Show simple item record

dc.contributor.authorRodríguez-Molinero, Alejandro
dc.contributor.authorSamà, Albert
dc.contributor.authorPérez-López, Carlos
dc.contributor.authorRodríguez-Martín, Daniel
dc.contributor.authorAlcaine, Sheila
dc.contributor.authorMestre, Berta
dc.contributor.authorQuispe, Paola
dc.contributor.authorGiuliani, Benedetta
dc.contributor.authorVainstein, Gabriel
dc.contributor.authorBrowne, Patrick
dc.contributor.authorSweeney, Dean
dc.contributor.authorMoreno Arostegui, J. Manuel
dc.contributor.authorBayes, Àngels
dc.contributor.authorLewy, Hadas
dc.contributor.authorCosta, Alberto
dc.contributor.authorAnnicchiarico, Roberta
dc.contributor.authorCounihan, Timothy
dc.contributor.authorLaighin, Gearòid Ò.
dc.contributor.authorCabestany, Joan
dc.date.accessioned2018-09-20T16:23:07Z
dc.date.available2018-09-20T16:23:07Z
dc.date.issued2017-09-01
dc.identifier.citationRodríguez-Molinero, Alejandro; Samà, Albert; Pérez-López, Carlos; Rodríguez-Martín, Daniel; Alcaine, Sheila; Mestre, Berta; Quispe, Paola; Giuliani, Benedetta; Vainstein, Gabriel; Browne, Patrick; Sweeney, Dean; Moreno Arostegui, J. Manuel; Bayes, Àngels; Lewy, Hadas; Costa, Alberto; Annicchiarico, Roberta; Counihan, Timothy; Laighin, Gearòid Ò. Cabestany, Joan (2017). Analysis of correlation between an accelerometer-based algorithm for detecting parkinsonian gait and updrs subscales. Frontiers in Neurology 8 ,
dc.identifier.issn1664-2295
dc.identifier.urihttp://hdl.handle.net/10379/13688
dc.description.abstractBackground: Our group earlier developed a small monitoring device, which uses accelerometer measurements to accurately detect motor fluctuations in patients with Parkinson's (On and Off state) based on an algorithm that characterizes gait through the frequency content of strides. To further validate the algorithm, we studied the correlation of its outputs with the motor section of the Unified Parkinson's Disease Rating Scale (UPDRS-III). Method: Seventy-five patients suffering from Parkinson's disease were asked to walk both in the Off and the On state while wearing the inertial sensor on the waist. Additionally, all patients were administered the motor section of the UPDRS in both motor phases. Tests were conducted at the patient's home. Convergence between the algorithm and the scale was evaluated by using the Spearman's correlation coefficient. Results: Correlation with the UPDRS-III was moderate (rho 0.56;p<0.001). Correlation between the algorithm outputs and the gait item in the UPDRS-III was good (rho 0.73; p < 0.001). The factorial analysis of the UPDRS-III has repeatedly shown that several of its items can be clustered under the so-called Factor 1: "axial function, balance, and gait." The correlation between the algorithm outputs and this factor of the UPDRS-III was 0.67 (p<0.01). Conclusion: The correlation achieved by the algorithm with the UPDRS-III scale suggests that this algorithm might be a useful tool for monitoring patients with Parkinson's disease and motor fluctuations.
dc.publisherFrontiers Media SA
dc.relation.ispartofFrontiers in Neurology
dc.subjectparkinson's disease
dc.subjectobjective monitoring
dc.subjectaccelerometers
dc.subjectgait
dc.subjectupdrs
dc.subjectambulatory activity
dc.subjectdisease
dc.subjectaccuracy
dc.subjectlevodopa
dc.titleAnalysis of correlation between an accelerometer-based algorithm for detecting parkinsonian gait and updrs subscales
dc.typeArticle
dc.identifier.doi10.3389/fneur.2017.00431
dc.local.publishedsourcehttps://www.frontiersin.org/articles/10.3389/fneur.2017.00431/pdf
nui.item.downloads0


Files in this item

This item appears in the following Collection(s)

Show simple item record