TY  - JOUR
AU  - Del Din, Silvia
AU  - Elshehabi, Morad
AU  - Galna, Brook
AU  - Hobert, Markus A
AU  - Warmerdam, Elke
AU  - Sünkel, Ulrike
AU  - Brockmann, Kathrin
AU  - Metzger, Florian
AU  - Hansen, Clint
AU  - Berg, Daniela
AU  - Rochester, Lynn
AU  - Maetzler, Walter
TI  - Gait analysis with wearables predicts conversion to parkinson disease.
JO  - Annals of neurology
VL  - 86
IS  - 3
SN  - 0364-5134
CY  - Hoboken, NJ
PB  - Wiley-Blackwell
M1  - DZNE-2020-00328
SP  - 357-367
PY  - 2019
AB  - 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.
KW  - Disease Progression
KW  - Early Diagnosis
KW  - Female
KW  - Gait Analysis
KW  - Humans
KW  - Linear Models
KW  - Longitudinal Studies
KW  - Male
KW  - Middle Aged
KW  - Parkinson Disease: diagnosis
KW  - Parkinson Disease: physiopathology
KW  - Prodromal Symptoms
KW  - Prospective Studies
KW  - Time Factors
KW  - Walking
KW  - Wearable Electronic Devices
LB  - PUB:(DE-HGF)16
C6  - pmid:31294853
C2  - pmc:PMC6899833
DO  - DOI:10.1002/ana.25548
UR  - https://pub.dzne.de/record/144964
ER  -