=Paper=
{{Paper
|id=Vol-2265/paper9
|storemode=property
|title=Towards Attention-based Design of Mental Health Interventions in Virtual Reality
|pdfUrl=https://ceur-ws.org/Vol-2265/paper9.pdf
|volume=Vol-2265
|authors=Maartje Hendriks,Lisa E. Rombout
}}
==Towards Attention-based Design of Mental Health Interventions in Virtual Reality==
https://ceur-ws.org/Vol-2265/paper9.pdf
Towards Attention-based Design of Mental
Health Interventions in Virtual Reality
Maartje Hendriks and Lisa E. Rombout1
Tilburg University, Department of Cognitive Science and Artificial Intelligence,
Warandelaan 2, 5037 AB Tilburg, Netherlands,
l.e.rombout@uvt.nl
Abstract. Virtual reality (VR) mental health therapy has been studied
extensively, and some of the resulting interventions are already used in
clinical practice. However, high-quality VR systems are still relatively ex-
pensive. There are some indications that a cheaper, low-quality VR sys-
tem produces different effects in its users, though studies remain scarce.
We theorize that the effectiveness of a VR intervention is determined
partly by how well it directs attention towards or away from the users
real body. We therefore propose a study comparing how image-quality
and biofeedback during a breathing task affect anxiety levels. We hy-
pothesize that high image quality distracts from the task, making the
intervention less effective. Conversely, placing biofeedback in the virtual
environment could direct attention back to the users body, increasing ef-
fectiveness. Initial results suggest individual differences in how attention
is managed. A full-scale study could inform a more structured, attention-
based design approach towards creating VR interventions.
Keywords: attention, anxiety, virtual reality, biofeedback, image-quality,
mental health
1 Introduction
1.1 Virtual Reality and Mental Health
Over the last couple of decades, entertainment based health interventions have
been studied extensively and some are already successfully used in clinical prac-
tice [1, 2]. Virtual Reality (VR) has been used as a technology and medium for
these purposes. Targeted VR experiences can be effective treatment methods for
individuals suffering from a range of mental health issues, suggesting that VR
has huge potential within this field [3]. For example, virtual reality can quickly
transport the user out of a stressful situation and into a personalized experience
made to induce positive emotions [4].
Immersive virtual reality, where the user is viewing the virtual world through
a head mounted display, can induce a strong sense of presence - the feeling of
actually entering the computer generated world [5]. Such immersive VR has
been shown to enhance the control over ones emotional response to stressful or
difficult situations [6]. It has also been effectively used as exposure therapy for
a range of phobia’s, performing on par with real life exposure therapy in some
instances [7].
VR can be used as a diagnostic tool, as treatment and as resilience training
[8]. For example, virtual scenes depicting nature provide more relaxation than
control scenes [9], suggesting that VR might provide the advantageous mental
health effects of nature to people living in environments where it is not readily
available, such as in submarines. In terms of therapy retention, VR breathing
exercises are not only preferred over standard breathing techniques by patients,
they are also rated as more fun and more likely to be used at home [4].
1.2 Cost and Quality
However, although the cost associated with VR has fallen dramatically in the
past couple of decades, even commercially available immersive VR systems are
still relatively expensive, making them inaccessible for everyday use [10]. Con-
ventional mental health treatments rely on the affected individual’s interaction
with a social support group or therapist. Giving these individuals the opportu-
nity to experience VR treatment at home gives them freedom and autonomy [11].
This makes the accessibility of the VR systems an important factor in producing
these health interventions.
The accessibility of a VR system is mostly connected to its price. But before
the use of cheaper systems is encouraged, we need to know whether these sys-
tems differ in their performance from their more expensive counterparts, and in
what way. Research on the difference between higher and lower quality VR Head
Mounted Displays (HMD’s) is unfortunately scarce. Hoffman et al. [12] showed
that, when using VR experiences as a form of pain relief, there was more reduc-
tion of pain reported in the higher quality HMD group than in the lower quality
one. However, other studies show that that high fidelity isn’t always superior to
lower fidelity [13]. Instead, it can be dependent on the type of task.
As VR technology is advancing rapidly, recent studies make use of cheaper
VR systems more often than before, often showing that there is big potential
in developing smart-phone based VR health applications [10, 14]. However, the
mechanisms behind what does and does not work in these different kinds of VR
systems are still unclear.
1.3 Attention
The direction of attention is one mechanism that seems to strongly influence the
effectiveness of the VR intervention. We theorize that different types of virtual
reality interventions have different goals as to the direction of the attention of
the user. For instance, in pain relief the goal is to distract the user from their own
body and the pain it is experiencing [15]. In anxiety-reducing breathing tasks,
the goal is to have the user focus on their own body and adjust their breathing
accordingly. To accomplish this, distractions from outside must be limited [5].
However, if distractions from outside the virtual world must be limited to allow
the user to focus on their own body, one could argue that the virtual environment
itself can not be too complex or interesting, as it would also start to distract the
user in a similar way.
There are multiple ways of reducing feelings of anxiety and stress, one of
which is through deep, diaphragmatic, slow breathing techniques [16]. The regu-
lar practice of breathing exercises decreases sympathetic and increases parasym-
pathetic activity in the body, improves cardiovascular and respiratory functions,
decreases the effect of stress and overall improves physical and mental health [17].
It can however be hard to sustain ones attention during the exercises [18], and
the techniques can be challenging for novices [1]. It would therefore be beneficial
to create an experience in which the individual could focus their attention to
the exercise at hand, without being distracted by any auditory or visual stimuli,
preferably an experience that they would enjoy spending time in [2, 19].
The creation of an interesting and engaging VR experience that is nonethe-
less not too distracting is a problem that has been approached several times
through the addition of biofeedback [20, 18]. A study by Zafar et al. [21] sug-
gests that enhancing games with biofeedback makes them more effective than
traditional stress self-regulation therapies, and that these games can even train
breathing control in a stressful setting. In the ’DEEP’ VR experience, breathing
is monitored and users are shown a visualization of their own breathing, situated
in the virtual environment. This feedback results in reduced anxiety levels and
an increase in deep breathing skill acquisition [22].
1.4 Experimental Paradigm
Redirecting the users attention away from the real body and unto a complex,
high-quality virtual environment might be useful in pain relief, but is likely
not ideal for acquiring deep breathing techniques and reducing anxiety. In these
cases, a low-quality VR environment might suffice or even work better. However,
adding biofeedback visualizations to the virtual environment might bring the
attention back to the body, regardless of how engaging the virtual world itself
is. We therefore propose to study both the image-quality and the presence of
biofeedback in the context of a virtual breathing exercise to reduce anxiety.
We hypothesize that a higher quality VR environment with biofeedback vi-
sualizations will results in the highest reduction of anxiety levels, followed by a
lower quality VR with biofeedback. We expect the high-quality VR experience to
draw more of the users attention, which is then directed towards their breathing
via the biofeedback. We hypothesize that both non-biofeedback conditions will
perform poorly, with the higher quality VR being the worst. We expect that
the high quality virtual environment would distract the user more, making them
less focused on their body and breathing and thus reducing the effects of the
intervention.
By studying the mechanisms behind the different effects of image-quality and
the presence of biofeedback, we hope to gain more knowledge on how to design
future VR health care applications and develop the basis of design practices that
can make them more accessible.
2 Methods
12 participants (5 female, 7 male) were recruited on the campus of Tilburg Uni-
versity. For this initial exploratory pilot, participants were not selected specifi-
cally for anxiety disorders.
There are four conditions: high quality VR with biofeedback, high quality
VR without biofeedback, low quality VR with biofeedback and low quality VR
without biofeedback. Each participant is assigned to two conditions; either high
quality VR with and without biofeedback or low quality VR with and without
biofeedback, making this study a mixed 2x2 design with the image-quality as the
between subjects variable and the presence of biofeedback as the within-subject
variable. The order of the conditions is randomized and counterbalanced over
the participants.
2.1 Materials
Anxiety Questionnaires Level of anxiousness is measured with two different
kinds of questionnaires. At the beginning of the experiment the participant are
asked to fill in the Trait Anxiety Inventory, with items such as I am content; I
am a steady person. [23]. The results of this questionnaire are used as a general
anxiety baseline.
State anxiety is measured with the use of Subjective Units of Discomfort
Scale, or SUDS [24]. The SUDS is a one-item 11-point Likert-type scale in which
the participant is asked On a scale from 0 being absolutely calm and 10 being
the worst anxiety you could ever feel, how do you rate yourself at this moment?.
The participants rate their anxiety levels vocally three times per condition: once
before entering the virtual experience, once during, and once after. We also use
the delta of before-after as a measurement.
Anxiety Induction In between the two conditions we lower the induced re-
laxation to avoid carry-over from the first condition into the second. To achieve
this we use the Velten Mood Induction Procedure - the participants read and
try to imagine a number of negatively loaded sentences for one minute [25].
Diaphragmatic Breathing Rate As an objective measure, the diaphrag-
matic breathing rate is collected by measuring diaphragm expansion with a
stretch sensor that functions as a variable resistor, connected to an Arduino
micro-controller. If diaphragmatic breathing is present, a clear waveform can be
distinguished in the data. The percentage of time that the participant is breath-
ing correctly according to the task is taken as a measurement. Additionally, we
examine the development of the waveform over time.
2.2 Virtual Environment
Our virtual environment is created with Unity 2017.2.0f3. and looks like a calm
forest. This forest was made with the ’FantasyEnvironments’ asset in Unity.
The participant sits in the virtual forest at one predetermined spot. A total of
13 randomly placed lights are visible.
In the conditions with biofeedback, the intensity of these lights reflects the
participants diaphragm expansion through a connection between the Arduino
and Unity, using the ARDUnity 1.0.8 asset. Breathing in correctly brightens the
lights, and breathing out dims them. In the conditions without biofeedback the
lights are off.
The head mounted display used in this experiment is the HTC Vive (2160x1200
pixels max, 90Hz, 110-degree field of view). We created a high and a low qual-
ity image within this same HDM by adjusting quality aspects within Unity,
specifically the texture quality, shadow resolution, and density of details in the
environment (see figure 1 and 2).
2.3 Procedure
Informed consent is obtained from all participants. Participants then receive
a short instruction on deep, diaphragmatic breathing techniques and practice
these shortly. Then the participant fills in the Trait Anxiety Inventory, puts
on the stretch-sensor band and read the Velten Mood Induction sentences. The
participant then rates the SUDS, the VR HMD is placed on the head of the
participant and the first condition starts. Near the end of virtual experience
the participant is asked to rate the SUDS again, followed by the removal of the
HMD. The participant now again rates the SUDS and take a short break. Then,
the same procedure is followed for condition two. After both the conditions the
participant is debriefed.
Fig. 2. Virtual environments. Top = high quality,
bottom = low quality. Left = with biofeedback, right
= no biofeedback.
Fig. 1. Experimental setup
3 Discussion
3.1 Preliminary results
During this exploratory pilot study, several trends were observed. Most impor-
tantly, there seems to be a wide range in individual responses to attention-
management within virtual reality. While some participants indicated that they
found the biofeedback lights calming and fun, others found them to be dis-
tracting. In general, if a participant did not achieve the correct deep breathing
patterns, they tended to describe the lights as random, in one case even as an-
noyingly so. There were also some reports of novelty bias, were participants who
hadn’t experienced VR before reported feeling excited or nervous for that reason.
This could potentially cloud the measurements.
SUDS scores tended to decrease during all virtual reality experiences, al-
though this effect was not significant. In the non-biofeedback conditions, the
SUDS score was slightly higher during the high-quality condition compared to
lower quality, but this difference was also not significant. For these measure-
ments, a full-scale study will hopefully bring more clarity.
Strong deep breathing patterns were very clearly observed in the diaphragm
expansion data from several participants. In figure 3 you see an example of a
participant who maintained deep breathing throughout the experiment, whereas
4 shows the patterns of a participant who self-reported that they didn’t manage
to achieve deep breathing. There were no strong trends observed in the deep
breathing data related to the different conditions. There seem to be quite strong
individual differences, so a large full-scale study might identify potential patterns
more effectively.
Fig. 3. Data from a participant who Fig. 4. Data from a participant who
maintained deep breathing throughout did not achieve deep breathing patterns
3.2 Research in Virtual Reality
The use of relatively new technologies and media in cognitive science comes with
several challenges pertaining to experimental design. We will recount a few of
our considerations here.
Firstly, the use of questionnaires could be detrimental to a users sense of
presence in the virtual environment. It becomes more difficult to switch between
virtual and non-virtual tasks, and could have unforeseen effects. Tools like the
SUDS scale allow for a shorter assessment and verbal answer, allowing the user
to maintain their sense of presence in the virtual world.
Secondly, the biofeedback used (breathing) can be measured in a variety of
ways. One especially non-invasive option we explored was a temperature sensor
under the nose - warm air signifies breathing out, cold air breathing in. We found
however that the sensor did not responds quickly enough to the temperature
changes. Another option would be to work with a microphone. This has the
added advantage that it would be relatively easy to use with a smart-phone
HMD, as the microphone is already a part of the device. However, this does
not allow you to distinguish between deep diaphragmatic breathing and shallow
chest breathing. The stretch sensor band is slightly constricting around the users
abdomen, but does allow us to use biofeedback only when the user is breathing
deeply. We constructed the stretch sensor ourselves using conductive thread, yarn
and elastic band. This, together with the use of the Arduino micro-controller,
results in a relatively affordable and accessible, yet reliable deep breathing sensor.
3.3 Future Applications
Should the full-scale version of this study show that not only high but also
low quality VR indeed reduces anxiety significantly, it would be interesting to
continue testing with actual smart-phones, preferably with low-priced accessible
HMD’s such as the Google Cardboard. Future research could additionally ex-
amine the influences of biofeedback lag, a smaller field-of-view, and alternative
biofeedback sensors on the reduction of anxiety.
If our study shows that the way in which attention is redirected within the
virtual world has an effect on anxiety reduction, the next logical step would be
to repeat the study with an intervention that has a different goal in mind, such
as directing attention away from the body. For example, one would hypothesize
that for pain relief environments, a higher quality VR experience would work
best, while adding biofeedback would negate the effects. In other words, the
exact opposite result from the current study.
Another interesting avenue is the way biofeedback is placed within the vir-
tual environment. Instead of the biofeedback visualization affecting the external
virtual environment, it would be interesting to look at the effect of placing the
biofeedback on the users virtual body. This could direct the users attention to
their body even stronger. Alternatively, the biofeedback could be perceptualized
in a way other than visually, for instance via audio.
If we elucidate the mechanisms behind what makes virtual reality health
care intervention work in different scenarios, it could give us more information on
which interventions can easily be made accessible and in which way. For instance,
breathing exercises might be suitable to use with low-quality devices, while pain
relief experiences might not. Giving the patient the possibility to experience the
treatment wherever and whenever they want enormously increases their freedom
and autonomy.
The development of the technology itself also means that these outcomes will
continue to shift. The image-quality and processing capabilities of smart-phones
is increasing every year. Rather than wait until smart-phone based HMD’s have
the same specifications as the high quality HMD’s of today, we believe well
researched, accessible VR based health applications can already be created in
the coming years.
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