This Provisional PDF corresponds to the article as it appeared upon acceptance. Fully formatted
PDF and full text (HTML) versions will be made available soon.
V-TIME: a treadmill training program augmented by virtual reality to decrease fall
risk in older adults: study design of a randomized controlled trial
BMC Neurology 2013, 13:15doi:10.1186/1471-2377-13-15
Anat Mirelman (firstname.lastname@example.org)
Lynn Rochester (email@example.com)
Miriam Reelick (M.Reelick@ger.umcn.nl)
Freek Nieuwhof (F.Nieuwhof@ger.umcn.nl)
Elisa Pelosin (firstname.lastname@example.org)
Giovanni Abbruzzese (email@example.com)
Kim Dockx (Kim.firstname.lastname@example.org)
Alice Nieuwboer (Alice.Nieuwboer@faber.kuleuven.be)
Jeffrey M Hausdorff (email@example.com)
26 August 2012
15 January 2013
6 February 2013
Like all articles in BMC journals, this peer-reviewed article can be downloaded, printed and
distributed freely for any purposes (see copyright notice below).
Articles in BMC journals are listed in PubMed and archived at PubMed Central.
For information about publishing your research in BMC journals or any BioMed Central journal, go to
© 2013 Mirelman et al.
This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
V-TIME: a treadmill training program augmented
by virtual reality to decrease fall risk in older adults:
study design of a randomized controlled trial
Jeffrey M Hausdorff1,7,8
1 Department of Neurology, Laboratory for Gait Analysis & Neurodynamics,
Movement Disorders Unit, Tel Aviv Sourasky Medical Center, 6 Weizmann
Street, Tel Aviv 64239, Israel
2 School of Health Related Professions, Ben Gurion University, Beer Sheba,
3 Institute for Aging and Health, University of Newcastle, Newcastle, UK
4 Department of Geriatric Medicine and Neurology, Radboud University
Nijmegen Medical Center, Nijmegen, The Netherlands
5 Department of Neurosciences, Universita Degli Studi Di Genova, Genova, Italy
6 Department of Rehabilitation Science, Katholieke Universiteit Leuven, Leuven,
7 Department of Physical Therapy, Sackler Faculty of Medicine, Tel-Aviv
University, Tel-Aviv, Israel
8 Harvard Medical School, Boston, MA, USA
* Corresponding author. Department of Neurology, Laboratory for Gait Analysis
& Neurodynamics, Movement Disorders Unit, Tel Aviv Sourasky Medical
Center, 6 Weizmann Street, Tel Aviv 64239, Israel
Recent work has demonstrated that fall risk can be attributed to cognitive as well as motor
deficits. Indeed, everyday walking in complex environments utilizes executive function, dual
tasking, planning and scanning, all while walking forward. Pilot studies suggest that a multi-
modal intervention that combines treadmill training to target motor function and a virtual
reality obstacle course to address the cognitive components of fall risk may be used to
successfully address the motor-cognitive interactions that are fundamental for fall risk
reduction. The proposed randomized controlled trial will evaluate the effects of treadmill
training augmented with virtual reality on fall risk.
Three hundred older adults with a history of falls will be recruited to participate in this study.
This will include older adults (n=100), patients with mild cognitive impairment (n=100), and
patients with Parkinson’s disease (n=100). These three sub-groups will be recruited in order
to evaluate the effects of the intervention in people with a range of motor and cognitive
deficits. Subjects will be randomly assigned to the intervention group (treadmill training with
virtual reality) or to the active-control group (treadmill training without virtual reality). Each
person will participate in a training program set in an outpatient setting 3 times per week for
6 weeks. Assessments will take place before, after, and 1 month and 6 months after the
completion of the training. A falls calendar will be kept by each participant for 6 months after
completing the training to assess fall incidence (i.e., the number of falls, multiple falls and
falls rate). In addition, we will measure gait under usual and dual task conditions, balance,
community mobility, health related quality of life, user satisfaction and cognitive function.
This randomized controlled trial will demonstrate the extent to which an intervention that
combines treadmill training augmented by virtual reality reduces fall risk, improves mobility
and enhances cognitive function in a diverse group of older adults. In addition, the
comparison to an active control group that undergoes treadmill training without virtual reality
will provide evidence as to the added value of addressing motor cognitive interactions as an
Falls, Ageing, Gait, Cognitive function, Prevention, Virtual reality
Gait impairments and falls are ubiquitous among older adults and patients with common
neurological diseases. Approximately 30% of community-dwelling adults over the age of 65
fall at least once a year [1,2]. In persons with Parkinson’s disease (PD), mild cognitive
impairment (MCI) or dementia, falls are even more frequent with annual incidence rising to
60–80% [2,3]. The consequences of these falls may be severe, leading to institutionalization,
loss of functional independence, disability, fear of falling, depression and social isolation .
Most falls occur during walking [5,6] and, not surprisingly, gait impairment has been
associated with an increased risk of falls [7,8]. Gait abnormalities in elderly fallers and
patients with PD include reduced gait speed, stride length, and increased stride symmetry .
Fear of falling, cautious gait [10,11], gait unsteadiness, or dysrhythmicity of stepping have
also been recognized as mediators of fall risk [12-15].
There is a growing body of research that specifically links the cognitive sub-domains of
attention and executive function (EF) to gait alterations and fall risk [15-21]. EF apparently
plays a critical role in the regulation of gait especially under challenging conditions where
decisions need to be made in real-time . Walking while avoiding obstacles and walking
while simultaneously performing another task, i.e., dual tasking (DT), place greater demands
on cognitive resources such as divided attention and executive control, judgment, and
reasoning, compared to “single task” walking [23-25]. EF scores and dual tasking gait
performance have been associated with fall history and have been shown to predict future
falls, even over several years of follow-up [17,21,26]. Although there is no universal
agreement, many studies in patients with PD have reported that EF and dual tasking gait
abilities are associated with fall risk [27-29] and attention-deficits predict future falls in
patients with PD . This may explain why falls occur so frequently among older adults,
and even more so in patients with PD and patients with MCI. We suggest that these three
groups share cognitive deficits that contribute to and exacerbate their fall risk. MCI patients
are cognitively impaired, by definition. As much as 60% of patients who receive the
diagnosis of PD already have cognitive deficits [31,32], and many older adults suffer from
age-associated decline in cognitive function.
Another risk factor identified as a cause for falls in the elderly is obstacle crossing. Compared
to healthy young adults, older adults walk more slowly during obstacle crossing [5,33-36],
with smaller steps [34-36] landing dangerously closer to the obstacle with their lead limb [36-
38]. Age-related deficits in vision, proprioception and visual-spatial orientation can also
negatively impact postural stability and lower limb kinematics when crossing obstacles
[5,34,36,37,39]. Obstacle negotiation heavily relies on the availability of ample cognitive
resources, due to the need for motor planning and visually dependent gait regulation [40,41].
Many intervention programs based on reported multiple risk factors have been proposed and
evaluated to reduce fall risk . However, despite the extensive knowledge on fall risk
obtained in recent years, there is no consensus as to the most efficacious or optimal treatment
approach [43,44]. Common treatments include exercise programs to improve strength or
balance, educational programs, medication optimization, environmental modification and
multi-factorial interventions involving a combination of several modalities. To date, however,
the effects on fall risk tend to be small and the reported changes are largely focused on motor
aspects with limited long-term retention [45-47].
Mahoney  suggested that perhaps the reason that multi-factorial interventions are not
consistently successful is because they fail to address three major concepts: 1) training should
be intensive, focused on the key impairment and become progressively more rigorous; 2) the
training should fit the target population; 3) delivery of the intervention should include
mechanisms to maximize motor learning and induce a behavioural change. We propose that
insufficient focus on cognitive aspects, in particular, the motor-cognitive interactions that
contribute to fall risk, might contribute to the sub-optimal success of previous fall risk
interventions. Even if cognitive function is targeted, it is generally done so in isolation and
the motor-cognitive interactions are not directly addressed in an integrated fashion needed to
successfully and safely ambulate in daily living.
To address this challenge, a multi-modal treadmill training program augmented by virtual
reality (VR) (see Figure 1) was developed to deal with both the motor and cognitive aspects
of fall risk and to promote motor learning critical for key tasks of safe ambulation. In general,
VR is defined as a “high-end-computer interface that involves real time simulation and
interactions through multiple sensorial channels” [49-51]. VR can be used to provide training
in a more stimulating and enriching environment than traditional rehabilitation whilst
providing feedback about performance to assist with learning new motor strategies of
movement. Therefore, treadmill training augmented by VR is, theoretically, well-suited as a
multi-factorial intervention for fall risk since it is designed to focus on the motor-cognitive
aspects of fall risk such as dual tasking, obstacle negotiation and executive function.
Figure 1 The V-TIME multi-modal intervention solution for reducing fall risk. Current
treatment of fall risk focuses on motor, e.g., gait, problems. V-TIME focuses on both gait and
cognitive deficits to optimally treat multiple, critical fall risk aspects and enhance mobility,
physical activity and cognitive function. The current working version of V-TIME is shown. A
patient trains on a treadmill while viewing a virtual environment that presents obstacles,
different types of challenges, and feedback . Written informed consent was obtained from
the patient for publication of this case report and any accompanying images. A copy of the
written consent is available for review by the Editor-in-Chief of this journal.
In a pilot study , 20 patients with PD participated in an intervention based on a VR
system for an obstacle navigation task. Patients walked on a treadmill while negotiating
obstacles in a VR scene projected on a wall in front of them. They trained for 3 times a week
for 6 weeks for about 45 minutes in each session. Visual and auditory feedback was provided
by the VR simulation upon error or success and at the end of each walk. After 6 weeks of
training, comfortable gait speed significantly improved, as did stride length, gait variability,
and over-ground obstacle negotiation. Dual task (DT) performance improved and there was
evidence of enhanced task planning and set shifting. Increased gait speeds under all
conditions (i.e., comfortable, fast, DT and six minute walk) were not only maintained at
follow-up, but also continued to improve 4 weeks later, suggesting that the training generated
a positive feedback loop that modified behaviour and overall mobility . Encouraged by
these results, an additional pilot study was carried out. Five elderly women who sustained at
least 2 falls in the 6 months prior to the study trained using the same treadmill training with
VR protocol. Here too, after training, improvements were observed in dual tasking, cognitive
function, gait, and mobility, but perhaps the most promising finding was a decrease of 73% in
the frequency of falls in the 6 months post-training as compared to 6 months pre-training
The accumulating evidence on the importance of cognitive function to gait and falls
combined with these initial findings formed the basis of the present study. The primary aim is
to demonstrate that six weeks of treadmill training augmented by VR (TT+VR) reduces the
risk of falls in a relatively large and diverse group of older adults (n=300), many of whom
will likely have a spectrum of motor and cognitive deficits. The study will compare training
effects of TT+VR against an active control paradigm (TT without VR) in a randomized
controlled trial. We hypothesize that a 6 week intervention with TT+VR compared to TT
alone will reduce the incidence of falls and decrease the risk of falls in elderly adults, patients
with PD and individuals with MCI. As a secondary question, we will also explore the neural
correlates associated with dual task activation and any plastic effects resulting from the
training using imaging techniques. However, protocols for these studies will not be presented
in this manuscript.
A prospective, single blinded, parallel group, randomized controlled trial (RCT) with 6
month follow-up will be employed. The study will include 300 older participants who have
experienced two or more falls in the previous 6 months. Participants will be randomized to
either the intervention or control group. The intervention group will receive 18 sessions of
Treadmill Training with Virtual Reality (TT+VR) and the active control comparison will
receive 18 training sessions of treadmill training alone (TT) without the VR simulation (see
Figure 2 Summary of the study design and training protocol. TT: treadmill training.
TT+VR: treadmill training augmented by the virtual reality simulation.
Participants and setting
The participants will be recruited from three groups: older adults with no cognitive
impairment (n=100); older adults with mild cognitive impairment (MCI) (n=100) and people
with Parkinson’s disease (PD) with no cognitive impairment (n=100). Subjects will be
recruited if they meet to the following criteria:
Common inclusion criteria
• 2 or more falls within 6 months prior to the beginning of the study
• Aged 60–85 years
• Able to walk for 5 minutes unassisted
• Adequate hearing (as evaluated by the whisper test) and vision capabilities (as
measured using a Snellen chart).
• Stable medication for the past 1 month and anticipated over a period of 6 months
Common exclusion criteria
• Psychiatric co-morbidity (e.g., major depressive disorder as determined by DSM IV
• Clinical diagnosis of dementia or other severe cognitive impairment (Mini Mental
State Exam score <24)
• History of stroke, traumatic brain injury or other neurological disorders (other than
PD and MCI, for those groups)
• Acute lower back or lower extremity pain, peripheral neuropathy, rheumatic and
• Unstable medical condition including cardio-vascular instability in the past 6 months
• Unable to comply with the training or currently participating in another interfering
therapy or a fall clinics program
• Interfering therapy
Group specific criteria
Participants with PD
• Diagnosis of idiopathic PD, as defined by the UK Brain Bank criteria
• Hoehn and Yahr stage II-III
• Taking anti-Parkinsonian medication
• Stable medication for past 1 month and anticipated over next 6 months or stable Deep
Brain Stimulation for at least one month and expected following 6 months
• Severe freezing of gait (defined as having >15 on the new FOG questionnaire) 
• Score 0.5 on the Clinical Dementia Rating Scale (CDR)
• Free from any neurological disorders that may have caused the cognitive impairment
Sample size calculation
The primary outcome measure is fall incidence rates during the 6 month post-training follow-
up period. The sample size estimate is based on extrapolations from our pilot studies  and
other related promising pilot work (e.g., Rosenblat et al. , Pai et al.  and,
Weerdesteyn et al. ). Power was set at 80%, alpha was set at 5% and we accounted for
drop off rate of 20%. Using a relatively conservative estimate, we assume that the control
group fall incidence rate, after intervention, will be three falls per year. If we consider a 40%
reduction for the treatment group relative to this, then, during the 6 month monitoring of falls
incidence, a total of 166 subjects would be required for 80% power (83 in each group) to
detect differences between the two treatment groups assuming non-inferiority with moderate
correlations among covariates (R-squared = 0.50). A much smaller sample is needed to detect
between group differences for the secondary outcomes. For example, with 22 subjects in the
intervention and control arms, we will have 90% power to detect an intervention effect
(assuming Cohen’s f for ANOVA of 0.21), in dual tasking gait speed. To enhance our ability
to examine the effects of the intervention on fall incidence within the three sub-groups
(seniors, PD, MCI), we will aim to recruit 100 subjects per group, for a total of 300 subjects.
Recruitment and randomization procedure
The study will be conducted in 5 clinical centres across Europe (Lab for Gait &
Neurodynamics, Tel Aviv Sourasky Medical Centre, Israel; Department of Rehabilitation
Sciences, Katholieke Universiteit Leuven, Belgium; Institute for Ageing and Health,
Newcastle University UK; Department of Neurosciences, University of Genoa, Italy;
Departments of Geriatric Medicine & Neurology, Radboud University Nijmegen Medical
Centre, The Netherlands).
Ethical approval was obtained by ethics committees of each of the above clinical sites.
Eligible subjects, who agree to participate, will be asked to provide an informed written
consent after which they will be randomized to one of two arms of the study: 1) TT+VR; 2)
TT. A permuted blocked randomization procedure will be used selecting randomly from a
block size of 4, 6 or 8. Group allocation will be performed by a third party not involved in the
day to day running of the study; the treating therapist will be notified by e-mail to ensure
All interventions will be delivered by therapists trained in the standard protocols across
centres in the consortium countries. Consistent with the motor learning literature and the pilot
studies [49,52], all subjects will be trained 3 times a week for 6 weeks, each session will last
approximately 45 minutes.
Virtual reality system
Details of the instrumentation are provided elsewhere [49,52]. Briefly, the system includes a
camera based motion capture (Kinect) and a computer generated simulation. The camera will
be used to collect the movement of the participant’s feet while walking on the treadmill.
These images will be transferred into the computer simulation and projected to the patient on
a large screen while training, enabling the subjects to see their feet walking within the VR
simulation. The virtual environment (VE) will consist of obstacles, different pathways,
narrow corridors and distracters, requiring modulations of step amplitude in two planes (i.e.,
height and width) coordinated with walking behaviour. The speed, orientation, size,
frequency of appearance and shape of the targets will be manipulated according to individual
needs following a standardized protocol. Environmental features (e.g., visibility, settings and
distractions) will be adjusted to increase training complexity. The VE will impose a cognitive
load requiring attention and response selection as well as processing of rich visual stimuli
involving several perceptual processes. The system will provide visual and auditory feedback
of successful or unsuccessful task performance to enhance motor learning. Adaptability of the
system is foreseen to adjust training parameters to the clinical needs of the individual
TT+VR group (the intervention group)
Motor aspect of training
Gait speed over-ground will be measured over 10 meters at the beginning of each week of
training. This speed will be registered and the treadmill speed will be set accordingly, as
detailed below. Training will be divided into bouts of walking and rest breaks in between.
The duration of the initial session should be ideally 20 minutes of walking time. A safety
harness will be attached to an over head suspension system, but no weight support will be
Training progression will be based on increasing both motor and cognitive challenges,
individualized to the participant’s level of performance. The motor component of training
progression will include an increase in the treadmill speed and duration of training. Treadmill
speed in the first week will be set at 80% of over ground gait speed. In the second week, the
treadmill speed will increase to 90% of over ground speed. Another 10-minutes in duration
will be added. From the third week, the speed goal will increase by 10% every week and 1
minute of walking will be added to each of the walking bouts (a total of an additional 3–5
minutes per session compared to the previous session). This progression is subject to the
performance and the patient’s ability.
The cognitive components of training progression will include changing the number, size and
shape of obstacles, and the frequency, speed and direction at which they appear. The Virtual
Environment characteristics will also be manipulated by reducing visibility and adding
distracters (e.g., birds, cars). During week 1, obstacles will appear infrequently (e.g., every 30
seconds, at low level of difficulty), be unilateral in direction and the environmental features
will be minimal (i.e., high visibility, day time walking, minimal distracters). Based on the
subject’s performance in weeks 2 and 3, the frequency of appearance of the obstacles will
increase, obstacles will appear on the more challenged side and their features (horizontal vs.
vertical) will be individualized. Environmental features will appear with some minimal
distracters during weeks 2 and 3. In week 4, subjects will be introduced to a new environment
to allow for more diversity in training and to maximize transfer to the real-world.
Throughout, training should maintain the ratio of 80:20 success/failure rates in order to
enhance motor learning. If subjects are not successful, the difficulty level will be decreased to
the level previously achieved and vice versa.
Treadmill training (TT) (active control) group
The participants will walk on the treadmill without receiving the feedback from the VR. As in
the TT+VR group, their gait speed over-ground will be measured at the beginning of each
week of training. Progression and the time spent with the trainer will follow the same
guidelines as the motor progression of the TT+VR group and will include increasing the
duration of each of the walking bouts and increasing walking speed.
A repeated measures design will be employed with assessments performed 1 week pre-
training, post-training, and at 1 month and 6 months post intervention (Table 1). A trained
assessor in each centre, not involved in training and blinded to group allocation, will perform
all assessments. Each participant will be assessed at about the same time of day to avoid
variability of performance due to any circadian rhythms or medication intake cycle.
Table 1 Assessment of outcome measures across the protocol
x Primary outcome
Falls Fall frequency
Gait Gait speed
Mindstreams tests of
Balance and mobility
Healthy Related Quality
User satisfaction and
2MWT- 2 Minute Walk Test, FSST-Four Square Step Test, SPPB- Short Physical
Performance Battery, mini-BEST- The mini-Balance Evaluation Systems Test, MOCA-
Montreal Cognitive Assessment, TMT- Trail Making Test, FES-I- Fall Efficacy Scale
Primary outcome measures
The primary outcome measure of the study is fall rate. Participants will keep a falls calendar
for 6 months post intervention. Consistent with the recommendations of the Prevention of
Falls Network Europe (ProFaNE), a fall will be defined as “an unexpected event in which the
participant comes to rest on the ground, floor or lower level”. Each time the participant falls
he/she will tick the date on the calendar. These calendars will be returned to the researchers
once a month in a pre-addressed envelope. Periodic contact by the research staff with each
participant will be used to maximize compliance with the fall calendars.
Secondary outcome measures
Gait speed and gait variability under usual and DT conditions and while negotiating physical
obstacles will be measured. Participants will be asked to walk in a well-lit corridor under 3
conditions each of 1 minute: i) walking in a comfortable speed, ii) walking while subtracting
3 s from a predefined number (dual task), iii) walking while negotiating two obstacles placed
on the floor at specific locations. The GaitRite mat, a sensorized 7 meter carpet (CIR
Systems, Inc. Haverton MA), will capture individual footfall data using embedded pressure
sensors. This is a valid and reliable method of assessing the spatiotemporal parameters of gait
in healthy older adults and in patients with Parkinson’s disease . Spatiotemporal gait
characteristics (e.g., gait speed (m/s), stride length (m), stride time (s), swing time (%),
asymmetry, and step width (cm)) will be determined. Over-ground obstacle negotiation will
be evaluated by placing physical obstacles on the GaitRite. The distance between the heel and
the physical obstacle during the loading response of the lead foot will be measured to assess
clearance and efficient obstacle negotiation.
Small, lightweight 3 axial accelerometers (APDM, Oregon, USA) will be worn on both feet,
both wrists and on the lower back of the participants during all gait measurements to quantify
temporal measures such as stride time and gait variability . Gait variability (i.e., the
inconsistency from one stride to the next) will be determined by calculating the magnitude of
stride-to-stride fluctuations, normalized to each subject’s mean stride time to define the
Coefficient of Variation (Coefficient of Variation (CV = (standard deviation/mean) × 100)).
Gait variability is a validated and reliable measure reflecting fall risk that has been used with
patients with PD , older adults  and individuals with MCI . Data will be collected
at 240 HZ, saved onto a computer and analysed using proprietary software.
Endurance will be assessed using the 2 Minute Walk Test. This performance-based tool was
originally developed to assess exercise tolerance among individuals with respiratory disease,
but has shown high test retest reliability and validity in assessment of gait endurance in older
adults  and in individuals with neurological conditions [61,62].
Balance and mobility
The Four Square Step Test (FSST) requires subjects to rapidly change direction while
stepping forward, backward, and sideway, over a low obstacle. Time to complete the test is
measured. The test has been validated in older adults  with sensitivity of 85% and
specificity of 88-100% in predicting fall risk .
The Short Physical Performance Battery (SPPB) consists of three types of physical
maneuvers: the balance tests, the gait speed test, and the chair stand test. The SPPB is highly
reliable in older adults (ICC=0.83-0.89) and has demonstrated a strong and consistent
association with health status measures, in spite of the socioeconomic and cultural differences
The mini-Balance Evaluation Systems Test (mini-BESTest) is a performance based measure
differentiating balance problems into 6 underlying systems that may be impaired:
biomechanical, stability limits, postural responses, anticipatory postural adjustments, sensory
orientation, dynamic balance during gait and cognitive effects. The mini-BESTest has been
shown to be a reliable (ICC=0.91) and valid measure of balance in individuals with PD .
Community ambulation will be assessed using 1) the Physical Activity Scale for the Elderly
(PASE). This 27 item self report questionnaire assess habitual physical activity in the home
and community environment. The questionnaire was designed to address cultural differences,
is available in multiple languages and has been validated for older adults ; 2) a tri-axial
accelerometer (Axivity Ltd.) will be worn by the participants for 7 days to quantify and
monitor stepping and physical activity. The device which records at 100Hz will be mounted
on the trunk (L5) and will derive the following outcome measures: step count, postural
transitions, sedentary time, percentage walking time, number and time of walking and
sedentary bouts. Data will be obtained one-week pre and post training.
Cognitive function will be assessed using a computerized neuropsychological test battery
(Mindstreams®, NeuroTrax Corp., NJ) . The battery assesses different cognitive domains
including memory, attemtion, executive function, visual spatial processing and a global
cognitive composite. The test battery generates age and education adjusted composite indices
of each cognitive domain on an IQ like scale, with the score of 100 representing the estimated
population mean normalized for age and education level. The battery has been validated in
elderly adults, patients with mild cognitive impairment, and patients with PD and has shown
to be useful in predicting falls [17,21,68-70].
In addition, we will also include standardized neuropsychological tests such as the Montreal
Cognitive Assessment (MoCA); a rapid screening instrument for global cognitive
dysfunction. Different cognitive domains are assessed (attention and concentration, executive
functions, memory, language, visuo-constructional skills, conceptual thinking, calculations,
and orientation). The MOCA was found to be a valid instrument for cognitive screening in
MCI and PD [71,72]. In this study the MoCA will be used as a descriptive measure.
The Trail Making Test (TMT) is a neuropsychological test of visual attention and task
switching. It consists of two parts in which the subject is instructed to connect a set of 25 dots
as fast as possible while still maintaining accuracy. The test provides information about
visual search speed, scanning, speed of processing, mental flexibility, and executive
functioning. The TMT is valid and reliable for older adults [73,74] and has been previously
associated with decreased gait speed, dual task activity, and obstacle clearance .This is a
well suited outcome measure given the nature of the training.
Verbal Fluency is a test of working memory and language in which participants have to say
as many words as possible from a category in a given time (usually 60 seconds). The test
includes both semantic and phonemic sections, is related to executive function, and has been
shown to be highly reliable and valid in the elderly population .
Health-related quality of life
The SF-36 Health Survey is a generic self-report questionnaire designed to address health
related quality of life. The SF-36 includes one multi-item scale measuring several constructs
such as physical functioning; bodily pain; social functioning; general mental health
(psychological distress and psychological wellbeing); vitality (energy/fatigue); and general
health perceptions. Criterion validity has been established but the scores could also be
divided into two aggregate summary measures; the Physical Component Summary (PCS) and
the Mental Component Summary (MCS). SF-36 has been validated for older adults and
patients with PD [77,78].
Fear of Falling will be evaluated using the Falls Efficacy Scale-International. The FES-I has
been significantly associated with performance-based measures of balance and mobility
including gait speed and medial-lateral sway. The scale has been shown to be sensitive to
change in older adults with and without cognitive impairments [79,80].
User satisfaction and views
A questionnaire was developed by the researchers to assess the satisfaction of the participants
from the training and to try and obtain subjective information regarding the usability and
efficacy of such an intervention in reducing fear of falling, fall risk and frequency of falls.
Statistical analysis will be undertaken using SPSS version 19.0 (SPSS Corp, Chicago, IL,
USA). All analysis will be conducted on an intention-to-treat principle using all randomized
participants. Demographic characteristics and baseline data will be summarized by
descriptive statistics using means, standard deviations and 95% confidence intervals for
continuous variables, median and inter-quartile ranges for non-normal continuous or ordinal
data and percentages for categorical data, and will be evaluated for normalcy and
homogeneity. For the primary outcome measure, fall rate will be analyzed by calculating
relative risk using negative bionomial regression models that adjust for any potential
confounders . Fall rate and fall status (none faller, faller and multiple faller) will then be
compared within and between groups. The secondary outcome measures will be analyzed
using repeated measures analysis of variance (RMANOVA) to assess differences between
groups (intervention) and across time (follow up) for each group of participants and then
compared across groups. All data will be adjusted for multiple comparisons.
Safety considerations and adverse events
All measurements are non-invasive and place the subject at no risk other than those that
normally may occur during walking. For some of the patients, in particular those who were
not practicing any kind of physical exercise prior to the intervention; there is a slight
possibility that subjects might feel some muscle soreness and fatigue after training. To
prevent excessive fatigue, subjects will be encouraged to take breaks as needed throughout all
study procedures. In addition, the study was designed for gradual increases in intensity which
will help to increase endurance and build muscle strength. Virtual reality may cause cyber
sickness, a sensation similar to motion sickness. This phenomenon is, however, very rare and
is related to highly immersive technology. The proposed study will use a 2D projection to
deliver the VR simulation which decreases the risk of developing cyber sickness.
The aim of this study is to establish a practical and feasible solution for enhancing mobility,
preventing falls and reducing disability among diverse groups of older adults using a unique
intervention that combines treadmill training and virtual reality. What sets this project apart
from previous work in this field is that the study simultaneously addresses both motor and
cognitive function and their interactions that are key to falls using a large, RCT study design,
with an active comparison control for assessing efficacy. Training is provided in a virtual
environment that implicitly challenges, teaches, and enhances visual scanning, planning, DT
abilities and obstacle negotiation. The additional training goals that aim to enhance the
cognitive aspects of mobility have not yet been integrated into common practice and are one
of the important added features of the proposed intervention. The unique training program
takes onboard all aspects of motor learning in that it probes retention as well as transfer of
training to real-world activities to maximize resilient effects.
In a sense, this study also addresses the concepts and concerns raised by Mahoney et al. .
The proposed training is intensive, focuses on the key impairment and becomes progressively
more rigorous. The training and protocol were designed to meet the needs of a diverse group
of older adults including those with cognitive deficits and motor impairment due to
neurodegeneration. The intervention maximizes motor learning in order to induce a
behavioural change. Moreover, training in the computer-controlled virtual environment
makes the therapy and protocol standardized and reproducible.
The training protocol that is at the basis of this study was developed based on recently
established guidelines on complex interventions in geriatrics [82-87]. The proposed protocol
is based on the needs of the three groups who share a high risk of falls, in part due to
cognitive deficits. Focus groups and questionnaires have been used to refine the intervention.
Feasibility of using such an intervention was assessed and pilot studies were carried out.
Further, the outcome measures are validated and selected to evaluate the effects of the
intervention on falls and the motor cognitive interactions that contribute to fall risk. This
process enables us to confidently advance into a large randomized controlled trial to explore
efficacy in comparison to an active training control group.
Evidence on the efficacy of fall prevention in geriatrics is not yet ideal and large randomized
control trials are needed. To promote motor learning required for safe ambulation, fall
prevention interventions should include motor and cognitive aspects relating to falls, task-
specific and generalized training, with the intervention centred around the user’s needs. The
proposed intervention set out to bridge all these needs. The knowledge that will be generated
by the results of this study are likely to inform new models of care that combine technology,
mobility training, and cognitive remediation to reduce risk of falls and enhance mobility even
in a chronic disease profile.
This randomized controlled trial will demonstrate the extent to which an intervention that
combines treadmill training augmented by virtual reality reduces fall risk, improves mobility
and enhances cognitive function in a diverse group of older adults. In addition, the
comparison to an active control group that undergoes treadmill training without the added
virtual reality will provide evidence as to the added value of an intervention that addresses
motor cognitive interactions as an integrated unit.
All authors declare that they have no competing interests.
AM and JMH participated in designing the study, designing the VR system to be used,
writing and reviewing of the manuscript. LR participated in designing the study and writing
and reviewing the manuscript. MR, FN and KD participated in reviewing the manuscript, EP,
GA and AN participated in designing the study and reviewing the manuscript. All authors
read and approved the final manuscript.
Besides the clinical partners, V-TIME also includes technical partners without which the
project would not be successful. We would like to thank our partners from the University of
Sassari in Sardinia (UNISS), the partners from Inition 3D technologies (INITION), Advanced
Drug Development Services (ADDS) and Beacon Tech Limited (BTL) for their contribution.
The project was funded by the European Commission (FP7 project V-TIME- 278169).
1. Blake AJ, Morgan K, Bendall MJ, et al: Falls by elderly people at home: prevalence and
associated factors. Age Ageing 1988, 17(6):365–372.
2. Tinetti ME, Speechley M, Ginter SF: Risk factors for falls among elderly persons living
in the community. N Engl J Med 1988, 319(26):1701–1707.
3. Wood BH, Bilclough JA, Bowron A, Walker RW: Incidence and prediction of falls in
Parkinson’s disease: a prospective multidisciplinary study. J Neurol Neurosurg
Psychiatry 2002, 72(6):721–725.
4. Rubenstein LZ, Josephson KR: Falls and their prevention in elderly people: what does
the evidence show? Med Clin North Am 2006, 90(5):807–824.
5. Mackenzie L, Byles J, D’Este C: Validation of self-reported fall events in intervention
studies. Clin Rehabil 2006, 20(4):331–339.
6. Sartini M, Cristina ML, Spagnolo AM, et al: The epidemiology of domestic injurious
falls in a community dwelling elderly population: an outgrowing economic burden. Eur
J Public Health 2010, 20(5):604–606.
7. Axer H, Axer M, Sauer H, Witte OW, Hagemann G: Falls and gait disorders in geriatric
neurology. Clin Neurol Neurosurg 2010, 112(4):265–274.
8. Hausdorff JM, Rios D, Edelberg HK: Gait variability and fall risk in community-living
older adults: a 1-year prospective study. Arch Phys Med Rehabil 2001, 82:1050–1056.