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dc.contributor.authorIluz, Talen_US
dc.contributor.authorGazit, Eranen_US
dc.contributor.authorHerman, Taliaen_US
dc.contributor.authorSprecher, Elioten_US
dc.contributor.authorBrozgol, Marinaen_US
dc.contributor.authorGiladi, Niren_US
dc.contributor.authorMirelman, Anaten_US
dc.contributor.authorHausdorff, Jeffrey Men_US
dc.date.accessioned2014-05-06T16:18:44Z
dc.date.issued2014en_US
dc.identifier.citationIluz, Tal, Eran Gazit, Talia Herman, Eliot Sprecher, Marina Brozgol, Nir Giladi, Anat Mirelman, and Jeffrey M Hausdorff. 2014. “Automated detection of missteps during community ambulation in patients with Parkinson’s disease: a new approach for quantifying fall risk in the community setting.” Journal of NeuroEngineering and Rehabilitation 11 (1): 48. doi:10.1186/1743-0003-11-48. http://dx.doi.org/10.1186/1743-0003-11-48.en
dc.identifier.issn1743-0003en
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:12153048
dc.description.abstractBackground: Falls are a leading cause of morbidity and mortality among older adults and patients with neurological disease like Parkinson’s disease (PD). Self-report of missteps, also referred to as near falls, has been related to fall risk in patients with PD. We developed an objective tool for detecting missteps under real-world, daily life conditions to enhance the evaluation of fall risk and applied this new method to 3 day continuous recordings. Methods: 40 patients with PD (mean age ± SD: 62.2 ± 10.0 yrs, disease duration: 5.3 ± 3.5 yrs) wore a small device that contained accelerometers and gyroscopes on the lower back while participating in a protocol designed to provoke missteps in the laboratory. Afterwards, the subjects wore the sensor for 3 days as they carried out their routine activities of daily living. An algorithm designed to automatically identify missteps was developed based on the laboratory data and was validated on the 3 days recordings. Results: In the laboratory, we recorded 29 missteps and more than 60 hours of data. When applied to this dataset, the algorithm achieved a 93.1% hit ratio and 98.6% specificity. When we applied this algorithm to the 3 days recordings, patients who reported two falls or more in the 6 months prior to the study (i.e., fallers) were significantly more likely to have a detected misstep during the 3 day recordings (p = 0.010) compared to the non-fallers. Conclusions: These findings suggest that this novel approach can be applied to detect missteps during daily life among patients with PD and will likely help in the longitudinal assessment of disease progression and fall risk.en
dc.language.isoen_USen
dc.publisherBioMed Centralen
dc.relation.isversionofdoi:10.1186/1743-0003-11-48en
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3978002/pdf/en
dash.licenseLAAen_US
dc.subjectParkinson’s diseaseen
dc.subjectGaiten
dc.subjectFall risken
dc.subjectBody-worn sensorsen
dc.subjectMonitoringen
dc.subjectAccelerometersen
dc.titleAutomated detection of missteps during community ambulation in patients with Parkinson’s disease: a new approach for quantifying fall risk in the community settingen
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden
dc.relation.journalJournal of NeuroEngineering and Rehabilitationen
dash.depositing.authorHausdorff, Jeffrey Men_US
dc.date.available2014-05-06T16:18:44Z
dc.identifier.doi10.1186/1743-0003-11-48*
dash.contributor.affiliatedHausdorff, Jeffrey M


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