Mobility evaluation in Parkinson’s disease: from disease study to therapeutic application

Parkinson's disease (PD) is the second most common neurodegenerative disease, and the fastest growing. In PD, mobility decline represents a key disabling feature, therefore studying how people with PD (PwPD) move is crucial for disease assessment, monitoring and management. PD symptoms could fluctuate within and between days; therefore, capturing mobility impairment only through laboratory-based tests (capacity) could not be sufficient and requires integrating monitoring in real-world conditions (performance), across the full 24-hour cycle and for multiple days to collect a full picture of PwPD conditions. The advent of consumer wearables, such as smartwatches, made this assessment viable, but most of these devices require validation. Additionally, mobility is not only determined by motor function but is influenced by several personal and environmental factors. This is particularly true in PD, where non-motor symptoms (NMS) are highly impactful. However, the extent to which motor symptoms (MS) and NMS of PD impact both capacity and performance is still unclear. This doctoral thesis addressed these challenges in PD through three complementary sections: • Validation of a commercial smartwatch for step counting. Four studies were included in this section. • Assessment of the relationship between in-clinic walking capacity and real-world walking performance, and the contribution of MS and NMS to mobility. Three studies were included in this section. • Assessment of nocturnal mobility, its association with diurnal mobility, and response to dopaminergic treatment. Two studies were included in this section. We built a large multimodal dataset of 121 people with PD including demographic, anthropometric, and clinical measures exploring both MS and NMS. We also collected wearable-based measures of supervised (including turning, forward and backward walking) and unsupervised mobility. In the first section, we demonstrated that average daily steps (AvDS) were accurate and reliable in mild-to-moderate PD under ecological conditions, though influenced by disease stage, phenotype, device placement, and pharmacological state. Importantly, we established, for the first time, the minimal clinically important difference (MCID) for AvDS in PD, providing a benchmark to interpret change over time or in response to interventions. Taken together, these findings confirmed the feasibility of smartwatch-based measures as digital mobility outcomes while highlighting disease-specific limitations. In the second section, we first compared forward walking, backward walking, and turning tasks with AvDS, using Bayesian model averaging. We revealed the limited power of supervised mobility tests in predicting unsupervised mobility. Furthermore, multivariate analyses showed that NMS, particularly executive dysfunction, depression, and fatigue, are major determinants of supervised and unsupervised mobility. Their influence was most evident in complex tasks but also in AvDS, sometimes outweighing MS. These results underscore that mobility in PD is multidimensional, shaped by MS, NMS, and environmental factors. In the third section, we reported that patients with impaired nocturnal mobility also exhibited poorer daytime mobility, indicating that motor limitations extend across the full 24-hour cycle. Additionally, in a second pilot study, we showed that dopaminergic therapy improved subjective sleep quality but not objective nocturnal mobility, suggesting partially distinct mechanisms. These findings highlight the importance of considering 24-hour mobility patterns in both research and care in PD. In conclusion, the findings of the present PhD thesis pave the way for more ecologically valid, multidimensional, and patient-centered mobility monitoring strategies. These results can inform clinical decision-making, therapeutic interventions, and the design of meaningful digital mobility outcomes, ultimately improving independence and quality of life in people living with PD.