StepuP: Steps Against the Burden of Parkinson’s Disease

Publication date: Jul 06, 2025

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.

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