Next Article in Journal
Research on High-Precision Dynamic Modeling and Performance Evaluation of Inertially Stabilized Platforms
Next Article in Special Issue
Exploring Usability, Emotional Responses, Flow Experience, and Technology Acceptance in VR: A Comparative Analysis of Freeform Creativity and Goal-Directed Training
Previous Article in Journal
Laser Processing of Intraocular Lenses
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Deceptive Modulation of Actual and Perceived Effort While Walking Using Immersive Virtual Reality: A Teleoanticipatory Approach

1
Airway & UCFit-Digital Health-Exercise Physiology Research Laboratory, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
2
Chobanian and Avedisian School of Medicine, Boston University, Boston, MA 02215, USA
3
Washington University School of Medicine, Washington University, Saint Louis, MO 63110, USA
4
Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hofstra University, Hempstead, NY 11549, USA
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(14), 6072; https://doi.org/10.3390/app14146072
Submission received: 24 June 2024 / Revised: 6 July 2024 / Accepted: 8 July 2024 / Published: 12 July 2024
(This article belongs to the Special Issue Virtual and Augmented Reality: Theory, Methods, and Applications)

Abstract

:
Virtual reality and technology-driven ‘exergaming’ have grown in tandem to expand the possibilities of exercise. However, few studies have investigated these aspects together in the context of treadmill walking. This study examined the effects of immersive virtual reality (IVR) on heart rate (HR) and ratings of perceived exertion (RPE) while walking on a treadmill at three different speeds, only one of which was disclosed to participants. Using a single-blinded, randomized control trial with a crossover research design, sixteen college-aged adults (eight females, mean age of 20.5 ± 1.2 years) were randomly assigned a testing order for six trials of treadmill walking. Three 10 min trials were conducted with IVR and three were conducted without on two separate days; a 3 min break was given in between trials. Borg RPE scores were assessed after each trial, while HR was monitored throughout. The IVR trials produced lower mean RPE scores in comparison to the non-IVR group at 3.5 and 3.7 mph. The IVR trials also yielded a lower mean HR compared to the non-IVR trials at the highest speed of 3.7 mph. These findings suggest that IVR may be able to attenuate RPE scores and HR while walking on a treadmill.

1. Introduction

Awareness of the physical demands of exercise will often determine the strategies used to successfully complete it. Based on a combination of prior experience, ratings of perceived exertion (RPE), biomechanical and cardio-metabolic feedback while performing the exercise, participants will engage in a self-pacing strategy known as teleoanticipation [1]. By incorporating feedback and feed-forward mechanisms that encompass a variety of physiological systems, the brain will engage in selective neuromuscular regulation to efficiently and effectively complete the exercise [2]. However, predetermining an endpoint for the exercise is necessary to ensure optimal performance under the teleoanticipatory model [3]. In the absence of an endpoint, physiological resources will be conserved to account for extended or intensified exercise [4,5]. Conversely, knowledge of when an endpoint is approaching will allow this conservation to subside while subsequently increasing the work rate [6].
An individual’s RPE, which primarily drives teleoanticipation, is commonly measured using the Borg RPE scale [7]. The Borg RPE scale ranges from ‘6’ to ‘20’, with each score designed to correspond to a tenfold increase in heart rate (e.g., 8 equals 80 beats/min) [8]. In previous research, RPE scores have been used to drive accurate, individualized exercise prescriptions [9]. Although RPE scores are subjective, there is a general consensus that these are largely attributed to the intensity of the exercise being performed [10]. This trend has been demonstrated to be consistent with running and resistance training, as a higher RPE has been associated with higher-intensity exercise [11].
Utilizing deception to manipulate an exercise experience has become increasingly common. Whether through the incorporation of a digital competitor [12] or hypnotic manipulation [13], deception has demonstrated the potential to change how people perceive exercise. Recently, visual hill gradients observed through virtual reality while cycling have been shown to alter perceptions of effort and breathlessness [14]. In this particular study conducted by Finnegan et al., participants who observed a steeper hill gradient experienced a higher level of breathlessness and cycled with more power despite the resistance of the cycle ergometer remaining consistent throughout. However, a systematic review conducted by Jones et al. found that while a majority of research has investigated manipulations of the exercise endpoint, there is little research available examining the effects of exercise intensity deception [15]. Only two studies were identified within this review that evaluated the effects of pre-exercise intensity deception, both of which yielded no significant differences in selected outcome measures (RPE and heart rate, respectively) [16,17]. However, Jones et al. contend that the methodologies used in these investigations varied greatly, such as the way in which participants were deceived and differences in training experience of the participants. As a result, further study is required to ascertain if there is a relationship between the deception of exercise intensity and exercise variables.
Virtual reality (VR) has emerged as a viable tool for exercise studies involving deception. VR allows individuals to engage with simulated content in virtual environments outside of the real world [18]. More recently, as VR technology has improved, immersive virtual reality (IVR) has been developed to enhance the experience. IVR, unlike its predecessor, incorporates unique sensorimotor elements (i.e., visual, auditory, and/or tactile stimuli) that alter a user’s experience to make them feel as if what they are perceiving is their actual surroundings [19]. From feeling a touch on the shoulder induced by a small actuator to ostensibly depicted arteries and organs in a training simulation for surgeons, IVR can provide sensations that mirror reality. In a study conducted by North and North (2016), traditional VR and IVR apparatuses depicting the sights and sounds of an airplane interior and exterior were compared against one another [20]. Mean Likert Scale ratings of presence revealed a stronger sense of immersion within the IVR environment compared to the traditional VR environment. However, these authors noted that physiological measurements were not used within their study and could be used to further evaluate a participant’s sense of presence. Furthermore, IVR has the unique potential to produce “place illusion”, which refers to the sensation of “being there” in the surroundings [21]. Early study employing IVR during exercise has yielded encouraging implications, as it was found to yield lower RPE levels while improving satisfaction during biking [22,23]. These findings, while salient, highlight the need for more study employing deception tactics with IVR to determine if they extend to other training modalities, such as a treadmill.
To our knowledge, there are no studies that have investigated the efficacy of IVR in conjunction with deception strategies during treadmill exercise. This study investigated the performance of participants exposed to speed deception on heart rate and ratings of perceived exertion (RPE) during immersive virtual reality while walking on a treadmill. We hypothesized that participants in the IVR condition would report lower RPE scores and equally yield lower heart rates (HRs) in comparison to the non-IVR condition.

2. Materials and Methods

2.1. Participants

Sixteen college-aged healthy and physically active volunteers (aged 19–23 years, eight females) were recruited from the University of California, Los Angeles (UCLA) campus. Exclusion criteria included the presence of any disorders that would preclude moderate- to high-intensity exercise participation and testing. All participants provided written informed consent at the beginning of the study. This research was approved by the UCLA Institutional Review Board and carried out in accordance with the ethical standards of the Helsinki Declaration.

2.2. Study Design

Using a single-blinded, crossover research design, participants were tested during two separate days, each consisting of three, 10 min treadmill walking trials separated by a 3 min rest period, with a 24 h washout period in between each day. All participants were randomly assigned to a participant number produced from a random number generator, which was operated by an investigator independent from recruitment and testing. Each participant number corresponded to a randomized sequence of testing for the two different days. The order for the speeds of the treadmill (3.3 mph, 3.5 mph, and 3.7 mph) were randomized, as well as the order for the IVR or non-IVR trials (i.e., IVR trials occurred on either the first day or second day of testing). The non-IVR trials used the same treadmill albeit with no head-mounted display (HMD). Therefore, while the non-IVR trials did not contain the same sensorimotor elements utilized with the HMD, the same speed order and testing sequence used in the IVR trials was applied for consistency. Participants were told (and deceived) that all trials occurred at 3.5 mph for blinding purposes. While one researcher was responsible for monitoring the participant during their rest periods in a nearby room without the treadmill, the other researcher was tasked with changing the speeds based on the assigned order. The first researcher, blinded to the speeds of the treadmill and their order, was solely responsible for assessing the RPE and HR data to ensure that a blinded approach was achieved. All testing was administered by trained research personnel under the supervision of the lab director in the UC Fit-Digital Health Exercise Physiology Research Laboratory at UCLA during the early spring of 2023 for a one-month period. Study participants were asked to refrain from completing moderate–vigorous physical activity at least 24 h prior to beginning the study until its conclusion. For baseline measures, body mass was measured on a calibrated medical scale (accuracy ± 0.1 kg), and height was determined using a precision stadiometer (Seca, Hanover, MD, USA; accuracy ± 0.01 m). Body fat percentage was measured using a validated octipolar, multi-frequency, multi-segmental bioelectrical impedance analyzer (InBody Co., Seoul, Republic of Korea) [24]. The instrument measured resistance and reactance using proprietary algorithms. To ensure accuracy, participants adhered to standard pre-measurement BIA guidelines recommended by the American Society of Exercise Physiologists [25].

2.3. IVR Intervention

The IVR intervention made use of an innovative 2.0 software application (Octonic VR, New York, NY, USA) developed to enable a variety of viewing experiences while walking on a treadmill. On an HMD (Meta Quest 2, Menlo Park, CA, USA), a standardized, pre-selected immersive environment portraying an island in the sky (i.e., “Sky Island”) was projected, as shown in Figure 1. As participants walked on the treadmill, in-app music played as their view dynamically changed in response to their pace and direction of gaze, as shown in Figure 2. The visual input while wearing the HMD entirely concealed the participants’ immediate surroundings, as is customary with IVR. However, a mixed reality feature unique to this program permitted a real-world visual perspective of the treadmill overlayed by the IVR environment to ensure safety while walking.

2.4. Equipment

Participants completed both IVR and non-IVR trials on a treadmill (Redliro, New York, NY, USA) while donning a forearm-worn optical heart rate monitor (Rhythm + TM 2.0, Scosche Industries, Oxnard, CA, USA). HR data were collected and graphically displayed using a fitness app (iCardio®, Cupertino, CA, USA) monitored by research administrators.

2.5. Experimental Procedures

Two independent research administrators assessed each participant individually during the 6 trials. A designated study administrator read a standardized set of instructions prior to the start of the first experiment. Participants were instructed that they would be performing three trials, separated by three-minute rest periods, while always walking at 3.5 mph on a treadmill on two different days (six trials total). One day of testing included three trials while wearing the IVR HMD, whereas the second day included three trials sans IVR. After each 10 min session, participants were asked to rate their perceived exertion (RPE) using the Borg RPE Scale, with a score ranging from 6 to 20. Furthermore, individuals’ heart rates were monitored throughout every trial. After completion of the first day of testing, a 24 h washout period was given prior to the subsequent trials.
Deception occurred in several ways, the first of which was the treadmill’s speed. Participants were instructed that all treadmill trials would be completed at 3.5 mph. However, each day of testing included two deceptive speed conditions (3.3 and 3.7 mph) in addition to the ‘true’ 3.5 mph speed condition. The ‘manual’ option in the app menu was chosen so that the HMD did not affect the treadmill speed. A separate research administrator modified the in-app display beforehand so that participants always saw a speed of 3.5 mph regardless of the deceptive pace they were actually reaching. The same research administrator altered the speeds for each participant, whereas the other research administrator was blinded to the speeds.
Participants were also given false information in regard to the IVR trials. In order to ‘deceive’ the participants from the true aim of the study, they were informed that there was a component of the IVR background that was changing in between trials. No further details were provided, and research administrators were not permitted to answer any questions pertaining to the background. After the completion of the second day of testing, participants were asked if they noticed any differences between the first and second days of testing. Responses were recorded and transcribed.

2.6. Statistical Analysis

Descriptive statistics are presented as mean and standard deviation (SD). A 3 × 2 repeated-measures analysis of variance (ANOVA) was utilized: independent variables were treadmill speed (3 levels) and presence of IVF (2 levels), while dependent variables were RPE and HR. Effect sizes of main effects and interactions were measured by partial eta squared (η2). These calculations were followed up by paired t-tests using the Benjamini–Hochberg procedure to maintain the familywise error rate. Effect sizes were measured by Cohen’s d. Statistical significance was determined by α = 0.05 and all tests were two-tailed. All data were exported to IBM SPSS Statistics for Windows, version 22 (IBM Corp., Armonk, NY, USA) for analysis.

3. Results

3.1. Demographics

All sixteen participants (mean age = 20.5 ± 1.2 years old) successfully completed the study. Mean height, weight, and body fat collectively for all participants were 171 ± 14 cm, 69 ± 15 kg, and 19 ± 7.5%, respectively.

3.2. RPE Scores

Although there was no significant difference in RPE between the 3.3 and 3.5 mph trials within the non-IVR group, these scores were lower compared to those associated with the 3.7 mph trials (p = 0.002, 95% CI: (−1.3, −0.7), d = 0.71; p = 0.002, 95% CI: (−1.4, −0.5), d = 0.75, respectively). No significant differences in RPE were observed within the IVR group. During the 3.5 and 3.7 mph trials, the IVR group reported lower RPE scores in comparison to the non-IVR group (p = 0.002, 95% CI: (0.6, 1.8), d = 1.40; p = 0.002, 95% CI (1.2, 2.7), d = 1.88, respectively) (Table 1). Correlational analysis revealed a weak relationship between RPE and HR (r = 0.27, p = 0.008). When partitioned by sex, a moderate correlation was found between RPE and HR in females (r = 0.45, p = 0.001), whereas a weak relationship was identified in males (r = 0.14, p = 0.361).

3.3. Heart Rate

In the non-IVR group, there was a significantly higher HR in the 3.7 mph trials compared to the 3.5 mph trials (p = 0.027, 95% CI: (−6.8, −1.8), d = 0.27). No significant differences in HR were observed within the IVR group. Between groups, only the 3.7 mph trials yielded a lower HR in the IVR group compared to the non-IVR group (p = 0.049, 95% CI: (2.0, 12.7), d = 0.56) (Table 1).

3.4. Post-IVR Session Responses

Through an in-person post-study interview, all but one of the sixteen participants reported “feeling no differences in treadmill speed while wearing the HMD” between trials. However, 75% (12 participants) reported feeling “a different treadmill speed” between trials while not donning the HMD. Moreover, participants commonly reported that “time went by faster” during the IVR trials, which were also deemed to be “more stimulating” and “more enjoyable”.

4. Discussion

IVR exercise has been linked to a variety of benefits. IVR exercise has been associated with reductions in fatigue, stress, and depression, as well as increases in tranquility and quality of life [26]. In addition, the capacity of IVR exergaming to be physically demanding while enjoyable allows it to be an effective and engaging form of exercise [27]. These advantages, when combined with generally high satisfaction and usage scores, have revealed the potential for IVR to vastly improve health and well-being [28]. Despite these advantages, there is a scarcity of studies testing the efficacy of deception via IVR exercise across different modalities. To the best of our knowledge, this study is the first of its kind to examine the effects of IVR in conjunction with treadmill speed deception.
The results of the present study support the first of our hypotheses, as the mean RPE scores corresponding to the IVR trials were significantly lower than those from the non-IVR trials at 3.5 and 3.7 mph (7.4 and 7.6 versus 8.6 and 9.5; p < 0.002, respectively). This finding is consistent with a deceptive study using a training bike conducted by Löchtefeld et al., which found that the manipulation of visual and haptic cues could allow for an increase in the user’s speed by 15.2% without the user’s awareness [29]. Similarly, in regard to resistance training, both a 5% and 10% deceptive increase in loading while leg pressing have been shown to elicit nearly identical strength measures without negatively affecting physical self-efficacy [30]. In contrast to our results, however, Leininger et al. found no difference in RPE between exergaming and matched-intensity treadmill walking [31]. An important distinction between both studies is the use of immersive VR versus two-dimensional, VR-only. Being engaged in an IVR environment may heighten dissociation and/or distract the user from the perceived effort of the exercise.
The second of our hypotheses was partially supported. Only the HR response during the trials at 3.7 mph was lower in the IVR group than the non-IVR group (107 bpm versus 115 bpm; p < 0.049, respectively). Little research has assessed heart rate trends in protocols that integrate deception and exergaming together, but there is a limited amount of each in isolation. In an IVR exergaming study with geriatric participants, mean HR measurements increased for three out of four individuals between two different training sessions [32]. Furthermore, a comparison between four exergames found that 75% of the exergames produced a lower perceived exertion than actual exertion based on their average heart rate as a percentage of their maximum heart rate (66%, 78%, and 55% of maximum heart rate, respectively) [33]. Conversely, in a deceptive treadmill and cycling study, lower heart rate measurements were observed during the condition in which the endpoint was not specified compared to when an endpoint was predetermined [34]. Further research is warranted to evaluate trends in HR during deceptive training in order to provide informed exercise recommendations, such as appropriate training HR zones, for IVR exergaming users.
The findings of the present study may be attributed to several different factors; first, the preservation of teleoanticipation. Previous research has shown that manipulating the endpoint can negatively affect exercise performance by disrupting the anticipatory setting of the pace [35]. Teleoanticipatory pacing strategies are crucial to delay fatigue and optimize performance [36]. The protocol used in this study did not modify the duration of the exercise nor deceive the participants of the endpoint. Hence, participants should have been able to fully complete the exercise without calculated or spontaneous adjustments affecting the use of physiological resources in both non-IVR and IVR trials. It is possible that establishing a workload or endpoint may factor into attenuated RPE and HR.
Furthermore, the differences in RPE and heart rate between the two conditions may have been a result of the IVR intervention. A commonly reported benefit of IVR is the ability to empower one to escape negative emotions, stresses, and affairs [37]. IVR has even been shown to reduce discomfort and minimize pain to a degree comparable to clinical analgesic treatments [38]. However, the environment in which the user is immersed also matters. Simulated urban environments have been reported to produce feelings of increased fatigue and decreased levels of self-esteem, whereas nature-based environments have yielded decreased levels of negative emotions (i.e., fatigue, tension, depression, etc.) [39]. In the present study, the selected environment incorporated several different natural elements (e.g., waterfalls, rocks, beaches, unique cloud formations, etc.) into view. It is possible that this environment allowed participants to divert their attention away from daily stressors and the exercise they were performing. Consequently, as we showed, a faster deceptive speed would not be noticed by the user or reflected by their HR response (i.e., autonomic nervous system). Rutkowski et al. recently demonstrated a similar phenomenon through heart rate variability analysis wherein lower heart rate responses during IVR were attributed to reductions in stress levels [40]. They noted that the “calming environment” altered autonomic nervous system activity, which was responsible for the observed heart rate behavior with virtual reality immersion.
The implications of these results should serve as a guide for IVR exergaming as it continues to evolve. The trend of lower HR and RPE scores corresponding to exercising with IVR may suggest that it has the potential to lower the perceived difficulty of exercise, which may be particularly beneficial for sedentary individuals. Furthermore, the lower HR and RPE scores produced in the 3.7 mph trials may suggest that IVR may have utility as a ‘deceptive’ exercise intervention. None of the participants reported a conscious awareness of the 5.7% increase in speed, nor was it reflected in their RPE. These results suggest that participants may perform exercise at a greater intensity while using IVR. Further research is warranted to determine if prolonged IVR training in different simulated environments can yield similar results. Moreover, a threshold for how much intensity (i.e., speed) can be increased before a participant notices should also be studied.
This study is not without its limitations. This study could have benefited from a larger sample size to add statistical power. To this end, the sample was almost entirely composed of fit, college-aged individuals. A more diverse sample would have been ideal to improve generalizability. Additionally, due to the design of the protocol, the findings of the present study may be limited to short-term effects of IVR. Future research should be conducted to examine if these effects would be consistent over a prolonged period of time and/or in response to repeated exercise. Notwithstanding, several other confounding variables may have affected the results of the present study and should be controlled in future studies. Given that walking styles and centers of gravity may vary across individuals, standardized walking patterns should be established in future studies to ensure that biomechanical loads are similar. Furthermore, since the Borg RPE score is inherently a subjective measurement, it is not without its limitations and may be subject to a nonnegligible degree of variability. Alternative measures of perceived exertion should also be considered in future research.

5. Conclusions

The findings of this study suggest that using deception via an immersive virtual reality exergaming experience may attenuate participant perceived effort and heart rate response without the user noticing the effect.

Author Contributions

Conceptualization, T.Y., T.K.S., C.A.M. and B.A.D.; methodology, T.Y., T.K.S., C.A.M. and B.A.D.; formal analysis, T.Y., T.K.S., C.A.M., M.S.M., E.V.N. and B.A.D.; data curation, T.Y., T.K.S., C.A.M., T.L.N., A.E.B., R.J.L., T.H.N., D.M.B. and J.P.F.; writing—original draft preparation, T.Y., T.K.S., C.A.M., M.S.M., E.V.N. and B.A.D.; writing—review and editing, T.Y., T.K.S., C.A.M., A.E.B., R.J.L., T.H.N., D.M.B., J.P.F., M.S.M., E.V.N. and B.A.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of UCLA (protocol code: 11-003190, 22 April 2020).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Acknowledgments

We are appreciative of the willingness of devoted participants to give so generously their time and effort to support this work.

Conflicts of Interest

The authors declare no conflicts of interest. Octonic had no role in the study design, data collection, analysis, interpretation, or writing of the manuscript.

References

  1. Wittekind, A.L.; Micklewright, D.; Beneke, R. Teleoanticipation in all-out short-duration cycling. Br. J. Sports Med. 2011, 45, 114–119. [Google Scholar] [CrossRef] [PubMed]
  2. Edwards, A.M.; Bentley, M.B.; Mann, M.E.; Seaholme, T.S. Self-pacing in interval training: A teleoanticipatory approach. Psychophysiology 2011, 48, 136–141. [Google Scholar] [CrossRef] [PubMed]
  3. Lambert, E.V.; St Clair Gibson, A.; Noakes, T.D. Complex systems model of fatigue: Integrative homoeostatic control of peripheral physiological systems during exercise in humans. Br. J. Sports Med. 2005, 39, 52–62. [Google Scholar] [CrossRef] [PubMed]
  4. Baden, D.A.; McLean, T.L.; Tucker, R.; Noakes, T.D.; St Clair Gibson, A. Effect of anticipation during unknown or unexpected exercise duration on rating of perceived exertion, affect, and physiological function. Br. J. Sports Med. 2005, 39, 742–746. [Google Scholar] [CrossRef] [PubMed]
  5. Coquart, J.B.; Garcin, M. Knowledge of the endpoint: Effect on perceptual values. Int. J. Sports Med. 2008, 29, 976–979. [Google Scholar] [CrossRef] [PubMed]
  6. Tucker, R. The anticipatory regulation of performance: The physiological basis for pacing strategies and the development of a perception-based model for exercise performance. Br. J. Sports Med. 2009, 43, 392–400. [Google Scholar] [CrossRef]
  7. Day, M.L.; Mcguigan, M.R.; Brice, G.; Foster, C. Monitoring Exercise Intensity During Resistance Training Using the Session RPE Scale. J. Strength. Cond. Res. 2004, 18, 353–358. [Google Scholar] [CrossRef] [PubMed]
  8. Williams, N. The Borg Rating of Perceived Exertion (RPE) scale. Occup. Med. 2017, 67, 404–405. [Google Scholar] [CrossRef]
  9. Glass, S.C.; Knowlton, R.G.; Becque, M.D. Accuracy of RPE from graded exercise to establish exercise training intensity. Med. Sci. Sports Exerc. 1992, 24, 1303–1307. [Google Scholar] [CrossRef] [PubMed]
  10. Lagally, K.M.; Robertson, R.J.; Gallagher, K.I.; Gearhart, R.; Goss, F.L. Ratings of perceived exertion during low- and high-intensity resistance exercise by young adults. Percept. Mot. Ski. 2002, 94, 723–731. [Google Scholar] [CrossRef]
  11. Ceci, R.; Hassmen, P. Self-monitored exercise at three different RPE intensities in treadmill vs field running. Med. Sci. Sports Exerc. 1991, 23, 732–738. [Google Scholar] [CrossRef] [PubMed]
  12. Corbett, J.; Barwood, M.J.; Ouzounoglou, A.; Thelwell, R.; Dicks, M. Influence of Competition on Performance and Pacing during Cycling Exercise. Med. Sci. Sports Exerc. 2012, 44, 509–515. [Google Scholar] [CrossRef]
  13. Williamson, J.W.; McColl, R.; Mathews, D.; Mitchell, J.H.; Raven, P.B.; Morgan, W.P. Hypnotic manipulation of effort sense during dynamic exercise: Cardiovascular responses and brain activation. J. Appl. Physiol. 2001, 90, 1392–1399. [Google Scholar] [CrossRef] [PubMed]
  14. Finnegan, S.L.; Dearlove, D.J.; Morris, P.; Freeman, D.; Sergeant, M.; Taylor, S.; Pattinson, K.T.S. Breathlessness in a virtual world: An experimental paradigm testing how discrepancy between VR visual gradients and pedal resistance during stationary cycling affects breathlessness perception. PLoS ONE 2023, 18, e0270721. [Google Scholar] [CrossRef] [PubMed]
  15. Jones, H.S.; Williams, E.L.; Bridge, C.A.; Marchant, D.; Midgley, A.W.; Micklewright, D.; McNaughton, L.R. Physiological and Psychological Effects of Deception on Pacing Strategy and Performance: A Review. Sports Med. 2013, 43, 1243–1257. [Google Scholar] [CrossRef] [PubMed]
  16. Pires, F.O.; Hammond, J. Manipulation effects of prior exercise intensity feedback by the Borg scale during open-loop cycling. Br. J. Sports Med. 2012, 46, 18–22. [Google Scholar] [CrossRef] [PubMed]
  17. Hampson, D.B.; St Clair Gibson, A.; Lambert, M.I.; Dugas, J.P.; Lambert, E.V.; Noakes, T.D. Deception and Perceived Exertion during High-Intensity Running Bouts. Percept. Mot. Ski. 2004, 98, 1027–1038. [Google Scholar] [CrossRef] [PubMed]
  18. Han, D.I.D.; Bergs, Y.; Moorhouse, N. Virtual reality consumer experience escapes: Preparing for the metaverse. Virtual Real. 2022, 26, 1443–1458. [Google Scholar] [CrossRef]
  19. Slater, M.; Sanchez-Vives, M.V. Enhancing Our Lives with Immersive Virtual Reality. Front. Robot. AI 2016, 3, 74. [Google Scholar] [CrossRef]
  20. North, M.M.; North, S.M. A Comparative Study of Sense of Presence of Traditional Virtual Reality and Immersive Environments. Australas. J. Inf. Syst. 2016, 20. [Google Scholar] [CrossRef]
  21. Slater, M.; Pérez Marcos, D.; Ehrsson, H.; Sanchez-Vives, M.V. Inducing illusory ownership of a virtual body. Front. Neurosci. 2009, 3, 214–220. [Google Scholar] [CrossRef] [PubMed]
  22. Zang, N.; Pope, Z.; Gao, Z. Acute Effect of Virtual Reality Exercise Bike Games on College Students’ Physiological and Psychological Outcomes. Cyberpsychology Behav. Soc. Netw. 2017, 20, 453–457. [Google Scholar] [CrossRef] [PubMed]
  23. Mestre, D.R.; Ewald, M.; Maiano, C. Virtual reality and exercise: Behavioral and psychological effects of visual feedback. Annu. Rev. Cybertherapy Telemed. 2011, 2011, 122–127. [Google Scholar]
  24. Dolezal, B.A.; Lau, M.J.; Abrazado, M.; Storer, T.W.; Cooper, C.B. Validity of two commercial grade bioelectrical impedance analyzers for measurement of body fat percentage. J. Exerc. Physiol. Online 2013, 16, 74. [Google Scholar]
  25. Heyward, V. ASEP methods recommendation: Body composition assessment. J. Exerc. Physiol. 2001, 4, 1–12. [Google Scholar]
  26. Qian, J.; McDonough, D.J.; Gao, Z. The Effectiveness of Virtual Reality Exercise on Individual’s Physiological, Psychological and Rehabilitative Outcomes: A Systematic Review. Int. J. Environ. Res. Public Health 2020, 17, 4133. [Google Scholar] [CrossRef] [PubMed]
  27. Yoo, S.; Kay, J. VRun: Running-in-place virtual reality exergame. In Proceedings of the 28th Australian Conference on Computer-Human Interaction, Launceston, Tasmania, Australia, 29 November–2 December 2016; Association for Computing Machinery: New York, NY, USA, 2016; pp. 562–566. [Google Scholar] [CrossRef]
  28. Xu, W.; Liang, H.N.; Baghaei, N.; Ma, X.; Yu, K.; Meng, X.; Wen, S. Effects of an Immersive Virtual Reality Exergame on University Students’ Anxiety, Depression, and Perceived Stress: Pilot Feasibility and Usability Study. JMIR Serious Games 2021, 9, e29330. [Google Scholar] [CrossRef] [PubMed]
  29. Löchtefeld, M.; Krüger, A.; Gellersen, H. DeceptiBike: Assessing the Perception of Speed Deception in a Virtual Reality Training Bike System. In Proceedings of the 9th Nordic Conference on Human-Computer Interaction, Gothenburg, Sweden, 23–27 October 2016; Association for Computing Machinery: New York, NY, USA, 2016; p. 40. [Google Scholar] [CrossRef]
  30. Piper, T.; Radlo, S.; Gerhardt, K.; Brooks, D.; Schnaiter, J. The Effects of Deception on Maximal Strength, Goals, and Physical Self-Efficacy. Int. J. Strength Cond. 2023, 3. [Google Scholar] [CrossRef]
  31. Leininger, L.; Coles, M.G.; Gilbert, J.N. Comparing enjoyment and perceived exertion between equivalent bouts of physically interactive video gaming and treadmill walking. Health Fit. J. 2010, 3, 12–18. [Google Scholar] [CrossRef]
  32. Campo-Prieto, P.; Rodríguez-Fuentes, G.; Cancela-Carral, J.M. Immersive Virtual Reality Exergame Promotes the Practice of Physical Activity in Older People: An Opportunity during COVID-19. Multimodal Technol. Interact. 2021, 5, 52. [Google Scholar] [CrossRef]
  33. Yoo, S.; Ackad, C.; Heywood, T.; Kay, J. Evaluating the Actual and Perceived Exertion Provided by Virtual Reality Games. In Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems, Denver, CO, USA, 6–11 May 2017; Association for Computing Machinery: New York, NY, USA, 2017; pp. 3050–3057. [Google Scholar] [CrossRef]
  34. Eston, R.; Stansfield, R.; Westoby, P.; Parfitt, G. Effect of deception and expected exercise duration on psychological and physiological variables during treadmill running and cycling. Psychophysiology 2012, 49, 462–469. [Google Scholar] [CrossRef] [PubMed]
  35. Ansley, L.; Robson, P.J.; St Clair Gibson, A.; Noakes, T.D. Anticipatory pacing strategies during supramaximal exercise lasting longer than 30 s. Med. Sci. Sports Exerc. 2004, 36, 309–314. [Google Scholar] [CrossRef] [PubMed]
  36. Stone, M.R.; Thomas, K.; Wilkinson, M.; Jones, A.M.; St Clair Gibson, A.; Thompson, K.G. Effects of deception on exercise performance: Implications for determinants of fatigue in humans. Med. Sci. Sports Exerc. 2012, 44, 534–541. [Google Scholar] [CrossRef] [PubMed]
  37. Melancon, J. Consumer profiles in reality vs fantasy-based virtual worlds: Implications for brand entry. J. Res. Interact. Mark. 2011, 5, 298–312. [Google Scholar] [CrossRef]
  38. Gromala, D.; Tong, X.; Choo, A.; Karamnejad, M.; Shaw, C.D. The Virtual Meditative Walk: Virtual Reality Therapy for Chronic Pain Management. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, Seoul, Republic of Korea, 18–23 April 2015; Association for Computing Machinery: New York, NY, USA, 2015; pp. 521–524. [Google Scholar]
  39. Yu, C.-P.; Lee, H.-Y.; Luo, X.-Y. The effect of virtual reality forest and urban environments on physiological and psychological responses. Urban For. Urban Green 2018, 35, 106–114. [Google Scholar] [CrossRef]
  40. Rutkowski, S.; Szary, P.; Sacha, J.; Casaburi, R. Immersive Virtual Reality Influences Physiologic Responses to Submaximal Exercise: A Randomized, Crossover Trial. Front. Physiol. 2021, 12, 702266. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Snapshot view of Sky Island (Octonic VR app) environment from the head-mounted display (HMD) during the immersive virtual reality intervention while walking on a treadmill.
Figure 1. Snapshot view of Sky Island (Octonic VR app) environment from the head-mounted display (HMD) during the immersive virtual reality intervention while walking on a treadmill.
Applsci 14 06072 g001
Figure 2. (a) Experimental setup during the IVR intervention mirroring participant’s HMD to a display for researchers; (b) perspective from the participant’s HMD showing the treadmill speed settings and overlays in the IVR environment.
Figure 2. (a) Experimental setup during the IVR intervention mirroring participant’s HMD to a display for researchers; (b) perspective from the participant’s HMD showing the treadmill speed settings and overlays in the IVR environment.
Applsci 14 06072 g002
Table 1. Mean heart rate and Borg RPE scores corresponding to deceptive treadmill speed in the non-immersive virtual reality (IVR) and IVR trials.
Table 1. Mean heart rate and Borg RPE scores corresponding to deceptive treadmill speed in the non-immersive virtual reality (IVR) and IVR trials.
Non-IVR IVR
Deceptive
Treadmill Speed (mph)
3.3 3.5 3.7 3.3 3.5 3.7
HR (±SD) (bpm) 111 (13) 110 (16) 115 (16) 107 (14) 105 (10) 107 (10)
Borg RPE (±SD) 8.5 (1.5) 8.6 (1.1) 9.0 (1.3) 7.5 (0.5) 7.4 (0.5) 7.6 (0.6)
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Yamamoto, T.; Sahni, T.K.; McCabe, C.A.; Nguyen, T.L.; Blatney, A.E.; Lechner, R.J.; Nguyen, T.H.; Benna, D.M.; Farrales, J.P.; Mologne, M.S.; et al. Deceptive Modulation of Actual and Perceived Effort While Walking Using Immersive Virtual Reality: A Teleoanticipatory Approach. Appl. Sci. 2024, 14, 6072. https://doi.org/10.3390/app14146072

AMA Style

Yamamoto T, Sahni TK, McCabe CA, Nguyen TL, Blatney AE, Lechner RJ, Nguyen TH, Benna DM, Farrales JP, Mologne MS, et al. Deceptive Modulation of Actual and Perceived Effort While Walking Using Immersive Virtual Reality: A Teleoanticipatory Approach. Applied Sciences. 2024; 14(14):6072. https://doi.org/10.3390/app14146072

Chicago/Turabian Style

Yamamoto, Trent, Trinabh K. Sahni, Corinne A. McCabe, Trevor L. Nguyen, August E. Blatney, Ross J. Lechner, Thalia H. Nguyen, Dominic M. Benna, Jason P. Farrales, Mitchell S. Mologne, and et al. 2024. "Deceptive Modulation of Actual and Perceived Effort While Walking Using Immersive Virtual Reality: A Teleoanticipatory Approach" Applied Sciences 14, no. 14: 6072. https://doi.org/10.3390/app14146072

APA Style

Yamamoto, T., Sahni, T. K., McCabe, C. A., Nguyen, T. L., Blatney, A. E., Lechner, R. J., Nguyen, T. H., Benna, D. M., Farrales, J. P., Mologne, M. S., Neufeld, E. V., & Dolezal, B. A. (2024). Deceptive Modulation of Actual and Perceived Effort While Walking Using Immersive Virtual Reality: A Teleoanticipatory Approach. Applied Sciences, 14(14), 6072. https://doi.org/10.3390/app14146072

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop