001     144964
005     20250415093352.0
024 7 _ |a 10.1002/ana.25548
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024 7 _ |a 0364-5134
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024 7 _ |a 1531-8249
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037 _ _ |a DZNE-2020-00328
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a Del Din, Silvia
|0 P:(DE-HGF)0
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245 _ _ |a Gait analysis with wearables predicts conversion to parkinson disease.
260 _ _ |a Hoboken, NJ
|c 2019
|b Wiley-Blackwell
264 _ 1 |3 online
|2 Crossref
|b Wiley
|c 2019-07-27
264 _ 1 |3 print
|2 Crossref
|b Wiley
|c 2019-09-01
336 7 _ |a article
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520 _ _ |a Quantification of gait with wearable technology is promising; recent cross-sectional studies showed that gait characteristics are potential prodromal markers for Parkinson disease (PD). The aim of this longitudinal prospective observational study was to establish gait impairments and trajectories in the prodromal phase of PD, identifying which gait characteristics are potentially early diagnostic markers of PD.The 696 healthy controls (mean age = 63 ± 7 years) recruited in the Tubingen Evaluation of Risk Factors for Early Detection of Neurodegeneration study were included. Assessments were performed longitudinally 4 times at 2-year intervals, and people who converted to PD were identified. Participants were asked to walk at different speeds under single and dual tasking, with a wearable device placed on the lower back; 14 validated clinically relevant gait characteristics were quantified. Cox regression was used to examine whether gait at first visit could predict time to PD conversion after controlling for age and sex. Random effects linear mixed models (RELMs) were used to establish longitudinal trajectories of gait and model the latency between impaired gait and PD diagnosis.Sixteen participants were diagnosed with PD on average 4.5 years after first visit (converters; PDC). Higher step time variability and asymmetry of all gait characteristics were associated with a shorter time to PD diagnosis. RELMs indicated that gait (lower pace) deviates from that of non-PDC approximately 4 years prior to diagnosis.Together with other prodromal markers, quantitative gait characteristics can play an important role in identifying prodromal PD and progression within this phase. ANN NEUROL 2019;86:357-367.
536 _ _ |a 344 - Clinical and Health Care Research (POF3-344)
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542 _ _ |i 2019-07-27
|2 Crossref
|u http://creativecommons.org/licenses/by/4.0/
542 _ _ |i 2019-07-27
|2 Crossref
|u http://doi.wiley.com/10.1002/tdm_license_1.1
588 _ _ |a Dataset connected to CrossRef, PubMed,
650 _ 2 |a Disease Progression
|2 MeSH
650 _ 2 |a Early Diagnosis
|2 MeSH
650 _ 2 |a Female
|2 MeSH
650 _ 2 |a Gait Analysis
|2 MeSH
650 _ 2 |a Humans
|2 MeSH
650 _ 2 |a Linear Models
|2 MeSH
650 _ 2 |a Longitudinal Studies
|2 MeSH
650 _ 2 |a Male
|2 MeSH
650 _ 2 |a Middle Aged
|2 MeSH
650 _ 2 |a Parkinson Disease: diagnosis
|2 MeSH
650 _ 2 |a Parkinson Disease: physiopathology
|2 MeSH
650 _ 2 |a Prodromal Symptoms
|2 MeSH
650 _ 2 |a Prospective Studies
|2 MeSH
650 _ 2 |a Time Factors
|2 MeSH
650 _ 2 |a Walking
|2 MeSH
650 _ 2 |a Wearable Electronic Devices
|2 MeSH
700 1 _ |a Elshehabi, Morad
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700 1 _ |a Galna, Brook
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700 1 _ |a Hobert, Markus A
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700 1 _ |a Warmerdam, Elke
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700 1 _ |a Sünkel, Ulrike
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700 1 _ |a Metzger, Florian
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700 1 _ |a Hansen, Clint
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700 1 _ |a Berg, Daniela
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700 1 _ |a Rochester, Lynn
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700 1 _ |a Maetzler, Walter
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773 1 8 |a 10.1002/ana.25548
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|t Annals of Neurology
|v 86
|y 2019
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773 _ _ |a 10.1002/ana.25548
|g Vol. 86, no. 3, p. 357 - 367
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|t Annals of neurology
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|x 0364-5134
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LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21