Parkinson's Disease (PD) is a major cause of disability, globally. PD affects a person's movement speed, fluency, quality, and ease of walking. PD has the fastest-growing incidence rate, with its prevalence expected to double over the next three decades, currently affecting 10 million people worldwide. PD often leads to disturbances in walking/gait characteristics such as abnormal/variable stride lengths and step times. Those disturbances increase the risk of falls, with about 39% of people with PD (PwPD) experiencing an average of 20.8 falls/year. Research has examined cueing by leveraging auditory, visual, and tactile cues to normalize variable gait characteristics and improve mobility to reduce falls. Auditory cueing is the most effective at improving gait and most practical to apply in all settings (via headphones) but one size does not fit all when using auditory cueing paradigms i.e., there is a need for personalised approaches to ensure cueing interventions are tailored to the individual and their specific functional limitations. Furthermore, the long-term effectiveness of auditory mechanisms (e.g., metronome-based repetitive beep) suffer from their lack of continuous engagement. This research project aims to examine personalised auditory cueing to improve gait in PwPD. Inertial sensors will capture and analyze validated gait-related characteristics and personalised auditory cues will be examined for their ability to correct variable gait. To reduce burden on PwPD (i.e., minimal number of wearable sensors) and to streamline data capture and deliver auditory cues, a single smartphone will be used only. The project involves a multidisciplinary study between Computing and Exercise and Rehabilitation at Northumbria University, testing cueing modalities in a controlled laboratory environment under trained researcher supervision. The study will enrol PwPD, focusing on the ability of personalised auditory cueing to improve gait and PwPD preference of auditory cues.
Age range
50 Years – 85 Years
Sex
ALL
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Gait speed (m/s)
Timeframe: 1 hour
Step length (m)
Timeframe: 1 hour
Step time variability
Timeframe: 1 hour
Cadence (steps/minute)
Timeframe: 1 hour