An Objective Fluctuation Score for Parkinson's Disease
Open accessAbstract
INTRODUCTION: Establishing the presence and severity of fluctuations is important in managing Parkinson's Disease yet there is no reliable, objective means of doing this. In this study we have evaluated a Fluctuation Score derived from variations in dyskinesia and bradykinesia scores produced by an accelerometry based system. METHODS: The Fluctuation Score was produced by summing the interquartile range of bradykinesia scores and dyskinesia scores produced every 2 minutes between 0900-1800 for at least 6 days by the accelerometry based system and expressing it as an algorithm. RESULTS: This Score could distinguish between fluctuating and non-fluctuating patients with high sensitivity and selectivity and was significant lower following activation of deep brain stimulators. The scores following deep brain stimulation lay in a band just above the score separating fluctuators from non-fluctuators, suggesting a range representing adequate motor control. When compared with control subjects the score of newly diagnosed patients show a loss of fluctuation with onset of PD. The score was calculated in subjects whose duration of disease was known and this showed that newly diagnosed patients soon develop higher scores which either fall under or within the range representing adequate motor control or instead go on to develop more severe fluctuations. CONCLUSION: The Fluctuation Score described here promises to be a useful tool for identifying patients whose fluctuations are progressing and may require therapeutic changes. It also shows promise as a useful research tool. Further studies are required to more accurately identify therapeutic targets and ranges.
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In Parkinson's disease, wearables were used to detect and quantify medication-evoked adverse symptoms such as dyskinesia and motor fluctuations.
“In PD, wearables were used to detect and quantify cardinal motor symptoms including bradykinesia [19], tremor [20, 21], and postural sway [22, 23] as well as medication-evoked adverse symptoms such as dyskinesia [24–27] and motor fluctuations [28, 29].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [28]
The commercial proprietary algorithm could detect changes in fluctuations before and after deep brain stimulation.
“A more recent study showed that a commercial proprietary algorithm could discriminate between individuals with and without motor fluctuations, and detect changes in fluctuations before and after deep brain stimulation [28].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [28]
A commercial proprietary algorithm could discriminate between individuals with and without motor fluctuations.
“A more recent study showed that a commercial proprietary algorithm could discriminate between individuals with and without motor fluctuations, and detect changes in fluctuations before and after deep brain stimulation [28].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [28]
Promising results have been reported for capturing motor fluctuations in Parkinson's disease using a single wrist sensor.
“Promising results have been reported for capturing motor fluctuations in PD using a single wrist sensor [28]”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [28]
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- Authors
- Horne, M. K., McGregor, S., & Bergquist, F.
- Affiliations
- The University of Melbourne (AU)
- St Vincent's Hospital Melbourne (AU)
- University of Gothenburg (SE)
- Corresponding author
- Horne, Malcolm K.
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-04-30
- Volume
- 10
- Issue
- 4
- Pages
- e0124522
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC4416005
- OpenAlex
- W2086628291
- Cited by
- 88
- References
- 10
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 12 · 2023: 14 · 2022: 8 · 2021: 8 · 2020: 10 · 2019: 6 · 2018: 10 · 2017: 7 · 2016: 6 · 2015: 1
- FWCI
- 5.64
- 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)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Health and Medical Research Council
- Topics
- Health Sciences › Medicine › Neurology › Neurological disorders and treatments
- Health Sciences › Medicine › Neurology › Parkinson's Disease Mechanisms and Treatments
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Keywords
- Parkinson's disease, motor fluctuations, accelerometry, dyskinesia, bradykinesia, deep brain stimulation, interquartile range, objective assessment, disease progression
- MeSH terms
- Aged, Aged, 80 and over, Female, Humans, Male, Middle Aged, Parkinson Disease, Hypokinesia, Dyskinesias
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