Feasibility of large-scale deployment of multiple wearable sensors in Parkinson's disease
Open accessAbstract
Wearable devices can capture objective day-to-day data about Parkinson's Disease (PD). This study aims to assess the feasibility of implementing wearable technology to collect data from multiple sensors during the daily lives of PD patients. The Parkinson@home study is an observational, two-cohort (North America, NAM; The Netherlands, NL) study. To recruit participants, different strategies were used between sites. Main enrolment criteria were self-reported diagnosis of PD, possession of a smartphone and age≥18 years. Participants used the Fox Wearable Companion app on a smartwatch and smartphone for a minimum of 6 weeks (NAM) or 13 weeks (NL). Sensor-derived measures estimated information about movement. Additionally, medication intake and symptoms were collected via self-reports in the app. A total of 953 participants were included (NL: 304, NAM: 649). Enrolment rate was 88% in the NL (n = 304) and 51% (n = 649) in NAM. Overall, 84% (n = 805) of participants contributed sensor data. Participants were compliant for 68% (16.3 hours/participant/day) of the study period in NL and for 62% (14.8 hours/participant/day) in NAM. Daily accelerometer data collection decreased 23% in the NL after 13 weeks, and 27% in NAM after 6 weeks. Data contribution was not affected by demographics, clinical characteristics or attitude towards technology, but was by the platform usability score in the NL (χ2 (2) = 32.014, p<0.001), and self-reported depression in NAM (χ2(2) = 6.397, p = .04). The Parkinson@home study shows that it is feasible to collect objective data using multiple wearable sensors in PD during daily life in a large cohort.
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The Parkinson@home study deployed the Fox Wearable Companion app to 953 participants in the Netherlands and North America; adherence was 68% in the Netherlands and 62% in North America, and not affected by demographics, clinical characteristics, or attitude toward technology.
“The Parkinson@home study26, 30 deployed the Fox Wearable Companion app to 953 participants in the Netherlands (13‐week study) and North America (6‐week study) to passively monitor behavior and rate the severity of selected symptoms. Adherence was 68% in the Netherlands and 62% in North America, and not affected by demographics, clinical characteristics, or attitude toward technology.26, 30”
From Evaluation of smartphone‐based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial (2018) · cites this paper as [30]
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Digital biomarkers for non-motor symptoms in Parkinson’s disease: the state of the art
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An Ultra-Low Energy Human Activity Recognition Accelerator for Wearable Health Applications
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Torrado, J. C., Husebo, B. S., Allore, H. G., Erdal, A., Fæø, S. E., Reithe, H., Førsund, E., Tzoulis, C., & Pătraşcu, M. (2022) · PLoS ONE · doi:10.1371/journal.pone.0275747 · Cited by 38 · see citing works
Big Data From Small Devices: The Future of Smartphones in Oncology
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Digital Health and the Care of the Patient With Arrhythmia
Tarakji, K. G., Silva, J. N. A., Chen, L. Y., Turakhia, M. P., Perez, M. V., Attia, Z. I., PASSMAN, R. S., Boissy, A. R., Cho, D. J., Majmudar, M. D., et al. (2020) · Circulation Arrhythmia and Electrophysiology · doi:10.1161/circep.120.007953 · Cited by 34 · see citing works
Impact of motor fluctuations on real-life gait in Parkinson’s patients
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Self-citationMultidisciplinary Care to Optimize Functional Mobility in Parkinson Disease
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Self-citationDigital phenotyping in Parkinson's disease: Empowering neurologists for measurement-based care
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Sensor Validation and Diagnostic Potential of Smartwatches in Movement Disorders
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Smart wearables addressing gait disorders: A review
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Applications of deep learning methods in digital biomarker research using noninvasive sensing data
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A Smart Device System to Identify New Phenotypical Characteristics in Movement Disorders
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Continuous Movement Monitoring at Home Through Wearable Devices: A Systematic Review
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Martinez, G. J., Mattingly, S. M., Robles-Granda, P., Saha, K., Sirigiri, A., Young, J., Chawla, N. V., Choudhury, M. D., D’Mello, S. K., Mark, G. J., et al. (2021) · JMIR mhealth and uhealth · doi:10.2196/22218 · Cited by 24 · see citing works
The Use of Digital Technology and Media in German Parkinson’s Disease Patients
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Multilevel Features for Sensor-Based Assessment of Motor Fluctuation in Parkinson's Disease Subjects
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Movement Disorders and Smart Wrist Devices: A Comprehensive Study
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Hunter, A., Leckie, T., Coe, O., Hardy, B., Fitzpatrick, D., Gonçalves, A., Standing, M.-K., Koulouglioti, C., Richardson, A., & Hodgson, L. E. (2022) · JMIR Rehabilitation and Assistive Technologies · doi:10.2196/25494 · Cited by 21 · see citing works
The Best Digital Biomarkers Papers of 2017
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Domotics, smart homes and Parkinson’s disease
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Validation of Commercial Activity Trackers in Everyday Life of People with Parkinson’s Disease
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Domotics, Smart Homes, and Parkinson’s Disease
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Assessing Sports Performances Using an Artificial Intelligence-Driven System
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Wearable devices in palliative care for people 65 years and older: A scoping review
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- Authors
- Silva de Lima, A. L., Hahn, T., Evers, L. J. W., de Vries, N. M., Cohen, E., Afek, M., Bataille, L., Daeschler, M., Claes, K., Boroojerdi, B., Terricabras, D., Little, M. A., Baldus, H., Bloem, B. R., & Faber, M. J.
- Affiliations
- Radboud University Nijmegen (NL)
- Intel (Israel) (IL)
- Michael J. Fox Foundation (US)
- UCB Pharma (Belgium) (BE)
- Aston University (GB)
- ORCID
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2017-12-20
- Volume
- 12
- Issue
- 12
- Pages
- e0189161
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC5738046
- OpenAlex
- W2779593300
- Cited by
- 162
- References
- 40
- Citations by year
- 2026: 2 · 2025: 16 · 2024: 18 · 2023: 23 · 2022: 20 · 2021: 23 · 2020: 27 · 2019: 23 · 2018: 10
- FWCI
- 6.81
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Aston Publications Explorer (Aston University) (acceptedVersion, cc-by)
- Radboud Repository (Radboud University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Radboud Repository (Radboud University) (submittedVersion)
- Funders
- Intel Corporation
- Parkinson's UK (J-1403, J-0901)
- ZonMw
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (0428-140)
- Governo Brasil
- UCB Pharma
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Neurology › Parkinson's Disease Mechanisms and Treatments
- Keywords
- Parkinson's disease, wearable sensors, smartwatches, accelerometer, feasibility, participant recruitment, data contribution, device usability, depression, medication adherence, self-reported symptoms, mobile application
- MeSH terms
- Aged, Feasibility Studies, Female, Gait, Humans, Male, Middle Aged, Movement, Parkinson Disease, Biosensing Techniques
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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