# Effect of Gameplay Uncertainty, Display Type, and Age on Virtual Reality
Exergames

Wenge Xu  Xi’an Jiaotong-Liverpool UniversitySuzhouJiangsuChina
<EMAIL_ADDRESS>,  Hai-Ning Liang  Xi’an Jiaotong-Liverpool
UniversitySuzhouJiangsuChina<EMAIL_ADDRESS>,  Kangyou Yu  Xi’an
Jiaotong-Liverpool UniversitySuzhouJiangsuChina
<EMAIL_ADDRESS>and  Nilufar Baghaei  Massey
UniversityAucklandNew Zealand<EMAIL_ADDRESS>
(2021)

###### Abstract.

Uncertainty is widely acknowledged as an engaging gameplay element but rarely
used in exergames. In this research, we explore the role of uncertainty in
exergames and introduce three uncertain elements (false-attacks, misses, and
critical hits) to an exergame. We conducted a study under two conditions
(uncertain and certain), with two display types (virtual reality and large
display) and across young and middle-aged adults to measure their effect on
game performance, experience, and exertion. Results show that (1) our designed
uncertain elements are instrumental in increasing exertion levels; (2) when
playing a motion-based first-person perspective exergame, virtual reality can
improve performance, while maintaining the same motion sickness level as a
large display; and (3) exergames for middle-aged adults should be designed
with age-related declines in mind, similar to designing for elderly adults. We
also framed two design guidelines for exergames that have similar features to
the game used in this research.

exergame, uncertainty, virtual reality, young adults, middle-aged adults

††journalyear: 2021††copyright: acmcopyright††conference: CHI Conference on
Human Factors in Computing Systems; May 8–13, 2021; Yokohama,
Japan††booktitle: CHI Conference on Human Factors in Computing Systems (CHI
’21), May 8–13, 2021, Yokohama, Japan††price: 15.00††doi:
10.1145/3411764.3445801††isbn: 978-1-4503-8096-6/21/05††ccs: Software and its
engineering Interactive games††ccs: Human-centered computing Virtual
reality††ccs: Applied computing Computer games

##  1\. Introduction

Motion-based exergames, a combination of “motion-based exercise” and “gaming”,
is a promising approach to encourage regular exercise, especially for
unmotivated or inactive target groups (Bogost, 2005; Sinclair et al., 2007).
Previous literature has shown the benefits of playing motion-based exergame,
which include but are not limited to enhanced postural stability (Sheehan and
Katz, 2013), muscle strength (Soares et al., 2016), and working memory
(Eggenberger et al., 2015). Because of the potential of these exergames in
eliciting health benefits, much work has been conducted with different age
groups (including children (Hernandez et al., 2013), young individuals (Xu et
al., 2020d), and older adults (Gerling et al., 2012)).

Age-related declines are common in older adults (i.e., aged 65 and above) and
middle-aged adults (i.e., aged 45 to 65) as previous studies show that
reductions (e.g., cognitive abilities) could start even before the age of 50
(Ferreira et al., 2015; Verhaeghen and Salthouse, 1997). These age-related
declines affect the elderly’ game performance and experience and could also
affect middle-aged adults in a similar way. Although there have been some
attempts to understand whether middle-aged adults could obtain the same health
benefits from playing videogame as elderly adults (Rosney and Horvath, 2018;
Xu et al., 2020a), there is very limited research on exploring the performance
and experience of middle-aged adults.

Designing an enjoyable and effective exergame is challenging. Studies
(Berkovsky et al., 2010; Ioannou et al., 2019; Barathi et al., 2018) have been
conducted to improve the motivation and experience of these games. For
instance, Ioannou et al. (Ioannou et al., 2019) proposed a virtual performance
augmentation method for exergames and found that it increased players’
immersion and motivation. Barathi et al. (Barathi et al., 2018) implemented an
interactive feedforward method to an exergame and found that it improved
players’ performance.

One factor that has been widely applied in games is uncertainty, which has
long been recognized as a key ingredient of engaging gameplay (Costikyan,
2013; Power et al., 2019; Caillois, 2001; Johnson, 2018). Costikyan
(Costikyan, 2013) argues that games require uncertainty to hold players’
interest and that the struggle to master uncertainty is central to games’
appeal. Most importantly, he suggested that game designers can harness
uncertainty to frame gameplay’s design. Several game designers and researchers
have tried to identify uncertainty sources that can lead to a good gameplay
experience (Malone, 1982; Juul, 2011; Tekinbas and Zimmerman, 2003; DeKoven,
2002). Drawing on many of these sources and practical experience, Costikyan
(Costikyan, 2013) listed an influential categorization of eleven sources of
uncertainty found or can be used in games. Recently, Kumari et al. (Kumari et
al., 2019) presented a grounded theory of uncertainty sources which can
partially map onto existing taxonomies, especially Costikyan’s (Costikyan,
2013), providing converging evidence of the validity of Costikyan’s
categorization of uncertainty sources. Although uncertainty is recognized as a
core component of the gaming experience, there is relatively little research
that has looked specifically into the effect of uncertainty in games,
especially exergames. Based on uncertainty sources identified in (Costikyan,
2013), in this research, we propose the use of three uncertain elements for
exergames, that cover four sources of uncertainty, and evaluate their effect
in exergames on performance, game experience (and sickness when implemented in
virtual reality), and exertion levels.

Given the recent emergence of affordable virtual reality (VR) head-mounted
displays (HMDs), VR exergames have been gaining rapid attention (Barathi et
al., 2018; Ioannou et al., 2019; Xu et al., 2020c). For instance, VR exergames
are useful in promoting physical activity in sedentary and obese children
(Rizzo et al., 2011), especially to increase their motivation to exercise
(Plante et al., 2003; Mestre et al., 2011). Existing literature has outlined
that there are additional benefits of playing motion-based exergames in VR
than non-VR. In VR, players could achieve a higher exertion and experience a
game more positively in areas like the challenge, flow, immersion and a lower
negative affect (Xu et al., 2020d). However, a major drawback is that VR might
lead to a higher level of simulator sickness, which must be taken into account
during the design process to mitigate its effects.

The aim of our research is to explore the effect of uncertain versus certain
elements and VR versus a typical TV large display (LD) on two main player
groups of exergames regarding their game performance, experience, and
exertion. In this paper, we first introduce GestureFit, the game we developed
for this research. We describe the rules and logic behind it, the game
procedure, and risk control for middle-aged adults. We then present the study
we conducted to investigate the effect of display type and game condition,
focusing on differences between young adults and middle-aged adults. We then
report the results and present a discussion of our findings that are framed
based on existing literature. Two main design guidelines derived from the
results are then proposed, followed by the conclusions.

The contributions of the paper include: (1) an empirical evaluation of the
effects of display type and game condition on exergame performance,
experience, and exertion between young and middle-aged adults; (2) a set of
uncertain elements that can help increase the exertion level for motion-based
exergames; and (3) two recommendations that can help frame the design of
motion-based exergames to contain uncertain gameplay elements and how to
motivate middle-age and older adults to engage with exergames more
meaningfully.

##  2\. Related Work

###  2.1. VR and Non-VR Motion-based Exergames

Many motion-based exergames have been developed for non-VR displays since the
introduction of Kinect. A typical motion-based exergame requires players to
move their body or perform certain gestures to interact with the game world.
For instance, in GrabApple (Gao and Mandryk, 2012), users need to jump or duck
to pick up apples; they also need to move around to locate them but also avoid
touching other objects, like bombs. In a game reported in Gerling et al.
(Gerling et al., 2012), users need to perform static and dynamic gestures to
grow plants and flowers and catch birds. In Sternataler (Smeddinck et al.,
2013), players use their hands to collect stars that appear sequentially in
some predefined paths.

Recent advances and the growing popularity of VR HMDs have created a
substantial demand for motion-based exergames. For instance, games like
Virtual Sports111https://www.vrgamerankings.com/virtual-sports for the HTC
VIVE allow a user to play sports with his/her full body in fully immersive
virtual environments. In another commercial game, FitXR222https://fitxr.com/,
the users need to jab, weave, and do uppercuts following rhythmic music. In
the research exergame KIMove (Xu et al., 2019), the players need to move their
hands to hit fruits floating in midair and use their feet to step on cubes
moving towards them on the ground. In GestureStar (Xu et al., 2020d), users
need to perform 6 different gestures to eliminate the objects, like cubes,
flying towards them.

Previous research has reported inconsistent findings when looking at the
effect of display type on gameplay experience and performance. Xu et al. (Xu
et al., 2020d) suggested that players achieved a higher exertion and
experienced a game more positively in VR than LD. However, they also found
that VR could lead to a higher level of simulator sickness. Results from (Xu
et al., 2019) suggested that there was no effect of display type on gameplay
performance and experience. Therefore, we have included this factor in our
experiment to investigate it further and provide more insights.

###  2.2. User Experience in Exergames

Exergames integrate physical activity to engage players (Mueller et al.,
2011). Because findings from other types of games may not be applicable to
exergames (Monteiro et al., 2018; Xu et al., 2020d), efforts have been focused
on studying user experience in exergames. For instance, it is reported in (Xu
et al., 2019) that task mode (single- and multi-tasking) could affect users’
exergame experience; in particular, multi-tasking could not only make the game
more challenging and cause a higher sickness, but also lead to worse
performance than single-tasking. Koulouris et al. (Koulouris et al., 2020)
investigated the effect of customization and identification in a VR exergame,
and found that customization significantly increased identification, intrinsic
motivation, and performance in the exergame. Further, playing pose (i.e.,
standing and seated), performance augmentation (i.e., enabling players with
superhuman capabilities in the virtual game) could also affect the gameplay
experience (e.g., sickness) (Ioannou et al., 2019; Xu et al., 2020b). On the
other hand, although uncertainty is a crucial element in gameplay, it is
underexplored in exergames. It is this reason that we are interested in
studying the effect of uncertainty in exergames for both immersive VR and
large displays.

###  2.3. Design Elements of Exergames

Several design guidelines have been proposed by researchers in HCI and sport
sciences for designing more attractive and effective full-body motion-based
exergames (Marshall et al., 2016; Márquez Segura et al., 2013; Hardy et al.,
2015). According to these, to design a playful exergame experience, designers
should focus on (1) the player’s body (movement concept), (2) the mediating
controller technology (transferring movement input into the virtual world and
providing feedback), and (3) the game scenario (audio-visual and narrative
design and feedback) (Martin-Niedecken et al., 2019).

####  2.3.1. The Player’s Body

After criticizing existing exertion games and commercial exergames, Marshall
et al. (Marshall et al., 2016) proposed three design strategies based on the
idea of movement, which are (1) the design of exertion trajectories (e.g., to
create a trajectory across individual play sessions for skill-learning that
takes into account players’ cognitive load and the exertion patterns), (2)
design for, with, and around pain (e.g., celebrating positive pain), and (3)
design leveraging the social nature of exertion (e.g., players to be
surrounded by other players like friends and family members or game
enthusiasts).

####  2.3.2. The Mediating Controller Technology

Studies have suggested that the participation of the body is a crucial
variable not only in the efficacy of exergames in affecting users’ emotional
experience (Vara et al., 2016), but also in improving user experience, energy
expenditure, and intention to repeat the experience (Kim et al., 2014). To
achieve these positive gaming experiences, body-centered controllers should be
designed to serve as an additional physical playground, so that they can be
easily integrated into players’ body scheme (Pasch et al., 2009) and provide a
balance of guided and free movements (Martin-Niedecken et al., 2019).

####  2.3.3. The Game Scenario

Exergame should involve specific preferences for game mechanics, levels,
visuals, audio, and narrative. This requirement will unavoidably make it
essential to involve the target group in the design process from the start
(Martin-Niedecken and Mekler, 2018; Martin-Niedecken, 2018). The literature
offers suggestions for key elements of game scenarios. For instance, games
should include an immediate celebration of movement articulation by providing
direct and constrained amounts of feedback (Mueller and Isbister, 2014). Also,
games should involve achievable short-term challenges to foster long-term
motivation and help players identify rhythm in their movements, for example,
by setting movements that are mapped to specific sounds and visualizing
previous and upcoming movements (Mueller and Isbister, 2014; Mueller et al.,
2016). It is also important to provide a challenge that matches individual
skill levels, for instance, balancing the challenge level by monitoring the
player’s heart rate (Mueller et al., 2012).

###  2.4. Uncertainty in Games

Caillois (Caillois, 2001) says that the outcome of a game should be uncertain
for it to be enjoyable. Similarly, Costikyan (Costikyan, 2013) argues about
the importance of uncertainty in the overall game experience and has developed
an influential categorization of 11 sources of uncertainty within games.
Typical uncertainty sources are (1) Performative uncertainty: uncertainty of
physical performance (e.g., hand-eye coordination); (2) Solver’s uncertainty:
weighting a group of options against potential outcomes; (3) Player
unpredictability: not knowing how the opponents/teammates will act; (4)
Randomness: uncertainty emanating from random game elements. Recently, Kumari
et al. (Kumari et al., 2019) developed an empirically-based grounded taxonomy
of seven sources of uncertainty across the input-output loop that involves the
game, the player, and their interaction in an outcome. This taxonomy partially
maps onto existing taxonomies, especially the one proposed by Costikyan
(Costikyan, 2013). This, in turn, provides further evidence of its validity.
Hence, in this research, we used Costikyan’s sources of uncertainty to guide
the design of the uncertainty elements in our exergame.

To explore the effects of uncertainty in exergames, we applied three uncertain
elements in an exergame we developed: (1) False-Attacks: this concept is
originally from sports (e.g., basketball) and has been applied widely in
sports videogames (e.g., NBA 2K series). (2) Misses: this concept has been
widely used in games (e.g., Dungeon & Fighter) where an attack hits the
opponent but is counted as a miss by the system. (3) Critical Hits: this
concept has also been widely used in games (e.g., Dungeon & Fighter). When a
critical hit happens, the player issuing the hit causes more damages to the
opponent that a normal successful blow.

###  2.5. Game Experience for Different Age Groups

Users from different age groups often perceive gameplay elements
differently—for instance, what is motivating for one group may not be so for
another. Motivations can change with age: fantasy is a powerful motivational
factor in younger children (Greenberg et al., 1999), whereas competition and
challenge-related motives are stronger in older children and adolescents
(Sherry et al., 2006). Young adults are more motivated by rewarding
experiences, while older adults are more inspired by perceived benefits to
their health (Subramanian et al., 2019). Young adults tend to prefer visually
appealing graphics and music that fit the theme and nature of the game, but
older adults pay more attention to the feedback that helps them complete a
game (Subramanian et al., 2019). Furthermore, there is an increased
appreciation for the enjoyment that a game brings, greater satisfaction for
autonomy, and decreased competence as users age, especially after a certain
threshold (Birk et al., 2017). In other words, young adults prefer exergames
that allow them to challenge themselves physically and cognitively, but older
adults preferred exergames that are fun to play and are beneficial to their
health (Subramanian et al., 2019).

Gajadhar et al. (Gajadhar et al., 2008) investigated the social elements of
gameplay for young adults. They found that gameplay is most enjoyable when
gamers are co-located, less satisfying in mediated co-play, and the least
enjoyable in virtual co-play. However, these three social contexts (virtual,
mediated, and co-located co-play) do not positively influence older users like
younger adults (Blocker et al., 2014; Gajadhar et al., 2010). Gerling et al.
(Gerling et al., 2013) explored the effect of sedentary and motion-based
control tasks in games (such as pointing and tracking) for older adults and
younger adults, and found that older adults performed worse than young adults.

There is a large body of work on the experience of children (Andries and
Robertson, 2019; Duh et al., 2010; Eriksson et al., 2019) and young adults (Xu
et al., 2020b; Xu et al., 2019; Xu et al., 2020d), and older adults (De
Schutter, 2011; De Schutter and Vanden Abeele, 2010; Gerling et al., 2012)
with videogames. However, there is only limited attention given to middle-aged
players. Previous research suggested age-related declines could start when
people are in their mid-age; for instance, age-related memory impairment and
executive dysfunction can be found in people before they reach 50 (Ferreira et
al., 2015; Verhaeghen and Salthouse, 1997). Middle-aged adults suffer from
several age-related declines, including but not limited to lower working
memory (Meguro et al., 2000), grip strength (Kozakai et al., 2016), and muscle
mass (Brown and Hasser, 1996). Given this above research, our work involves
two groups, young adults (18-30) and middle-aged adults (45-65), to explore
the effect of age on exergames.

Table 1. Features and requirement for each move by the playera and the monsterb. Name |  Description of the move
---|---
Kicka |  An attack move that inflicts 10 hp damage to the opponent in the kicking direction and requires a 3-second cooldown.
Puncha,b |  An attack move that inflicts 10 hp damage to the opponent on the punching direction and requires a 3-second cooldown.
Zoom+Kicka |  A ranged attack move that inflicts 30 hp damage to the opponent in that attack range (1m) and requires a 5-second cooldown.
Squatb |  A ranged attack move that deals 30 hp damage and requires a 5-seconds to cooldown.
Zoom+Squata |  A defense move that releases a sphere to protect the user for 2 seconds and heals 20 hp if it could successfully defend the player from the monster’s attack. This move requires a 3-second cooldown.

##  3\. GestureFit: A Gestured-based Game

The game was implemented in Unity3D with the Oculus Integration
plugin333https://assetstore.unity.com/packages/tools/integration/oculus-
integration-82022 and the Kinect v2 Unity
plugin444https://assetstore.unity.com/packages/3d/characters/kinect-v2-examples-
with-ms-sdk-and-nuitrack-sdk-18708.

###  3.1. Rules and Logic

The design of our game was inspired by Nintendo Ring Fit
Adventure555https://www.nintendo.com/games/detail/ring-fit-adventure-switch/.
The goal of the game is for the player to stay alive and defeat a monster
three times. To do this, the player needs to perform gestures to make attacks
against the monster and defend themselves from being attacked by it. The
player begins with 100 health points (HP) while the monster has 500 HP. The
monster or player dies when their HP reaches 0. Both the monster and the
player have 3 lives. The monster could move leftward or rightward within a
2-meter range prior to its game starting position. Players’ lateral movement
is limited so that they are always within the operational tracking range
(Ioannou et al., 2019; Xu et al., 2020d). The game is designed to take this
into account so that the gameplay experience is not affected. Both visual and
audio feedback is provided to give a fuller range of sensory experience to
players.

####  3.1.1. Selected Gestures and Corresponding Attack/Defense Moves

There are three attack moves and one defense move. All moves can be released
by performing their corresponding gestures. These four moves are (i) Kick:
kicking using any leg, (ii) Punch: single hand punching, (iii) Zoom+Kick:
kicking using any leg and leaning arms forward and stretching them out, and
(iv) Zoom+Squat: performing a squat and leaning arms forward and stretching
them out. The selected gestures were chosen based on design recommendations
from previous studies on young adults (Xu et al., 2020d) and older adults
(Gerling et al., 2012). Table 1 lists pre-defined features and their
requirements.

####  3.1.2. The Use of Uncertainty

The uncertain condition includes three uncertain elements, which covers four
uncertainty sources (Costikyan, 2013):

  * •

`False-Attacks`: There is a 20% chance that the monster would perform a false-
attack (which lasts around 0.8 seconds) when the system triggers an attack-
related animation to trick the player into performing the defense move. False-
attacks cover the following uncertainty sources: a) Performative uncertainty:
our game challenges eye-body coordination (i.e., would the players be able to
cancel their defense move when they realize the monster is performing a false-
attack?), b) Solver’s uncertainty: it is concerned with whether performing or
not performing a defense move against potential outcomes (i.e., wasting a
defense move to a false-attack or being successful in defending from an actual
attack), and c) Player unpredictability: this is about the uncertainty of the
opponent’s movements (e.g., whether it is a false or real attack).

  * •

`Misses`: There is a 10% chance that the player’s or monster’s attack would be
regarded as a miss even if it hits the opponent. Randomness: misses act as a
random element in the game.

  * •

`Critical Hits`: There is a 10% chance that the player’s or monster’s attack
could be a critical hit, which would deal 50% more damage than a normal attack
move. Randomness: critical hits act as another random element in the game.

The only difference between the certain and non-certain conditions is that the
former does not include the above three uncertain features.

####  3.1.3. Monster Attack Design

In both conditions, the monster would perform an action every 2 sec. In the
certain condition, if any attack skill is available, there is 80% chance that
the action is an attack (either 100% for the only skill that is available or
50% for each skill that is available); otherwise, it is a walk. The uncertain
condition also follows this attack mechanism; the only difference is that if
an attack skill is available, there is 80% chance the action is attack-related
(i.e., 8/10 = a real attack, 2/10 = a false attack).

###  3.2. Game Procedure

The game starts with a training (warm-up) phase (see Figure 1a-b), where the
player needs to use attack and defense moves. The order of the moves required
for the player to perform is Kick, Punch, Zoom+Kick, Zoom+Squat. For attack
moves, the player needs to perform the corresponding gesture, and its attack
must damage the monster twice before proceeding to the next move. For the
defense moves, the player must successfully defend themselves from the
monster’s attacks twice to finish the training. The player needs to perform a
Zoom gesture between each move training to switch to the next move training.
After the training phase, the player needs to perform another Zoom gesture to
start the gameplay phase.

Figure 1. Screenshots of GestureFit: (a) LD training phase, (b) VR training
phase, (c) LD gameplay phase, and (d) VR gameplay phase. All variables are the
same in all versions except in VR the player information is slightly tilted.

Game procedure LD and VR versions

During the gameplay phase (see Figure 1c-d), players need to perform the
gestures to attack and defend themselves. If the players have no HPs, they
need to perform Zoom+Squat five times to regain life and perform Zoom once to
confirm they are ready to return. If the monster has no HPs, the game will
play an animation of the monster falling to the ground and is destroyed. After
a 5-second wait, the monster uses its second or third life and the game re-
starts. The game ends when the monster or the player has no lives and HPs
left.

###  3.3. Risk Control for Middle-aged Adults

We controlled the risk, if any, to a minimal level. As pointed in (Martin-
Niedecken, 2018; Martin-Niedecken and Mekler, 2018), having users involved in
the development process is useful. As such, for our game prototype, we had two
middle-aged adults frequently involved during the development process to test
the gestures’ suitability, tune parameters (e.g., cooldown time, shield
protection’s duration) and ensure accurate and meaningful execution of
movements. The selected gesture worked quite well since all middle-aged
participants had no issues performing them during the experimental gaming
sessions (as our results would show; more on this later).

Besides, we minimized any risks by (1) making a first-person viewing
perspective game so that players can see their motions, (2) limiting the
number of monster’s attack skills and having gaps in its attacks, (3)
restricting players’ position, (4) allowing them 5 sec rests after they took a
monster’s life, (5) allowing them to rest as much as they want after they lost
one life, and (6) displaying information (user’s skills, player’s HP, and
monster’s HP) in front of the users without the need for additional head
movement.

##  4\. Experiment

###  4.1. Experiment Design and Outcome Measures

The experiment followed a 2 × 2 within-subjects design with two within-
subjects factors: Display Type (DT: VR and LD) and (2) Game Condition (GC:
certain and uncertain). The order of DT × GC was counterbalanced in the
experiment.

To determine participants’ task performance, we collected the following (1)
completion time on each of the three lives of the monster; (2) success rate of
each move; and (3) the total number of each type of gestures performed.

Participants’ experience was measured with Game Experience Questionnaire (GEQ)
(IJsselsteijn et al., 2008) and Simulator Sickness Questionnaire (SSQ)
(Kennedy et al., 1993). We used the 33-item core module of the GEQ to measure
game experience, which consists of seven components: competence, immersion,
flow, tension, challenge, negative affect, and positive affect. Simulator
sickness was assessed using the 16-item SSQ, which produces 3 measures of
cybersickness (nausea, oculomotor, and disorientation).

Exertion was evaluated by (1) the average heart rate (avgHR%) expressed as a
percentage of a participant’s estimated maximum heart rate
(211-0.64$\times$age) (Nes et al., 2013), (2) calories burned, and (3) Borg
RPE 6-20 scale (Borg, 1982).

We measured the acceptability of the uncertain elements used in our games with
three questions: “I like the design of the false-attacks”, “I like the design
of attacks that could be missed by chance”, and “I like the design of attacks
that could be a critical hit by chance”. The questions followed a 1-7 Likert
scale, with 1 indicating “extremely disagree” and 7 indicating “extremely
agree”.

After completing the above questionnaires, we conducted a semi-structured
interview for participants with the following open-ended questions: “Overall,
what did you think about the game?”, “What did you like about the game?”,
“What did you not like about the game?”, “Was there anything more difficult
than you expected in the game?”, and “Was there anything more confusing than
you expected in the game?” (Drachen et al., 2018). Answers were recorded and
transcribed in text and later analyzed by two of the researchers following an
informal, simplified inductive open coding approach (Sanders and Stappers,
2013). Themes were concluded by the two researchers independently and agreed
in a post-coding meeting with a third researcher. Details of the themes can be
found in the feedback section (Section 4.5.5). There was no limit for the
length of participants’ responses.

###  4.2. Apparatus and Setup

We used an Oculus Rift CV1 as our VR HMD and a 50-inch 4K TV as our LD. Both
devices were connected to an HP Z workstation with an i7 CPU, 16GB RAM, and a
Nvidia Quadro P5200 GPU. Players’ gestures were detected via a Microsoft
Kinect 2, which was also connected to the HP Z workstation. The heart rate
(HR) was monitored by a Polar OH1 optical HR sensor, which has been proven to
be reliable compared to the gold standard of HR measurement with an
electrocardiography device (Hettiarachchi et al., 2019; Schubert et al.,
2018). Figure 2 shows the experiment setup and devices used in the experiment.

Figure 2. Experiment setup and the devices used in the experiment: (1) the
Oculus Rift CV1; (2) a 50-inch 4K TV; (3) the HP Z backpack; (4) the Microsoft
Kinect 2; and (5) Polar OH1.

Experiment setup

The experiment was conducted in an indoor laboratory room that could not be
seen from the outside. The laboratory room was well illuminated, and its
temperature was controlled by an air conditioner that regulated the room
temperature to 24℃ during the experiment.

###  4.3. Participants

####  4.3.1. Inclusion and Exclusion Criteria

Participants were recruited from a local university campus and a local
community center through posters, social media platforms, and a mailing list
for young adults between 18 and 30 years old and middle-aged adults between 45
to 65 years old. The study included participants who were not disabled, were
not pregnant (because of the physical exertion required to play the game), and
had not consumed any alcohol during the day (because blood alcohol level of
approximately 0.07% could reduce symptoms of cybersickness (Iskenderova et
al., 2017), which might affect the results of our study).

Participants were excluded from the experiment if they (1) answered “yes” to
any of the Physical Activity Readiness Questionnaire (Thomas et al., 1993)
questions, (2) had resting blood pressure higher than 140/90 mmHg, and (3) had
an extremely good or poor resting heart rate (RestHR) level (i.e., heart rate
range were the top 10% or the last 10% of the population) depending on their
age and gender (Ostchega et al., 2011).

####  4.3.2. Participants Background

Thirty-two (32) participants participated in our study—16 young adults (6
females; mean age = 20.6, SD = 1.31, range 18 to 23; BMI = 20.3, SD = 2.62),
and 16 middle-aged adults (5 females; mean age = 47.7, SD = 2.68, range 45 to
54; BMI = 23.8, SD = 2.04). Among young adults, 7 of them had experience with
VR HMDs, but none were regular users. Fourteen of them played videogames
before; 6 of them played regularly. For middle-aged adults, none had
experience with VR HMDs and videogames. There were no dropouts in this
experiment.

###  4.4. Procedure and Task

The duration of each session was about one hour. Before the experiment began,
participants needed to fill out a pre-experiment questionnaire that gathered
demographic information (e.g., age, gender, and experience with the VR device)
and Physical Activity Readiness Questionnaire (Thomas et al., 1993). After a
brief description of the experimental procedure, participants signed the
consent to participate in the experiment and collected their RestHR and
resting blood pressure level. They were also asked to enter their age, gender,
height, and weight into the Polar Beat app.

Before each condition started, a researcher would help each participant to
wear the required devices (e.g., Polar OH1). Once their HR reached the
equivalent RestHR level, they were led to the experiment stage, beginning with
a training (warm-up) phase and then the gameplay phase (see Figure 1 and
Section 3.2). After each condition, they were asked to fill in post-condition
questionnaires (GEQ (IJsselsteijn et al., 2008), SSQ (Kennedy et al., 1993),
Borg RPE 6-20 scale (Borg, 1982)). They proceeded to the next condition when
they felt rested and their HR was at the resting level. Once they completed
all conditions, they needed to complete a post-experiment questionnaire and a
semi-structured interview.

###  4.5. Results

####  4.5.1. Statistical Analysis

We used SPSS version 24 for windows for data analysis. We employed a three-way
mixed ANOVA with GC (uncertain and certain) and DT (VR and LD) as within-
subjects variables and Age (young adults—YA and middle-aged adults—MA) as the
between-subjects variable. We applied Age as the between-subjects variable
because we want to follow existing approaches in the literature (Nacke et al.,
2009; Gerling et al., 2013; Wang et al., 2017). Bonferroni correction was used
for pairwise comparisons. Effect sizes ($\eta_{p}^{2}$) were added whenever
feasible. To minimize any impact on the readability of the paper, we have
placed all the data results in the tables of an appendix located after the
references.

####  4.5.2. Performance

Completion Time on Each Life. Figure 3a presents the mean completion time of
each life (i.e., monster’s life1, life2, life3). ANOVA tests yielded a
significant effect of Age on life2 ($F_{1,30}=7.246,p<.05,\eta_{p}^{2}=.195$)
and life3 ($F_{1,30}=9.088,p<.01,\eta_{p}^{2}=.232$). Post-hoc pairwise
comparisons revealed that YA could destroy the monster faster than MA on life2
and life3. No other significant effects were found.

Figure 3. (a) Mean completion time on each monster’s life according to age
group, (b) mean success rate of Kick and Zoom+Squat according to DT, and (c)
mean success rate of Zoom+Squat and Zoom+Kick according to GC and Age. Error
bars indicate ±2 standard errors.

Fig3

Success Rate. Table 2 shows the ANOVA tests of the success rate for
Zoom+Squat, Kick, Zoom+Kick. Corresponding success rate data can be found in
Figure 3b,c and Figure 4a. In summary, (1) participants have a higher defense
(i.e., Zoom+Squat) success rate in certain GC than uncertain GC, (2) YA have a
higher defense success rate in VR than LD, (3) participants have a higher Kick
success rate in VR than LD, (4) YA had a higher Zoom+Kick success rate than MA
in VR, (5) YA had a higher Zoom+Kick success rate in VR than LD, and (6) YA
had a higher Zoom+Kick success rate than MA in uncertain GC.

Total Number of Gestures Performed. Table 3 shows the ANOVA tests of the total
number of gestures performed for Zoom+Squat, Punch, Zoom+Kick. Corresponding
success rate data can be found in Figure 4b,c. In summary, (1) YA and MA both
performed more defense moves (i.e., Zoom+Squat) in uncertain GC than certain
GC, (2) MA performed more defense moves than YA in both certain and uncertain
GC, (3) YA performed more Punch than MA in LD, (4) MA performed more Punch in
VR than LD, (5) participants performed more Zoom+Kick in uncertain GC than in
certain GC.

Table 2. Three-way mixed ANOVA test results for success rate. Significant results where $p<.05$ are shown in light green, $p<.01$ in green, and $p<.001$ in dark green. Punch, Age, DT × GC, DT × Age × GC have no significant results and therefore not shown for better clarity. No sig indicates no significant results. |  Kick |  Zoom+Squat |  Zoom+Kick
---|---|---|---
DT |  $F_{1,30}=4.836,p<.05,\eta_{p}^{2}=.139$ |  $F_{1,30}=14.403,p<.001,\eta_{p}^{2}=.324$ |  No sig
GC |  No sig |  $F_{1,30}=21.799,p<.001,\eta_{p}^{2}=.421$ |  No sig
DT × Age |  No sig |  $F_{1,30}=7.942,p<.01,\eta_{p}^{2}=.209$ |  $F_{1,30}=5.008,p<.05,\eta_{p}^{2}=.143$
GC × Age |  No sig |  No sig |  $F_{1,30}=6.439,p<.05,\eta_{p}^{2}=.177$
Post-hoc |  DT: VR ¿ LD ($p<0.5$; see Figure 3b) |  GC: uncertain ¡ certain ($p<.001$; see Figure 3c); YA: VR ¿ LD ($p<.001$; see Figure 4a) |  VR: YA ¿ MA ($p<.05$; see Figure 4a); YA: VR ¿ LD ($p<.05$; see Figure 4a); Uncertain: YA ¿ MA ($p<.05$; see Figure 3c)
Table 3. Three-way mixed ANOVA test results for the total number of gestures performed. Significant results where $p<.05$ are shown in light green, $p<.01$ in green, and $p<.001$ in dark green. Kick, DT, GC × DT, Age × GC × DT have no significant results and therefore not shown for better clarity. No sig indicates no significant results. |  Punch |  Zoom+Squat |  Zoom+Kick
---|---|---|---
GC |  No sig |  $F_{1,30}=129.718,p<.001,\eta_{p}^{2}=.812$ |  $F_{1,30}=5.473,p<.05,\eta_{p}^{2}=.154$
Age |  $F_{1,30}=5.268,p<.05,\eta_{p}^{2}=.149$ |  $F_{1,30}=18.638,p<.001,\eta_{p}^{2}=.383$ |  No sig
GC × Age |  No sig |  $F_{1,30}=9.231,p<.01,\eta_{p}^{2}=.235$ |  No sig
DT × Age |  $F_{1,30}=4.981,p<.05,\eta_{p}^{2}=.142$ |  No sig |  No sig
Post-hoc |  LD: YA ¿ MA ($p<.01$; see Figure 4b); MA: VR ¿ LD ($p<.01$; see Figure 4b) |  YA and MA: uncertain ¿ certain (both $p<.001$; see Figure 4c); Uncertain and certain: MA ¿ YA (both $p<.001$; see Figure 4c) |  GC: uncertain ¿ certain ($p<.05$; see Figure 4c)

Figure 4. (a) Mean success rate of Zoom+Kick and Zoom+Squat according to DT
and Age, (b) mean total number of Punch performed according to DT and Age, and
(c) mean total number of Zoom+Kick and Zoom+Squat performed according to GC
and Age. Error bars indicate ±2 standard errors.

Fig4

####  4.5.3. Experience

Game Experience. ANOVA tests yielded a significant effect of Age on competence
($F_{1,30}=20.787,p<.001,\eta_{p}^{2}=.409$), immersion
($F_{1,30}=23.010,p<.001,\eta_{p}^{2}=.434$), tension
($F_{1,30}=20.815,p<.001,\eta_{p}^{2}=.410$), negative affect
($F_{1,30}=19.278,p<.001,\eta_{p}^{2}=.391$), positive affect
($F_{1,30}=20.810,p<.001,\eta_{p}^{2}=.410$). Post-hoc pairwise comparisons
showed that YA had a higher levels of competence, immersion, tension, negative
affect, and positive affect than MA (see Figure 5a).

Figure 5. (a) Game experience questionnaire rating of subscales according to
Age, (b) mean flow rating according to DT and Age, and (c) mean nausea and
oculomotor rating according to Age. Error bars indicate ±2 standard errors.

Fig5

There was a significant effect of DT
($F_{1,30}=40.298,p<.001,\eta_{p}^{2}=.573$) on flow, showing that
participants experienced a greater flow in VR than LD. Additionally, ANOVA
tests yielded a significant effect of DT × Age
($F_{1,30}=11.163,p<.01,\eta_{p}^{2}=.271$) on flow. Post-hoc pairwise
comparisons revealed that (1) YA experienced a lower flow than MA in LD
($p<.001$), (2) VR could lead to a greater flow experience than LD in both YA
($p<.05$) and MA ($p<.001$). Figure 5b depicts the corresponding flow values.
No other significant effects were found.

Simulator Sickness. ANOVA tests yielded a significant effect of Age on nausea
($F_{1,30}=7.049,p<.05,\eta_{p}^{2}=.190$) and oculomotor
($F_{1,30}=5.242,p<.05,\eta_{p}^{2}=.149$), but not on disorientation
($F_{1,30}=2.490,p=.125,\eta_{p}^{2}=.077$). Post-hoc pairwise comparisons
revealed that (1) YA experienced a higher nausea level than MA (see Figure
5c), and (2) YA experienced a higher oculomotor level than MA (see Figure 5c).
No other significant effects were found.

Uncertain Elements’ Ratings. We employed a two-way mixed ANOVA with Elements
(false-attack, hit, miss) as the within-subjects variable and Age as the
between-subjects variable. The ANOVA tests yielded a significant effect of
Elements ($F_{1.607,48.224}=3.547,p<.05,\eta_{p}^{2}=.106$), but not Elements
× Age ($F_{1.607,48.224}=1.656,p=.200$) on the ratings of the uncertain
elements. There was a significant effect of Age
($F_{1,30}=8.217,p<.001,\eta_{p}^{2}=.215$) on the uncertain elements’
ratings, showing that uncertainty settings were rated higher in YA (M = 5.88,
s.e. = 0.20) than MA (M = 5.08, s.e. = 0.20). However, post-hoc pairwise
comparisons could not find any significance between uncertain elements.

####  4.5.4. Exertion

Table 4 shows the ANOVA tests of all exertion measures. In summary, (1) YA had
lower avgHR% than MA in uncertain GC, (2) MA had a higher avgHR% in uncertain
GC than certain GC, (3) participants burned more calories in uncertain GC than
certain GC, (4) MA participants burned more calories than YA participants (see
Figure 6b), (5) Borg RPE for uncertain GC was higher than certain GC among YA
and MA, (6) the Borg RPE for YA was higher than MA in certain GC and uncertain
GC.

Table 4. Three-way mixed ANOVA test results for exertion measurements. Significant results where $p<.05$ are shown in light green, $p<.01$ in green, and $p<.001$ in dark green. DT, GC × DT, Age × DT, Age × GC × DT have no significant results and therefore not shown for better clarity. No sig indicates no significant results. |  avgHR% |  Calories Burned |  Borg RPE
---|---|---|---
GC |  $F_{1,30}=30.560,p<.001,\eta_{p}^{2}=.505$ |  $F_{1,30}=45.587,p<.001,\eta_{p}^{2}=.603$ |  $F_{1,30}=39.533,p<.001,\eta_{p}^{2}=.569$
Age |  $F_{1,30}=7.754,p<.01,\eta_{p}^{2}=.205$ |  $F_{1,30}=8.353,p<.01,\eta_{p}^{2}=.218$ |  $F_{1,30}=15.488,p<.001,\eta_{p}^{2}=.340$
GC × Age |  $F_{1,30}=8.279,p<.01,\eta_{p}^{2}=.248$ |  No sig |  $F_{1,30}=4.759,p<.05,\eta_{p}^{2}=.137$
Post-hoc |  Uncertain: YA ¡ MA (both $p<.01$; see Figure 6a); MA: uncertain ¿ certain ($p<.001$; see Figure 6a) |  GC: uncertain ¿ certain ($p<.001$; see Figure 6b); Age: MA ¿ YA ($p<.01$; see Figure 6b) |  YA: uncertain ¿ certain ($p<.01$; see Figure 6c); MA: uncertain ¿ certain ($p<.001$; see Figure 6c); Certain: YA ¿ MA ($p<.001$; see Figure 6c); Uncertain: YA ¿ MA ($p<.01$; see Figure 6c)

Figure 6. (a) Mean avgHR% according to GC and Age, (b) mean calories burned,
and (c) mean Borg RPE rating according to GC and Age. Error bars indicate ±2
standard errors.

Fig6

####  4.5.5. User Rankings and Feedback

The VR uncertain version was rated the best version among the four versions by
23 participants (12 YA). Only 5 participants (4 YA) selected VR certain as
their top option and 4 MA chose LD uncertain version as their top selection.

Feedback. From the coded transcripts, three main themes emerged (element of
the games, general gaming experience, and exercising for health) from the two
researchers, who first reviewed the transcripts independently. They were
agreed by a third researcher after a second discussion. Thirty-two
participants were labeled P1-P16 (YA group) and P17-P32 (MA group).

Overall, both user groups perceived the game as “enjoyable” (10 YA, 9 MA),
“novel” (9 YA, 8 MA), and “good for their health” (9 YA, 14 MA) and none of
them perceive anything that was confusing in the game. Both groups perceived
the false-attacks more difficult than expected (P3, P13, P20, P22, P24-27),
but only MA participants mentioned that sometimes they could not perform the
defense move in time.

Regarding the elements that they liked about the game, the comments from the
two groups came from two different perspectives. Most YA focused on the game
elements (e.g., “the false-attack by the opponents” [P3, P14, P16], “critical
hits” [P5], “misses” [P11], “using gestures to trigger attacks are fun and
easy to understand” [P6, P9, P13]) while only a few mentioned about the health
benefits as their preferred elements (P8, P10, P15). This is a completely
different for the MA, where 13 MA mentioned they liked the game because it
could be a good exercise activity while only 6 comments focused on design
elements (e.g., “false-attacks by the monster is a good design” [P23, P27,
P30], “it tricks me into performing defense moves, which is good for my
health” [P20, P24, P25]).

The two generations focused on the different perspectives again regarding the
elements that they did not like. Most comments from YA were about the graphics
and models used in the game, that they should be improved and more moves could
be added. On the other hand, most MA believed that the uncertain elements are
sometimes overused, which caused them to perform too many defense moves and
made them feeling exhausted during the game.

##  5\. Discussion

###  5.1. Effect of Age on Exergames

In general, the performance (i.e., completion times, success rates for both
attack and defense moves) of middle-aged adults were worse than young adults
in our motion-based first-person exergame, which is in line with previous
studies of similar games (Gerling et al., 2013). One possible reason could be
age-related declines in mobility; for instance, middle-aged adults typically
require more time to perform gestures (Ferrucci et al., 2016). They also were
not able to react to the monster’s attack sometimes or cancel their defense
moves when realizing that the monster was performing false-attacks; for
example, P20, P22, P24-25, P27-28: “I could not react in time.” Hence, it is
necessary to take into account age-related declines (e.g., working memory
(Meguro et al., 2000), grip strength (Kozakai et al., 2016), and muscle mass
(Brown and Hasser, 1996)) when designing exergames for middle-aged adults.

In addition, the two age groups perceived the game experience differently. We
found that young adults were more immersive (immersion, flow) in the game than
middle-aged adults and had a higher positive emotion, efficacy, competence.
However, young adults still felt more annoyed and experienced more negative
emotions than middle-aged adults even though they had a better performance
(e.g., the successful attack rate is much higher). One possible reason is that
young adults might have expected that they should perform much better due to
their competitive expectations of themselves and the game, while the
competition was downplayed in middle-aged adults (Subramanian et al., 2019).

Previous research has suggested that there may be a decline in susceptibility
to VR sickness as people age (Bardey et al., 2013). Our results also support
this, as we found that young adults felt sicker during gameplay than middle-
aged adults. Overall, sickness level for all participants were either
negligible or very low, with no participants experiencing severe simulator
sickness. That is, all participants had no issues in playing the game.

Existing literature in the exercise domain (e.g., tai chi (Lan et al., 2004),
arms training (Groslambert et al., 2006), arm abduction (Pincivero et al.,
2010)) have suggested that age does not affect the exertion level of the
exercise. However, this is not supported by our results because we found that
our two groups of participants produced different levels of exertion (middle-
aged adults had a higher avgHR% in the uncertain condition and burned more
calories than young adults but gave a lower Borg RPE ratings). Further study
is required to explain this.

###  5.2. Effect of Display Type on Exergames

Our results suggest that participants had a better performance in VR (i.e.,
higher success rates in attack and defense moves in VR than LD). This is
understandable because the greater flow experience brought by VR to the
players had a positive effect on performance in the game (Admiraal et al.,
2011). A previous study (Xu et al., 2020d) that also focused on the effect of
DT versus VR showed that VR could provide a greater positive game experience
(e.g., challenge, flow, immersion) to the players than LD, which was also
found in our results (i.e., VR led to a higher flow rating than LD). Existing
literature also indicated that game experience (from GEQ) could be perceived
the same in both VR and LD (Xu et al., 2019). One reason could be that in (Xu
et al., 2019), participants only experienced 4 minutes of gameplay, which is
relatively short for developing a fuller picture of the technologies. Hence,
we suggest that future studies consider a longer game duration, like 7- 8
minutes used in our research and in (Xu et al., 2020d), to let the players
experience a game in each technology more fully.

In addition, our findings indicate there was no significant difference
regarding the level of sickness that participants experienced between VR and
LD when playing the motion-based exergame, which is in line with (Xu et al.,
2019) but not (Xu et al., 2020d) where researchers reported that playing a
motion-based exergame in VR could lead to a higher sickness than LD. One
possible explanation could be that the type of game used in the experiment was
different. Our game and the game used in (Xu et al., 2019) involved more
interaction with the virtual world than the game in (Xu et al., 2020d). For
instance, players had direct contacts with the virtual objects (either through
attacking and defending against the monster in our game or directly using the
hands or feet to hit the objects in the game from (Xu et al., 2019)), which is
not the case for (Xu et al., 2020d) where the gestures performed by the users
did not have direct contact with the virtual objects in the form of cubes.

###  5.3. Effect of Uncertainty on Exergames

The purpose of the design of false-attacks, one uncertain element in our
exergame, was to trick the players into using the defense moves. Our results
show that this element achieved its intended goal because participants
performed more defense moves (Zoom+Squat) in the uncertain condition than the
counterpart condition. We also observed during the experiment that this design
tricked all players across both groups.

In addition, the design of misses had also forced them to perform more attack
moves in their attempts to kill the monster. Hence, participants had a higher
exertion level (i.e., avgHR%—MA, calories burned, Borg RPE) in the uncertain
condition. Furthermore, what is interesting to note is that participants did
not feel a worse experience by these design features since (1) they did not
complain about the features, and (2) the gameplay experience and sickness in
both game conditions were not significantly different. Therefore, we believe
that involving uncertain elements (i.e., false-attacks, misses, and critical
hits) in the type of exergame similar to ours could increase players’ energy
costs without incurring negative gameplay experiences in both VR and LD.

###  5.4. Design Guidelines

####  5.4.1. Applying Uncertainty to Exergames

As our results show, the proposed uncertain elements in our exergame could be
useful in enhancing exertion levels during game sessions. We list with
examples of how these uncertain elements can be applied to other exergames.

For sports exergames, false-attack can be used in several ways. For example,
in the boxing game Creed: Rise to
Glory666https://www.oculus.com/experiences/rift/1872428116153565/, a false-
attack can be directly applied to Creed’s attack strategy to trick players
into making defense moves. False-attacks can be enhanced further by following
a real attack after the animation of a false-attack. For Eleven Table Tennis
VR777https://www.oculus.com/experiences/rift/989106554552337/, this can be
added as a way for NPC to pretend they want to move into one direction but not
moving into that direction. This type of false moves can be used in designing
basketball and football exergames where trickery is a key to make a defending
player go into one direction so that the player can move into the opposite way
(e.g., Kinect Sports: Soccer888https://marketplace.xbox.com/en-
US/Product/Kinect-Sports/66acd000-77fe-1000-9115-d8024d5308c9).

For exergames that involve one-way interaction with the enemy (i.e., player to
NPC), critical hits and misses can be used. For instance, in the tower defense
game Longbowman (Xie et al., 2018), critical hits and misses can be designed
with additional features. A critical hit can deal additional damage and also
slow down the movement of the enemy. In contrast, a miss does not damage the
enemy and would make the enemy become angry and move faster.

For exergames that involve two-way interaction with the enemy (player to NPC
and NPC to player), all three elements can be used. For instance, in Ring Fit
Adventure, a motion-based active game for the Nintendo Switch, all these
elements can be added in a similar way that we did in our exergame since it is
designed based on this commercial game.

####  5.4.2. Highlighting Health Benefits to Middle-aged and Older Adults

Like older adults (Subramanian et al., 2019), middle-aged adults believe that
exergames are helpful to their health. We suggest making the potential health
benefits to middle-aged adults explicit and clear inside the game and as part
of the gameplay experience. For instance, designers could (1) introduce the
benefits of each gesture before the game, (2) present the energy cost like
calories burned during the game as part of any dynamic visual and audio
feedback, (3) give a summary report of the overall performance (e.g., for each
type of gestures, providing the total number the player performed) after the
game.

###  5.5. Limitations and Future Work

There are some limitations in this research, which can also serve as
directions for the future. One limitation is that we tested three elements of
uncertainty (false-attacks, misses, and critical hits) that covers four
uncertainty sources. Future work could explore more uncertainty sources
(Costikyan, 2013) in motion-based exergames. For example, we can use (1)
analytical complexity, by allowing more skills for the player but require the
player to kill the monster in a limited time so that the player needs to
analyze the best strategy to fight against their opponent carefully. It is
possible to integrate (2) hidden information, by not showing information of
the opponent’s attack moves. Addition, (3) narrative anticipation can be used
by adding a storyline to a game and fighting an opponent would reward them
with the corresponding piece of the storyline. By doing this, the player has
the desire to know the next piece of the storyline (Murnane et al., 2020).

In addition, there are some limitations related to the choice of VR HMD and
exergames in current commercial VR HMDs. We used the Oculus Rift CV1. Newer VR
HMDs (i.e., VIVE Pro Eye) that come with a higher resolution could impact
simulator sickness and game experience. We used the Oculus Rift CV1 because we
wanted to have consistency with prior studies (Xu et al., 2019; Xu et al.,
2020d). The Rift CV1, as a tethered helmet, has a limited range of motion
because of the attached cables. While standalone devices like Oculus Quest do
not have this limitation, they suffer from latency issues when used with
external motion sensors (i.e., Kinect) to capture motion data. In addition,
long gameplay sessions wearing any current HMDs could result in sweats in the
glasses; thus, the length of gameplay should be carefully designed to prevent
this issue. Also, to make MA-friendly exergames, future games should involve
more simple gestures (like zoom—hands stretching out, hands-up) to eliminate
any risks when wearing a VR HMD.

Our study only involved a single session. Longer-term studies will be useful
to determine if the same results hold and to determine additional effects that
may come with long-term exposures. In addition, due to the COVID-19, we cannot
to include the elderly adults (i.e., those 65 years old and above) in the
experiment. Future work could have all these three groups of adults (i.e.,
young, middle-aged, elderly) to assess their relative performance and
experience with exergames.

##  6\. Conclusion

In this research, we have investigated the effect of display type (virtual
reality and large display) with or without elements of uncertainty in motion-
based first-person perspective exergames. We also have explored the impact of
age by comparing game performance, gameplay experience, and level of energy
exertion between young adults and middle-aged adults. Our results suggest the
following three conclusions: (1) For the type of exergame like ours, virtual
reality could improve game performance while maintaining the same level of
sickness as large displays. (2) Uncertain elements like those used in this
research’s motion-based exergame might not help enhance the overall game
experience, but are instrumental in increasing exertion levels, which is one
of the essential features of exergames. (3) Exergames for middle-aged adults
should be carefully designed with consideration to age-related declines,
similar to older adults. We also proposed two main design guidelines which can
pave the way for improving the acceptability of VR exergames among young and
middle-aged adults.

###### Acknowledgements.

The authors would like to thank the anonymous reviewers for their valuable
comments and helpful suggestions and the Committee Member who guided the
revision of our paper. The work is supported in part by Xi’an Jiaotong-
Liverpool University (XJTLU) Key Special Fund (KSF-A-03) and XJTLU Research
Development Fund.

## References

  * (1)
  * Admiraal et al. (2011) Wilfried Admiraal, Jantina Huizenga, Sanne Akkerman, and Geert ten Dam. 2011.  The concept of flow in collaborative game-based learning.  _Group Awareness in CSCL Environments_ 27, 3 (May 2011), 1185–1194.  https://doi.org/10.1016/j.chb.2010.12.013
  * Andries and Robertson (2019) Valentina Andries and Judy Robertson. 2019.  Designing Social Play to Support Young Hospitalised Children. In _Proceedings of the 18th ACM International Conference on Interaction Design and Children_ _(IDC ’19)_. Association for Computing Machinery, New York, NY, USA, 550–555.  https://doi.org/10.1145/3311927.3325317 event-place: Boise, ID, USA.
  * Barathi et al. (2018) Soumya C. Barathi, Eamonn O’Neill, Christof Lutteroth, Daniel J. Finnegan, Matthew Farrow, Alexander Whaley, Pippa Heath, Jude Buckley, Peter W. Dowrick, Burkhard C. Wuensche, and James L. J. Bilzon. 2018.  Interactive Feedforward for Improving Performance and Maintaining Intrinsic Motivation in VR Exergaming. In _Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems - CHI ’18_. ACM Press, Montreal QC, Canada, 1–14.  https://doi.org/10.1145/3173574.3173982
  * Bardey et al. (2013) Aurore Bardey, Gaëlle Quarck, Paolino F., Pierre Denise, Michel Paolino, John Golding, and Venera GHULYAN-BEDIKIAN. 2013.  Motion sickness susceptibility in healthy subjects and vestibular patients: effects of gender, age and trait-anxiety.  _Journal of Vestibular Research_ 23, 4-5 (Nov. 2013), 203–209.  https://doi.org/10.3233/VES-130501
  * Berkovsky et al. (2010) Shlomo Berkovsky, Mac Coombe, Jill Freyne, Dipak Bhandari, and Nilufar Baghaei. 2010.  Physical Activity Motivating Games: Virtual Rewards for Real Activity. In _Proceedings of the SIGCHI Conference on Human Factors in Computing Systems_ (Atlanta, Georgia, USA) _(CHI ’10)_. Association for Computing Machinery, New York, NY, USA, 243–252.  https://doi.org/10.1145/1753326.1753362
  * Birk et al. (2017) Max V. Birk, Maximilian A. Friehs, and Regan L. Mandryk. 2017\.  Age-Based Preferences and Player Experience: A Crowdsourced Cross-Sectional Study. In _Proceedings of the Annual Symposium on Computer-Human Interaction in Play_ _(CHI PLAY ’17)_. Association for Computing Machinery, New York, NY, USA, 157–170.  https://doi.org/10.1145/3116595.3116608 event-place: Amsterdam, The Netherlands.
  * Blocker et al. (2014) Kenneth Blocker, Timothy Wright, and Walter Boot. 2014\.  Gaming Preferences of Aging Generations.  _Gerontechnology_ 12, 3 (June 2014), 174–184.  https://doi.org/10.4017/gt.2014.12.3.008.00
  * Bogost (2005) Ian Bogost. 2005\.  The Rhetoric of Exergaming. In _Proceedings of the Digital Arts and Cultures (DAC)_. Copenhagen, Denmark, 10.  https://doi.org/10.1.1.500.1480
  * Borg (1982) Gunnar Borg. 1982\.  Psychophysical bases of perceived exertion.  _Medicine & Science in Sports & Exercise_ 14, 5 (1982), 377–381.  insights.ovid.com
  * Brown and Hasser (1996) Marybeth Brown and Eileen M Hasser. 1996.  Complexity of age-related change in skeletal muscle.  _The Journals of Gerontology Series A: Biological Sciences and Medical Sciences_ 51, 2 (1996), B117–B123.  Publisher: The Gerontological Society of America.
  * Caillois (2001) Roger Caillois. 2001\.  _Man, Play and Games_ (reprint edition ed.).  University of Illinois Press, Urbana.
  * Costikyan (2013) Greg Costikyan. 2013\.  _Uncertainty in games_.  Mit Press.
  * De Schutter (2011) Bob De Schutter. 2011\.  Never Too Old to Play: The Appeal of Digital Games to an Older Audience.  _Games and Culture_ 6, 2 (May 2011), 155–170.  https://doi.org/10.1177/1555412010364978 Publisher: SAGE Publications.
  * De Schutter and Vanden Abeele (2010) Bob De Schutter and Vero Vanden Abeele. 2010.  Designing Meaningful Play within the Psycho-Social Context of Older Adults. In _Proceedings of the 3rd International Conference on Fun and Games_ _(Fun and Games ’10)_. Association for Computing Machinery, New York, NY, USA, 84–93.  https://doi.org/10.1145/1823818.1823827 event-place: Leuven, Belgium.
  * DeKoven (2002) Bernie DeKoven. 2002\.  _The Well-Played Game: A Playful Path to Wholeness_.  iUniverse, San Jose.
  * Drachen et al. (2018) Anders Drachen, Pejman Mirza-Babaei, and Lennart Nacke (Eds.). 2018\.  _Games User Research_.  Oxford University Press, Oxford, UK ; New York.
  * Duh et al. (2010) Henry Been-Lirn Duh, Sharon Lynn Chu Yew Yee, Yuan Xun Gu, and Vivian Hsueh-Hua Chen. 2010\.  A Narrative-Driven Design Approach for Casual Games with Children. In _Proceedings of the 5th ACM SIGGRAPH Symposium on Video Games_ _(Sandbox ’10)_. Association for Computing Machinery, New York, NY, USA, 19–24.  https://doi.org/10.1145/1836135.1836138 event-place: Los Angeles, California.
  * Eggenberger et al. (2015) Patrick Eggenberger, Vera Schumacher, Marius Angst, Nathan Theill, and Eling D de Bruin. 2015.  Does multicomponent physical exercise with simultaneous cognitive training boost cognitive performance in older adults? A 6-month randomized controlled trial with a 1-year follow-up.  _Clinical interventions in aging_ 10 (Aug. 2015), 1335–1349.  https://doi.org/10.2147/CIA.S87732
  * Eriksson et al. (2019) Eva Eriksson, Gökçe Elif Baykal, Staffan Björk, and Olof Torgersson. 2019.  Using Gameplay Design Patterns with Children in the Redesign of a Collaborative Co-Located Game. In _Proceedings of the 18th ACM International Conference on Interaction Design and Children_ _(IDC ’19)_. Association for Computing Machinery, New York, NY, USA, 15–25.  https://doi.org/10.1145/3311927.3323155 event-place: Boise, ID, USA.
  * Ferreira et al. (2015) Daniel Ferreira, Rut Correia, Antonieta Nieto, Alejandra Machado, Yaiza Molina Rodríguez, and José Barroso. 2015.  Cognitive decline before the age of 50 can be detected with sensitive cognitive measures.  _Psicothema_ 27, 3 (May 2015), 216–222.  https://doi.org/10.7334/psicothema2014.192
  * Ferrucci et al. (2016) Luigi Ferrucci, Rachel Cooper, Michelle Shardell, Eleanor Simonsick, Jennifer Schrack, and Diana Kuh. 2016.  Age-Related Change in Mobility: Perspectives From Life Course Epidemiology and Geroscience.  _The Journals of Gerontology Series A: Biological Sciences and Medical Sciences_ 71, 9 (March 2016), 1184–1194.  https://doi.org/10.1093/gerona/glw043
  * Gajadhar et al. (2008) Brian J. Gajadhar, Yvonne A. W. de Kort, and Wijnand A. IJsselsteijn. 2008\.  Shared Fun Is Doubled Fun: Player Enjoyment as a Function of Social Setting. In _Fun and Games_ , Panos Markopoulos, Boris de Ruyter, Wijnand IJsselsteijn, and Duncan Rowland (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 106–117.  https://doi.org/10.1007/978-3-540-88322-7_11
  * Gajadhar et al. (2010) Brian J. Gajadhar, Henk Herman Nap, Yvonne A. W. de Kort, and Wijnand A. IJsselsteijn. 2010\.  Out of Sight, out of Mind: Co-Player Effects on Seniors’ Player Experience. In _Proceedings of the 3rd International Conference on Fun and Games_ _(Fun and Games ’10)_. Association for Computing Machinery, New York, NY, USA, 74–83.  https://doi.org/10.1145/1823818.1823826 event-place: Leuven, Belgium.
  * Gao and Mandryk (2012) Yue Gao and Regan Mandryk. 2012.  The acute cognitive benefits of casual exergame play. In _Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems - CHI ’12_. ACM Press, Austin, Texas, USA, 1863\.  https://doi.org/10.1145/2207676.2208323
  * Gerling et al. (2012) Kathrin Gerling, Ian Livingston, Lennart Nacke, and Regan Mandryk. 2012.  Full-body motion-based game interaction for older adults. In _Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems - CHI ’12_. ACM Press, Austin, Texas, USA, 1873\.  https://doi.org/10.1145/2207676.2208324
  * Gerling et al. (2013) Kathrin M. Gerling, Kristen K. Dergousoff, and Regan L. Mandryk. 2013.  Is Movement Better? Comparing Sedentary and Motion-Based Game Controls for Older Adults. In _Proceedings of Graphics Interface 2013_ _(GI ’13)_. Canadian Information Processing Society, CAN, 133–140.  event-place: Regina, Sascatchewan, Canada.
  * Greenberg et al. (1999) Paul E. Greenberg, Tamar Sisitsky, Ronald C. Kessler, Stan N. Finkelstein, Ernst R. Berndt, Jonathan R. T. Davidson, James C. Ballenger, and Abby J. Fyer. 1999.  The economic burden of anxiety disorders in the 1990s.  _The Journal of Clinical Psychiatry_ 60, 7 (1999), 427–435.  https://doi.org/10.4088/JCP.v60n0702 Place: US Publisher: Physicians Postgraduate Press.
  * Groslambert et al. (2006) Alain Groslambert, Céline C Grange, Stéphane Perrey, Jérôme Maire, Nicolas Tordi, and Jean Denis Rouillon. 2006.  Effects of Aging on Perceived Exertion and Pain During Arm Cranking in Women 70 to 80 YEARS OLD.  _Journal of sports science & medicine_ 5, 2 (June 2006), 208–214.  https://pubmed.ncbi.nlm.nih.gov/24259993 Publisher: Asist Group.
  * Hardy et al. (2015) Sandro Hardy, Tim Dutz, Josef Wiemeyer, Stefan Göbel, and Ralf Steinmetz. 2015.  Framework for personalized and adaptive game-based training programs in health sport.  _Multimedia Tools and Applications_ 74, 14 (July 2015), 5289–5311.  https://doi.org/10.1007/s11042-014-2009-z
  * Hernandez et al. (2013) Hamilton A. Hernandez, Zi Ye, T.C. Nicholas Graham, Darcy Fehlings, and Lauren Switzer. 2013\.  Designing action-based exergames for children with cerebral palsy. In _SIGCHI Conference on Human Factors in Computing Systems_. ACM Press, Paris, France, 1261–1270.  https://doi.org/10.1145/2470654.2466164
  * Hettiarachchi et al. (2019) Imali T. Hettiarachchi, Samer Hanoun, Darius Nahavandi, and Saeid Nahavandi. 2019.  Validation of Polar OH1 optical heart rate sensor for moderate and high intensity physical activities.  _PLOS ONE_ 14, 5 (May 2019), e0217288.  https://doi.org/10.1371/journal.pone.0217288
  * IJsselsteijn et al. (2008) W. A. IJsselsteijn, Y. A. W. De Kort, and Karolien Poels. 2008\.  The game experience questionnaire.  _Eindhoven: Technische Universiteit Eindhoven_ (2008).
  * Ioannou et al. (2019) Christos Ioannou, Patrick Archard, Eamonn O’Neill, and Christof Lutteroth. 2019.  Virtual Performance Augmentation in an Immersive Jump & Run Exergame. In _Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems - CHI ’19_. ACM Press, Glasgow, Scotland Uk, 1–15.  https://doi.org/10.1145/3290605.3300388
  * Iskenderova et al. (2017) Aliya Iskenderova, Florian Weidner, and Wolfgang Broll. 2017\.  Drunk Virtual Reality Gaming: Exploring the Influence of Alcohol on Cybersickness. In _Proceedings of the Annual Symposium on Computer-Human Interaction in Play_ _(CHI PLAY ’17)_. Association for Computing Machinery, New York, NY, USA, 561–572.  https://doi.org/10.1145/3116595.3116618 event-place: Amsterdam, The Netherlands.
  * Johnson (2018) Mark R. Johnson. 2018\.  _The Unpredictability of Gameplay_.  Bloomsbury Academic, New York.
  * Juul (2011) Jesper Juul. 2011\.  _Half-Real: Video Games between Real Rules and Fictional Worlds_.  The MIT Press, Cambridge, Mass.
  * Kennedy et al. (1993) Robert S. Kennedy, Norman E. Lane, Kevin S. Berbaum, and Michael G. Lilienthal. 1993.  Simulator Sickness Questionnaire: An Enhanced Method for Quantifying Simulator Sickness.  _The International Journal of Aviation Psychology_ 3, 3 (July 1993), 203–220.  https://doi.org/10.1207/s15327108ijap0303_3
  * Kim et al. (2014) Sung Yeun (Su) Kim, Nathan Prestopnik, and Frank A. Biocca. 2014\.  Body in the interactive game: How interface embodiment affects physical activity and health behavior change.  _Computers in Human Behavior_ 36 (2014), 376 – 384.  https://doi.org/10.1016/j.chb.2014.03.067
  * Koulouris et al. (2020) Jordan Koulouris, Zoe Jeffery, James Best, Eamonn O’Neill, and Christof Lutteroth. 2020.  Me vs. Super(Wo)Man: Effects of Customization and Identification in a VR Exergame. In _Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems_ _(CHI ’20)_. Association for Computing Machinery, New York, NY, USA, 1–17.  https://doi.org/10.1145/3313831.3376661 event-place: Honolulu, HI, USA.
  * Kozakai et al. (2016) Rumi Kozakai, Fujiko Ando, Heung Kim, Atsumu Yuki, Rei Otsuka, and Hiroshi Shimokata. 2016\.  Sex-differences in age-related grip strength decline: A 10-year longitudinal study of community-living middle-aged and older Japanese.  _The Journal of Physical Fitness and Sports Medicine_ 5, 1 (March 2016), 87–94.  https://doi.org/10.7600/jpfsm.5.87
  * Kumari et al. (2019) Shringi Kumari, Sebastian Deterding, and Jonathan Freeman. 2019\.  The Role of Uncertainty in Moment-to-Moment Player Motivation: A Grounded Theory. In _Proceedings of the Annual Symposium on Computer-Human Interaction in Play_ (Barcelona, Spain) _(CHI PLAY ’19)_. Association for Computing Machinery, New York, NY, USA, 351–363.  https://doi.org/10.1145/3311350.3347148
  * Lan et al. (2004) Ching Lan, Ssu-Yuan Chen, and Jin-Shin Lai. 2004.  Relative Exercise Intensity of Tai Chi Chuan is Similar in Different Ages and Gender.  _The American Journal of Chinese Medicine_ 32, 1 (Jan. 2004), 151–160.  https://doi.org/10.1142/S0192415X04001746 Publisher: World Scientific Publishing Co.
  * Malone (1982) Thomas W. Malone. 1982\.  Heuristics for Designing Enjoyable User Interfaces: Lessons from Computer Games. In _Proceedings of the 1982 Conference on Human Factors in Computing Systems_ (Gaithersburg, Maryland, USA) _(CHI ’82)_. Association for Computing Machinery, New York, NY, USA, 63–68.  https://doi.org/10.1145/800049.801756
  * Márquez Segura et al. (2013) Elena Márquez Segura, Annika Waern, Jin Moen, and Carolina Johansson. 2013.  The Design Space of Body Games: Technological, Physical, and Social Design. In _Proceedings of the SIGCHI Conference on Human Factors in Computing Systems_ (Paris, France) _(CHI ’13)_. Association for Computing Machinery, New York, NY, USA, 3365–3374.  https://doi.org/10.1145/2470654.2466461
  * Marshall et al. (2016) Joe Marshall, Florian ‘Floyd’ Mueller, Steve Benford, and Sebastiaan Pijnappel. 2016\.  Expanding exertion gaming.  _International Journal of Human-Computer Studies_ 90 (2016), 1 – 13\.  https://doi.org/10.1016/j.ijhcs.2016.02.003
  * Martin-Niedecken (2018) Anna Lisa Martin-Niedecken. 2018\.  Designing for Bodily Interplay: Engaging with the Adaptive Social Exertion Game ”Plunder Planet”. In _Proceedings of the 17th ACM Conference on Interaction Design and Children_ (Trondheim, Norway) _(IDC ’18)_. Association for Computing Machinery, New York, NY, USA, 19–30.  https://doi.org/10.1145/3202185.3202740
  * Martin-Niedecken and Mekler (2018) Anna Lisa Martin-Niedecken and Elisa D. Mekler. 2018.  The ExerCube: Participatory Design of an Immersive Fitness Game Environment. In _Serious Games_ , Stefan Göbel, Augusto Garcia-Agundez, Thomas Tregel, Minhua Ma, Jannicke Baalsrud Hauge, Manuel Oliveira, Tim Marsh, and Polona Caserman (Eds.). Springer International Publishing, Cham, 263–275.
  * Martin-Niedecken et al. (2019) Anna Lisa Martin-Niedecken, Katja Rogers, Laia Turmo Vidal, Elisa D. Mekler, and Elena Márquez Segura. 2019.  ExerCube vs. Personal Trainer: Evaluating a Holistic, Immersive, and Adaptive Fitness Game Setup. In _Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems_ (Glasgow, Scotland Uk) _(CHI ’19)_. Association for Computing Machinery, New York, NY, USA, 1–15.  https://doi.org/10.1145/3290605.3300318
  * Meguro et al. (2000) Yuko Meguro, Toshikatsu Fujii, Atsushi Yamadori, Takashi Tsukiura, Kyoko Suzuki, Jiro Okuda, and Mariko Osaka. 2000.  The Nature of Age-Related Decline on the Reading Span Task.  _Journal of Clinical and Experimental Neuropsychology_ 22, 3 (June 2000), 391–398.  https://doi.org/10.1076/1380-3395(200006)22:3;1-V;FT391 Publisher: Routledge.
  * Mestre et al. (2011) Daniel R. Mestre, Virginie Dagonneau, and Charles-Symphorien Mercier. 2011\.  Does Virtual Reality Enhance Exercise Performance, Enjoyment, and Dissociation? An Exploratory Study on a Stationary Bike Apparatus.  _Presence: Teleoperators and Virtual Environments_ 20, 1 (Feb. 2011), 14.  https://doi.org/10.1162/pres_a_00031
  * Monteiro et al. (2018) Diego Monteiro, Hai-Ning Liang, Wenge Xu, Marvin Brucker, Vijayakumar Nanjappan, and Yong Yue. 2018\.  Evaluating enjoyment, presence, and emulator sickness in VR games based on first- and third- person viewing perspectives.  _Computer Animation and Virtual Worlds_ 29, 3 (2018), 12\.  https://doi.org/10.1002/cav.1830
  * Mueller and Isbister (2014) Florian Mueller and Katherine Isbister. 2014.  Movement-Based Game Guidelines. In _Proceedings of the SIGCHI Conference on Human Factors in Computing Systems_ (Toronto, Ontario, Canada) _(CHI ’14)_. Association for Computing Machinery, New York, NY, USA, 2191–2200.  https://doi.org/10.1145/2556288.2557163
  * Mueller et al. (2016) Florian Mueller, Rohit Ashok Khot, Kathrin Gerling, and Regan Mandryk. 2016.  Exertion Games.  _Found. Trends Hum.-Comput. Interact._ 10, 1 (Dec. 2016), 1–86.  https://doi.org/10.1561/1100000041
  * Mueller et al. (2012) Florian Mueller, Frank Vetere, Martin Gibbs, Darren Edge, Stefan Agamanolis, Jennifer Sheridan, and Jeffrey Heer. 2012.  Balancing Exertion Experiences. In _Proceedings of the SIGCHI Conference on Human Factors in Computing Systems_ (Austin, Texas, USA) _(CHI ’12)_. Association for Computing Machinery, New York, NY, USA, 1853–1862.  https://doi.org/10.1145/2207676.2208322
  * Mueller et al. (2011) Florian “Floyd” Mueller, Darren Edge, Frank Vetere, Martin R. Gibbs, Stefan Agamanolis, Bert Bongers, and Jennifer G. Sheridan. 2011.  Designing Sports: A Framework for Exertion Games. In _Proceedings of the SIGCHI Conference on Human Factors in Computing Systems_ _(CHI ’11)_. Association for Computing Machinery, New York, NY, USA, 2651–2660.  https://doi.org/10.1145/1978942.1979330 event-place: Vancouver, BC, Canada.
  * Murnane et al. (2020) Elizabeth L. Murnane, Xin Jiang, Anna Kong, Michelle Park, Weili Shi, Connor Soohoo, Luke Vink, Iris Xia, Xin Yu, John Yang-Sammataro, Grace Young, Jenny Zhi, Paula Moya, and James A. Landay. 2020.  Designing Ambient Narrative-Based Interfaces to Reflect and Motivate Physical Activity. In _Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems_ _(CHI ’20)_. Association for Computing Machinery, New York, NY, USA, 1–14.  https://doi.org/10.1145/3313831.3376478 event-place: Honolulu, HI, USA.
  * Nacke et al. (2009) Lennart E. Nacke, Anne Nacke, and Craig A. Lindley. 2009\.  Brain Training for Silver Gamers: Effects of Age and Game Form on Effectiveness, Efficiency, Self-Assessment, and Gameplay Experience.  _CyberPsychology & Behavior_ 12, 5 (2009), 493–499.  https://doi.org/10.1089/cpb.2009.0013 PMID: 19772440.
  * Nes et al. (2013) Bjarne. M Nes, Imre Janszky, Ulrik Wisløff, Asbjørn Støylen, and Trine Karlsen. 2013\.  Age-predicted maximal heart rate in healthy subjects: The HUNT Fitness Study.  _Scandinavian Journal of Medicine & Science in Sports_ 23, 6 (2013), 697–704.  https://doi.org/10.1111/j.1600-0838.2012.01445.x
  * Ostchega et al. (2011) Yechiam Ostchega, Kathryn Porter, Jeffery Hughes, Charles F Dillon, and Tatiana Nwankwo. 2011\.  Resting pulse rate reference data for children, adolescents, and adults: United States, 1999-2008.  _National health statistics reports_ 41 (Aug. 2011), 1–16.
  * Pasch et al. (2009) Marco Pasch, Nadia Bianchi-Berthouze, Betsy van Dijk, and Anton Nijholt. 2009.  Movement-based sports video games: Investigating motivation and gaming experience.  _Entertainment Computing_ 1, 2 (2009), 49 – 61.  https://doi.org/10.1016/j.entcom.2009.09.004 Intelligent Technologies for Interactive Entertainment.
  * Pincivero et al. (2010) Danny Pincivero, Mark Timmons, and D Elsing. 2010.  RPE Angle Effects in Young and Middle-Aged Adults.  _International journal of sports medicine_ 31, 4 (Feb. 2010), 257–260.  https://doi.org/10.1055/s-0030-1247551
  * Plante et al. (2003) Thomas G. Plante, Arianna Aldridge, Denise Su, Ryan Bogdan, Martha Belo, and Kamran Kahn. 2003\.  Does Virtual Reality Enhance the Management of Stress When Paired With Exercise? An Exploratory Study.  _International Journal of Stress Management_ 10, 3 (2003), 203–216.  https://doi.org/10.1037/1072-5245.10.3.203
  * Power et al. (2019) Christopher Power, Paul Cairns, Alena Denisova, Themis Papaioannou, and Ruth Gultom. 2019\.  Lost at the edge of uncertainty: Measuring player uncertainty in digital games.  _International Journal of Human–Computer Interaction_ 35, 12 (2019), 1033–1045.  https://doi.org/10.1080/10447318.2018.1507161 Publisher: Taylor & Francis.
  * Rizzo et al. (2011) Albert Skip Rizzo, Belinda Lange, Evan A. Suma, and Mark Bolas. 2011\.  Virtual Reality and Interactive Digital Game Technology: New Tools to Address Obesity and Diabetes.  _Journal of Diabetes Science and Technology_ 5, 2 (March 2011), 256–264.  https://doi.org/10.1177/193229681100500209
  * Rosney and Horvath (2018) Daniel M Rosney and Peter J Horvath. 2018.  Exergaming Intervention in Sedentary Middle-Aged Adults Improves Cardiovascular Endurance, Balance and Lower Extremity Functional Fitness.  _Health Science Journal_ 12, 6 (2018), 1–10.  https://doi.org/10.4172/2165-7904-C1-55 Publisher: Technological Educational Institute of Athens.
  * Sanders and Stappers (2013) Liz Sanders and Pieter Jan Stappers. 2013.  _Convivial Toolbox: Generative Research for the Front End of Design_ (illustrated edition ed.).  Laurence King Publishing, Amsterdam.
  * Schubert et al. (2018) Matt Schubert, Amy Clark, and Annie Rosa. 2018.  The Polar OH1 Optical Heart Rate Sensor is Valid during Moderate-Vigorous Exercise.  _Sports Medicine International Open_ 2, 3 (March 2018), 67–70.  https://doi.org/10.1055/a-0631-0920
  * Sheehan and Katz (2013) Dwayne P. Sheehan and Larry Katz. 2013.  The effects of a daily, 6-week exergaming curriculum on balance in fourth grade children.  _Journal of Sport and Health Science_ 2, 3 (Sept. 2013), 131–137.  https://doi.org/10.1016/j.jshs.2013.02.002
  * Sherry et al. (2006) John L. Sherry, Kristen Lucas, Bradley S. Greenberg, and Ken Lachlan. 2006.  Video Game Uses and Gratifications as Predicators of Use and Game Preference.  In _Playing video games: Motives, responses, and consequences._ Lawrence Erlbaum Associates, New York, US, 213–224.  Place: Mahwah, NJ, US Publisher: Lawrence Erlbaum Associates Publishers.
  * Sinclair et al. (2007) Jeff Sinclair, Philip Hingston, and Martin Masek. 2007\.  Considerations for the design of exergames. In _Proceedings of the 5th international conference on Computer graphics and interactive techniques in Australia and Southeast Asia - GRAPHITE ’07_. ACM Press, Perth, Australia, 289.  https://doi.org/10.1145/1321261.1321313
  * Smeddinck et al. (2013) Jan Smeddinck, Sandra Siegel, and Marc Herrlich. 2013\.  Adaptive Difficulty in Exergames for Parkinson’s Disease Patients. In _Proceedings of Graphics Interface 2013_ _(GI ’13)_. Canadian Information Processing Society, CAN, 141–148.  event-place: Regina, Sascatchewan, Canada.
  * Soares et al. (2016) Antônio Soares, Noé Borges Júnior, Marcelo Hounsell, Elessandra Marcelino, Gabriel Rossito, and Yoshimasa Sagawa. 2016.  A serious game developed for physical rehabilitation of frail elderly.  _European Research in Telemedicine / La Recherche Européenne en Télémédecine_ 5, 2 (June 2016), 45–53.  https://doi.org/10.1016/j.eurtel.2016.05.003
  * Subramanian et al. (2019) Sruti Subramanian, Yngve Dahl, Nina Skjæret Maroni, Beatrix Vereijken, and Dag Svanæs. 2019\.  Assessing Motivational Differences Between Young and Older Adults When Playing an Exergame.  _Games for Health Journal_ 9, 1 (Sept. 2019), 24–30.  https://doi.org/10.1089/g4h.2019.0082 Publisher: Mary Ann Liebert, Inc., publishers.
  * Tekinbas and Zimmerman (2003) Katie Salen Tekinbas and Eric Zimmerman. 2003.  _Rules of Play: Game Design Fundamentals_.  The MIT Press, Cambridge, Mass.
  * Thomas et al. (1993) Scott Thomas, Jeff Reading, and Roy Shephard. 1993\.  Revision of the Physical Activity Readiness Questionnaire (PAR-Q).  _Canadian journal of sport sciences_ 17, 4 (Jan. 1993), 338–45.
  * Vara et al. (2016) Mª Dolores Vara, Rosa M. Baños, Paloma Rasal, Alejandro Rodríguez, Beatriz Rey, Maja Wrzesien, and Mariano Alcañiz. 2016.  A game for emotional regulation in adolescents: The (body) interface device matters.  _Computers in Human Behavior_ 57 (2016), 267 – 273.  https://doi.org/10.1016/j.chb.2015.12.033
  * Verhaeghen and Salthouse (1997) Paul Verhaeghen and Timothy Salthouse. 1997.  Meta-analyses of age-cognition relations in adulthood: Estimates of linear and nonlinear age effects and structural models.  _Psychological bulletin_ 122, 3 (Dec. 1997), 231–249.  https://doi.org/10.1037//0033-2909.122.3.231
  * Wang et al. (2017) Ping Wang, Xing-Ting Zhu, Han-Hui Liu, Yi-Wen Zhang, Yang Hu, Hui-Jie Li, and Xi-Nian Zuo. 2017.  Age-Related Cognitive Effects of Videogame Playing Across the Adult Life span.  _Games for Health Journal_ 6, 4 (June 2017), 237–248.  https://doi.org/10.1089/g4h.2017.0005 Publisher: Mary Ann Liebert, Inc., publishers.
  * Xie et al. (2018) Biao. Xie, Yongqi. Zhang, Haikun. Huang, Elisa. Ogawa, Tongjian. You, and Lap. Fai. Yu. 2018.  Exercise Intensity-Driven Level Design.  _IEEE Transactions on Visualization and Computer Graphics_ 24, 4 (2018), 1661–1670.  https://doi.org/10.1109/TVCG.2018.2793618
  * Xu et al. (2020a) Wenge Xu, Hai-Ning Liang, Nilufar Baghaei, Bing Wu Berberich, and Yong Yue. 2020a.  Health Benefits of Digital Videogames for the Aging Population: A Systematic Review.  _Games for Health Journal_ 9, 6 (2020), 389–404.  https://doi.org/10.1089/g4h.2019.0130 PMID: 32589482.
  * Xu et al. (2020b) Wenge Xu, Hai-Ning Liang, Qiuyu He, Xiang Li, Kangyou Yu, and Yuzheng Chen. 2020b.  Results and Guidelines From a Repeated-Measures Design Experiment Comparing Standing and Seated Full-Body Gesture-Based Immersive Virtual Reality Exergames: Within-Subjects Evaluation.  _JMIR Serious Games_ 8, 3 (2020), 15.  https://doi.org/10.2196/17972
  * Xu et al. (2020c) Wenge Xu, Hai-Ning Liang, Xiaoyue Ma, and Xiang Li. 2020c.  VirusBoxing: A HIIT-Based VR Boxing Game. In _Extended Abstracts of the 2020 Annual Symposium on Computer-Human Interaction in Play_ (Virtual Event, Canada) _(CHI PLAY ’20)_. Association for Computing Machinery, New York, NY, USA, 98–102.  https://doi.org/10.1145/3383668.3419958
  * Xu et al. (2019) Wenge Xu, Hai-Ning Liang, Yifan Yu, Diego Monteiro, Khalad Hasan, and Charles Fleming. 2019\.  Assessing the Effects of a Full-body Motion-based Exergame in Virtual Reality. In _Proceedings of the Seventh International Symposium of Chinese CHI on - Chinese CHI 2019_. ACM Press, Xiamen, China.  https://doi.org/10.1145/3332169.3333574
  * Xu et al. (2020d) Wenge Xu, Hai-Ning Liang, Zeying Zhang, and Nilufar Baghaei. 2020d.  Studying the Effect of Display Type and Viewing Perspective on User Experience in Virtual Reality Exergames.  _Games for Health Journal_ 9, 4 (Feb. 2020), 10.  https://doi.org/10.1089/g4h.2019.0102 Publisher: Mary Ann Liebert, Inc., publishers.

##  Appendix A Appendix

###  A.1. Data Results

We list all data in Table 5 and 6. VR_Cer represents VR certain game condition
(GC), VR_Unc represents VR uncertain GC, TV_Cer represents TV certain GC,
TV_Unc represents TV uncertain GC.

Table 5. Means (SDs) of participants’ performance data regarding the completion time on each of the three lives of the monster, total number of gestures performed, and success rate of each move. | Young Adults | Middle-aged Adults
---|---|---
Type |  VR_Cer |  VR_Unc |  TV_Cer |  TV_Unc |  VR_Cer |  VR_Unc |  TV_Cer |  TV_Unc
Completion Time on Each of The Three Lives of The Monster
Life1 |  126.83 (34.74) |  126.14 (36.79) |  133.69 (54.42) |  140.05 (51.73) |  149.38 (41.71) |  152.82 (48.91) |  148.16 (60.72) |  144.64 (44.30)
Life2 |  113.80 (22.07) |  114.60 (33.05) |  119.71 (33.22) |  121.71 (37.82) |  133.96 (25.06) |  138.09 (39.38) |  131.36 (29.37) |  142.75 (47.57)
Life3 |  105.45 (19.23) |  109.30 (35.73) |  112.21 (29.10) |  115.39 (23.88) |  134.27 (36.88) |  126.20 (28.26) |  121.08 (19.63) |  139.17 (46.41)
Total Number of Gestures Performed
Kick |  33.19 (7.88) |  35.13 (7.44) |  38.00 (8.33) |  34.75 (10.08) |  36.50 (10.41) |  36.56 (8.60) |  35.19 (9.09) |  35.00 (9.35)
Push |  45.56 (13.77) |  44.25 (8.31) |  47.50 (9.64) |  44.13 (14.06) |  40.94 (10.97) |  41.31 (8.54) |  34.31 (13.80) |  35.88 (11.91)
Zoom+Kick |  35.25 (3.62) |  35.75 (5.01) |  32.81 (3.29) |  35.50 (3.12) |  34.06 (4.55) |  35.75 (5.42) |  35.06 (5.20) |  35.56 (5.67)
Zoom+Squat |  29.19 (7.88) |  33.88 (13.50) |  24.56 (9.24) |  36.63 (13.44) |  33.75 (4.93) |  46.69 (7.43) |  34.44 (5.68) |  50.44 (12.93)
Success Rate of Each Move
Kick |  82.19% (12.31%) |  83.72% (10.28%) |  77.28% (13.50%) |  75.87% (21.27%) |  80.03% (12.01%) |  80.44% (13.10%) |  75.96% (11.01%) |  76.04% (7.98%)
Push |  52.04% (25.06%) |  55.34% (24.32%) |  61.05% (18.84%) |  60.43% (20.57%) |  54.76% (13.12%) |  49.18% (18.52%) |  56.79% (15.61%) |  55.31% (15.83%)
Zoom+Kick |  98.32% (2.14%) |  99.50% (1.08%) |  96.16% (3.46%) |  98.13% (2.17%) |  97.61% (4.08%) |  97.31% (4.03%) |  99.14% (2.20%) |  96.19% (3.81%)
Zoom+Squat |  74.07% (13.17%) |  73.03% (13.36%) |  61.88% (17.56%) |  55.46% (18.25%) |  73.51% (10.20%) |  64.12% (9.21%) |  70.13% (11.33%) |  63.10% (6.78%)
Table 6. Means (SDs) of participants’ experience and exertion data regarding each game experience questionnaire subscale, simulator sickness questionnaire subscale, and exertion measurement. | Young Adults | Middle-aged Adults
---|---|---
Type |  VR_Cer |  VR_Unc |  TV_Cer |  TV_Unc |  VR_Cer |  VR_Unc |  TV_Cer |  TV_Unc
Game Experience Questionnaire
Competence |  12.00 (3.72) |  9.00 (4.52) |  10.25 (2.70) |  11.44 (3.44) |  6.69 (4.09) |  6.75 (4.60) |  7.38 (4.18) |  6.56 (2.92)
Immersion |  9.88 (5.64) |  10.44 (4.83) |  9.19 (4.86) |  10.06 (5.28) |  3.44 (2.45) |  3.50 (2.76) |  3.50 (2.90) |  3.50 (2.13)
Flow |  10.63 (4.11) |  10.31 (3.79) |  7.69 (3.52) |  8.75 (3.75) |  10.94 (5.63) |  11.81 (5.83) |  3.94 (1.98) |  4.31 (2.09)
Tension |  1.31 (1.45) |  1.88 (2.45) |  1.63 (1.59) |  1.69 (2.02) |  0.19 (0.54) |  0.06 (0.25) |  0.00 (0.00) |  0.06 (0.25)
Challenge |  6.13 (3.88) |  7.25 (2.59) |  6.69 (2.96) |  6.25 (3.04) |  6.06 (2.69) |  6.00 (2.76) |  5.44 (2.68) |  6.44 (2.58)
Negative Affect |  2.00 (2.19) |  2.56 (2.56) |  2.44 (2.76) |  2.81 (3.69) |  0.56 (1.21) |  0.63 (1.15) |  0.31 (0.60) |  0.19 (0.40)
Positive Affect |  10.56 (4.57) |  9.38 (3.90) |  8.88 (4.08) |  9.69 (3.59) |  4.94 (2.21) |  5.19 (2.14) |  5.38 (2.39) |  5.63 (2.09)
Simulator Sickness Questionnaire
Nausea |  11.93 (13.71) |  14.31 (17.42) |  11.93 (13.26) |  13.71 (16.33) |  2.98 (9.68) |  2.98 (8.33) |  1.19 (3.26) |  5.37 (14.77)
Oculomotor |  12.79 (14.57) |  11.84 (16.60) |  14.21 (13.80) |  14.21 (14.87) |  3.32 (7.81) |  6.16 (5.69) |  4.26 (9.17) |  7.11 (11.23)
Disorientation |  11.31 (23.41) |  6.96 (15.25) |  6.96 (21.56) |  7.83 (24.88) |  0.00 (0.00) |  0.00 (0.00) |  3.48 (9.51) |  1.74 (4.75)
Total SSQ |  14.03 (16.64) |  13.32 (17.43) |  13.56 (15.62) |  14.49 (18.16) |  2.81 (7.54) |  4.21 (5.27) |  3.51 (8.25) |  6.08 (12.13)
Exertion
avgHR% |  62.61% (8.87%) |  66.12% (11.72%) |  62.39% (10.15%) |  63.79% (10.14%) |  67.75% (7.22%) |  76.26% (6.12%) |  67.83% (8.73%) |  74.92% (6.90%)
maxHR% |  70.79% (9.76%) |  72.63% (12.60%) |  71.33% (12.60%) |  72.83% (10.65%) |  76.26% (6.60%) |  84.78% (6.84%) |  75.56% (7.51%) |  83.64% (5.93%)
Calories |  41.31 (10.73) |  50.88 (14.68) |  42.19 (14.79) |  46.06 (11.79) |  50.31 (10.26) |  60.25 (14.09) |  46.56 (9.61) |  60.25 (8.89)
Borg RPE 6-20 |  8.88 (2.28) |  10.19 (2.71) |  9.19 (1.83) |  8.88 (1.67) |  7.06 (0.44) |  8.19 (0.75) |  7.06 (0.44) |  8.00 (0.73)