A new study from the University of Oxford demonstrates how clinicians could use data to predict the risk of falls in people with Parkinson’s Disease (PD) to help improve effective longer term care planning.
Falls are a common problem for people living with Parkinson’s. A recent review estimated that some 60% of all people living with PD have experienced at least one fall. These can lead to injury and hospitalisation, as well as reduced mobility, quality of life, and life expectancy.
Accurate fall risk assessment is vital for effective care planning in Parkinson’s Disease (PD), yet traditional methods are subjective and time-consuming. A new study explored whether wearable sensors could predict fall risk over five years using brief clinic tests. Researchers from the NeuroMetrology lab at the Nuffield Department of Clinical Neurosciences gathered data from 104 PD patients without prior falls. Participants completed a two-minute walk and a 30-second postural sway task, alongside questionnaires and clinical scales. Machine Learning analysed sensor data, identifying key features of walking and posture linked to future falls.
This innovative, three-minute assessment offers accurate predictions, reducing burden on clinicians and patients. Earlier detection enables tailored care, helps prevent life-threatening falls, and optimises resource planning. It also improves participant selection for trials of fall-prevention therapies, enhancing their efficiency and outcomes. Lead author, Professor Chrystalina Antoniades, said: ‘I am absolutely delighted to see this work published. It is well documented that Parkinson’s does increase the risk of falling. This is work in the making from the last few years following patients from our OxQUIP cohort and shows great promise in accurately assessing falls and therefore giving us the opportunity to start thinking of effective care planning. This is a great opportunity for enhancing PD management and starting to develop realistic and effective prevention strategies.’