Abstract
People with Parkinson’s disease (PD) experience significant impairments to gait and balance; as a result, the rate of falls in people with Parkinson’s disease is much greater than that of the general population. Falls can have a catastrophic impact on quality of life, often resulting in serious injury and even death. The number (or rate) of falls is often used as a primary outcome in clinical trials on PD. However, falls data can be unreliable, expensive and time-consuming to collect. We sought to validate and test a novel digital biomarker for PD that uses wearable sensor data obtained during the Timed Up and Go (TUG) test to predict the number of falls that will be experienced by a person with PD. Three datasets, containing a total of 1057 (671 female) participants, including 71 previously diagnosed with PD, were included in the analysis. Two statistical approaches were considered in predicting falls counts: the first based on a previously reported falls risk assessment algorithm, and the second based on elastic net and ensemble regression models. A predictive model for falls counts in PD showed a mean R2 value of 0.43, mean error of 0.42 and a mean correlation of 30% when the results were averaged across two independent sets of PD data. The results also suggest a strong association between falls counts and a previously reported inertial sensor-based falls risk estimate. In addition, significant associations were observed between falls counts and a number of individual gait and mobility parameters. Our preliminary research suggests that the falls counts predicted from the inertial sensor data obtained during a simple walking task have the potential to be developed as a novel digital biomarker for PD, and this deserves further validation in the targeted clinical population.
Highlights
Parkinson’s disease (PD), a progressive neurodegenerative disease, has significant deleterious effects on gait and balance
We considered an ensemble of regression models where the ensemble prediction is completed by predicting falls count as a linear combination of the estimates from the TDNoPD and training data (TD)-Fallers-NoPD models
We report the results for a range of statistical models intended to predict the fall rates in PD patients
Summary
Parkinson’s disease (PD), a progressive neurodegenerative disease, has significant deleterious effects on gait and balance. People with PD are at a much higher risk of falls compared to the general population [6]. They are twice as likely to fall as patients with other neurological conditions [7,8], falling more frequently in the advanced stages of the disease. It has been estimated that 38–68% of PD patients will fall at some point during the course of their disease [6,9,10,11]; a range of novel medications and non-pharmacological interventions are under development to address this unmet need [12,13,14,15]. A lengthy study burdens the patients and may delay the time to market for a compound or intervention that could potentially reduce the frequency of falls
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
