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Review
. 2021;11(s1):S95-S101.
doi: 10.3233/JPD-202407.

Digital Therapeutics in Parkinson's Disease: Practical Applications and Future Potential

Affiliations
Review

Digital Therapeutics in Parkinson's Disease: Practical Applications and Future Potential

Terry D Ellis et al. J Parkinsons Dis. 2021.

Abstract

Digital therapeutics, treatments delivered remotely and enabled by modern technology, facilitate the provision of personalized, evidence-based, interdisciplinary interventions to manage the complexities associated with Parkinson's disease. In the context of the COVID-19 pandemic, the need for digital therapeutics has arguably never been greater. However, despite new advances in technology and a heightened interest due to the pandemic, digital therapeutics remain underdeveloped and underutilized. In this paper, we briefly review practical applications and emerging advances in digital therapeutic platforms that target motor and non-motor signs and healthy lifestyle behaviors such as regular exercise, a healthful diet and optimal sleep hygiene habits. Future applications which could transform personalized self-management and patient care are presented. Opportunities, drawbacks and barriers to access are discussed.

Keywords: Parkinson’s disease; digital therapeutics; mobile health; virtual coach.

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Conflict of interest statement

The authors have no conflict of interest to report.

Figures

Fig. 1
Fig. 1
Digital Closed Loop Gait Training Therapeutic In the top portion of the figure, a sensor is worn on the shoe to collect real-time walking cadence. A smartphone (lower right) houses the music playlist and delivers the acoustic signals through headphones worn (lower left) by the patient. Algorithms time shift the music to match the patient’s cadence and modulate according to entrainment outcomes.
Fig. 2
Fig. 2
Proposed Digital Therapeutic Platform Provides Centralized Hub for PD Management: “PD PAL” Artificially Intelligent Digital Therapeutic Platform provides a centralized hub (“one-stop shop”) for PD management by integrating data from physical activity, sleep, eating habits, motor (e.g., gait) and non-motor (e.g., mood) symptoms in relationship to medication intake to provide personalized digital interventions that address the areas of most concern or to achieve pre-determined goals.

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