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000144964 037__ $$aDZNE-2020-00328
000144964 041__ $$aEnglish
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000144964 1001_ $$0P:(DE-HGF)0$$aDel Din, Silvia$$b0
000144964 245__ $$aGait analysis with wearables predicts conversion to parkinson disease.
000144964 260__ $$aHoboken, NJ$$bWiley-Blackwell$$c2019
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000144964 520__ $$aQuantification 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.
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000144964 542__ $$2Crossref$$i2019-07-27$$uhttp://creativecommons.org/licenses/by/4.0/
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000144964 650_2 $$2MeSH$$aDisease Progression
000144964 650_2 $$2MeSH$$aEarly Diagnosis
000144964 650_2 $$2MeSH$$aFemale
000144964 650_2 $$2MeSH$$aGait Analysis
000144964 650_2 $$2MeSH$$aHumans
000144964 650_2 $$2MeSH$$aLinear Models
000144964 650_2 $$2MeSH$$aLongitudinal Studies
000144964 650_2 $$2MeSH$$aMale
000144964 650_2 $$2MeSH$$aMiddle Aged
000144964 650_2 $$2MeSH$$aParkinson Disease: diagnosis
000144964 650_2 $$2MeSH$$aParkinson Disease: physiopathology
000144964 650_2 $$2MeSH$$aProdromal Symptoms
000144964 650_2 $$2MeSH$$aProspective Studies
000144964 650_2 $$2MeSH$$aTime Factors
000144964 650_2 $$2MeSH$$aWalking
000144964 650_2 $$2MeSH$$aWearable Electronic Devices
000144964 7001_ $$0P:(DE-2719)9000064$$aElshehabi, Morad$$b1
000144964 7001_ $$0P:(DE-HGF)0$$aGalna, Brook$$b2
000144964 7001_ $$0P:(DE-2719)9000133$$aHobert, Markus A$$b3
000144964 7001_ $$0P:(DE-HGF)0$$aWarmerdam, Elke$$b4
000144964 7001_ $$0P:(DE-2719)2811825$$aSünkel, Ulrike$$b5$$udzne
000144964 7001_ $$0P:(DE-2719)2811916$$aBrockmann, Kathrin$$b6
000144964 7001_ $$0P:(DE-2719)9001194$$aMetzger, Florian$$b7
000144964 7001_ $$0P:(DE-HGF)0$$aHansen, Clint$$b8
000144964 7001_ $$0P:(DE-2719)2000059$$aBerg, Daniela$$b9
000144964 7001_ $$0P:(DE-HGF)0$$aRochester, Lynn$$b10
000144964 7001_ $$0P:(DE-2719)2810915$$aMaetzler, Walter$$b11$$eLast author
000144964 77318 $$2Crossref$$3journal-article$$a10.1002/ana.25548$$b : Wiley, 2019-07-27$$n3$$p357-367$$tAnnals of Neurology$$v86$$x0364-5134$$y2019
000144964 773__ $$0PERI:(DE-600)2037912-2$$a10.1002/ana.25548$$gVol. 86, no. 3, p. 357 - 367$$n3$$p357-367$$tAnnals of neurology$$v86$$x0364-5134$$y2019
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