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@ARTICLE{DelDin:144964,
      author       = {Del Din, Silvia and Elshehabi, Morad and Galna, Brook and
                      Hobert, Markus A and Warmerdam, Elke and Sünkel, Ulrike and
                      Brockmann, Kathrin and Metzger, Florian and Hansen, Clint
                      and Berg, Daniela and Rochester, Lynn and Maetzler, Walter},
      title        = {{G}ait analysis with wearables predicts conversion to
                      parkinson disease.},
      journal      = {Annals of neurology},
      volume       = {86},
      number       = {3},
      issn         = {0364-5134},
      address      = {Hoboken, NJ},
      publisher    = {Wiley-Blackwell},
      reportid     = {DZNE-2020-00328},
      pages        = {357-367},
      year         = {2019},
      abstract     = {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.},
      keywords     = {Disease Progression / Early Diagnosis / Female / Gait
                      Analysis / Humans / Linear Models / Longitudinal Studies /
                      Male / Middle Aged / Parkinson Disease: diagnosis /
                      Parkinson Disease: physiopathology / Prodromal Symptoms /
                      Prospective Studies / Time Factors / Walking / Wearable
                      Electronic Devices},
      cin          = {AG Maetzler / Ext UKT / ICRU / AG Gasser / AG Berg},
      ddc          = {610},
      cid          = {I:(DE-2719)5000024 / I:(DE-2719)5000058 /
                      I:(DE-2719)1240005 / I:(DE-2719)1210000 /
                      I:(DE-2719)5000055},
      pnm          = {344 - Clinical and Health Care Research (POF3-344) / 345 -
                      Population Studies and Genetics (POF3-345)},
      pid          = {G:(DE-HGF)POF3-344 / G:(DE-HGF)POF3-345},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:31294853},
      pmc          = {pmc:PMC6899833},
      doi          = {10.1002/ana.25548},
      url          = {https://pub.dzne.de/record/144964},
}