Objective: to develop a FOG detection and prediction algorithm under free-living conditions.
ID
Source
Brief title
Condition
- Movement disorders (incl parkinsonism)
Synonym
Research involving
Sponsors and support
Intervention
Outcome measures
Primary outcome
Machine learning and deep learning techniques will be applied to outcomes
derived from clinical assessments, wearable motion sensors (e.g. gait related:
abnormal gait classification, gait cadence, gait velocity, freezing index,
number of FOG episodes, percentage time spent on FOG, heart rate variability,
and skin conductivity), and video (presence of FOG) in order to create an
algorithm aiming at predicting an upcoming FOG episode and detecting an
existing FOG episode. Afterwards, ensemble technique will be applied to combine
the results from multiple different algorithms or parameters into a single
result. This step will increase algorithm precision. The same techniques will
be applied to data collected during the follow-up, in order to develop an
algorithm robust predicting an upcoming FOG episode and detecting an existing
FOG episode under free-living circumstances.
Secondary outcome
To investigate the influence of different sensor types, sensor number, and
sensor locations on the performance of a FOG detection and prediction algorithm
performance we will create different prediction algorithms using data from
clinical assessments (see table 1) and data from wearable sensors collected in
different numbers and sensor location. Models will be created using Machine
learning and/or Deep learning approaches. Machine learning algorithms build a
mathematical model based on sample data, here outcomes extracted from the
sensors and clinical assessments, in order to make predictions or decisions
without being explicitly programmed to perform the task. Self-reported FOG,
falls and balance problems, extracted from the smartphone application, will
increase model performance. Afterwards, model performance measures from models
including data from all sensors versus data from sensor from one location only,
as well as models from semi-free-living condition versus free-living conditions
will be compared in order to stablish the best model.
Next, we will apply the model created using data from study visit 1 (see
paragraph 10.1) to the data collected in study visit 2. We will then compare
the performance measures of each model and determine whether the model is
robust for a population that shows changes over time (i.e. the test-retest
reliability of the algorithm for prediction FOG episodes).
Finally, we will correlate balance-related outcomes (see table 1) to gait
variability measures (i.e. stride time, stride length, gait cadence, and gait
velocity). To achieve that we will apply extract from the raw sensor data
outcomes such as: level of chaos in the signal (maximal Lyapunov exponent),
stride time variability, stride length variability, and time in double stance.
To increase reliability, all outcomes will be averages over a 3-days period.
Those outcomes will them be used to detect a change on balance against clinical
measurements (collected in study-visit 1 and 2) and patients* self-reports
collected during the 7-days study follow-up.
Background summary
Freezing of Gait (FOG) is the most bothersome gait difficulty experienced by
people with Parkinson*s disease (PD). FOG is assessed by visual gait analysis
during clinical consultations. However, the episodic character of FOG makes it
difficult to accurately assess the severity of FOG. Therefore, reliable
detection of FOG outside hospital environments would enhance care for this
disabling symptom.
Study objective
Objective: to develop a FOG detection and prediction algorithm under
free-living conditions.
Study design
Observational longitudinal cohort study.
Study burden and risks
Nature and extent of the burden and risks associated with participation,
benefit and group relatedness: All procedures are non-invasive. The study visit
will begin at an OFF status because FOG happens more predominantly when
participants are without medication. The study visit is expected to last no
more than 5 hours in total, with 2.0 hours in OFF state. There is a small risk
of participants feeling overwhelmed or, for instance, experiencing a fall
episode. To diminish the risk, we have minimized the number of assessments in
an OFF state. In addition, participants will be advised to take a 40 minutes
break. Finally, to ensure safety and efficiency, the assessments will be
performed by a trained and experienced physiotherapist. In any case, if the
participant wishes so, the assessment can be stopped or cancelled at any time.
Participants are not expected to directly benefit from the study. However,
under their request, clinical and technical data collected during the study can
be made available to them. Once in their possession, participants may share the
data with any health professional or family member if they wish to.
Malvert 6913
Nijmegen 3538ET
NL
Malvert 6913
Nijmegen 3538ET
NL
Listed location countries
Age
Inclusion criteria
In order to be eligible to participate in this study, a subject must meet all
of the following criteria:
1. Self-reported diagnosis of Parkinson*s disease;
2. Use of levodopa or other Parkinson*s disease medication;
3. 18 years or older;
4. Freezing of gait episodes experienced on a daily basis (New freezing of gait
questionnaire answer to question 2 * *How frequently do you experience freezing
episodes?* * * Very often, more than once a day);
5. No cognitive or psychiatric impairment as judged by the researcher;
6. Possession of a smartphone with suitable Android operating system;
7. Able to provide informed consent.
Exclusion criteria
A potential subject who meets any of the following criteria will be excluded
from participation in this study:
1. Incapacitating dyskinesias or dystonia;
2. Comorbidities that cause severe gait impairment (e.g. severe arthrosis or
neuropathy);
3. Usage of advance therapies such as Deep Brain Stimulation;
4. Freezing of gait episodes exclusively in ON period (because this is thought
to have a different pathophysiologic mechanism than the more regular version of
FOG which occurs predominantly during OFF).
Design
Recruitment
Followed up by the following (possibly more current) registration
No registrations found.
Other (possibly less up-to-date) registrations in this register
No registrations found.
In other registers
| Register | ID |
|---|---|
| CCMO | NL71352.091.19 |