A roadmap for implementation of patient-centered digital outcome measures in Parkinson's disease obtained using mobile health technologies
- PMID: 30901495
- PMCID: PMC6520192
- DOI: 10.1002/mds.27671
A roadmap for implementation of patient-centered digital outcome measures in Parkinson's disease obtained using mobile health technologies
Abstract
Obtaining reliable longitudinal information about everyday functioning from individuals with Parkinson's disease (PD) in natural environments is critical for clinical care and research. Despite advances in mobile health technologies, the implementation of digital outcome measures is hindered by a lack of consensus on the type and scope of measures, the most appropriate approach for data capture (eg, in clinic or at home), and the extraction of timely information that meets the needs of patients, clinicians, caregivers, and health care regulators. The Movement Disorder Society Task Force on Technology proposes the following objectives to facilitate the adoption of mobile health technologies: (1) identification of patient-centered and clinically relevant digital outcomes; (2) selection criteria for device combinations that offer an acceptable benefit-to-burden ratio to patients and that deliver reliable, clinically relevant insights; (3) development of an accessible, scalable, and secure platform for data integration and data analytics; and (4) agreement on a pathway for approval by regulators, adoption into e-health systems and implementation by health care organizations. We have developed a tentative roadmap that addresses these needs by providing the following deliverables: (1) results and interpretation of an online survey to define patient-relevant endpoints, (2) agreement on the selection criteria for use of device combinations, (3) an example of an open-source platform for integrating mobile health technology output, and (4) recommendations for assessing readiness for deployment of promising devices and algorithms suitable for regulatory approval. This concrete implementation guidance, harmonizing the collaborative endeavor among stakeholders, can improve assessments of individuals with PD, tailor symptomatic therapy, and enhance health care outcomes. © 2019 International Parkinson and Movement Disorder Society.
Keywords: Parkinson's disease; mobile health technologies; remote monitoring; wearable technology.
© 2019 International Parkinson and Movement Disorder Society.
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References
-
- Merola A, Sturchio A, Hacker S, et al. Technology-based assessment of motor and nonmotor phenomena in Parkinson disease. Expert Rev Neurother. 2018;18(11):825–845. - PubMed
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