Project 458867

Parkinson's disease (PD) is the second most common neurodegenerative disease worldwide. Current clinical care relies on managing symptoms with medication, such as a drug called levodopa. Despite taking medication, individuals with PD experience changes in their symptoms between medication doses, known as "ON-OFF" fluctuations. To lessen the disruptions to daily life, clinicians must make careful decisions about the dose of medication. These decisions are based on questionnaires or observations in a clinic, including a Levodopa Challenge Test (LCT), which is used to determine how an individual responds to their medication. Relying on "snapshot" assessments are not ideal and clinicians and patients report the need to find better ways to determine medication dose. Wearable sensor technology can improve how clinicians make decisions about medication and care by providing data about the impact of medication on PD symptoms over longer time periods. Early studies using remote monitoring in PD have been limited by use of only a single wearable sensor and/or short periods of capturing data under highly controlled conditions. Optimizing the use of wearable technology for clinical care and self-management requires a different approach. Therefore, my research will use multiple wearable sensors to increase knowledge about the impact of medication on "ON-OFF" fluctuations in PD, as they occur in daily life, outside of a clinic. In light of the COVID-19 pandemic and the need to extend clinical care to those with less access, a focus will also be on whether it is useful to conduct a virtual LCT at home. Conducting a LCT at home would minimize the patient and care partner burden associated with a clinic visit and improve access to care for underserviced groups. Conclusions from this line of work have the potential to improve clinical care and patient well-being, leading to improvements in healthcare delivery and quality of life for Canadians living with PD.