Proprioception is the sense of body position in space (Gilman, 2002; Goble, Coxon, et al., 2012), and can be evaluated using both clinical assessments and laboratory based tasks. To date, normal aging has been shown to lead to a decline in proprioceptive acuity as assessed via laboratory based proprioceptive matching tasks, while proprioceptive deficits have been assumed to be present in people with multiple sclerosis (PwMS) based on performance on clinical assessments. The objective of the current study was to determine if performance on clinical assessments and laboratory based proprioceptive matching tasks is similar across older adults (OA) and PwMS (Adamo et al., 2007; Goble, 2010; Herter et al., 2014; Jamali et al., 2017; Khan et al., 2018; Scherder et al., 2018). Twenty-four OA participants (70+ years old) and twenty PwMS from the Ottawa community were recruited to take part in this study. Proprioceptive sense was assessed using clinical assessments (i.e., superficial sensation, vibration sense and joint position sense) and laboratory based proprioceptive matching tasks. Analysis revealed that while OA performed better on the clinical assessments, PwMS were more accurate in the laboratory matching tasks. Furthermore, analysis of goal directed movements in the matching tasks, revealed that PwMS spent more time in the initial, planning stage of the movement compared to OA, who spent more time executing their movements. These results indicate that OA and PwMS do not demonstrate similar deficits across clinical assessments and laboratory based proprioceptive tasks, and in fact plan and execute their movements differently. Moreover, results also call into question the relationship between clinical and laboratory based assessments of proprioception.

Background: The yearly incidence of people with Spinal Cord Injury (SCI) is between250,000 and 500,000, according to the World Health Organization (WHO). The injury often reduces the ability to walk. Various consequences affect the nervous system and, thus, the entire body. Therefore, the patient population with SCI is highly heterogeneous also in their gait patterns. Multiple tools are used to classify and understand the walking impairments caused by the injury. Objective: To underline the added value brought by the integration of 3D gait analysis to more standard methods (GDI, GPS, GVS, spatiotemporal parameters, ASIAgrade, muscle strength, and spasticity) in the evaluation and interpretation of gait patterns of subjects with SCI. Methods: 3D gait analysis with a passive optical motion capture system (Vicon)and four force plates was performed in 7 control subjects and 3 with SCI. The model used for marker placement and pre-processing was CGM 2.3. Matlab was used to analyze and plot the kinematic and kinetic joints’ data and calculate the GDI, GPS, and GVS indexes and spatiotemporal parameters for subjects with SCI and the control group. A specialized physiotherapist conducted the clinical assessment of the patients with SCI in a rehabilitation center. This included: ASIA grade and review, muscle strength, and spasticity with Daniels Whorthingham scale and Modified Ashworth scale, respectively. The evaluation of the result was qualitative. Results: The integration of 3D gait analysis show further understanding in the assessment of walking impairments. The indexes resumed the impairments and classified the subjects but lacked temporal and functional perspective. Gait phases and spatiotemporal parameters suggested difficulties in stability and balance but could not identify the problem’s origin. Lastly, clinical assessment enlightened the singular motor and sensory function impairments. 3D gait analysis contextualized these results identifying gait patterns and functional implications. Conclusion: Integrating 3D gait analysis might give a deeper understanding of subjects with SCI’s gait strategies and impairments. Indeed this complex technique links the other methods’ results, contextualizing them and gaining information.