Understanding balance issues in Veterans with Parkinson's Disease

Kinematic and Neural Dynamics of Postural Instability in Parkinson's Disease

This study looks at how balance problems affect Veterans with Parkinson's Disease to see if new treatments can help them stay steady on their feet and avoid falls.

Quick facts

• Study type: Observational
• Enrollment: 100 (estimated)
• Ages: 18 Years and up
• Sex: All
• Sponsor: VA Office of Research and Development (Federal)
• Locations: 1 site (Minneapolis, Minnesota)
• Trial ID: NCT06405334 on ClinicalTrials.gov

What this trial studies

This observational study focuses on Veterans diagnosed with Parkinson's Disease (PD) to investigate the dynamics of postural instability and its underlying neural mechanisms. By utilizing wearable sensors and deep brain stimulation electrodes, the study aims to collect data on balance and gait in real-world settings. The goal is to enhance the understanding of brain pathways related to balance problems, which could lead to the development of new treatments to improve stability and reduce falls in this population.

Who should consider this trial

Good fit: Ideal candidates for this study are Veterans with a clinical diagnosis of Parkinson's Disease in Hoehn and Yahr stages 2-3 who can provide informed consent.

Not a fit: Patients who are beyond stage 3 of Parkinson's Disease may not benefit from this study due to the nature of the measures being assessed.

Why it matters

Potential benefit: If successful, this study could lead to improved treatments for balance issues in Veterans with Parkinson's Disease, potentially reducing the risk of falls.

How similar studies have performed: While this approach is innovative, prior studies have shown challenges in measuring balance effectively, indicating that this study may pave the way for novel insights rather than relying on established methods.

Eligibility criteria

Inclusion Criteria:

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

Inclusion criteria:

* In addition to the inclusion criteria for Aims 1 and 2, Veterans with an implanted STN DBS lead with a Percept device in place.

Exclusion Criteria:

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 (PSP, 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:

Exclusion criteria:

* In addition to the exclusion criteria for Aims 1 and 2, Veterans with DBS leads implanted in other locations will be excluded.

Where this trial is running

Minneapolis, Minnesota

• Minneapolis VA Health Care System, Minneapolis, MN — Minneapolis, Minnesota, United States (Recruiting)

Study contacts

• Principal investigator: Robert A McGovern — Minneapolis VA Health Care System, Minneapolis, MN
• Study coordinator: Robert A McGovern

Conditions Parkinson's Disease; Parkinson Disease; subthalamic nucleus; gait; postural instability; deep learning