved performance (Martens & Landers, 1970).

A sensation of nausea was perceived by all participants, with an increase in VR conditions compared to SCREEN (Fig. 4d; ERG-VR > ERG-S and SIT-VR > S). Most of this nausea may be the result of cybersickness, namely the discomfort induced by virtual environments (LaViola, 2000). It is often characterized by headaches, dizziness, eye strain, vomiting, nausea and even malaise. There is much theorizing about the origins of this discomfort, but the most widely held theory is that cybersickness arises from a sensory conflict, i.e. a paradox between what is observed (the body in motion) and body immobility (Davis et al., 2014). As this discomfort triggers activation of the central nervous system, it could represent per se a factor that modulates the subjective and physiological markers of stress.

4.4. Study limitations & perspectives

One of the limitations of this study is the lack of measurement of cybersickness. The SSQ (Simulator Sickness Questionnaire) can be used to detect sufferers (Kennedy et al., 1993). Furthermore, the lack of a proper analysis of the sense of presence in the present study, for example through a validated questionnaire, may also limit the interpretation of our results. The choice to use a simple and quick visual analog scale for these markers was made based on the multiplication of conditions in the same experimental session, and the time that would have been required to complete a full questionnaire. Finally, performing all four conditions (6 with resting measurements) on the same day may also have led to contamination from one situation to another. In this regard, randomization of the order of the conditions may limit this bias, although this could lead to under-estimation of the stress effect of each condition on average.

In terms of perspectives, it would be interesting to perform this experiment while the participant is actually interacting with the virtual environment by being able to pedal and/or brake when necessary. In the present protocol, this possibility was excluded by using passive immersion to avoid the contamination of physiological markers with movements and exercise effects. However, it is known that immersion effects on brain dynamics are greater when the participant can interact with the virtual environment (Djebbara et al., 2019). To this end, a target intensity or power can be used to control the exerted effort.

The present study aimed to assess the potential autonomic, muscular, and subjective stress induced by VR immersion, according to the level of realism of the situation (seated on a chair or on an Cycle Ergometer, with 2D or virtual 3D display), in cyclists and non-cyclist athletes. Our results showed greater induced stress in the most immersive situations (VR), and also that stress was greater in the less experienced athletes. Therefore, VR can induce physiological and perceived stress in athletes during passive downhill cycling immersion. However, it is important to note the importance of body position during immersion. This exploratory study demonstrates the value of implementing VR in cyclists for stress induction and downhill training, especially in novices. In professional cyclists, VR could add significant value in managing post-traumatic stress during re-training programs after a fall. Future experiments are warranted, however, to test the stress induced while participants are exercising.

ANS	Autonomic Nervous System	PL	Palmaris Longus
BB	Biceps Brachii	RMS	Root Mean Sqare
ECG	Electro-Cardiography	RMSSD	Root Mean Sqare of Successive R-R
EMG	Electromyography	SIT/S	Seated
ERG	Ergometer	TB	Triceps Brachii
GSR	Galvanic Skin Response	TRAP	Trapezius
HR	Heart Rate	VAS	Visual Analog Scale
HRV	Heart Rate Variability	VR	Virtual Reality

Conflicts of Interest
All authors disclose professional relationships with companies or manufacturers who could benefit from the results of the present study. Results of the present study do not constitute endorsement by ACSM. All authors declare that the results of the study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation.

Ethical Approval
Each participant gave writen informed consent, and the study was conducted following the ethical principles of the 1964 Helsinki declaration. This study was approved by the regional ethic committee (CRUBFC).

Author Contribution

Conceptualization: SG; Methodology: SG, MB, AG, FG, NB; Data Collection: MB, JC, SG; Formal analysis : MB, SG; Writing - original draft preparation: MB; Writing - review and editing: SG, MB, AG, FG, NB, JC; Funding acquisition: SG, AG, FG; Supervision: SG.

Acknowledgments

This research was funded in the framework of the LABCOM REAPCY project, funded by the National Research Agency of France (ANR). This project involves a partnership between the French professional cycling team GROUPAMA-FDJ and the Laboratory C3S – Culture, Sport, Health and Society.

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No competing interests reported.

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Neurophysiological and subjective responses to a virtual downhill cycling exercise

Status:

Version 1

Abstract

Figures

1. Introduction

2. Methods

2.1. Participants

2.2. Study protocol

2.3. Electrodermal activity

2.4. Electrocardiography

2.5. Muscle activity

2.6. Subjective markers of stress

2.7. Statistical analysis

3. Results

3.1. Autonomic Nervous System (ANS)

3.2. Myoelectrical activity

3.3. Subjective markers

4. Discussion

4.1. Autonomic Nervous System

4.2. Muscular activity

4.3. Subjective markers

4.4. Study limitations & perspectives

5. Conclusion

Abbreviations

Declarations

Ethical Approval
Each participant gave writen informed consent, and the study was conducted following the ethical principles of the 1964 Helsinki declaration. This study was approved by the regional ethic committee (CRUBFC).

Author Contribution

Acknowledgments

References

Additional Declarations

Status:

Version 1

Neurophysiological and subjective responses to a virtual downhill cycling exercise

Status:

Version 1

Abstract

Figures

1. Introduction

2. Methods

2.1. Participants

2.2. Study protocol

2.3. Electrodermal activity

2.4. Electrocardiography

2.5. Muscle activity

2.6. Subjective markers of stress

2.7. Statistical analysis

3. Results

3.1. Autonomic Nervous System (ANS)

3.2. Myoelectrical activity

3.3. Subjective markers

4. Discussion

4.1. Autonomic Nervous System

4.2. Muscular activity

4.3. Subjective markers

4.4. Study limitations & perspectives

5. Conclusion

Abbreviations

Declarations

Ethical Approval Each participant gave writen informed consent, and the study was conducted following the ethical principles of the 1964 Helsinki declaration. This study was approved by the regional ethic committee (CRUBFC).

Author Contribution

Acknowledgments

References

Additional Declarations

Status:

Version 1

Ethical Approval
Each participant gave writen informed consent, and the study was conducted following the ethical principles of the 1964 Helsinki declaration. This study was approved by the regional ethic committee (CRUBFC).