# Beyond technology: How XR quality features shape user experience and physical activity determinants in exergaming

Anaïck Perrochon, Karim El Ayoubi, Arnaud Boujut, Elpidio Attoh-Mensah

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Anaïck Perrochon, Karim El Ayoubi, Arnaud Boujut, Elpidio Attoh-Mensah. Beyond technology: How XR quality features shape user experience and physical activity determinants in exergaming. Acta Psychologica, 2025, 260, pp.105764. 10.1016/j.actpsy.2025.105764. hal-05310157

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Submitted on 11 Oct 2025

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# Beyond technology: How XR quality features shape user experience and physical activity determinants in exergaming

Anaïck Perrochon<sup>a</sup>, Karim El Ayoubi<sup>a</sup>, Arnaud Boujut<sup>a,b</sup>, Elpidio Attoh-Mensah<sup>a,*</sup>

## ARTICLE INFO

**Keywords:**

Exergames

Extended reality

Physical activity

User experience

Healthcare technology

## ABSTRACT

Promoting physical activity (PA) in healthcare is essential, yet inactivity remains widespread. Exergames (EGs) offer a playful strategy to foster PA. Implementation technologies range from screen-based consoles to Extended Reality (XR) devices. Because XR environments differ in experiential quality features (e.g., immersivity, interactivity, coherence), they may affect individual-level PA determinants (physical, psychological, biological). This mini-review examines if different XR environments influence the user experience and individual-level PA determinants in EGs, highlighting areas for further research. Eleven eligible studies compared at least two exergaming environments (PC/screen, AR, or VR) via head-mounted displays [HMDs] or CAVE) and reported outcomes on user experience (UX) and/or individual-level PA determinants. Compared with VR, AR was associated with fewer adverse effects (e.g., motion sickness, anxiety). Within VR environments, headset technology tends to enhance movement execution, elicit stronger neurophysiological responses (e.g., brain activity, heart rate) compared to large-scale systems, and enhance UX through the sense of presence. Compared to computer-based setups, VR generally improved performance (e.g., higher game scores), immersion and motivation. Most studies were limited to young, healthy participants, revealing a gap in research on clinical and older populations. Current evidence suggests that XR should be considered not only based on hardware specifications, but also on experiential quality features that shape UX and, in turn, influence PA determinants. Future studies should systematically leverage such features to fully grasp the impact on PA determinants particularly in EGs.

## 1. Introduction

The message “stay active to stay healthy” is supported by extensive research evidence (McPhee et al., 2016), emphasizing the importance of promoting physical activity (PA) across healthcare. Regular practice of PA prevents chronic diseases, especially in older adults, and plays a central role in managing health issues in rehabilitation (Eckstrom et al., 2020; Kalb et al., 2020). However, many people struggle to maintain PA due to various barriers such as low motivation, effort minimization tendency, limited access to safe spaces, infrastructure challenges, and insufficient post-discharge support (Bethancourt et al., 2014; Cheval et al., 2024). These barriers are linked to PA determinants which encompass individual, interpersonal, environmental, and political factors that shape one's ability to engage in PA (Condello et al., 2017; Dygrýn et al., 2025). Addressing these challenges requires innovative and accessible approaches capable of strengthening motivation and sustaining long-term participation.

In this context, Exergames (EG), active video games that require PA from the player, have gained attention recently as a promising solution to overcome some of these barriers (Attoh-Mensah & Perrochon, 2025; Mazeas et al., 2022; Manser et al., 2025). EG can enhance engagement and motivation by incorporating gamification elements and adding a ludic dimension (Baranowski et al., 2008; Sardi et al., 2017). This is especially relevant, as engagement and motivation are among the key individual-level PA determinants (Cheval et al., 2024; Dygrýn et al., 2025), making EG a particularly compelling. In a recent position paper, Šlosar and colleagues have developed a classification for EG mostly based on the technology used for implementation, defining them as systems that combine the use of a digital device, the existence of human-computer interaction, and a PA level above 1.5 metabolic equivalent (MET) (Šlosar et al., 2022). In their proposition, traditional gaming consoles, such as the Nintendo Wii, Xbox Kinect, and 2D screen displays, are regrouped under the computer category (Šlosar et al., 2022). In this category, the players interact with a screen through controllers,

<footnote><sup>a</sup> Univ. Limoges, HAVAE, UR 20217, F-87000 Limoges, France</footnote>

<footnote><sup>b</sup> 3iL Ingénieurs, Limoges, France</footnote>

<footnote>* Corresponding author at: 123 Avenue Albert Thomas, 87000, Limoges, France.
E-mail address: kouakou.attoh-mensah@unilim.fr (E. Attoh-Mensah).</footnote>
treadmills, or other input devices.

The other category proposed by Šlosar et al. is Extended Reality (XR), which offers innovative possibilities for EG as it includes more immersive solutions and various sensory and cognitive stimulations (Šlosar et al., 2022). XR environments enable the creation of realistic ecological settings that support safe PA practice while reducing the limitations associated with environmental PA determinants (e.g., limited access to safe spaces and infrastructure challenges) (Gallou-Guyot et al., 2020; Levac et al., 2019). XR is an umbrella term encompassing a continuum spanning from fully real environments at one end to entirely virtual environments (Virtual Reality (VR)). Between these extremes, solutions like Augmented Reality (AR) overlay virtual elements onto the real world, creating a blended experience. Mixed reality takes this further, incorporating virtual objects that not only appear in the real world but can also interact dynamically with physical elements (Skarbez et al., 2021). This classification of XR environments aligns with Milgram and Kishino's concept in their reality-virtuality continuum, which principally focuses on the hardware and the visual experience within the environment (Milgram & Kishino Milgram & Kishino, 1994).

Recent taxonomies have revisited XR, no longer framing it as a continuum between AR and VR but as an experience determined not only by the visual inclusion of the physical environment, but also by the external senses (auditory, tactile, taste...), the interoceptive senses that monitor the body's internal state, and the overall user experience (UX) (Rauschnabel et al., 2022; Skarbez et al., 2021). Some authors have emphasized two foundational quality features of XR environments: immersivity and interactivity. Immersivity refers to system specifications, such as field of view or sensory fidelity, that determine the extent to which a user can be perceptually and cognitively absorbed by the environment. Interactivity, by contrast, denotes the degree to which users can act upon and manipulate items in the environment. Variations in these features give rise to different subjective first-level UX such as immersion (the phenomenological state of being absorbed) and interaction (the enacted engagement with the environment). Consequently, as for immersivity for example, XR environments can be classified along a continuum ranging from non-immersive to semi-immersive to fully immersive systems. Beyond these previous two core quality features, coherence is a third construct proposed to capture the broader ecological validity of XR environments. Such coherence is typically operationalized through several interrelated quality features: explorability (the degree of navigability and freedom of movement within the virtual space), plausibility (the consistency and logical integrity of environmental events and dynamics), and believability (the extent to which the environment resembles real-world structures) (Flavián et al., 2019; Hameed et al., 2024; Lee, 2025). Taken together, immersivity, interactivity, and features within the coherence construct constitute the structural properties of XR environments. Their combined influence generates higher-order UX such as presence (the psychological sense of “being there”) and embodiment (the subjective experience of ownership, agency, and self-location within a virtual body) which will complete the overall experience.

The complex framework of XR environments means that users may exhibit different behaviors when engaging in PA or, more specifically, EG. For example, a complete VR environment can be implemented using wearable devices such as head-mounted displays (HMDs), or through large-scale systems like Cave Automatic Virtual Environments (CAVEs). A CAVE is a room-sized immersive system in which participants wear stereoscopic glasses to experience a 3D environment projected onto the surrounding walls, floor, and ceiling. Thus, HMDs and CAVEs differed in terms of fields of view and interaction modalities ultimately impacting perceptual and motor responses in distinct ways (Molina et al., 2022). Furthermore, varying immersivity in any type of environment can lead to sensory conflicts between exteroceptive and interoceptive inputs (Skarbez et al., 2021), potentially affecting perceptual and physical performance through sensorimotor adaptations (Barbour et al., 2024; Flores-Cortes et al., 2023). In addition, the effects of the quality features

of XR environments on the first level (immersion, interaction) or higher-order (presence, embodiment) can influence key PA determinants such as motivation, engagement, performance and ability (Chenais & Görgen, 2024; Perez-Marcos et al., 2017).

Overall, the features of XR environments influence multiple determinants of PA, primarily at the individual level. Notably, these determinants are central to the effectiveness of EG (Torre & Temprado, 2022). In the context of exergaming, no review to date has directly considered the different features of XR environments despite the existing literature on their impact on several outcomes (Barbour et al., 2024; Flores-Cortes et al., 2023). The aim of this mini-review is to explore the current understanding of the effects of the features of different XR environments on UX and the individual-level PA determinants during EG.

## 2. Methods

The search was conducted across the following electronic database covering the last 10 years: PubMed, Embase, IEEE Xplore, Scopus, Web of Sciences, ScienceDirect, and PsychINFO. The search included terms combined with “OR” and “AND” operators as follows: (Exergame OR Exergaming OR interactive video game) AND (Virtual Reality OR Mixed Reality OR Augmented Reality OR Extended Reality OR Wii OR Kinect OR PlayStation OR Xbox) AND (Comparison OR Compared OR Comparing OR Compare OR Compared OR vs). Studies were eligible for inclusion in this review if they met the following criteria: 1) written in English, 2) compared an XR environment to another during EG execution, and 3) Individual-level determinants of PA or UX were among the outcomes. For each article included, the following elements were extracted: a) Metadata (Authors, year of publication), b) Characteristics of the study population, c) Type of immersive environment, d) Description of the EG, and e) main findings.

## 3. Results

From 2152 records identified in the initial search, and after removing duplicates (n = 975) and the title and abstract screening (n = 1117 removed), we searched for the full texts of 60 reports that were screened for eligibility criteria, resulting in 49 additional removals. Fig. 1 displays the study selection procedure. Consequently, 11 studies are included in this mini-review, and their characteristics and main findings are summarized in Table 1.

The type of technological devices was the main feature that drove the comparison between the environments. Most studies (n = 5) compared VR to the computer category (Microsoft Kinect system or TV condition). Some compared two VR environments (n = 4) implemented by either HMDs (mostly HTC Vive) and CAVE. Only two studies have compared VR to AR using HMDs.

The participants were young and healthy in most studies (n = 9), with only two focusing on aging or clinical populations (Elor et al., 2020; Lieze et al., 2020). Some of the EG applied included full-body activities with coordination, spatial navigation, fitness movements, running, stepping, and cycling tasks (Born et al., 2019, 2021; Lieze et al., 2020; Ring & Masuch, 2023; Xu et al., 2021, 2019, 2020; Yang et al., 2022). Some others required upper extremities exercises and coordination (Cordeil et al., 2017; Elor et al., 2020; Molina et al., 2022).

The studies evaluated UX using first-order factors such as immersion, interaction, and usability (Elor et al., 2020; Molina et al., 2022; Xu et al., 2020; Yang et al., 2022), as well as higher-order factors like the sense of presence (Born et al., 2019; Lieze et al., 2020). The individual-level determinants of PA identified across the studies included physical factors (e.g., movement execution, gesture efficiency, task completion time, physical exertion), psychological factors (e.g., engagement, anxiety, usability, embodiment), and physiological factors (e.g., brain activity, heart rate).

When comparing AR to VR environments, one study reported more disorientation in VR mode than in AR (Ring & Masuch, 2023). The
<figure><img src="image_2.png" /><figcaption>Fig. 1. Flowchart of study selection.</figcaption></figure>

disorientation assessed through the Collision Anxiety Questionnaire is associated with players' awareness of their position and surrounding and the location of objects during gameplay. Another study found an advantage of AR in terms of interaction efficiency and reduced motion sickness (Yang et al., 2022).

When comparing two VR environments, the sense of presence varied across studies, with sometimes better immersion in HMDs compared with CAVE (Elor et al., 2020) or no difference between these systems (Lieze et al., 2020). Furthermore, participants using HMDs devices consistently demonstrated better performance than within CAVE, showing more efficient gestures and faster task completion (Cordeil et al., 2017; Elor et al., 2020; Molina et al., 2022). Elor et al. (2020) found that heart rate was higher in HMD users than in CAVE users. Additionally, they also utilized electroencephalography (EEG) to monitor brain activity. They found that HMD users exhibited higher activity than CAVE users in the alpha and gamma bands of the prefrontal cortex, indicating enhanced focus and cognitive processing (Elor et al., 2020).

Quite interestingly, the comparison between VR and computer category (e.g., Microsoft Kinect or TV conditions) suggest that playing an EG in a VR environment led to significantly enhanced UX, such as

immersion (Xu et al., 2020) and presence (Born et al., 2021). This advantage of VR is still in terms of determinants such as motivation, engagement, embodiment (Born et al., 2019; Xu et al., 2020). Participants also made fewer errors in their gestures (Xu et al., 2021) and elicited enhanced brain activity (Xu et al., 2019) when using VR. Interestingly, two studies revealed lower physical exertion in VR than in screen condition with Kinect (Born et al., 2019, 2021).

## 4. Discussion

This mini-review aimed to synthesize current knowledge on the effects of different types of XR environments on UX and key individual-level determinants of PA during EG. First, we found studies on UX indicating that AR environments offer better interaction compared to VR. At the same time computer-based setups are generally less immersive and associated with a lower presence than VR. Regarding PA determinants, AR tends to cause fewer side effects than VR. Within VR settings, HMDs are linked to more favorable outcomes across physical, psychological, and even biological domains compared to CAVE systems. Lastly, while computer-based exergaming may elicit greater physical exertion, it is generally less engaging and motivating than VR-based
<table><caption>**Table 1**
Summary of the characteristics and the findings of studies.</caption><thead><tr><th>First author and year of publication</th><th>Characteristics of the population</th><th>1 /Type of environments<br>2/ Technology</th><th>Description of EG</th><th>1 / User experience<br>2/ Individual-level determinant</th></tr></thead><tbody><tr><td>Yang et al., 2022</td><td>N = 28<br>Age: 20–48</td><td>1/AR vs VR<br>2/ HTC Vive or Meta Quest 2 to perform AR and VR</td><td>Hit a target (virus) and avoid it at the same time</td><td>1/ Interaction efficiency (game score) → AR > VR (t = 2.591, p < 0.01, d = 0.44).<br>Flow experience → No significant difference</td></tr><tr><td>Ring & Masuch, 2023</td><td>N = 37<br>Mean Age: 24.8</td><td></td><td>Hit a target (ghost) and throw them away</td><td>2/ Number of side effects [Motion sickness (SSQ), anxiety level (STAIY)] → VR > AR (t = 1.86 to 2.591, p = 0.01 to 0.07, d = 0.21 to 0.44).<br>1/ NR</td></tr><tr><td>Cordeil et al., 2017</td><td>N = 21<br>Mean Age: 36.0</td><td>1/Two VRs<br>2/ CAVE vs HMD (HTC Vive)</td><td>Connecting and finding the shorter paths between dots and triangles using hand motion</td><td>2/ Disorientation measured by the Collision Anxiety Questionnaire (CAQ) → VR > AR (t(35) = -2.16, p = 0.019),<br>1/ NR</td></tr><tr><td>Elor et al., 2020</td><td>N = 40<br>Age: 20–31<br>27 participants with cognitive disabilities (intellectual disabilities, cerebral palsy, epilepsy, autism, and Down syndrome)</td><td></td><td>Catching shooting stars with hands gesture in a cosmic immersive</td><td>2/ Performance (Completion time of tasks) → HMD > CAVE (t(93) = -4.56, p < 0.001)<br>1/ System preference and self-reported immersion → HMD > CAVE</td></tr><tr><td>Lieze et al., 2020</td><td>N = 108<br>Mean age: 70.3</td><td>1/ Two VRs</td><td>Cycling through the streets</td><td>2/ Focus and cognitive process (frontal gamma activity), heart rate, hand gesture precision, → HMD > CAVE (regardless of cognitive status) (p = 0.05 to 0.001)<br>1/ Sense of presence (IPQ) and perception → no difference between HMD and CAVE</td></tr><tr><td>Molina et al., 2022</td><td>N = 17<br>Mean Age: 36.8</td><td>2/ CAVE vs HMD (HTC Vive)<br>1/ VR vs Microsoft Kinect<br>2/ HMD (HTC Vive) vs (PC-desktop screen)<br>1/ VR vs TV condition</td><td>Upper extremity exercise: moving colored balls to corresponding boxes</td><td>2/ NR<br>1/ system usability → HMD > CAVE<br>Chi 2 = 5.93 to 21.23 and p = 0.001 to 0.051<br>2/ Performance (completion time, errors, gesture precision) → HMD > CAVE<br>z = -3.2 to -2.09 and p = 0.001 to 0.02<br>1/ Sense of presence (IPQ) → VR > Kinect</td></tr><tr><td>Born et al., 2019</td><td>N = 44<br>Mean age: 21.4</td><td>2/ with HMD (HTC Vive) vs TV condition (Full HD 50 in.)</td><td>Fit into body-shaped holes in walls</td><td>2/ Motivation (IMI), Engagement (GEQ), and embodiment → VR > Kinect<br>Physical exertion (Borg scale) → VR < Kinect (p = 0.001 to 0.04)<br>1/ Sense of presence (IPQ) → VR > TV condition z = -4.25 to -2.32, p = 0.001 to 0.02,</td></tr><tr><td>Born et al., 2021</td><td>N = 32<br>Mean age: 22.6</td><td>2/ with HMD (HTC Vive) vs TV condition (Full HD 50 in.)</td><td>Running and sword combat in a simulated medieval village</td><td>2/ Motivation (IMI), → VR > TV condition z = -4.25 to -2.32, p = 0.001 to 0.02,<br>Physical exertion (Borg scale) → VR < TV condition (t(18.98) = -2.51, p = 0.021, d = -0.89).<br>1/ NR</td></tr><tr><td>Xu et al., 2019</td><td>N = 12<br>Mean age: 22.4</td><td>1/ VR with vs Microsoft Kinect</td><td>Hand movements and stepping to catch objects</td><td>2/ Brain activity (Theta waves) → VR > Kinect (F(1,11 = 7.415, p < 0.05, ηp2 = 0.403),<br>1/ Immersion (GEQ) → VR > Kinect (p = 0.02)</td></tr><tr><td>Xu et al., 2020</td><td>N = 16<br>Mean age: 22.0</td><td>2/ HMD (Oculus Rift CV1) vs (50-in. 4 K)</td><td>Fitness movements to remove objects</td><td>2/ Heart rate → VR > Kinect (p = 0.001)<br>1/ NR</td></tr><tr><td>Xu et al., 2021</td><td>N = 32<br>Mean age: 20.6</td><td></td><td>Leg Kick, single hand punching, squat in an adventure game</td><td>2/ Success rates (attack and defense move) → VR > Kinect<br>F<sub>1.30</sub> = 5.473 to 14.403, p 0.05 to 0.001, η<sub>p</sub><sup>2</sup> = 0.154 to 0.324</td></tr></tbody></table>

EG: Exergame; VR: Virtual Reality; AR: Augmented Reality; HMD: Head Mounted Display; CAVE: Cave Automatic Virtual Environments; SUS: System Usability Scale; SSQ: Stimulator Sickness Questionnaire; STAIY: State-Trait Anxiety Inventory- Form Y; CAQ: Collision Anxiety Questionnaire; IMI: IPQ: Igroup Presence Questionnaire; GEQ: Game Engagement Questionnaire; NR: Non-reported.

experiences.

## 4.1. AR vs VR

Some authors propose that the user's visual awareness of their surroundings in AR may contribute to the advantage of this environment over VR in limiting motion sickness and enhancing interaction (Ring & Masuch, 2023; Yang et al., 2022). This may be explained by the

increased likelihood of conflict between interoceptive and exteroceptive sensory inputs in VR environments compared to AR due to reduced environmental awareness and sensory mismatch (Skarbez et al., 2021). Such environmental awareness is valuable for older adults or individuals with clinical conditions, as it offers reassurance and a sense of safety. Consequently, AR is a compelling choice for designing EG environments in XR applications within healthcare settings. However, the studies (Ring & Masuch, 2023; Yang et al., 2022) identified in this mini-review
are preliminary and require further validation through controlled trials that include specific EG outcomes, such as improvements in cognitive and motor functions. In addition, the studies still relied on Milgram's virtuality continuum to classify XR environments, even though updated taxonomies now incorporate other features to grasp the overall UX (Chenais & Görgen, 2024; Hameed et al., 2024; Lee, 2025; Rauschnabel et al., 2022; Skarbez et al., 2021).

## 4.2. VR environments

Users tend to score higher for presence and usability in HMDs rather than CAVE (Elor et al., 2020; Molina et al., 2022), likely due to the level of immersivity in HMDs. Indeed, Although HMDs and CAVE can foster VR experiences, they differ in perceptual aspects, such as field of view and body part visualization which are important for immersivity.

In addition, we found that the use of HMD devices rather than CAVE allowed better physical performances in participants (accurate gestures, faster completion time ...) (Cordeil et al., 2017; Molina et al., 2022). Some researchers suggest that HMDs may enhance performance in EG because visualizing one's body, as seen in CAVE setups, can occasionally introduce inaccuracies in gesture and movement execution (Molina et al., 2022). Indeed, seeing one's own body in the virtual environment may disrupt users' sense of embodiment and lead to more errors. In contrast, a fully immersive virtual environment, as in HMDs, might better support focused movement by eliminating this visual distraction (Molina et al., 2022). Nevertheless, this hypothesis warrants further exploration as the technology used for the VR environment may vary in how users perceive body visualization depending on immersivity or interactivity. Additionally, the presence of a virtual avatar might enhance self-embodiment (Perez-Marcos et al., 2009), and this should be evaluated depending on the type of EG.

Another important consideration is the effect of XR environments on the underlying neurophysiology of EG. Here again a difference is apparent between VR environments implemented by HMD and CAVE, with higher values for the former in heart rate and brain activity in the frontal area (Elor et al., 2020). An increase in heart rate might be a physiological response to several events (physical exertion, stress, motion sickness...), which are not clearly studied in the paper discussed here. The authors proposed that this may be a result of increased arousal within the HMD compared to CAVE since physical exertion is marginally the same within the two environments. Nevertheless, this should be confirmed while using a setup with specific content enabling the increase of arousal. It would also be important in future studies to investigate how this heart rate response is related to the global UX in the EG. Furthermore, the increase in brain activity in the frontal area is interesting. Indeed, our recent research demonstrated that EG interventions can impact brain functions on structural, functional, and molecular levels (Attoh-Mensah et al., 2025). In the context of EG in XR, it thus remains to understand if considering specific quality features (e.g., immersivity, interactivity, explorability...) beyond hardware specification (e.g., CAVE vs HMDs) in VR but also in other XR environments (e.g., AR, Mixed Reality) would also impact brain functions differently.

## 4.3. VR vs computer category

The advantage of VR over computer category EG in terms of immersion, engagement (Born et al., 2019, 2021), motivation (Born et al., 2019, 2021; Xu et al., 2020), and presence (Born et al., 2021) is unsurprising since VR has significant potential to offer multimodal stimulations and enhance UX. One study even found that this effect of VR has a neurophysiological background characterized by greater theta activity compared to the computer category (Xu et al., 2019). However, some studies have noted that physical exertion in VR-based EG tends to be lower compared to computer category (Born et al., 2019, 2021). This reinforces the need to choose the technology based specifically on the user's goals (e.g., rehabilitation, increasing PA levels) and their

individual capacities. Nevertheless, VR-based EG should be designed to ensure a minimum threshold of physical exertion while leveraging key determinants such as motivation and engagement, which are crucial for sustaining long-term PA levels.

As comparisons between XR environments discussed above have relied mainly on technological devices, several features remain largely unaddressed despite their significant relevance to individual-level PA. Indeed, updated XR taxonomies distinguish computer-based environments from XR ones such as AR and VR based on several other features beyond hardware specifications (Rauschnabel et al., 2022; Skarbez et al., 2021; Slosar et al., 2022). Although definitions vary across studies (Chenais & Görgen, 2024; Hameed et al., 2024; Lee, 2025), two features are most commonly highlighted: immersivity and interactivity. These can impact individual-level PA determinants directly by shaping physical, biological, or psychological variables, and indirectly, by modifying the UX, notably through first-level (e.g., immersion, interaction) or higher order inputs (i.e., sense of presence, embodiment). In Fig. 2 we propose a framework that highlights the link between such quality features and PA determinants through direct effects and mediating mechanisms involving UX. Interestingly, the experimental studies included in our mini-review did not appear to focus on features within the coherence construct, such as plausibility, believability or exportability even though certain environmental designs might implicitly mobilize these dimensions. For example, scenarios involving running and sword combat in a simulated medieval village (Born et al., 2021), or catching shooting stars with hand gestures in a cosmic immersive setting (Elor et al., 2020), may lack coherence or internal logic in the narrative. While these features were not explicitly assessed, they could potentially influence user engagement or the perceived meaningfulness of the experience.

Noteworthy, the differences in sensorimotor behaviors, particularly across XR environments while playing EG, have significant clinical implications. For example, some environments may better suit individuals with motor impairment depending on the goal of the rehabilitation. Tailoring these environments to accommodate specific mobility challenges could enhance accessibility and effectiveness for users with varying levels of impairment. Indeed, the degree to which users remain aware of their environment during XR-based EG likely depends on several factors, including the type of movement involved, the characteristics of the target population, and the specific therapeutic or functional objectives of the EG. However, the studies included in this review offer limited insight into such variability, as they predominantly involved healthy young adults. This narrow focus restricts our ability to draw practical recommendations or generalize findings, particularly regarding the comparative effects of AR and VR for other populations, such as older adults or individuals with clinical conditions.

Finally, in the present mini-review, some tasks included in the EG (e. g., hand gestures only (Cordeil et al., 2017; Elor et al., 2020; Molina et al., 2022)) fall below the 1.5 MET threshold proposed by Slosar et al., highlighting the need to assess EGs with sufficient physical exertion to be beneficial for prevention or rehabilitation purposes. Furthermore, additional investigation is necessary to determine whether the choice of XR environment directly impacts the effectiveness of EGs in achieving their intended outcomes within the context of prevention and rehabilitation.

## 5. Limitations

A key limitation of this paper is the relatively small number of studies included in the analysis, which restricts the breadth and depth of the conclusions that can be drawn. Moreover, most studies focused on young, healthy participants, with only a few on aging (Lieze et al., 2020) or clinical populations (Elor et al., 2020). While it is valuable to understand the impact of XR on healthy individuals, there is a clear need for more research involving frail populations that require greater attention regarding their PA levels. Indeed, it remains unclear whether
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** A framework of the effects of quality features of extended reality on UX and individual-level PA determinant. The arrows indicate that these features can affect directly or indirectly multiple elements within the framework.</figcaption></figure>

some XR environments might better suit specific pathological populations with unique needs when using EG to increase PA. Despite these limitations, this review highlights an important gap in the literature and underscores the need for future research that expands to more diverse populations and includes methodologically robust designs. Such efforts are essential to fully understand the applicability and impact of immersive technologies across different demographic and clinical groups.

Regarding the studies included in the review while they provide important foundational insights, their findings should be interpreted with caution. Small and unjustified sample sizes are a common methodological limitation across the studies. This can be attributable to the exploratory nature of the research; many studies were pilot in scope and constrained by limited resources, which restricted their capacity for rigorous experimental design. As a result, the outcomes reported, while promising, should be viewed as preliminary and interpreted within the

context of these methodological constraints. As the fields of XR and EG continue to evolve, future high-quality research will likely emerge to enhance our knowledge of the effects of XR features on PA determinants in EG. Furthermore, many of the devices used to implement EG are now practically obsolete due to the rapid pace of technological advancements in the field. New EG platforms and tools are introduced each year (Attoh-Mensah & Perrochon, 2025; Campo-Prieto et al., 2022; Gallou-Guyot et al., 2023), making past comparisons quickly outdated. Therefore, it is essential to use this current analysis as a foundation and regularly update the comparison to include emerging technologies and more recent exergaming system

## 6. Future works

This mini-review reveals that many studies continue to define XR environments primarily based on hardware characteristics, without
adequately considering the broader experiential qualities that shape UX and outcomes. A critical avenue for future research lies in moving beyond this hardware-centric perspective to systematically incorporate the full range of quality features, both when evaluating existing XR environments and when designing new ones. Specifically, aspects within the construct coherence such as scene representation, self-representation, and environmental coherence should be carefully considered, as their significance extends beyond mere technical specifications and directly impacts UX. Consequently, the overall construct of an XR environment should be understood as the combination of hardware and software specifications in order to grasp their true impact. In the context of EG, evaluating these underexplored dimensions is particularly important, as they are likely to influence UX and key determinants of PA that are central to EG outcomes. Moreover, while the enhanced UX in XR is likely to influence key determinants such as engagement, motivation, and adherence, these factors have rarely been directly assessed. However, it is crucial to provide a comprehensive understanding of the effects of XR environments on these determinants for the effective adoption of EG, especially among older adults or clinical populations (Nawaz et al., 2015). Deepening our understanding of how different XR environments affect these factors is a critical area for future research.

## 7. Conclusion

The features of XR environments influence physical, biological, and psychological determinants of PA, as well as aspects of UX, such as immersion and presence during exergaming. Current evidence suggest that AR may be preferable when minimizing adverse effects is a priority, whereas VR offers advantages when maximizing engagement, motivation, and movement quality is desired. However, several key features of XR environments remain unexplored in terms of their direct effects on these determinants or their indirect effects through UX. In addition, the findings in the current studies are largely preliminary and come with methodological limitations that future research should address. To advance this field, future studies should adopt the updated frameworks for XR environments that incorporate not only hardware specifications but also specific quality features. These refined classifications will be crucial for understanding how various XR environments impact EG outcomes.

### CRediT authorship contribution statement

**Anaëck Perrochon:** Visualization, Validation, Methodology, Data curation, Conceptualization, Writing – review & editing, Writing – original draft. **Karim El Ayoubi:** Methodology, Formal analysis, Data curation, Conceptualization, Writing – review & editing. **Arnaud Boujut:** Validation, Methodology, Writing – review & editing, Writing – original draft. **Elpidio Attoh-Mensah:** Visualization, Validation, Supervision, Methodology, Investigation, Formal analysis, Data curation, Conceptualization, Writing – review & editing, Writing – original draft.

### Funding

This work received no specific funding.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Data availability

Data extracted from the studies included in this review are available upon request from the corresponding author.

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