JMIR Publications

ABSTRACT

Background:

Clinical motion characterization in patients with Parkinson’s disease (PD) is challenging: symptom progression, suitability of medication, and level of independence in the home environment can vary across time and from patient to patient. Appointments at the neurological outpatient clinic provide a limited understanding of the overall situation. In order to follow up the variations in the state of PD and heterogenity of symptoms, longer-term measurements performed outside of the clinic could help to optimize and personalize therapies. Several wearable sensors have been used and algorithms have been developed to estimate the severity of symptoms in PD; however, longitudinal recordings, even for shorter duration of few days are rare. Home recordings have the potential benefit of providing a more thorough and objective follow-up about the disease, while providing more information about the possible need to change medications or consider invasive secondary treatments.

Objective:

The primary objective of this study is to collect a dataset for developing methods for detecting PD-related symptoms that are visible in walking patterns at home. The movement data are collected continuously and remotely at home during the normal lives of patients with Parkinson’s disease, as well as controls. The secondary objective is to use the dataset to study whether the registered medication intakes can be identified from the collected movement data by looking for and analyzing short term changes in the walking patterns.

Methods:

This observational case-control study protocol measures the activity using three different devices: (1) a smartphone with a built-in accelerometer, gyroscope, and phone orientation sensor, (2) a Movesense® smart sensor to measure movement data from the wrist, and (3) a Forciot® smart insole to measure the temporal and spatial distributions of the forces applied on the feet. The measurements are first collected during the appointment at the clinic through background interviews, a comprehensive Unified Parkinson’s Disease Rating Scale (UPDRS) test, and a 20-step walking test conducted by a trained clinical physiotherapist. After which the subjects wear the smartphone at home for three consecutive days. Wrist and insole sensors are not used in the home recordings.

Results:

Data collection began in March 2018. The subject recruitment and data collection will continue in spring 2019. The intended sample was 150 subjects. In 2018, we collected a sample of 103 subjects, 66 diagnosed with PD and 37 control subjects.

Conclusions:

This study aims to produce an extensive movement sensor dataset recorded from PD patients in various phases of the disease, as well as a group of control subjects for effective and impactful comparison studies. The study also aims to develop data analysis methods to monitor PD symptoms and the effects of medication intake during normal life and outside of the clinic. Further applications of these methods may include tools for healthcare professionals to better monitor PD remotely and applying them also to other movement disorders. Clinical Trial: This study is registered under ClinicalTrials.gov (NCT03366558). https://clinicaltrials.gov/ct2/show/NCT03366558?term=k%C3%A4veli&rank=1. Accessed: 2018-08-29. Archived at: http://www.webcitation.org/721n3uIqR