Virginia Bandini

Dr. Virginia Bandini

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Research Focus & Metrics

Academic Career Length

4Years (2023-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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20 January 2024 in Sensors
Verified
Journal Article
Open access
PDF file
GBGaia Bailo
Gaia Bailo
FSFrancesca Lea Saibene
Francesca Lea Saibene
VBVirginia Bandini
Virginia Bandini
PAPietro Arcuri
Pietro Arcuri
ASAnna Salvatore
Anna Salvatore
ICIlaria Carpinella
Ilaria Carpinella
Although the 6-Minute Walk Test (6MWT) is among the recommended clinical tools to assess gait impairments in individuals with Parkinson’s disease (PD), its standard clinical outcome consists only of the distance walked in 6 min. Integrating a single Inertial Measurement Unit (IMU) could provide additional quantitative and objective information about gait quality complementing standard clinical outcome. This study aims to evaluate the test–retest reliability, validity and discriminant ability of gait parameters obtained by a single IMU during the 6MWT in subjects with mild PD. Twenty-two people with mild PD and ten healthy persons performed the 6MWT wearing an IMU placed on the lower trunk. Features belonging to rhythm and pace, variability, regularity, jerkiness, intensity, dynamic instability and symmetry domains were computed. Test–retest reliability was evaluated through the Intraclass Correlation Coefficient (ICC), while concurrent validity was determined by Spearman’s coefficient. Mann–Whitney U test and the Area Under the receiver operating characteristic Curve (AUC) were then applied to assess the discriminant ability of reliable and valid parameters. Results showed an overall high reliability (ICC ≥ 0.75) and multiple significant correlations with clinical scales in all domains. Several features exhibited significant alterations compared to healthy controls. Our findings suggested that the 6MWT instrumented with a single IMU can provide reliable and valid information about gait features in individuals with PD. This offers objective details about gait quality and the possibility of being integrated into clinical evaluations to better define walking rehabilitation strategies in a quick and easy way.

22 August 2023 in Sensors
Verified
Journal Article
Open access
PDF file
VBVirginia Bandini
Virginia Bandini
ICIlaria Carpinella
Ilaria Carpinella
AMAlberto Marzegan
Alberto Marzegan
JJJohanna Jonsdottir
Johanna Jonsdottir
Carlo Albino FrigoCarlo Albino Frigo
Carlo Albino Frigo
TLTiziana Lencioni
Tiziana Lencioni
Persons post-stroke experience excessive muscle co-contraction, and consequently the arm functions are compromised during the activities of daily living. Therefore, identifying instrumental outcome measures able to detect the motor strategy adopted after a stroke is a primary clinical goal. Accordingly, this study aims at verifying whether the surface electromyography (sEMG)-based co-contraction index (CCI) could be a new clinically feasible approach for assessing and monitoring patients’ motor performance. Thirty-four persons post-stroke underwent clinical assessment and upper extremity kinematic analysis, including sEMG recordings. The participants were randomized into two treatment groups (robot and usual care groups). Ten healthy subjects provided a normative reference (NR). Frost’s CCI was used to quantify the muscle co-contraction of three different agonist/antagonist muscle pairs during an object-placing task. Persons post-stroke showed excessive muscle co-contraction (mean (95% CI): anterior/posterior deltoid CCI: 0.38 (0.34–0.41) p = 0.03; triceps/biceps CCI: 0.46 (0.41–0.50) p = 0.01) compared to NR (anterior/posterior deltoid CCI: 0.29 (0.21–0.36); triceps/biceps CCI: 0.34 (0.30–0.39)). After robot therapy, persons post-stroke exhibited a greater improvement (i.e., reduced CCI) in proximal motor control (anterior/posterior deltoid change score of CCI: −0.02 (−0.07–0.02) p = 0.05) compared to usual care therapy (0.04 (0.00–0.09)). Finally, the findings of the present study indicate that the sEMG-based CCI could be a valuable tool in clinical practice.