Biomechanical aspects that precede freezing episode during gait in individuals with Parkinson's disease: A systematic review

Efficacy of exercise interventions combined with Selegiline in ameliorating freezing of gait in Parkinson's disease patients

OBJECTIVE

To evaluate the efficacy of exercise interventions combined with Selegiline in ameliorating freezing of gait (FOG) in Parkinson's disease (PD) patients.

METHODS

A total of 60 PD patients with FOG treated in the First People's Hospital of Fuyang District from January 2020 to January 2023 were retrospectively collected and analyzed. Patients were divided into a control group (n = 28, treated with Selegiline alone) and an observation group (n = 32, treated with Selegiline and exercise interventions). Gait parameters, FOG indices, motor and balance functions, Berg Balance, psychological status, and quality of life were compared between the groups pre- and post-treatment.

RESULTS

After treatment, the observation group exhibited longer step length, higher step speed, and lower step frequency (P = 0.000, 0.003, 0.001, respectively), with enhanced balance as indicated by lower Timed Up and Go Test and higher Berg Balance Scale scores than the control group (P = 0.000, 0.000, respectively). The Beck Depression Inventory and Beck Anxiety Inventory scores were notably lower in the observation group than those in the control group (P = 0.000, 0.004, respectively). Additionally, the observation group showed better quality of life across several dimensions of the Parkinson's Disease Quality of Life Questionnaire, including mobility, activities of daily living, emotional well-being, stigma, social support, cognition, communication, and bodily discomfort (P = 0.000, 0.000, 0.000, 0.000, 0.017, 0.000, 0.000, 0.000, respectively) than the control group.

CONCLUSION

The combination of exercise interventions and Selegiline effectively rectifies the gait parameters, enhances the balance function, alleviates psychological distress, and improves the overall quality of life in PD patients experiencing FOG.

Soft robotic apparel to avert freezing of gait in Parkinson's disease

Freezing of gait (FoG) is a profoundly disruptive gait disturbance in Parkinson's disease, causing unintended stops while walking. Therapies for FoG reveal modest and transient effects, resulting in a lack of effective treatments. Here we show proof of concept that FoG can be averted using soft robotic apparel that augments hip flexion. The wearable garment uses cable-driven actuators and sensors, generating assistive moments in concert with biological muscles. In this n-of-1 trial with five repeated measurements spanning 6 months, a 73-year-old male with Parkinson's disease and substantial FoG demonstrated a robust response to robotic apparel. With assistance, FoG was instantaneously eliminated during indoor walking (0% versus 39 ± 16% time spent freezing when unassisted), accompanied by 49 ± 11 m (+55%) farther walking compared to unassisted walking, faster speeds (+0.18 m s-1) and improved gait quality (-25% in gait variability). FoG-targeting effects were repeatable across multiple days, provoking conditions and environment contexts, demonstrating potential for community use. This study demonstrated that FoG was averted using soft robotic apparel in an individual with Parkinson's disease, serving as an impetus for technological advancements in response to this serious yet unmet need.

Context expectation influences the gait pattern biomechanics

Beyond classical aspects related to locomotion (biomechanics), it has been hypothesized that walking pattern is influenced by a combination of distinct computations including online sensory/perceptual sampling and the processing of expectations (neuromechanics). Here, we aimed to explore the potential impact of contrasting scenarios ("risky and potentially dangerous" scenario; "safe and comfortable" scenario) on walking pattern in a group of healthy young adults. Firstly, and consistently with previous literature, we confirmed that the scenario influences gait pattern when it is recalled concurrently to participants' walking activity (motor interference). More intriguingly, our main result showed that participants' gait pattern is also influenced by the contextual scenario when it is evoked only before the start of walking activity (motor expectation). This condition was designed to test the impact of expectations (risky scenario vs. safe scenario) on gait pattern, and the stimulation that preceded walking activity served as prior. Noteworthy, we combined statistical and machine learning (Support-Vector Machine classifier) approaches to stratify distinct levels of analyses that explored the multi-facets architecture of walking. In a nutshell, our combined statistical and machine learning analyses converge in suggesting that walking before steps is not just a paradox.