Clinical Trial Details
— Status: Active, not recruiting
Administrative data
| NCT number |
NCT04479761 |
| Other study ID # |
20-0312 |
| Secondary ID |
|
| Status |
Active, not recruiting |
| Phase |
N/A
|
| First received |
|
| Last updated |
|
| Start date |
September 15, 2021 |
| Est. completion date |
June 30, 2024 |
Study information
| Verified date |
November 2023 |
| Source |
New York University |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Interventional
|
Clinical Trial Summary
More than 1/3 of adults in the United States seek medical attention for vestibular disorders
and hearing loss; disorders that can triple one's fall risk and have a profound effect on
one's participation in activities of daily living. Hearing loss has been shown to reduce
balance performance and could be one modifiable risk factor for falls. Patients with
vestibular hypofunction tend to avoid busy, hectic, visually complex, and loud environments
because these environments provoke dizziness and imbalance. While the visual impact on
balance is well known, less is known about the importance of sounds. In search for a possible
mechanism to explain a relationship between hearing and balance control, some studies
suggested that sounds may serve as an auditory anchor, providing spatial cues for balance,
similar to vision. However, the majority of these studies tested healthy adults' response to
sounds with blocked visuals. It is also possible that a relationship between hearing loss and
balance problems is navigated via an undetected vestibular deficit. By understanding the role
of auditory input in balance control, falls may be prevented in people with vestibular
disorders and hearing loss. Therefore, there is a critical need for a systematic
investigation of balance performance in response to simultaneous visual and auditory
perturbations, similar to real-life situations.
To answer this need, the investigators used recent advances in virtual reality technology and
developed a Head Mounted Display (HMD) protocol of immersive environments, combining specific
manipulations of visuals and sounds, including generated sounds (i.e., white noise) and
real-world recorded sounds (e.g., a train approaching a station). This research will answer
the following questions: (1) Are sounds used for balance and if yes, via what mechanism? (2)
Do individuals with single-sided hearing loss have a balance problem even without any
vestibular issues? (3) Are those with vestibular loss destabilized by sounds? To address
these questions, the following specific aims will be investigated in individuals with
unilateral peripheral vestibular hypofunction (n=45), individuals with single-sided deafness
(n=45), and age-matched controls (n=45): Aim 1: Establish the role of generated and natural
sounds in postural control in different visual environments; Aim 2: Determine the extent to
which a static white noise can improve balance within a dynamic visual environment.
Description:
Introduction: Aim 1 is to establish the role of generated and natural sounds in postural
control given the visual environment and sensory loss. For that the investigators will
measure postural sway in individuals with unilateral peripheral vestibular hypofunction
(n=45), individuals with SSD (n=45) and age-matched controls (n=45). They will be tested in
an immersive virtual reality environment displaying an abstract 3-wall display of stars or a
subway station. Within each environment, we will compare changes in postural sway in response
to visual (static, dynamic) and auditory perturbations (no sound, dynamic sound, i.e.,
rhythmic white noise in the stars environment or natural sounds, such as moving trains, in
the subway environment). Aim 2 is to determine the extent to which a static white noise can
improve balance (reduce postural sway) within a dynamic visual environment in individuals
with and without sensory loss. To accomplish this aim, the 3 groups of participants will be
tested within the same visual environment but here we will compare their sway within a
sound-free dynamic visual environment to that with static white noise.
System: Visuals were designed in C# language using standard Unity Engine version
2018.1.8f1(64-bit) (©Unity Tech., San Francisco, CA, USA). The scenes will be delivered via
an HTC Vive headset (Taoyuan City, Tai-wan) controlled by a Dell Alienware laptop 15 R3
(Round Rock, TX, USA). The Vive has built-in positional track-ing operating at 60Hz and a
refresh rate at 90 Hz. Sounds will be delivered via Bose (Bose Corporation, Fram-ingham, MA,
USA) QuietComfort 35 wireless headphones II with active noise cancellation and 360º spatial
audio. The process of creating auditory cues included over 20 hours of sound field recording
based on the targeted scenes and their intensity levels in New York City. Auditory cues were
captured with the Sennheiser Ambeo microphone in first order Ambisonics format. The
background sounds merged with a sound design process which involved simulating the detailed
environmental sounds that exist within the natural environment to develop a real-world sonic
representation. The audio files were processed in Wwise and integrated into Unity. Postural
sway will be recorded at 100 Hz by Qualisys software for a Kistler 5233A force-platform
(Winterthur, Switzer-land).
Data Collection: Potentially eligible participants will complete a demographics form and go
through the following diagnostic screening at the Ear Institute: Caloric Test, Video Head
Impulse Test (vHIT), Ocular / Cervical Vestibular Evoked Myogenic Potential, and Audiogram.
Visual and somatosensory screening will be done at the Ear Institute as well. This first
session is expected to take 2.5 hours to complete. Participants will receive questionnaires
to complete at home or on the next session. The Dizziness Handicap Inventory (DHI) was
designed to identify difficulties that a patient may be experiencing because of dizziness.
The Activities-Specific Balance Confidence (ABC) is a measure of confidence in performing
various ambulatory activities without falling or feeling 'unsteady'. The State-Trait Anxiety
Inventory (STAI) assesses the severity of anxiety symptoms and a generalized tendency to be
anxious. The Speech, Spatial and Quality of Hearing 12-item Scale (SSQ12) is a valid, short
version of the original SSQ which provides insights on day-to-day hearing loss impact. The
virtual reality protocol (testing by the PI at the NYU Human Performance Laboratory) includes
12 conditions: 2 environments (an abstract display of stars, a subway station) X 2 visuals
(moving, static) X 3 sounds (dynamic, none, static white noise) each repeated 3 times for a
total of 36 trials. It will be randomized and completed over 1-2 sessions, as needed of up to
90 minutes each. Sounds will be played at the highest level that is comfortable to the
participant. Scenes are 60 seconds long. Throughout all sessions, the patients will complete
the Simulator Sickness Questionnaire, used to monitor participants' symptoms.
Data Analysis: For each of the 3 measures of interest and for each environment, we will fit a
linear mixed effects model. Each model will include main effects of group, visual condition,
and auditory condition, as well as all 2 and 3-way interactions. The models will also control
for caloric and Video Head Impulse Test (vHIT) test results as well as Age Related Hearing
Loss and age. For aim 1, we will assess the significance of contrasts between no sounds /
dynamic sounds for the different visual conditions and groups. For aim 2, the same will be
done for contrasts between no sounds / static sounds. These models estimate the difference in
visual weighting and reweighting between the groups, maximizing the information we can obtain
from the data by accounting for the inherent multi-level study design (person, conditions,
repetitions). Since each person completes various trials for each condition, the linear mixed
effects model accounts for these sources of variability. P-values for the fixed effects will
be calculated using the Satterthwaite approximation for the degrees of freedom for the
T-distribution80. In addition, we will descriptively explore the relationship between DP,
area, self-reported outcomes (DHI, ABC, STAI, SSQ12), and age.
