Monitoring Parkinson’s disease symptoms during daily life: a feasibility study

Abstract

Parkinson’s disease symptoms are most often charted using the MDS-UPDRS. Limitations of this approach include the subjective character of the assessments and a discrepant performance in the clinic compared to the home situation. Continuous monitoring using wearable devices is believed to eventually replace this golden standard, but measurements often lack a parallel ground truth or are only tested in lab settings. To overcome these limitations, this study explores the feasibility of a newly developed Parkinson’s disease monitoring system, which aims to measure Parkinson’s disease symptoms during daily life by combining wearable sensors with an experience sampling method application. Twenty patients with idiopathic Parkinson’s disease participated in this study. During a period of two consecutive weeks, participants had to wear three wearable sensors and had to complete questionnaires at seven semi-random moments per day on their mobile phone. Wearable sensors collected objective movement data, and the questionnaires containing questions about amongst others Parkinson’s disease symptoms served as parallel ground truth. Results showed that participants wore the wearable sensors during 94% of the instructed timeframe and even beyond. Furthermore, questionnaire completion rates were high (79,1%) and participants evaluated the monitoring system positively. A preliminary analysis showed that sensor data could reliably predict subjectively reported OFF moments. These results show that our Parkinson’s disease monitoring system is a feasible method to use in a diverse Parkinson’s disease population for at least a period of two weeks. For longer use, the monitoring system may be too intense and wearing comfort needs to be optimized.

Theoretical analysis predicts an optimal therapeutic strategy in distinct parkinsonian landscapes of the striatum

“…In contrast, in the SID model, denervation slowed with time after an initial rapid loss of dopaminergic neurons (Figure 2). The temporal evolution of the PLD and SID models correlate remarkably well with the clinical progression pattern seen in the early and middle stages of idiopathic PD and atypical parkinsonian syndromes, respectively (Eckert et al, 2007;Heijmans et al, 2019;Liu et al, 2015;Mcfarland, 2016;Payan et al, 2011). Our model thus predicts that the dopaminergic denervation pattern may be a central determinant for the disease progression variability and etiology seen in patients.…”

Gait Kinematic Parameters in Parkinson’s Disease: A Systematic Review

“…Since only two studies [9,10] presented values for spatiotemporal gait parameters in free-living assessments, and patients are known to perform differently in the laboratory and free-living contexts, these values were excluded from data analysis [11]. Table 3 lists the gait parameters using NWS reported in the included studies; the most frequently used unit of measurement and the mean and standard deviations of the reported values are also listed.…”

Parkinson’s Disease Tremor Detection in the Wild Using Wearable Accelerometers

“…Heijmans et al [ 33 ] include a study for tremor detection using wearable sensors during daily life. The proposed method compared the information collected from subjects’ questionnaires (weak labels) using an Experience Sampling Method (ESM) app named PsyMate TM , with objective metrics computed from inertial signals: logarithmic signal energy between 3.5 and 7.5 Hz, root mean square of the low-pass-filtered (3 Hz) time series, dominant frequency and dominant energy ratio, amplitude range of the raw time series, and maximum normalized cross-correlation and corresponding temporal offset between all accelerometer and gyroscope channels.…”