Steps Against the Burden of Parkinson's Disease

Summary

Parkinson's Disease Treadmill Training RCT Summary

Parkinson's disease (PD) affects over 10 million people globally. Despite optimal pharmacological treatment, approximately 70% of individuals experience unstable gait and falls, leading to loss of confidence, social isolation, fractures, and frequent hospitalisations. Treadmill training-especially when augmented by mechanical or virtual-reality perturbations-has shown promise in improving gait and reducing fall risk. However, the mechanisms underlying these benefits remain poorly understood, limiting the ability to personalise interventions effectively.

This randomised controlled trial (RCT) forms part of the broader Steps Against the Burden of Parkinson's Disease project (CT-IDs: 6ef2e427b002, 6ef2e427b003, 6ef2e427b004), comprising three harmonised but independently conducted RCTs. All sites follow a shared core protocol, allowing for pooled data analysis while preserving site-specific perturbation adaptations. Findings from this trial will be reported both independently and as part of the combined dataset.

In this trial, participants with PD will undergo 12 sessions of treadmill training, with or without virtual reality and perturbation-based adaptations. Assessments will be conducted at baseline, post-training, and follow-up. The intervention aims to enhance gait through improved sensorimotor integration and balance control. During the follow-up period, a smartphoneapp "Walking Tall" will be used to encourage continued exercises and long-term retention of training effects.

Biomechanical analyses will focus on changes in foot placement control. Neurophysiological outcomes will be examined using EEG and EMG, targeting reductions in beta-band EEG power and enhanced EEG-EMG coherence as markers of improved gait stability.

Recognising that laboratory-based improvements may not always translate to daily life, this study will also investigate gait self-efficacy as a potential moderator of transfer. Remote monitoring tools will capture real-world mobility outcomes over a week. Machine learning techniques will be employed to identify factors differentiating those who improve in both settings from those who do not. These insights will inform the development of personalised interventions capable of translating training effects into meaningful real-life outcomes.

Conditions

• Parkinson Disease (PD)

Interventions

OTHER

Exercise

SDTT adjusts the treadmill's speed in real time to match an individual's walking pace, creating a dynamic and adaptive training environment. This approach simulates real-world walking conditions, promoting neuromuscular coordination, balance, and functional mobility. By tailoring speed to the user's natural gait, SDTT supports the development of efficient and more natural walking patterns. It has shown promise across clinical populations, including those with neurological disorders, musculoskeletal conditions, or recovering from injury. Its flexibility allows for progressive challenge as walking ability improves, making SDTT a valuable tool for optimising gait and mobility outcomes.

OTHER

Exercise

The SDTT+ program combines speed-dependent treadmill training with perturbations and VR-triggered adaptations. Reactive gait responses are elicited through controlled accelerations and decelerations of treadmill belts, simulating real-life balance challenges.

Sponsors & Collaborators

• Neuroscience Research Australia

  collaborator OTHER

• VU University of Amsterdam

  collaborator OTHER

• IRCCS Azienda Ospedaliero-Universitaria di Bologna

  collaborator OTHER

• Shake it up Australia Foundation

  collaborator UNKNOWN

• Tel Aviv Medical Center

  collaborator OTHER

• The University of New South Wales

  lead OTHER

Principal Investigators

• Matthew Brodie, PhD · University of New South Wales

• Yoshiro Okubo, PhD · Neuroscience Research Australia, University of New South Wales

• Daniel Chan, PhD, MD · University of New South Wales

• Luca Modenese, PhD · University of New South Wales

• Frederic von Wegner, PhD, MD · University of New South Wales

• Phu Hoang, PhD, MD · Neuroscience Research Australia

• Husna Razee, PhD · University of New South Wales

• Paulo Silva Pelicioni, PhD · University of New South Wales

• Vicki Miller · Shake it up Australia Foundation

• Carolyn Sue, PhD, MD · Neuroscience Research Australia

• Martin Ostrowski, PhD · University of New South Wales

• Mayna Ratanapongleka · Neuroscience Research Australia

Study Design

Allocation

RANDOMIZED

Purpose

BASIC_SCIENCE

Masking

DOUBLE

Model

PARALLEL

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2025-07-09

Primary Completion

2026-09-30

Completion

2026-11-30

Countries

• Australia

Study Locations