Objective:
To identify objective biomarkers to improve clinical phenotyping and differential diagnosis in patients with PD and ET using wearable sensors.

Background:
At present, the diagnosis of PD and ET depends on clinical characteristics, but the symptoms can overlap in various stages of diseases. Although some sophisticated imaging approaches can detect of neurodegeneration, their relatively higher cost and semi-quantitative results limit their clinical application. Wearable sensors were proved to be a quantitative and reliable method for evaluating PD patient’s motion [1]. Using similar method, we expect to quantify the motion in different disease groups/phenotypes, and find objective biomarkers for disease differentiation.

Method:
21 PD and 5 ET participants were recruited from outpatients in neurology department. PD patients were identified as tremor dominant (TD, n=10), postural instability/gait difficulty (PIGD, n=7) and indeterminate (INTER, n=4) according to TD/PIGD ratios derived from MDS-UPDRS [2]. The participants with 10 body-fixed sensors walked straight back and forth twice in a 3.6-meter-long ground comfortably. Characteristics of limbs and trunk pertinent to velocity, angle, amplitude, and symmetry were derived from sensor analysis system. Pearson correlation test was first performed to detect the potential parameters for differential diagnosis. Then one-way ANOVA was used for comparisons among different groups.

Results:
In preliminary results of this observational study, two of fifty-seven parameters—arm swing velocity asymmetry (Arm Swing Vel Asym) and arm rom symbolic symmetry index (Arm Rom SSI), were significantly correlated with TD/PIGD ratios (p<0.05). Further group comparison demonstrated Arm Swing Vel Asym was significantly higher in TD group than that in PIGD (46.42 vs 25.81, p =0.0125) and ET (46.42 vs 22.05, p = 0.0076) group (Figure1). As for Arm Rom SSI, TD group showed the highest value among four groups. Additionally, significantly higher values in both TD and PIGD group were observed when compared with ET group (44.67 vs 31.11, p <0.0001; 38.39 vs 31.11, p = 0.02) (Figure2).

Conclusion:
Arm swing characteristics based on wearable sensors detection could serve to more sensitively classify clinical PD subtypes and serve as potential biomarkers of differential diagnosis between PD and ET.

References: [1] Din S D , Elshehabi M , Galna B , et al. Gait analysis with wearables predicts conversion to Parkinson disease[J]. Annals of Neurology, 2019, 86(3). [2] Stebbins G T , Goetz C G , Burn D J , et al. How to identify tremor dominant and postural instability/gait difficulty groups with the movement disorder society unified Parkinson\”s disease rating scale: Comparison with the unified Parkinson\”s disease rating scale[J]. Movement Disorders, 2013, 28(5):668-670.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/arm-swing-differences-in-patients-with-pd-and-et-during-walking/