Detailed Description:
Mobility issues affect 1/3 of the adult population requiring rehabilitation. Current
shortages in skilled rehabilitation professionals require novel approaches to address this
unmet rehabilitation need. Rehabilitation typically sets out to monitor and support people to
live life as they wish using appropriate therapies. Here the investigators propose to develop
and test a gait device for people with mobility issues. The investigators will first focus on
people with Parkinsons' (PwP) and then on people with other conditions affecting their
movement, including stroke and arthritis. There are several practical challenges to bring
cueing to the daily lives of PwP: PwP have difficulty multi-tasking. Basic rhythmic cueing
methods do not adapt to changes in gait quality or the activities undertaken.
PwP habituates to cues that are constantly in action. These challenges limit the
effectiveness and adoption of current cueing products by PwP. gaitQ is developing a smart
cueing device that: 1) directly addresses these challenges enabling an effective, practical
solution for everyday use; 2) aims to improve quality of life, and mental well-being and
reduce fall risks; 3) supports more effective and accurate disease management; and 4) does
this in a way that preserves PwP's discretion and privacy.
GaitQ:
The gaitQ system comprises two wearable devices worn on the back of the user's legs. The
devices provide vibrational cues with specific patterns tailored to the user's walking
characteristics to help them overcome FOG and FSG, and improve their normal gait quality,
such as stride length and step symmetry. As Parkinson's gait symptoms contribute to a
two-fold risk of falling in PwP, by enabling a more fluid and stable gait while reducing the
occurrence of FOG and FSG, the gaitQ system sets out to help reduce the risk of falling. By
the time of the trial, gaitQ device will be certified with UKCA certificate.
Rationale: Considering Parkinson's alone, an analysis has shown that 1 in every 37 people
will be diagnosed with PD in their lifetime. FOG and FSG are severely debilitating aspects of
the disease which greatly reduce the quality of life of PwP and contribute to the two-fold
increased fall risk and related injuries. It is vital that investigators provide a solution
to support PwP with a more fluid and safer gait, greater independence and better disease
management and care. Cueing with visual, auditory, or somatosensory stimuli is a
well-documented and clinically validated method to overcome FOG and FSG. Research studies
have also shown that cueing modalities improve gait in PwP during both free and treadmill
walking, whilst improving balance and reducing the need for stabilising support. Gait
parameters, such as step frequency, stride length and gait symmetry, have been shown to be
measurable with inertial motion sensors for gait quality assessment. Studies have shown that
objective measurement of the disease can improve treatment outcomes in PD. There are basic
visual and rhythmic cueing products available (such as laser shoes, a metronome app and a
vibrational button), however, they are still based on simple continuous cues which have
profound limitations on usability and effectiveness in the everyday environment and do not
include gait analytics systems to facilitate better patient outcome and experience. The gaitQ
system will be the first unique solution that uses artificial intelligence and smart adaptive
cueing to help patients effectively overcome FOG and FSG in their daily environment while
improving gait quality.
STUDY DESIGN Clinical trials will be conducted in close collaboration with the Royal Devon
University Healthcare NHS Foundation Trust to collect clinical evidence and usability data on
the impact of the gaitQ product. The project will be underpinned by the new MRC guidelines
for developing a complex intervention with a participatory design methodology that uses
evidence-based research and behaviour change models to identify intrinsic and extrinsic
factors that contribute to a given outcome in a specific population to collect clinical
evidence and usability data on the impact of the gaitQ product. The project will be
underpinned by the new MRC guidelines for developing a complex intervention
- (1) with a
participatory design methodology that uses evidence-based research and behaviour change
models to identify intrinsic and extrinsic factors that contribute to a given outcome in a
specific population.
Key outcomes:
Improvement in gait metrics, reducing freezing & festination episodes comparing with and
without the gaitQ device.
Improvement in gait quality comparing with and without the gaitQ device, in terms of stride
length, step frequency, step symmetry, walking speed.
User feedback on usability and acceptability Data for developing/verifying gait metrics
algorithms & for developing FOG detection algorithms Acceptability and safety will be
recorded by engagement with the device and completion of >70% of planned sessions.
Within the proposed NIHR i4i project, the researchers will investigate how the gaitQ product
can be potentially integrated into the clinical practices for these conditions.
STUDY SETTING This study will be based at the University of Exeter's VSimulator facility and
testing site (https://vsimulators.co.uk), at Exeter Science Park, Clyst Honiton, Exeter, EX5
2FN. Or at the Oxford University Hospitals NHS Trust. Gait facility.
People with conditions will be recruited from the Royal Devon University Healthcare NHS
Foundation Trust, University of Oxford, University Hospitals Plymouth NHS Trust and Bristol
and Weston NHS Foundation Trust and open recruitment through adverts and social media.
Sampling technique Convenience sampling is used for this study. Participants need to be
identified within the timeline and scope of the project. Obtaining volunteers that are easily
available and willing is a sensible sampling strategy for the scope of the project.
Convenience sampling is appropriate because the research is exploratory in nature and/or the
conclusions to be drawn from the data will not be threatened by issues concerning selection
bias, generalisability, sampling error, and/or statistical power.
Recruitment Eligible participants will be approached by their treating clinician and asked if
they'd like to hear more about the study from a member of the research team. If happy to hear
more about the study, and have their details passed on to the study team, participants will
be offered a participant information sheet and informed consent form and have a chance to
discuss the study in more detail with the research team. Patient consent may be taken at this
appointment, or the completed consent form can be returned online or via post.
Data collection/data processing Motion capture and force plate data will be collected using
the VSimulator force plate and motion capture systems (Optitrack). This data will then be
recorded onto the system computer. Recordings will not contain any identifiable data of each
participant, with participant numbers being used for file names. Video data will be collected
on participants using a camera during activities to assess the FOG. The recorded video will
be saved locally inside the camera memory SD card, and after each test, it will be
transferred to a University encrypted laptop.
All participants will be given a participant number with all data pseudonymised. A
participant number document will be kept separate from all data, which has information on the
identity of each participant should follow-up contact be needed.
Questionnaire data: Data will be collected using an Excel spreadsheet with participant
identity pseudonymised. Participant numbers will be used for researchers to identify the
corresponding answers in the follow-up analysis. The filled Excel files will be kept stored
on an online university drive with the name list stored on a separate SharePoint.
Optitrack data will be recorded on the VSimulator system computer. Upon completion of
recording the data will be transferred to a password protected university drive. Data file
name will use participant number and will have no identifiable elements. Upon transfer of
data to university drive the data will be deleted from the system computer.
Motion capture data will be kept on the university onedrive. The written informed consent
form will be scanned, uploaded directly to the encrypted University of Exeter servers as soon
as it is signed and the hard copy immediately destroyed in the confidential waste, leaving no
physically identifiable information at the VSimulator.
The gaitQ devices contain 6-degrees-of-freedom motion sensors and high-performance
micro-controllers to enable collection, analysis and storage of the user's motion data. This
will enable the gaitQ product to deliver automated and adaptive/personalised smart cueing to
the user. IMU data from the gaitQ devices will be stored in the gaitQ secured cloud server.
This data will be used, in conjunction with the motion capture data from the VSimulator
(non-identifiable), to develop the automatic, adaptive cueing algorithms. It will not contain
any identifiable information of each participant with participant numbers used as labels.
Following the testing procedure participants will be made comfortable and supervised in a
separate room for as long as required, until they feel able to continue with normal
activities. At this point they will remove the motion capture suit and their participation in
the study will be completed. The investigators will monitor how long the return to normal
activities takes.
Analysis Primary analysis: Interview data will be transcribed verbatim and analysed using a
thematic approach with nvivo. Lab data will be cleaned and processed using a standardised
procedure .
The system will be validated in the lab to determine point estimates and means for system
measures for all parameters and all groups Criterion Validity of categorised groups will be
based on agreement between classification/value with overall accuracy explored using Fisher's
exact test [Categories of agree/disagree] or Bland-Altman plots or with ICC (3).
Descriptive and frequency statistics will be reported for demographic, usability,
acceptability and feasibility data and SUS questionnaire, as well as reporting of missing
data.
Absolute and relative reliability, relative reliability ICC
- (3) and absolute reliability by
standard error of measurement (SEM) and minimal detectable change at the 95% confidence
interval.
Potential for effect will be determined with responsiveness of gait metrics, using the device
Feasibility of measure use: this will focus on the proportion of participants who used the
system successfully ( 1) identify design limitations when the system is used in the manner
intended for future use within the home setting 2) determine safety during testing by
monitoring adverse events 3) explore feasibility (usability/acceptability) of the technology
to measure people A [Acceptability and safety will be recorded by recruitment rate >20%,
engagement with the FA-IMAGINE and completion of >70% of sessions/measures over the 12 months
and monitoring of any adverse events. Usability will be assessed through the successful
establishment of a usability SUS target score >68
(https://www.usability.gov/how-to-and-tools/methods/system-usability-scale.html).
Determine the potential for effect: to determine the amount of change in metrics:
Participants will be classified as those changing/not changing ≥ minimal detectable change
(MDC) [MDC = 1.96 x SEM x square root of 2, MDC95 based on a 95% confidence interval ] at
testing and calculate the significance of the change using repeated measure ANOVA,
Generalised Estimating Equation (GEE)/ General Linear Model (GLM)as appropriate.
Peer review: The study has been peer-reviewed by the funder (NIHR).
