Predicting Wearing-Off of Parkinson’s Disease Patients Using a Wrist-Worn Fitness Tracker and a Smartphone: A Case Study

Abstract

Parkinson’s disease (PD) patients experience varying symptoms related to their illness. Therefore, each patient needs a tailored treatment program from their doctors. One approach is the use of anti-PD medicines. However, a “wearing-off” phenomenon occurs when these medicines lose their effect. As a result, patients start to experience the symptoms again until their next medicine intake. In the long term, the duration of “wearing-off” begins to shorten. Thus, patients and doctors have to work together to manage PD symptoms effectively. This study aims to develop a prediction model that can determine the “wearing-off” of anti-PD medicine. We used fitness tracker data and self-reported symptoms from a smartphone application in a real-world environment. Two participants wore the fitness tracker for a month while reporting any symptoms using the Wearing-Off Questionnaire (WoQ-9) on a smartphone application. Then, we processed and combined the datasets for each participant’s models. Our analysis produced prediction models for each participant. The average balanced accuracy with the best hyperparameters was at 70.0–71.7% for participant 1 and 76.1–76.9% for participant 2, suggesting that our approach would be helpful to manage the “wearing-off” of anti-PD medicine, motor fluctuations of PD patients, and customized treatment for PD patients.

A Digital Architecture for the Real-Time Tracking of Wearing off Phenomenon in Parkinson’s Disease

“…Many of the above solutions, however cutting-edge, are geared toward identifying general neuromuscular implications or symptoms related to PD. However, only a few stateof-the-art solutions [13][14][15][16][17][18] ensure the use of technologies aimed at providing real-time WO detection and objective measurement of the specific phenomenon in PD, which constitute the focus of the present paper.…”

“…A possible solution has been proposed by Victorino et al in [13]. They propose the use of a smart band Garmin vivosmart4 fitness tracker to extract the built-in heart rate, step cadence, stress score, and a sleep classification parameter to discriminate between the ON and OFF phases of WO.…”

“…Table 5 summarizes a comparison of the proposed system with the state-of-the-art solutions analyzed in Section 2, in terms of classification performance. Technological proposals for WO detection outlined in [13,14] have been excluded during the comparison, because they do not implement a classification step for the proposed features. Moreover, no details about the number of units per each of the 15 hidden layers composing the NN are provided in [15].…”

Computerized analysis of hypomimia and hypokinetic dysarthria for improved diagnosis of Parkinson's disease

“…Moreover, the fitness tracker and smartphone questionnaires were utilized to forecast the wearing-off period when the PD patient needs to take the next dose of levodopa. The researchers obtained average balanced accuracy of 70.0–71.7% for participant 1 and 76.1–76.9% for participant 2 [43] . Furthermore, Shimmer3 wearable device was used to collect data to predict the tremor severity level manifesting in PD.…”

An Individualized Multi-Modal Approach for Detection of Medication “Off” Episodes in Parkinson’s Disease via Wearable Sensors

“…Most work related to the prediction of WO in PD has been with accelerometer data [ 16 , 17 , 18 , 19 , 20 , 21 ]. However, there are potential issues with this approach.…”