Large-Scale Wearable Sensor Deployment in Parkinson’s Patients: The Parkinson@Home Study Protocol
Open accessAbstract
BACKGROUND: Long-term management of Parkinson's disease does not reach its full potential because we lack knowledge about individual variations in clinical presentation and disease progression. Continuous and longitudinal assessments in real-life (ie, within the patients' own home environment) might fill this knowledge gap. OBJECTIVE: The primary aim of the Parkinson@Home study is to evaluate the feasibility and compliance of using multiple wearable sensors to collect clinically relevant data. Our second aim is to address the usability of these data for answering clinical research questions. Finally, we aim to build a database for future validation of novel algorithms applied to sensor-derived data from Parkinson's patients during daily functioning. METHODS: The Parkinson@Home study is a two-phase observational study involving 1000 Parkinson's patients and 250 physiotherapists. Disease status is assessed using a short version of the Parkinson's Progression Markers Initiative protocol, performed by certified physiotherapists. Additionally, participants will wear a set of sensors (smartwatch, smartphone, and fall detector), and use these together with a customized smartphone app (Fox Insight), 24/7 for 3 months. The sensors embedded within the smartwatch and fall detector may be used to estimate physical activity, tremor, sleep quality, and falls. Medication intake and fall incidents will be measured via patients' self-reports in the smartphone app. Phase one will address the feasibility of the study protocol. In phase two, mathematicians will distill relevant summary statistics from the raw sensor signals, which will be compared against the clinical outcomes. RESULTS: Recruitment of 300 participants for phase one was concluded in March, 2016, and the follow-up period will end in June, 2016. Phase two will include the remaining participants, and will commence in September, 2016. CONCLUSIONS: The Parkinson@Home study is expected to generate new insights into the feasibility of integrating self-collected information from wearable sensors into both daily routines and clinical practices for Parkinson's patients. This study represents an important step towards building a reliable system that translates and integrates real-life information into clinical decisions, with the long-term aim of delivering personalized disease management support. CLINICALTRIAL: ClinicalTrials.gov NCT02474329; https://clinicaltrials.gov/ct2/show/NCT02474329 (Archived at http://www.webcitation.org/6joEc5P1v).
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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 [26]
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Sukhdev, N., Roesler, O., Neumann, M., Bartlett, M., Habberstad, D., & Ramanarayanan, V. (2024) · Studies in computational intelligence · doi:10.1007/978-3-031-63592-2_21 · Cited by 1 · see citing works
The Diagnosis of Parkinson’s Disease: Current Clinical Practice and Future Trends
Blanco, R. L., & Sánchez‐Ferro, Á. (2018) · Springer eBooks · doi:10.1007/978-981-13-0944-1_9 · Cited by 1 · see citing works
E-Health and Neurodegeneration
Intas, G., Platis, C. G., & Stergiannis, P. I. (2023) · Springer eBooks · doi:10.1007/978-3-319-75922-7_35 · Cited by 1 · see citing works
Gut Microbiota in Brain diseases
Salami, M. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-819265-8.00040-1
E-Health and Neurodegeneration
Intas, G., Platis, C. G., & Stergiannis, P. I. (2021) · Springer eBooks · doi:10.1007/978-3-319-75479-6_35-1
Parkinson’s Tremor Rating System Based on MLP
岳, 星. (2022) · Software Engineering and Applications · doi:10.12677/sea.2022.111003
Gaßner, H., Friedrich, J., Masuch, A., Jukic, J., Stallforth, S., Regensburger, M., Marxreiter, F., Winkler, J., & Klucken, J. (2022) · doi:10.2196/preprints.38994
Khardali, A., Lane, E. L., & Hughes, L. (2023) · doi:10.2196/preprints.49730
Lützow, L., Teckenburg, I., Koch, V., Marxreiter, F., Jukic, J., Stallforth, S., Regensburger, M., Winkler, J., Klucken, J., & Gaßner, H. (2023) · Figshare · doi:10.6084/m9.figshare.24465326
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Lützow, L., Teckenburg, I., Koch, V., Marxreiter, F., Jukic, J., Stallforth, S., Regensburger, M., Winkler, J., Klucken, J., & Gaßner, H. (2023) · Figshare · doi:10.6084/m9.figshare.26634033
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Telemedicine in deep brain stimulation
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- Authors
- Silva de Lima, A. L., Hahn, T., de Vries, N. M., Cohen, E., Bataille, L., Little, M. A., Baldus, H., Bloem, B. R., & Faber, M. J.
- Affiliations
- Radboud University Medical Center (NL)
- Intel (Israel) (IL)
- Michael J. Fox Foundation (US)
- Aston University (GB)
- Radboud University Nijmegen (NL)
- ORCID
- Venue
- JMIR Research Protocols
- Publisher
- JMIR Publications Inc.
- Published
- 2016-08-26
- Volume
- 5
- Issue
- 3
- Pages
- e172
- ISSN
- 1929-0748 (ISSN-L)
- PMC
- PMC5018102
- OpenAlex
- W2507704171
- Cited by
- 63
- References
- 39
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 2 · 2023: 10 · 2022: 6 · 2021: 6 · 2020: 12 · 2019: 4 · 2018: 13 · 2017: 7 · 2016: 1
- FWCI
- 5.62
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Research Protocols (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- Funders
- Intel Corporation
- Parkinson's UK (J-1403, J-0901)
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (0428-140)
- Stichting ParkinsonFonds
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Health Sciences › Medicine › Neurology › Parkinson's Disease Mechanisms and Treatments
- Keywords
- Parkinson's disease, wearable sensors, home monitoring, smartwatches, fall detection, mobile application, physical activity, tremor, sleep quality, medication adherence, falls, Parkinson's Progression Markers Initiative, physiotherapists, feasibility, sensor data analysis
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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