A roadmap for implementation of patient‐centered digital outcome measures in Parkinson's disease obtained using mobile health technologies

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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.

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Technology in Parkinson's disease: Challenges and opportunities.

Alberto Espay, Paolo Bonato, Fatta B. Nahab … (2016)

The miniaturization, sophistication, proliferation, and accessibility of technologies are enabling the capture of more and previously inaccessible phenomena in Parkinson's disease (PD). However, more information has not translated into a greater understanding of disease complexity to satisfy diagnostic and therapeutic needs. Challenges include noncompatible technology platforms, the need for wide-scale and long-term deployment of sensor technology (among vulnerable elderly patients in particular), and the gap between the "big data" acquired with sensitive measurement technologies and their limited clinical application. Major opportunities could be realized if new technologies are developed as part of open-source and/or open-hardware platforms that enable multichannel data capture sensitive to the broad range of motor and nonmotor problems that characterize PD and are adaptable into self-adjusting, individualized treatment delivery systems. The International Parkinson and Movement Disorders Society Task Force on Technology is entrusted to convene engineers, clinicians, researchers, and patients to promote the development of integrated measurement and closed-loop therapeutic systems with high patient adherence that also serve to (1) encourage the adoption of clinico-pathophysiologic phenotyping and early detection of critical disease milestones, (2) enhance the tailoring of symptomatic therapy, (3) improve subgroup targeting of patients for future testing of disease-modifying treatments, and (4) identify objective biomarkers to improve the longitudinal tracking of impairments in clinical care and research. This article summarizes the work carried out by the task force toward identifying challenges and opportunities in the development of technologies with potential for improving the clinical management and the quality of life of individuals with PD. © 2016 International Parkinson and Movement Disorder Society.

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From hype to reality: data science enabling personalized medicine

Holger Fröhlich, Rudi Balling, Niko Beerenwinkel … (2018)

Background Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media). While during recent years there has been a lot of enthusiasm about the potential of ‘big data’ and machine learning-based solutions, there exist only few examples that impact current clinical practice. The lack of impact on clinical practice can largely be attributed to insufficient performance of predictive models, difficulties to interpret complex model predictions, and lack of validation via prospective clinical trials that demonstrate a clear benefit compared to the standard of care. In this paper, we review the potential of state-of-the-art data science approaches for personalized medicine, discuss open challenges, and highlight directions that may help to overcome them in the future. Conclusions There is a need for an interdisciplinary effort, including data scientists, physicians, patient advocates, regulatory agencies, and health insurance organizations. Partially unrealistic expectations and concerns about data science-based solutions need to be better managed. In parallel, computational methods must advance more to provide direct benefit to clinical practice.

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A systematic review of the characteristics and validity of monitoring technologies to assess Parkinson’s disease

Catarina Godinho, Josefa Domingos, Guilherme Cunha … (2016)

Background There is growing interest in having objective assessment of health-related outcomes using technology-based devices that provide unbiased measurements which can be used in clinical practice and scientific research. Many studies have investigated the clinical manifestations of Parkinson’s disease using such devices. However, clinimetric properties and clinical validation vary among the different devices. Methods Given such heterogeneity, we sought to perform a systematic review in order to (i) list, (ii) compare and (iii) classify technological-based devices used to measure motor function in individuals with Parkinson's disease into three groups, namely wearable, non-wearable and hybrid devices. A systematic literature search of the PubMed database resulted in the inclusion of 168 studies. These studies were grouped based on the type of device used. For each device we reviewed availability, use, reliability, validity, and sensitivity to change. The devices were then classified as (i) ‘recommended’, (ii) ‘suggested’ or (iii) ‘listed’ based on the following criteria: (1) used in the assessment of Parkinson’s disease (yes/no), (2) used in published studies by people other than the developers (yes/no), and (3) successful clinimetric testing (yes/no). Results Seventy-three devices were identified, 22 were wearable, 38 were non-wearable, and 13 were hybrid devices. In accordance with our classification method, 9 devices were ‘recommended’, 34 devices were ‘suggested’, and 30 devices were classified as ‘listed’. Within the wearable devices group, the Mobility Lab sensors from Ambulatory Parkinson’s Disease Monitoring (APDM), Physilog®, StepWatch 3, TriTrac RT3 Triaxial accelerometer, McRoberts DynaPort, and Axivity (AX3) were classified as ‘recommended’. Within the non-wearable devices group, the Nintendo Wii Balance Board and GAITRite® gait analysis system were classified as ‘recommended’. Within the hybrid devices group only the Kinesia® system was classified as ‘recommended’. Electronic supplementary material The online version of this article (doi:10.1186/s12984-016-0136-7) contains supplementary material, which is available to authorized users.

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Author and article information

Journal

Title: Movement Disorders

Abbreviated Title: Mov Disord

Publisher: Wiley

ISSN (Print): 0885-3185

ISSN (Electronic): 1531-8257

Publication date Created: December 17 2018

Publication date Created: May 2019

Publication date (Electronic): March 22 2019

Publication date (Print): May 2019

Volume: 34

Issue: 5

Pages: 657-663

Affiliations

[1 ]James J. and Joan A. Gardner Family Center for Parkinson's Disease and Movement DisordersUniversity of Cincinnati Cincinnati Ohio USA

[2 ]Center for the Study of Movement, Cognition, and Mobility, Department of NeurologyTel Aviv Sourasky Medical Center Tel Aviv Israel

[3 ]Department of Physical Therapy, Sackler Faculty of Medicine and Sagol School of NeuroscienceTel Aviv University Tel Aviv Israel

[4 ]Rush Alzheimer's Disease Center and Department of Orthopedic SurgeryRush University Chicago Illinois USA

[5 ]HM CINACHospital Universitario HM Puerta del Sur Móstoles Madrid Spain

[6 ]Department of Molecular NeurologyUniversity Hospital Erlangen, Friedrich‐Alexander University Erlangen‐Nürnberg Erlangen Germany

[7 ]Fraunhofer Institut for Integrated CircuitsDigital Health Pathway Research Group Erlangen Germany

[8 ]Department of Physical Medicine and RehabilitationHarvard Medical School Boston Massachusetts USA

[9 ]Discipline of Physiotherapy, Faculty of Health SciencesThe University of Sydney Sydney New South Wales Australia

[10 ]Department of NeurologyOregon Health & Science University, Portland Veterans Affairs Medical System Portland Oregon USA

[11 ]APDM, Inc Portland Oregon USA

[12 ]Parkinson's Disease and Movement Disorders Center, Division of Neurology, Department of MedicineThe Ottawa Hospital Research Institute, University of Ottawa Ottawa ON Canada

[13 ]George‐Huntington‐Institute, Technology Park Muenster Germany

[14 ]Department of RadiologyUniversity of Muenster Muenster Germany

[15 ]Department of Neurodegenerative Diseases and Hertie‐Institute for Clinical Brain ResearchUniversity of Tuebingen Tuebingen Germany

[16 ]Neuromotor Rehabilitation Research Group, Department of Rehabilitation SciencesKU Leuven Leuven Belgium

[17 ]Department of NeurologyUniversity of Rochester Medical Center Rochester New York USA

[18 ]Institute of Neuroscience, Newcastle University, Newcastle Upon Tyne NE4 5PL UK

[19 ]Newcastle upon Tyne Hospitals National Health Service Foundation Trust Newcastle upon Tyne UK

[20 ]Radboud University Medical CenterDonders Institute for Brain, Cognition and Behaviour, Department of Neurology Nijmegen The Netherlands

[21 ]Department of NeurologyChristian‐Albrechts University Kiel Germany

Article

DOI: 10.1002/mds.27671

PMC ID: 6520192

PubMed ID: 30901495

SO-VID: 4c9cc855-cc07-4cbc-82e3-d061c44af95f

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http://doi.wiley.com/10.1002/tdm_license_1.1

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A roadmap for implementation of patient‐centered digital outcome measures in Parkinson's disease obtained using mobile health technologies

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Most cited references 13

Technology in Parkinson's disease: Challenges and opportunities.

From hype to reality: data science enabling personalized medicine

A systematic review of the characteristics and validity of monitoring technologies to assess Parkinson’s disease

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