Parkinson's DiseaseNCT06405334
Kinematic and Neural Dynamics of Postural Instability in Parkinson's Disease
An observational study of Parkinson's Disease, run by VA Office of Research and Development at 1 site in United States.
Trial summary
Balance problems and falls are among the most common complaints in Veterans with Parkinson's Disease (PD), but there are no effective treatments and the ability to measure balance and falls remains quite poor. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Parkinson's Disease
- How many people
- 100 participants
- Where
- 1 siteUnited States
- Study dates
- 1 Nov 2024to 31 Oct 2028
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Veterans with Parkinson's disease who experience postural instability and are treated at the Minneapolis VA Health Care System
Who may be able to join
- Aims 1 and 2 Inclusion criteria:
- All Veterans with a clinical diagnosis of Parkinson's disease as made by their treating neurologist in Hoehn and Yahr stage 2-3 with the ability to give informed consent will be considered for possible participation in this study
- Veterans cannot be past stage 3 as our measures depend on physical independence and fall risk prediction is less useful after stage 3
- Capacity to consent will be assessed with the University of California, San Diego Brief Assessment of Capacity to Consent (UBACC)
- A score of less than 14.5 will be used as the cut-off to decide whether a Veteran is capable of consenting as the false positive rate is zero below this score with marginal increases in sensitivity above this score (89% sensitivity, 100% specificity)
- Aim 3
- In addition to the inclusion criteria for Aims 1 and 2, Veterans with an implanted STN DBS lead with a Percept device in place
Who cannot join
- Aims 1 and 2 Exclusion criteria:
- Veterans with dementia of sufficient severity to impair their ability to make healthcare related decisions for themselves will be excluded
- Veterans with other forms of parkinsonism (progressive supranuclear palsy (PSP), cortical basal ganglionic degeneration (CBGD), etc.) will be excluded
- Veterans past H\&Y Stage 3 will be excluded
- Veterans with symptomatic orthostatic hypotension (defined as sustained drop in systolic blood pressure by 20 mmHg or diastolic blood pressure by 10 mmHg within 3 minutes of standing after being supine for five minutes) will also be excluded as this is an entity the investigators are not characterizing and could confound/bias the dataset
- Aim 3:
- In addition to the exclusion criteria for Aims 1 and 2, Veterans with DBS leads implanted in other locations will be excluded
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site, 1 recruiting
- Recruiting
Minneapolis VA Health Care System, Minneapolis, MN
Minneapolis, Minnesota, 55417-2309
Robert A McGovern, (612) 629-7904, Robert.McgovernIII@va.gov
Miranda J Johnson, BS, (612) 629-7878, miranda.johnson3@va.gov
About this study
Balance problems and falls are among the most common complaints in Veterans with Parkinson's Disease (PD), but there are no effective treatments and the ability to measure balance and falls remains quite poor. This study uses wearable sensors to measure balance and uses deep brain stimulation electrodes to measure electric signals from the brain in Veterans with PD. The investigators hope to use this data to better understand the brain pathways underlying balance problems in PD so that new treatments to improve balance and reduce falls in Veterans with PD can be designed.
Read the detailed description
Parkinson's disease (PD) is a progressive neurodegenerative disorder that manifests with the cardinal motor signs of bradykinesia, rigidity, tremor and postural instability (PI). Postural instability is a major cause of falls and as the disease process progresses, the most common patient complaints center on gait and balance difficulties leading to falls. Falls are the most common reason for hospitalization in PD patients, impose a significant economic burden to the US healthcare system and are a major cause of diminished quality of life, reduced mobility, disability and death. Both clinical and laboratory-based assessments of balance provide only a brief window of time into patients' function. Prior studies have demonstrated poor correlation between capacity based measurements in the clinic or lab (i.e., what can a patient do when asked) and performance-based measurements in the real world. The investigators have developed methods to use wearable sensors in the ambulatory setting that can accurately detect a variety of activities and have created a number of quantitative metrics that are specific to PI. In this manner, the investigators can monitor and analyze PI in the real world ambulatory setting in Veterans with PD. At present, there are no effective long term treatments for PI. Major impediments to progress in this field are an understanding of how patients actually experience PI at home and categorizing PI into meaningful phenotypic subtypes in order to understand its underlying pathophysiology and evaluate new treatments.
The goal of this project is to better understand the underlying kinematic and electrophysiological components of postural instability in Veterans with PD. Aim 1 sends PD patients home for one week with five wearable sensors and a neck-worn video camera to create a massive video-validated quantitative dataset of a variety of events that are relevant to analyzing PI at home (walking, turning, sit to stand transitions, near falls/stumbles). For each video-validated event, the investigators use deep learning algorithms to predict which activity occurred and create ROC curves to examine the algorithms' predictive accuracy. Aim 2 will use kinematic data obtained from the wearable sensors to develop "deep clinical phenotypes" of postural instability using principal component analysis (PCA) and unsupervised clustering machine learning methods. Using these deep clinical phenotypes, the investigators will then test specific hypotheses related to patients' future fall risk, their experience of PI at home and the relationship of these phenotypes to clinical data such as the presence of co-morbidities like peripheral neuropathy. In Aim 3, a subset of Veterans with PD from the first two aims will undergo subthalamic nucleus (STN) DBS. The investigators will use local field potential recordings from their leads to understand the physiological signature(s) that occur just prior to, during and after a perturbation evoking a reactive postural response. By recording from contacts in motor and associative regions while undergoing simultaneous kinematic recordings and associative STN stimulation, the investigators can investigate the physiological basis of postural instability in these patients.
- Conditions
- Parkinson's Disease
- Keywords
- Parkinson Disease, subthalamic nucleus, gait, postural instability, deep learning
- Medical subject headings
- Parkinson Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| PD-Postural Instability | Veterans with postural instability |
What is being measured
The primary outcome is the main question the study is designed to answer.
Event Identification Receiver Operating Characteristic Curve
All events are validated using the video camera recording. Using the CNN-LSTM algorithm, the investigators create a series of predicted events for the entire dataset of wearable sensor usage. For each event type, the investigators will then compare the predicted events based on this CNN-LSTM algorithm to the actual validated events. This will also allow us to assess the sensitivity, specificity, positive predictive value and negative predictive value for each event type.
Finally, the investigators will create separate receiver operating characteristic (ROC) curves and calculate the AUC for each event type.
Measured: 4 years
Silhouette Scores
All kinematic variables are first standardized using z-score conversion and then transformed into a new set of uncorrelated principal components that retain the original data's variation. Following dimensionality reduction with principal component analysis (PCA), the investigators utilize k-means clustering to identify potential subgroups. K-means clustering partitions the data into distinct, non-overlapping subgroups based on minimizing within-cluster variance, with the optimal number of clusters determined through the elbow method. While k-means is the most common method and has been effective thus far, small sample size datasets typically fare better using hierarchical clustering. This method, conversely, constructs a hierarchy of clusters by iteratively combining the most similar clusters. Silhouette scores are calculated to assess how well separated the clusters are from each other.
Measured: 4 years
Associative subthalamic nucleus (STN) alpha band power
Associative and motor STN will be parcellated and the DBS lead reconstructed based on our prior work. The investigators will then use bipolar LFP recordings from the appropriate contact pairs to construct time frequency histograms and examine the event-related modulation of power in the response preparation, movement execution and post-movement execution phases of the postural response.
Measured: 4 years
Postural step length response during associative subthalamic nucleus (STN) vs. motor STN stimulation vs. no stimulation
The investigators will assess changes in reactive postural response kinematics to associative vs. motor vs. no STN stimulation to test whether any stimulation or stimulation location can improve PI. Linear mixed-effects models are used to test for within-patient changes in pull test kinematic parameters between groups. These models are adjusted for pull intensity, and baseline step length values. Models use a Bonferroni p-value correction to account for multiple testing. The investigators have previously been able to determine within-patient kinematic differences using our variable pull test method in a sample size of 13 movement disorder patients. With ~15 pull test trials for each condition, the investigators can demonstrate within-patient kinematic differences of about 5 cm in initial step length and 100 ms in reaction time.
Measured: 4 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 May 2024
- Study start1 Nov 2024
- Primary completion31 Oct 2028 (estimated)
- Study completion31 Oct 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Parkinson's DiseaseStudies of the same condition, open and under way.
- More trials run by VA Office of Research and DevelopmentThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- VA Office of Research and Development
- Sponsor type
- US federal agency
- Collaborators
- University of Minnesota
- Responsible party
- Sponsor
- Investigators
- Robert A McGovern, Principal investigator, Minneapolis VA Health Care System, Minneapolis, MN
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06405334
- Sponsor's protocol ID
- E4810-R
- Other IDs
- 5I01RX004810-02
- Status last verified
- Sep 2026
- First submitted
- 3 May 2024
- Last updated
- 6 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Synchronized IMU, event and LFP data (when available) will be made publicly available by submitting the anonymized, synchronized, annotated dataset to the National Institute of Aging's AgingResearchBioBank for public use. The investigators will NOT make the video recorded data available except upon request for a specific purpose, such as validating event algorithms by another research group. See access criteria below.
Study at a glance
From the registry record, last updated by the sponsor on 6 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Kinematic and Neural Dynamics of Postural Instability in Parkinson's Disease
- ClinicalTrials.gov ID
- NCT06405334
- Acronym / sponsor ID
- E4810-R
- Conditions
- Parkinson's Disease
- Keywords
- Parkinson Disease, subthalamic nucleus, gait, postural instability, deep learning
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- VA Office of Research and DevelopmentUS federal agency
- Collaborators
- University of Minnesota
- Study start
- 1 Nov 2024 (actual)
- Primary completion
- 31 Oct 2028 (estimated)
- Study completion
- 31 Oct 2028 (estimated)
- First posted
- 8 May 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Balance problems and falls are among the most common complaints in Veterans with Parkinson's Disease (PD), but there are no effective treatments and the ability to measure balance and falls remains quite poor. This study uses wearable sensors to measure balance and uses deep brain stimulation electrodes to measure electric signals from the brain in Veterans with PD. The investigators hope to use this data to better understand the brain pathways underlying balance problems in PD so that new treatments to improve balance and reduce falls in Veterans with PD can be designed.
Detailed description
Parkinson's disease (PD) is a progressive neurodegenerative disorder that manifests with the cardinal motor signs of bradykinesia, rigidity, tremor and postural instability (PI). Postural instability is a major cause of falls and as the disease process progresses, the most common patient complaints center on gait and balance difficulties leading to falls. Falls are the most common reason for hospitalization in PD patients, impose a significant economic burden to the US healthcare system and are a major cause of diminished quality of life, reduced mobility, disability and death. Both clinical and laboratory-based assessments of balance provide only a brief window of time into patients' function. Prior studies have demonstrated poor correlation between capacity based measurements in the clinic or lab (i.e., what can a patient do when asked) and performance-based measurements in the real world. The investigators have developed methods to use wearable sensors in the ambulatory setting that can accurately detect a variety of activities and have created a number of quantitative metrics that are specific to PI. In this manner, the investigators can monitor and analyze PI in the real world ambulatory setting in Veterans with PD. At present, there are no effective long term treatments for PI. Major impediments to progress in this field are an understanding of how patients actually experience PI at home and categorizing PI into meaningful phenotypic subtypes in order to understand its underlying pathophysiology and evaluate new treatments.
The goal of this project is to better understand the underlying kinematic and electrophysiological components of postural instability in Veterans with PD. Aim 1 sends PD patients home for one week with five wearable sensors and a neck-worn video camera to create a massive video-validated quantitative dataset of a variety of events that are relevant to analyzing PI at home (walking, turning, sit to stand transitions, near falls/stumbles). For each video-validated event, the investigators use deep learning algorithms to predict which activity occurred and create ROC curves to examine the algorithms' predictive accuracy. Aim 2 will use kinematic data obtained from the wearable sensors to develop "deep clinical phenotypes" of postural instability using principal component analysis (PCA) and unsupervised clustering machine learning methods. Using these deep clinical phenotypes, the investigators will then test specific hypotheses related to patients' future fall risk, their experience of PI at home and the relationship of these phenotypes to clinical data such as the presence of co-morbidities like peripheral neuropathy. In Aim 3, a subset of Veterans with PD from the first two aims will undergo subthalamic nucleus (STN) DBS. The investigators will use local field potential recordings from their leads to understand the physiological signature(s) that occur just prior to, during and after a perturbation evoking a reactive postural response. By recording from contacts in motor and associative regions while undergoing simultaneous kinematic recordings and associative STN stimulation, the investigators can investigate the physiological basis of postural instability in these patients.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| PD-Postural Instability | – | –Veterans with postural instability |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Veterans with Parkinson's disease who experience postural instability and are treated at the Minneapolis VA Health Care System
Key inclusion criteria (7 listed)
- All Veterans with a clinical diagnosis of Parkinson's disease as made by their treating neurologist in Hoehn and Yahr stage 2-3 with the ability to give informed consent will be considered for possible participation in this study
- Veterans cannot be past stage 3 as our measures depend on physical independence and fall risk prediction is less useful after stage 3
- Capacity to consent will be assessed with the University of California, San Diego Brief Assessment of Capacity to Consent (UBACC)
- Aim 3
- In addition to the inclusion criteria for Aims 1 and 2, Veterans with an implanted STN DBS lead with a Percept device in place
Key exclusion criteria (7 listed)
- Veterans with dementia of sufficient severity to impair their ability to make healthcare related decisions for themselves will be excluded
- Veterans with other forms of parkinsonism (progressive supranuclear palsy (PSP), cortical basal ganglionic degeneration (CBGD), etc.) will be excluded
- Veterans past H\&Y Stage 3 will be excluded
- Veterans with symptomatic orthostatic hypotension (defined as sustained drop in systolic blood pressure by 20 mmHg or diastolic blood pressure by 10 mmHg within 3 minutes of standing after being supine for five minutes) will also be excluded as this is an entity the investigators are not characterizing and could confound/bias the dataset
- In addition to the exclusion criteria for Aims 1 and 2, Veterans with DBS leads implanted in other locations will be excluded
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Event Identification Receiver Operating Characteristic Curve
Time frame: 4 years
All events are validated using the video camera recording. Using the CNN-LSTM algorithm, the investigators create a series of predicted events for the entire dataset of wearable sensor usage. For each event type, the investigators will then compare the predicted events based on this CNN-LSTM algorithm to the actual validated events. This will also allow us to assess the sensitivity, specificity, positive predictive value and negative predictive value for each event type. Finally, the investigators will create separate receiver operating characteristic (ROC) curves and calculate the AUC for each event type.
Silhouette Scores
Time frame: 4 years
All kinematic variables are first standardized using z-score conversion and then transformed into a new set of uncorrelated principal components that retain the original data's variation. Following dimensionality reduction with principal component analysis (PCA), the investigators utilize k-means clustering to identify potential subgroups. K-means clustering partitions the data into distinct, non-overlapping subgroups based on minimizing within-cluster variance, with the optimal number of clusters determined through the elbow method. While k-means is the most common method and has been effective thus far, small sample size datasets typically fare better using hierarchical clustering. This method, conversely, constructs a hierarchy of clusters by iteratively combining the most similar clusters. Silhouette scores are calculated to assess how well separated the clusters are from each other.
Associative subthalamic nucleus (STN) alpha band power
Time frame: 4 years
Associative and motor STN will be parcellated and the DBS lead reconstructed based on our prior work. The investigators will then use bipolar LFP recordings from the appropriate contact pairs to construct time frequency histograms and examine the event-related modulation of power in the response preparation, movement execution and post-movement execution phases of the postural response.
Postural step length response during associative subthalamic nucleus (STN) vs. motor STN stimulation vs. no stimulation
Time frame: 4 years
The investigators will assess changes in reactive postural response kinematics to associative vs. motor vs. no STN stimulation to test whether any stimulation or stimulation location can improve PI. Linear mixed-effects models are used to test for within-patient changes in pull test kinematic parameters between groups. These models are adjusted for pull intensity, and baseline step length values. Models use a Bonferroni p-value correction to account for multiple testing. The investigators have previously been able to determine within-patient kinematic differences using our variable pull test method in a sample size of 13 movement disorder patients. With ~15 pull test trials for each condition, the investigators can demonstrate within-patient kinematic differences of about 5 cm in initial step length and 100 ms in reaction time.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Oct 2026.
- Individual participant dataSharing planned: Yes. Synchronized IMU, event and LFP data (when available) will be made publicly available by submitting the anonymized, synchronized, annotated dataset to the National Institute of Aging's AgingResearchBioBank for public use. The investigators will NOT make the video recorded data available except upon request for a specific purpose, such as validating event algorithms by another research group. See access criteria below.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Robert A McGovern principal investigator
Minneapolis VA Health Care System, Minneapolis, MN
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Parkinson's Disease, run by VA Office of Research and Development at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 7 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 6 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Robert A McGovern ((612) 629-7904, Robert.McgovernIII@va.gov), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Event Identification Receiver Operating Characteristic Curve (time frame: 4 years). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 6 Oct 2026.