AnReal: Towards an extended reality, low-cost system for kinesiophobia

doi:10.21203/rs.3.rs-4697453/v1

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Research Article

AnReal: Towards an extended reality, low-cost system for kinesiophobia

https://doi.org/10.21203/rs.3.rs-4697453/v1

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Background

Virtual reality (VR) has been successfully used as part of treatment for patients with chronic low back pain and kinesiophobia. However, the cost of VR systems is still too high to be used in interventions in low-income countries, and systems may produce adverse effects on patients, such as cybersickness and reduced sense of presence.

Objective

This study presents a novel mobile-based extended reality (XR) system that combines views of the real world with a simulated video trajectory that aims to be affordable, provide a good user experience, and provide the essential elements to treat kinesiophobia.

Methods

This paper presents the concept of the system, which is the first system aimed at reducing kinesiophobia that uses XR, as well as its development process and an initial evaluation in physical therapy students.

Results

The system was found to provide an acceptable user experience, but high cybersickness values as indicated by a score of 31.52 on the SSQ questionnaire.

Conclusions

This research proposes and implements a novel, low-cost alternative for treating kinesiophobia related to chronic pain, representing the first system for these purposes that uses extended reality instead of virtual reality.¹

Extended Reality

Kinesiophobia

Low-cost

Cybersickness

Mobile development

Pain is considered chronic if it has a duration of more than 3 months and has passed the expected tissue recovery time barrier (Treede et al. 2015). Epidemiological studies show that between 10.1% and 55.2% of the population have chronic pain (Ospina et al. 2002). Chronic pain has a wide range of implications, such as depression, anxiety, sleep disturbances (Kato et al. 2017), a decrease in quality of life (Hadi et al. 2019), and high costs (Andrew et al. 2014). The lower back is the most common source of pain; this type of pain is referred to as low back pain (LBP) (Johannes et al. 2010), and chronic low back pain (CLBP) when it becomes a chronic condition. CLBP patients may develop kinesiophobia (Picavet et al. 2002), an excessive and irrational fear related to movement (Baykal et al. 2021). One study found the incidence of kinesiophobia in LBP patients to be 57.3% (Felício et al. 2021).

The treatment of CBLP in patients with kinesiophobia is a challenge, as rehabilitation is hindered by the avoidance of performing the prescribed therapeutic exercises (Chen et al. 2017). In recent years, researchers have begun to use Virtual Reality (VR) as a rehabilitation tool for patients (Kizony et al. 2003). The use of VR demonstrated significant benefits, proving to be effective both for chronic pain (Spiegel et al. 2019; Matamala-Gomez et al. 2019; Malloy et al. 2010; Mallari et al. 2019) and kinesiophobia (Wang et al. 2023). This success can be attributed to various factors such as distraction (Garret et al. 2017; Garret et al. 2018), along with other factors such as somatosensory input manipulation (Riva et al. 2017), graded exposure (Thomas et al. 2016) and motivation (Sveistrup et al. 2004). Distraction, in particular, can cause down-regulation of nociceptive signaling (Triberti et al. 2014) due to a shift in attention and concentration, as human attention is limited and VR demands a large proportion of it (Arane et al. 2017).

VR systems for kinesiophobia can be categorized as non-immersive (e.g. Nintendo Wii), semi-immersive, and fully immersive (e.g. Oculus Rift), depending on how isolated the user is from the physical environment (Wang et al. 2023). In immersive VR systems, the user is involved and able to interact with a completely virtual world (Milgram et al. 1994). However, not all systems are completely aligned with this definition, but rather somewhere along a reality-virtuality continuum, which includes augmented reality, augmented virtuality (Milgram et al. 1994), and mixed reality systems, those in which a user perceives real and virtual objects across different senses (Skarbez et al. 2021). Extended reality (XR) is a broad term that is used to describe all types of immersive experiences that combine aspects of physical and virtual environments (Bordeleau et al. 2022), showing great potential for various applications. Recently, a systematic search of XR systems in patients with back pain found that all the included studies were more aligned with the VR side of the continuum (Bordeleau et al. 2022).

Even though VR systems have shown effectiveness in the treatment of kinesiophobia and CLBP, they may cause cybersickness (CS) (Luque-Moreno et al. 2015) and reduced sense of presence (SoP). Within virtual reality, SoP is the subjective feeling of being in a virtual place (Slater et al. 2018). Cybersickness is a constellation of symptoms of discomfort caused by VR, which patients may experience during therapy. In one study, 60% of participants experienced CS (Garret et al. 2018). The relationship between presence and cybersickness is complex and related to sensory mismatch, but increasing interaction intuitiveness may lower CS and increase SoP (Weech et al. 2019).

While fully immersive VR has been found to negatively affect some users due to cybersickness (Wang et al. 2023), non-immersive VR has been found to produce positive results and be well accepted by patients. However, one study with a fully immersive system consisting of an Oculus Rift and touch controllers, using three existing applications, found that participants found it acceptable, tolerable and beneficial (Kelly et al. 2022).

Most previous research has used commercial and readily available hardware and applications (e.g. Oculus, Nintendo Wii) or more specialized solutions (e.g. ProKin systems), ranging in cost from several hundred dollars to tens of thousands of dollars, for each system. However, these costs can be prohibitive for low-income countries, and the realities of rehabilitation using VR in marginalized populations has been seldom studied (Dy et al. 2023). Furthermore, these programs do not necessarily fit the needs of the patients as they are generic.

Prior Work

The impact of virtual reality on chronic pain management was analyzed through a review of previous work (Goudman et al. 2022). Among the evaluated studies, 56% employed immersive VR techniques, while 44% opted for non-immersive techniques. The study revealed statistically significant pain relief with the use of VR technology on rehabilitation (Goudman et al. 2022).

Also, some studies focus on the effect of kinesiophobia. A systematic review found several studies have used VR for patients with LBP, using Nintendo Wii sport games, head-mounted displays with an iPod and video glasses that showed a video of a walk, and a horse simulator machine with a video display (Ahern et al. 2020). These interventions found VR improved pain and fear of movement when compared to conventional physical therapy (Ahern et al. 2020). Another systematic review found three studies of patients with CLBP and kinesiophobia, finding significant improvement in patients who used VR when compared to a no-VR treatment (Brea-Gómez et al. 2021). Two of these studies used the Prokin System, with a balance training and shooting game, and one used a Nintendo Switch (Brea-Gómez et al. 2021).

While most studies focus on distracting patients from the source of pain, a few publications take an alternative path through visual illusions. The work of (Chen et al. 2017) examines individuals with and without chronic neck pain who perform VR exercises under the influence of proprioceptive mismatch. The simulation is based on a VR game where the range of motion of the person’s neck is artificially decreased in what is seen in the simulation. The proprioceptive mismatch allowed an increased neck range of motion. However, not all participants benefited similarly, concluding that VR could eventually help with the ultimate goal. Also, recent work by (Trujillo et al. 2020) focused on virtual embodiment, where feedback from the person’s movement is directly reflected in the movement of the virtual avatar. Therefore, modifications can be made to the avatar’s motion, such as exaggerating a particular motion and thus altering the motion feedback of the person itself. The results show an improvement in pain perception, evidencing that the augmentation of movement through virtual embodiment could be helpful in patients with chronic pain. However, the study is only performed with two patients with CLBP and no control group, underscoring the need for further studies.

Study Aims

This paper proposes a new type of system for kinesiophobia treatment, that aims to encourage users to decrease their avoidance of movement and kinesiophobia by exposing them to a view of a simulated movement that looks and feels real. The main goals of this research are to evaluate a technology that (1) provides a good user experience (UX) while minimizing cybersickness and (2) it is implemented at low cost, with widely available hardware, able to be broadly implemented in a low-income country. This low-cost extended reality system is situated closer to the real environment side of the reality-virtuality spectrum, which we believe can help achieve good usability and higher SoP. We present the concept of the system as well as an initial evaluation with healthy students, aimed at measuring usability aspects, as well as SoP and CS variables.

AnReal: an Android-based Extended Reality System

Main Features and Interaction Design

The proposed system, which we call AnReal, is an Android mobile application placed in a simple VR headset (approximately 32 USD). The application allows users to conduct one classic rehabilitation exercise, but the same concept can be applied to any other exercise that requires users to move their body in a certain expected way. This exercise is based on bending forward, which is one of the most common problems in patients with chronic low back pain (Osumi et al. 2019).

The system has two modes: camera preview and trajectory simulation. In the camera preview mode, the user observes the real environment, in its current state, through the headset. In the trajectory simulation mode, the trajectory is simulated by means of a pre-recorded video that simulates the expected trajectory of the user while carrying out the rehabilitation exercise (Fig. 1). The video is recorded on the same phone, but a WiFi connection is established with a computer so that the video is processed on the computer and can be accessed instantly through the phone. The system automatically switches between the camera preview mode and the trajectory simulation mode at a predetermined angle of movement, so that the user stops moving but continues to see as if he/she were still doing so, creating an illusion of movement.

In this situation, healthy participants are asked to stop at a certain angle to test the functionality of the trajectory continuation. In a later study, this transition will be automatic once the person with low back pain can no longer bend down.

Therefore, the participant interacts with the system by carrying out the rehabilitation exercise while wearing the headset, and the headset displays either the real environment through the camera lens, or a video of a simulated trajectory that continues the user’s movement. The idea behind this interaction is that, in a future study, a patient with pain and kinesiophobia would start to lose their fear of movement by watching themselves carry out movement trajectories beyond their current capabilities, as limited by their condition. In other words, utilizing a visual-vestibular mismatch, the aim is to make the person believe that they are finishing the expected movement completely without pain, therefore decreasing the fear of movement.

System Development

The Android mobile application was developed using a Samsung S21 FE phone. Figure 2 illustrates the software architecture, which can be segmented into three layers: (1) the application framework layer, (2) the runtime and libraries layer, and (3) the kernel layer (Yan et al. 2016). The application was developed using the React Native (RN) framework and a native module in Android Studio. Each component requires different libraries and abstraction levels to interact with the hardware abstraction layer and thus communicate with various sensors.

The native Java module establishes communication with the device's rear camera stream through the Camera2 API. The CaptureRequestBuilder class within this API allows duplicating the stream to segments of the screen, thus being able to segment the screen into 2 identical streams at a 30 Hz refresh. The accelerometer, gyroscope and magnetometer sensors were used to know the dynamic inclination of the headset through the SensorManager API on the native side. The RN framework displays the previously recorded video employing the react-native-video library and the expo-sensors library for the dynamic inclination. The Javascript code communicates with the native modules through the React Native Bridge, which allows access to features that are not available in Javascript (Native Modules, 2023).

The intention is to alternate, in a smooth and non-perceptible way, the transition of the view between the modules once a previously determined angle is reached. The cycle of each repetition of the exercise begins with the camera preview from the Android native module. Each time the person bends forward, there is a probability that the trajectory is simulated (i.e. is switched to the RN module showing the video), thus ending the activity in the native module. If so, the video is played until it reaches the same point from where it started and returns to the native module completing the cycle. The functions in charge of transitioning between modules sample the person's tilt at a frequency at least twice the refresh rate of the headset.

The goal is that once the person reaches this mark, the application simulates the trajectory artificially, creating the illusion that continue to lean until he/she see their own feet, even though they remain static.

Study Design

We evaluated the AnReal prototype in a laboratory setting in a classroom at the kinesiology department of Pontificia Universidad Católica de Chile, with artificial light and no moving elements in the field of view (e.g. people walking by). We defined a spot where the subjects would perform the exercise and marked a line on the wall indicating how far to bend down. This section describes the evaluation of the system.

Recruitment

This study used a convenience sample of healthy kinesiology students at Pontificia Universidad Católica de Chile in Santiago, Chile, through an open invitation sent through email. Therefore, the exclusion criteria was that they did not have any pathology that could be affected by the use of a headset, as well as no low back pain. This study was approved by the University Ethics Committee.

Measurements

We used four questionnaires to measure user experience, cybersickness, sense of presence, and immersive tendencies. All of the questionnaires were translated into Spanish by the researchers, except for the User Experience Questionnaire (UEQ), in which we used the official version in Spanish.

To capture the user experience, we used the Spanish version of the UEQ (Somrak et al. 2019). This consists of 26 items on a 7-point scale, which are summarized into six categories: attractiveness, perspicuity, efficiency, dependability, stimulation and novelty.

To measure cybersickness, we used the most commonly used questionnaire (Weech et al. 2019), the Simulator Sickness Questionnaire (SSQ) (Kennedy 1993). This questionnaire has 16 items on a 4-point scale. These elements can be summarized in subscales such as oculomotor discomfort, disorientation, and nausea. The total score represents the overall severity of the CS symptoms

To measure sense of presence, the Presence Questionnaire (Witmer et al. 1998) was used. This is the most widely used questionnaire in this category (Rhiu et al. 2020). We removed the components measuring sound and haptics, resulting in a reduced version of 18 items on a 7-point scale. From these 18 items, several subscales (e.g. realism, possibility to act, interface quality, possibility to examine and self-evaluation performance) can be obtained.

We also used the Immersive Tendencies Questionnaire (Witmer et al. 1998) to index an individual's likelihood of feeling immersed in virtual settings. It consists of 18 items on a 7-point scale. The subscales show the level of involvement, attentional focus and the tendency to play video games.

Finally, we asked two open questions: (1) “How did you feel wearing the headset?”, (2), “Did you feel you could move more than you actually did?”. After these questions, we explained the goal of the study to the participants, and then asked two final open questions: (3) “Do you think the goal was achieved?”, (4) “Do you have recommendations for improvements in the system?”.

Procedure

First, we recorded a video that simulated the trajectory and processed it so it would be ready for the participant. Then, the participant was asked to fill out the informed consent, basic demographic and activity level information and the Immersive Tendencies Questionnaire. We taught the participant the exercise they would be doing throughout the evaluation: they had to lean forward using their spine, and stop when they reached the mark on the wall (positioned so that the person would reach roughly 30 degrees of inclination), keeping their feet stretched and the head aligned with the trunk of the body (Fig. 1).

We then asked the participant to wear the headset so they could get used to seeing around them with the headset on. The participant then performed three sets of 10 repetitions of the aforementioned exercise. During the first set, the participant saw through the headset with the camera preview mode (real environment). The second set continued the participant’s trajectory through the video trajectory simulation at some of the repetitions according to a probabilistic function; as the participant went through the repetitions, there was a higher probability that the video will appear, thus achieving a surprise factor. The surprise factor is that people expect their vision to match their body position. But unexpectedly, their visual path continues on its own. This is a key attribute of the system because it helps to test the cybersickness parameters and the illusion of movement in healthy individuals. Additionally, in the future, the aim is to make the person believe that they are finishing the expected movement completely without pain, therefore decreasing the fear of movement.

Finally, in the third set, the trajectory was simulated in all repetitions. After the participants finished all of the exercises, they were asked to fill the remaining questionnaires, namely UEQ, SSQ and Presence Questionnaire. Finally, each participant was asked the four final open questions. This brief interview was audio recorded.

Participants

A total of 7 participants were enrolled in this evaluation; 3 men and 4 women (median age = 21, max: 24, min: 21). All of them were physical therapy students at the same university where the study took place. 54% of the participants had a moderate level of physical activity, 14.3% had a low level, and 28.6% did not do any physical activity at all.

Quantitative Data Analysis

The results of the questionnaires are shown in Table 1, along with the mean and standard deviation of the most relevant components of each one. The correlations between the most relevant variables are shown in Table 2.

The UEQ results are in the following categories: novelty is excellent, perspicuity is good, attractiveness and stimulation are above average, efficiency is below average and dependability is bad. The UEQ results point to high agreement in the perspicuity, efficiency, novelty and dependability scales, and medium agreement on stimulation and attractiveness (Handbook 2023).

SSQ values would optimally be zero, although the possibility of a zero-value in CS is considered an erroneous assumption (Brown et al. 2022). A categorization of SSQ scores states that scores between 10 and 15 are significant feelings of CS, between 15 and 20 are worrisome and above 20 indicate a problem in the display (Kennedy et al. 2003). Our total result is 31.52, so the level of cybersickness could be extremely high.

The results were analyzed using Spearman’s correlation to understand relationships between them. No relationship was found between immersive tendencies and the rest of the variables. The results show a statistically significant positive correlation between UX and SoP (P = .02), as well as a statistically significant negative correlation between UX and CS (P < .001). Finally, a moderately strong negative correlation is shown between CS and SoP (P = .06).

Quantitative

Table 1

Results for Sense of Presence, VR Sickness, User Experience and Immersive tendencies.
Questionnaire (range)		Mean	SD
User Experience Questionnaire
	Attractiveness (-3,3)	1.33	0.85
	Perspicuity (-3,3)	2.00	0.65
	Efficiency (-3,3)	1.00	0.38
	Dependability (-3,3)	0.54	0.65
	Stimulation (-3,3)	1.18	1.01
	Novelty (-3,3)	1.61	0.65
Simulator Sickness Questionnaire
	Nausea (0, 200.34)	8.17	9.44
	Oculomotor (0,181.92)	25.98	14.52
	Disorientation (0,200.34)	39.52	27.22
	Total (Range: 0–246.84)	31.52	20.58
Immersive Tendencies Questionnaire
	Focus (0,49)	31.33	1.97
	Involvement (0,49)	21.11	5.22
	Games (0,14)	3.67	2.25
	Total (Range: 0–112)	64.3	7.61
Presence Questionnaire
	Realism (0,49)	33.1	7.15
	Possibility to act (0,28)	21.7	2.60
	Quality of interface (0,21)	15.77	3.05
	Possibility to examine (0,21)	15.6	2.44
	Self-evaluation performance (0,14)	12.1	1.55
	Total (Range: 0–133)	98.28	14.96

Table 2

Spearman’s correlations between all questionnaires.
Variable		Sense of Presence	Immersion Propensity	User Experience	Cybersickness
Sense of Presence
	r	1	-0.25	0.82^a	-0.72
	P value	—^b	.58	.02	.06
Immersion propensity
	r	-0.25	1	-0.50	0.35
	P value	.58	—	.24	.43
User Experience
	r	0.82^a	-0.50	1	-0.95^a
	P value	.02	.24	—	.0008
Cybersickness
	r	-0.72	0.35	-0.95^a	1
	P value	.06	.43	.0008	—
^aThe correlation is significant at a significance level of .05
^bNot applicable
Qualitative Data Analysis

First, the interviews were transcribed by one of the researchers. Then, one researcher coded the interviews using the following method: each interview was read and coded, and then grouped by theme. Then, subcategories were created within each theme. Here, we discuss those subcategories that had a higher frequency of mentions to give an overview of the main results.

The main themes were related to their experience using the system and how they felt about the simulated trajectory of their movement. From these themes, we defined several subcategories, for example, the adaptability of the system, improvements to be made, among others.

The first few times of the second set, when the video appeared simulating the extension of the movement while the person remained static in place, there was a surprise factor that generated different reactions in people. Among these was the surprising feeling that the trajectory had been completed "It looked as if I had continued to descend"(Participant #7). This generated a discordance that for some was positive:

"I find it's good because you get to a certain part and then the video begins and it's striking to see as if you lowered your vision to the ground"... "When you go down it's like Oh! It's funny" [Participant #1]

In turn, this visual mismatch was an influential factor in the participants that reported feeling dizzy or confused:

"I went down to a certain point and one expects, obviously, that the vision stays there, but the vision keeps going down while you are still. So that eye-motion miscoordination made me a little bit dizzy" [Participant #3]

…"I was losing my proprioception, so to speak, of the movement. I didn't really know what range I was moving in, come to think of it." [Participant #2]

We found users gradually adapted to the transition between the camera preview and the simulated trajectory, and also to the general use of the headset, which meant an improvement in comfort and a decrease in negative factors:

"At first a little dizzy, but then I adapted easily" [Participant #5]

…" once I adjusted it and started looking more (...) it felt a little more normal". [Participant #2

There were some participants who took a little longer to adapt:

"At first it took me a couple of seconds to get used to the reaction time between me turning my head and actually seeing the camera turn as well. But other than that it was no problem." [Participant #7]

While some negative effects associated with the beginning of the sessions are mentioned, they did not feel a lack of safety or risk of falling.

Among the most frequently mentioned possible improvements was the smoothing of the transition between the camera view and the video. At the moment of the transition between when the video appeared and disappeared, there was a fraction of a second in which the screen was black, which was an obvious indicator that there was a change in the exercise mode. Also, there was a request to enlarge the field of view.

Comparative discussion of results

This research aimed to implement a low-cost extended reality system that provided a good user experience and low CS. We designed and implemented a system that combines a view of the real environment with a video view that simulates the movement trajectory corresponding to a bending forward exercise. We conducted an initial evaluation of the system with 7 healthy participants. The designed system is novel, in that (1) it does not use VR, but rather is an XR system that combines the real world with a pre-recorded video simulating movement, and that (2) it is low-cost and readily available, when compared to existing solutions. Although user experience was mostly above average, CS was high and SoP was not as high as expected. This section discusses the results, opening avenues for further improving the system.

Regarding CS (SSQ questionnaire), past studies evaluating VR systems found that the highest obtained values are close to 20 points (Ota et al. 2023), and that SSQ levels in several publications ranged between 14.3 and 35.27, with a mean of 28.00 (95%Cl 24.66-31-35), considering this as a high value (Saredakis et al. 2020). Therefore, even the categories that showed the lowest CS levels with a value of 14.3 would be categorized as considerable. The above shows that our implementation has considerable undesirable CS effects. These results are complemented by the opinion of the participants themselves, who stated there was some discomfort, especially at the beginning of the experience. This CS might be related to the nature of the system design, which relies on a visual-vestibular mismatch, thus having a considerable CS induction factor according to some studies (Rebenitsch et al. 2016; LaViola et al. 2000). Indeed, once one of the subjects became aware that what they were watching was a video, they stated they no longer felt conflicting sensations.

User experience varied across the aspects measured by UEQ. The comparison benchmark includes data from 452 evaluations of products assessed with this survey (Handbook 2023). First, the novelty of the HMD is in the top 10% range. This evaluation makes sense since there is no similar existing XR application. Second, the insight, attraction, and stimulation criteria are categorized as above average. This means that it was relatively easy for users to learn to interact with the device, which was reflected in their quick adaptation. At the same time, they found the product motivating and had a positive overall impression of it, which is consistent with their expressions of surprise. Thirdly, as negative aspects, they found efficiency to be below average; it took users unnecessary effort to perform their tasks. This makes quite a bit of sense with feelings of discomfort because while it is expected that there will be difficulties inherent in an exercise designed for rehabilitation, the side effects of feeling sick should not be present. Finally, dependability ranks in the worst 25% of the results. This category answers whether the user feels they have control over the interaction. Considering the functionality of simulating an extension of reality, it is part of the application design that the user feels that they have little control over.

According to the correlations, on the one hand, although previous studies showed an evident positive correlation between immersion tendency and SoP (Rosa et al. 2016; Slater et al. 1997), the low variability of the subjects in terms of age and profession, as well as the low number of participants, could have been a relevant factor in the difficulty of predicting who would have a higher or lower SoP. On the other hand, the user experience clearly reflects the consistency that when a person has side effects using an XR headset, they have a bad experience in general. At the same time, the inverse relationship between SoP and CS is consistent with previous studies (Weech et al. 2019), opening the possibility that, if there is a causality of SoP and CS, then there is a possibility of decreasing the symptoms of discomfort by directly addressing how to make the person feel more "present", according to (Weech et al. 2019). From a stimulus point of view, it would be on how to make the person's attention more focused on the visual feedback, especially at the moment when the video is played, rather than an intermediate state where they question, for a fraction of a second, whether they are still or moving, or in the worst case, knowing they are still and just watching a video that seems unrelated to their experience.

Limitations and Future Work

The main limitations of the present study are that the group of participants is small and homogeneous, and not representative of people with LBP, so the experience of patients, especially those with kinesiophobia and pain, may be widely different, especially as our participants were kinesiology students (i.e., who have studied body movement). Furthermore, since each evaluation was done in a single session for each participant, it was not possible to observe what happened in a second instance in terms of adaptability: there is a possibility that since they were accustomed to it, the levels of CS could be lower in future sessions. We did not measure CS before and after exercise (Bouchard et al. 2007), so we did not have a baseline value for each participant. Finally, there was a translation from English to Spanish in the questionnaires and a translation from Spanish to English in the qualitative part, which may have failed to capture some key elements of the essence of the language.

Potential improvements can be approached from a variety of perspectives. As participants suggest having a wider range of view and refresh rate, studies show that these affect SoP and CS sensation (Diemer et al. 2015; Huber et al. 2018). At the same time, a vital aspect of the improvements is that the person should be unaware of the transition between the camera view and the video. In the application design, to make the transition as seamless as possible, three key elements were addressed. First, the RN activity is always active, even when the native module is called asynchronously. Second, the video communication is active and paused at the precise millisecond at which it should start to give continuity to what the person sees when the camera activity in the native module is terminated (i.e. the person reaches 30 degrees). Finally, the position of the headset is sampled at a rate at least twice the refresh rate. Despite these measures, there was an experienced lack of continuity between the camera preview and prerecorded video, caused by the RN bridge, which generates an inefficient transition by being unnecessarily asynchronous, single-threaded and imposing extra overheads (React Native 2023), making it necessary to modify the software architecture and develop the App only in native code.

However, these technological improvements are not enough to generate a true SoP (Wallach et al. 2010). This is why the exercise itself should be made more flexible, and the transition should be triggered according to the user’s movement and not always at 30 degrees, considering, for example, the rate of movement of the user. Previous research has found there is a level of visual motion that achieves the vection effect but does not induce CS (Tanaka et al. 2004). Among healthy individuals, there is a threshold of tolerance for visual disparities between VR visual feedback and their inherent proprioceptive feedback (Burns et al. 2006). Finally, there is a gain (ratio between actual head rotation angle and visual rotation angle displayed) where the patient just notices a discrepancy in the visual feedback (Chen et al. 2017). Therefore, simulating the artificial range of motion as a percentage dependent on the inclination of the person in such a way that it does not generate such an obvious visual-vestibular mismatch may be an essential aspect instead of simulating the whole motion, as was done in the present evaluation.

This research proposes and implements a novel alternative for the treatment of kinesiophobia related to chronic pain. The system combines a view of the real environment with a video view simulating an expected movement trajectory, therefore representing the first system for these purposes that uses extended reality instead of virtual reality. The developed system has a reasonable level of usability, and is very low cost, therefore applicable in low-income countries, where resources for healthcare technology are limited. However, an initial evaluation of the system found that users felt a high level of cybersickness. We identified key elements that need to be modified to reduce the CS, both in the software architecture and in the movement delivery mechanisms such as graphics, SoP and the longitude of the simulated trajectory. We believe the initial results are promising and this system could represent an area of future research in the field.

Funding

This research was funded by Sociedad Chilena de Ortopedia y Traumatología (https://www.schot.org/). The funds were allocated to the following items:

Equipment

600 USD for a Android S21 FE for the development of the Augmented Reality device.

50 USD for two VR headsets compatible with the phone

Experiment setup: 5 USD per participant for parking and transportation costs for volunteers.

Fees: 800 USD for a software developer to assist with the communication system between the mobile device and the computer.

Administration: 200 USD for a person to transcribe the qualitative analysis and summarize the main outcomes.

Scholarship: 1,800 USD to cover the monthly expenses for the study of one of the publication's authors.

Financial interests

The authors have no financial interests.

Ethics Approval

This study was approved by the Ethics Committee of Health Sciences at Pontificia Universidad Católica de Chile. The official approval document is attached to the manuscript.

Author Contribution

Mauricio Campos and Gustavo Torres contributed to the study's conception and design. Catalina Vidal managed the study setup, material collection, and bibliographic references related to medical aspects. Valeria Herskovic and Diego Arredondo contributed to the software development and all technological approaches undertaken to address the issue.

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The code and the data analyzed are in the following repository: https://github.com/Diego-Arredondo/Anreal-App.git

No competing interests reported.

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AnReal: Towards an extended reality, low-cost system for kinesiophobia

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