Analysis of Trend Changes in Static Balance of Parkinson’s Disease Patients to Distinguish Drug States

Introduction

Maintaining static body balance is crucial for daily living, which requires coordination between different body segments. Any change or decline in balance ability increases the risk of falling. Patients with Parkinson’s disease (PD) experience motor symptoms that affect their ability to maintain stable postures. This study aimed to investigate the postural sway of body segments in static postures of PD patients and healthy adults, and apply Trend Change Analysis (TCA) to reveal the differences in their motor strategies.

Background

Maintaining an upright posture is a fundamental physiological process that relies on the precise coordination of sensory systems, including vision, vestibular, and somatosensory feedback, within the central nervous system. Posture not only represents the static arrangement of body segments but also reflects the continuous adjustment process required to maintain stability. With increasing age and the occurrence of neurological diseases, the ability to maintain an upright posture gradually declines, leading to an increased risk of falls and related injuries. This decline is influenced by various factors, including changes in sensory input, decreased muscle strength, reduced joint flexibility, and impaired neural processing functions.

In the case of Parkinson’s disease, a neurodegenerative disorder that causes the loss of dopaminergic neurons, postural control ability is severely impaired. PD patients face significant challenges in maintaining stable postures, as the depletion of dopaminergic neurons in the basal ganglia, which plays a crucial role in controlling upright posture, is the underlying cause.

Research Methods

The study included 61 healthy participants, comprising 40 young adults, 21 older adults, and 29 PD patients (13 in the off-medication state and 16 in the on-medication state). Participants were asked to stand still for 10 seconds, and researchers attached an inertial measurement unit (IMU) to their heads, sternums, and pelvic areas to measure postural parameters. Trend Change Analysis (TCA) was performed on the data to compare the differences between groups.

TCA was initially used in stock trading analysis and is now employed to quantify postural correction processes. It can detect small, rapid corrections, more frequent long-term corrections, and displacements between continuous postural corrections. The researchers proposed new biomechanical parameters based on this analysis method, including the Trend Change Index (TCI), TCI_DT (time intervals between consecutive trend changes), and TCI_DS (displacement changes between consecutive trend changes).

Research Results

1) When comparing different body locations, almost all parameters showed significant differences between the head, sternum, and pelvis, reflecting the complex coordination of body segments in maintaining balance.

2) When comparing the on-medication (PDON) and off-medication (PDOFF) states, TCA revealed differences, while other parameters did not. Specifically, in the PDOFF state, TCI was higher, and TCI_DT was smaller, indicating a more frequent postural correction process.

Research Discussion

While all parameters could distinguish postural sway in different body parts, only TCA could differentiate between the medication states of PD patients. The researchers speculated that TCA could capture postural control abnormalities caused by visual deficits, which might originate from the dysregulation of the dopaminergic system. Previous studies have shown that low-frequency postural sway is primarily associated with visual and vestibular regulation, while high-frequency sway is related to somatosensory regulation, and dopamine plays a crucial role in visual perception and integration.

Research Significance

This study is the first to apply TCA to assess postural stability in PD patients, revealing its ability to distinguish between on and off medication states, whereas traditional postural parameters could not. This finding provides a new analytical tool for evaluating disease progression, treatment response, and potentially even early stages of PD, offering important clinical application prospects. Exploring the potential applications of TCA in larger sample sizes and across different disease stages is a direction for future research.

This research provides a new perspective on utilizing innovative biomechanical analysis methods to evaluate motor abnormalities in neurological diseases and offers a novel approach for clinically distinguishing medication states, demonstrating significant theoretical and practical value.