Gait analysis with wearables predicts conversion to Parkinson disease

Objective 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. Methods 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. Results 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. Interpretation 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;86:357–36

Instrumented gait analysis identifies potential predictors for Parkinson’s disease converters [abstract]

Objective: This longitudinal prospective observational study investigated if gait can predict Parkinson’s disease (PD) conversion from a cohort of community-dwelling older adults. Background: PD is a progressive disorder including a prodromal period when definitive motor/non-motor symptoms to permit a diagnosis have not yet appeared. Quantification of gait with wearable technology (WT) may serve as an accurate tool to identify surrogate markers of incipient disease manifestation. Recently arm swing and selective gait characteristics measured with WT have been shown to be potential prodromal markers for people at risk for PD [1]; however these data were obtained from a cross-sectional assessment; the potential of gait to predict PD conversion has not been investigated yet in a longitudinal cohort. Methods: 16 participants (69±5 years (yrs)) who were diagnosed with PD on average 4.5 yrs after baseline assessment (converters (PDC)) and 48 age-matched old healthy adults (HA) recruited in the TREND study were included. Assessments were performed longitudinally 4 times at 2-year intervals. Participants were asked to walk at their preferred speed, performing 2 straight-line trials over 20m with a WT device placed on the lower back; 14 validated clinically relevant gait characteristics were quantified [2]. ANCOVA was used to examine gait between-group differences; the value of baseline gait in predicting PDC was explored using AUC and stepwise, forward, logistic regression analyses. Random effects linear mixed-models (RELM) were used to predict latency gait deterioration and diagnosis of PD. Results: PDC walked with significantly lower pace, higher variability and asymmetry than HA (p≤0.027). Pace, variability and asymmetry characteristics were able to significantly predict PDC (AUC≥0.695). Step time variability was the best predictor for the stepwise, forward, logistic regression (sensitivity 25%, specificity 98%, accuracy of 80%). RELMs indicate gait impairment (step velocity and step length) is evident 4-6 yrs prior to diagnosis. Conclusions: Our preliminary results suggest that pace, variability and asymmetry of gait represent sensitive predictors of prodromal PD and that gait impairment starts 4-5 years prior to diagnosis. Therefore, gait assessment may play an important role in concert with other biomarkers to identify people at high risk of PD and aid early diagnosis