Identificação de sinais de acelerometria como biomarcadores digitais de mobilidade em pessoas com esclerose múltipla

Abstract

The main objective of this dissertation is to identify digital biomarkers of mobility in individuals with multiple sclerosis. An integrative literature review was carried out, which looked at objective measures such as accelerometers and inertial measurement units (IMUs), which have shown sensitivity in assessing gait, balance and mobility. The review showed that the high granularity of these devices allowed the identification of digital biomarkers, such as walking speed, step count and gait index, which are considered safe and effective for monitoring the degree of mobility related to the severity and progression of multiple sclerosis complications. The second study in this dissertation was an original study involving 54 adult participants with relapsing remitting multiple sclerosis. The participants completed self-reported questionnaires, including the Patient Determined Disease Scale (PDDS), an assessment tool used to measure the progression of multiple sclerosis based on the patient's perspective. They also performed the Short Physical Performance Battery (SPPB), the Timed Up and Go (TUG) and the 6 Minute Walk Test (6MWT). The participants also wore an ActiGraph GT9X-Link accelerometer for a period of 7 days. In order to assess the association between the accelerometry variables in a free environment and the degree of mobility, Spearman's Rho correlation analyses of the PDDS and EDSS were carried out with the tests performed and the accelerometer variables, as well as linear regressions between dependent variables (PDDS, TUG and 6MWT) and various independent variables. The results showed negative correlations between the distance covered in the 6MWT and the Patient Determined Disease Scale (PDDS) (-0.448, p = 0.002) and the EDSS (r = -0.503, p < 0.001). Speed during the 6MWT was also negatively correlated with PDDS (-0.448, p = 0.002) and EDSS (-0.503, p < 0.001). In the linear regression analysis, the model explained 46.8% of the variation in PDDS in the third block (-0.002, p = 0.011), and Peak 30-Minute Cadence (PC30M) was identified as a significant predictor of PDDS. These results highlight the importance of PC30M as a digital mobility biomarker to help understand the severity of multiple sclerosis and identify therapeutic strategies to improve patients' quality of life and mobility.