A systematic review of the characteristics and validity of monitoring technologies to assess Parkinson’s disease
Open accessAbstract
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'.
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Common consumer technologies such as smartphones have the advantages of widespread availability, low cost, and high sensor quality.
“common consumer technologies such as smartphones have the advantages of widespread availability, low cost, and high sensor quality.”
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 [6]
Skew characteristic and clinical validation may vary among different equipment version.
“Skew characteristic and clinical validation may vary among different equipment version (Godinho et al., 2016).”
From A review of applications in federated learning (2020) · cites this paper as Godinho et al., 2016
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References 31 · Cited by 226
- Authors
- Godinho, C., Domingos, J., Cunha, G., Santos, A. T., Fernandes, R. M., Abreu, D., Gonçalves, N., Matthews, H., Isaacs, T., Duffen, J., Al-Jawad, A., Larsen, F., Serrano, A., Weber, P., Thoms, A., Sollinger, S., Graessner, H., Maetzler, W., & Ferreira, J. J.
- Affiliations
- University of Lisbon (PT)
- Cure Parkinson's Trust (GB)
- Hahn-Schickard-Gesellschaft für angewandte Forschung (DE)
- Hasomed (Germany) (DE)
- AbilityNet (United Kingdom) (GB)
- University of Tübingen (DE)
- Hertie Institute for Clinical Brain Research (DE)
- Corresponding author
- Ferreira, Joaquim J.
- ORCID
- Venue
- Journal of NeuroEngineering and Rehabilitation
- Published
- 2016-03-12
- Volume
- 13
- Issue
- 1
- Pages
- 24-24
- ISSN
- 1743-0003 (ISSN-L)
- PMC
- PMC4788909
- OpenAlex
- W2294748375
- Cited by
- 226
- References
- 31
- Citations by year
- 2026: 8 · 2025: 17 · 2024: 19 · 2023: 21 · 2022: 13 · 2021: 31 · 2020: 28 · 2019: 34 · 2018: 27 · 2017: 22 · 2016: 6
- FWCI
- 5.14
- Type
- journal article
- Language
- en
- Update notices
- correction (2016-08-01)
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of NeuroEngineering and Rehabilitation (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Multimedialen Archiv und Publikationsserver der Christian-Albrechts-Universität zu Kiel (Christian-Albrechts-Universität zu Kiel) (publishedVersion, cc-by)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, cc-by)
- Funders
- Seventh Framework Programme, Cooperation – ICT (288557)
- Topics
- 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
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- Parkinson's disease, wearable sensors, hybrid devices, motor function assessment, clinimetric properties, device validation, reliability statistics, sensitivity to change, gait analysis, accelerometer, Wii Balance Board, GAITRite
- MeSH terms
- Humans, Parkinson Disease, Reproducibility of Results, Monitoring, Ambulatory, Accelerometry
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