Standard versus innovative robotic balance assessment for people with multiple sclerosis: a correlational study

Introduction Balance disorders are common in people with Multiple Sclerosis (PwMS) and, together with other impairments and disabilities, often prevent PwMS from performing their daily living activities. Besides clinical scales and performance tests, robotic platforms can provide more sensitive, specific, and objective monitoring. Validated technologies have been adopted as gold standard, but innovative robotic solutions would represent an opportunity to detect balance impairment in PwMS. Aim Study's aim was to compare postural assessment of 46 PwMS with a relapsing-remitting form during static tasks performed with the novel robotic platform hunova((R)) and the gold standard EquiTest((R)), Methods Pearson's r was run on Center of Pressure (COP)-related parameters and global static balance measures computed from hunova((R)) and EquiTest((R)) in eyes-open (EO) and eyes-closed (EC) conditions. In addition, agreeableness level toward the use of both devices was tested through numeric rating scale. Results Considering COP-related parameters, correlations were significant for all measures (p <.001). Interestingly, in EO, a strong correlation was shown for sway area (r =.770), while Medio-Lateral (ML) and Anterior-Posterior (AP) oscillation range, path length, ML and AP speed, ML and AP root mean square distance had a relatively strong association (.454 = r =.576). In EC, except for ML oscillation range showing a relatively strong correlation (r =.532), other parameters were strongly associated (.603 = r =.782). Correlations between global balance indexes of hunova((R)) and EquiTest((R)) revealed a relatively strong association between the Somatosensory Score in EquiTest((R)) and the Somatosensory Index in hunova((R)) (r = -.488). While in EO Static Balance Index from hunova((R)) was highly correlated with Equilibrium score of EquiTest((R)) (r =.416), Static Balance Index had a relatively strong association with both the Equilibrium (r =.482) and Strategy Score (r =.583) of EquiTest((R)) in EC. Results from agreeableness rating scale revealed that hunova((R)) was highly appreciated compared to EquiTest((R)) (p =.044). Conclusions hunova((R)) represents an innovative adjunct to standard robotic balance evaluation for PwMS. This confirms that combining traditional and robotic assessments can more accurately detect balance impairments in MS.