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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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![Fig 1 (a) Fox Wearable Companion app main screen; (b) Fox Wearable Companion app activity graph; (c) Fox Wearable Companion app movement during sleep graph; (d) Fox Wearable Companion app symptom self-reports. “Reprinted from [Intel and Michael J Fox Foundation] under a CC BY license, with permission from [INTEL®], original copyright [2017].](/paper-figures/513312c109a04740adf0b5762912b0a6/feasibility-of-large-scale-deployment-of-multiple-wearable-sensors-in-parkinson-figure-1.jpg)
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“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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Metadata
- 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 › Clinical 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
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