JMIR Publications

Background:

Mobile health devices (MHDs), such as wearables and smartphones, have the potential to improve the monitoring of people with Parkinson’s disease (PD) and inform timely and individualized disease management decisions.

Objective:

This scoping review provides an overview of the recent technological developments behind MHDs and investigate their potential for clinical management of PD.

Methods:

The work was conducted following the PRISMA-ScR guidelines. A literature search using PubMed, Scopus, and IEEE Xplore was initially performed on October 30, 2023, and updated on November 16, 2023. Studies of MHD applications to measure cardinal PD motor features such as bradykinesia, gait disturbances, tremor, and dyskinesia were included. We excluded conference papers, editorials, case reports, reviews, study protocols, as well as studies involving participants from other neurological disorders than PD and collecting data using technologies not designed for mobile health applications.

Results:

In total, 1467 studies were identified, of which 138 (9.4%) met the eligibility criteria and were included in the final review. Of the 138 included studies, the most frequently motor features that were assessed with MHDs were gait disturbances (58%), bradykinesia (33%), and tremor (24.6%). All studies addressed clinical effectiveness, while overlooking aspects such as the operational efficiency (9.4%) or sustainable viability factors such as user adoption and cost-effectiveness (31.8%), with full assessments being rare. The main types of MHDs were wearables (92.8%) and smartphones (7.2%) where 50.8% of the studies reported the use of accelerometers, 37% gyroscopes, and 9.4% accelerometers, gyroscopes, and magnetomers.

Conclusions:

We found that MHDs were mostly used to collect objective measurements and assess cardinal motor features such as gait disturbances, bradykinesia, and tremor. We also found several gaps related to integration of MHDs into healthcare systems, assessing their cost-effectiveness and user adoption, which may suggest the need for further research to ensure that MHD-based interventions are feasible for real-world applications.