Innovative motor and cognitive dual-task approaches combining upper and lower limbs may improve dementia early detection.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
02 04 2021
Historique:
received: 26 08 2020
accepted: 17 03 2021
entrez: 3 4 2021
pubmed: 4 4 2021
medline: 30 10 2021
Statut: epublish

Résumé

Motor and Cognitive Dual-Task (MCDT) represents an innovative chance to assess Mild Cognitive Impairment (MCI). We compare two novel MCDTs, fore-finger tapping (FTAP), toe-tapping (TTHP), to gold standards for cognitive screening (Mini-Mental State Examination-MMSE), and to a well-established MCDT (GAIT). We administered the aforementioned MCDTs to 44 subjects (MCIs and controls). Motor parameters were extracted, and correlations with MMSE investigated. Logistic regression models were built, and AUC areas computed. Spearman's correlation demonstrated that FTAP and TTHP significantly correlate with MMSE, at each cognitive load. AUC areas computed report similar (FTAP, 0.87), and even higher (TTHP, 0.97) capability to identify MCIs, if compared to GAIT (0.92). We investigated the use of novel MCDT approaches to assess MCI, aiming to enrich the clinical repertoire with objective and non-invasive tools. Our protocol shows good correlations with MMSE, and reaches high performances in identifying MCI, adopting simpler exercises.

Identifiants

pubmed: 33811226
doi: 10.1038/s41598-021-86579-3
pii: 10.1038/s41598-021-86579-3
pmc: PMC8018979
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

7449

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Auteurs

Gianmaria Mancioppi (G)

The BioRobotics Institute, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio 34, Pontedera, 56025, Pisa, Italy.
The Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, piazza Martiri della Libertà 33, 56127, Pisa, Italy.

Laura Fiorini (L)

The Department of Industrial Engineering, University of Florence, via Santa Marta 3, 50139, Florence, Italy.

Erika Rovini (E)

The BioRobotics Institute, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio 34, Pontedera, 56025, Pisa, Italy.
The Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, piazza Martiri della Libertà 33, 56127, Pisa, Italy.

Radia Zeghari (R)

The CoBTeK, Université Cote d'Azur (UCA), 10 Rue Molière, 06100, Nice, France.

Auriane Gros (A)

The CoBTeK, Université Cote d'Azur (UCA), 10 Rue Molière, 06100, Nice, France.

Valeria Manera (V)

The CoBTeK, Université Cote d'Azur (UCA), 10 Rue Molière, 06100, Nice, France.

Philippe Robert (P)

The CoBTeK, Université Cote d'Azur (UCA), 10 Rue Molière, 06100, Nice, France.

Filippo Cavallo (F)

The BioRobotics Institute, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio 34, Pontedera, 56025, Pisa, Italy. filippo.cavallo@unifi.it.
The Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, piazza Martiri della Libertà 33, 56127, Pisa, Italy. filippo.cavallo@unifi.it.
The Department of Industrial Engineering, University of Florence, via Santa Marta 3, 50139, Florence, Italy. filippo.cavallo@unifi.it.

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