An Ambulatory Tremor Score for Parkinson’s Disease

Braybrook, Michelle; O'Connor, S.; Churchward, Philip; Perera, Thushara; Farzanehfar, Parisa; Horne, Malcolm

doi:10.3233/jpd-160898

Cited by 70 publications

(63 citation statements)

References 21 publications

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“…In contrast, Bazgir et al ( 2015 ), Kostikis et al ( 2015 ), and Daneault et al ( 2012 ) proposed smartphone-based systems that were mounted in a custom-made glove-case from which the acceleration signal was recorded. Finally, Braybrook et al ( 2016 ) proposed the Parkinson's Kinetigraph System, a wrist-worn device able to collect data continuously over an extended time period and detect tremor events. Data for tremor analysis were collected while the subjects performed standard diagnostic exercises according to UPDRS for assessment of RT, PT, KT; or conducted a reaching task (Alhamid et al, 2010 ) or an action task (Kwon et al, 2016 ).…”

Section: Resultsmentioning

confidence: 99%

“…In these exercises, it is crucial to recognize different typos of tremors (e.g., essential, resting, postural, re-emergent) accurately (Thanawattano et al, 2015 ; Surangsrirat et al, 2016 ), analyse the various frequency bands properly (Hossen et al, 2010 , 2013 ; Niazmand et al, 2011 ; Daneault et al, 2012 ; Hossen, 2012 ; Rigas et al, 2012 , 2016 ; Pierleoni et al, 2014 ; Bazgir et al, 2015 ; Ghassemi et al, 2016 ; Kwon et al, 2016 ; Mailankody et al, 2016 ; Zhou et al, 2016 ), and distinguish tremor correctly from other movements and disorders (e.g., dyskinesias, bradykinesia) (Salarian et al, 2007b ; Rigas et al, 2012 , 2016 ; Pierleoni et al, 2014 ), as well as recognize tremor severity accurately (Salarian et al, 2007b ; Daneault et al, 2012 ; Rigas et al, 2012 ; Pierleoni et al, 2014 ; Bazgir et al, 2015 ). For these purposes, a frequency analysis was the most appropriate approach (Salarian et al, 2007b ; Daneault et al, 2012 ; Hossen, 2012 ; Cavallo et al, 2013 ; Scanlon et al, 2013 ; Pierleoni et al, 2014 ; Bazgir et al, 2015 ; Braybrook et al, 2016 ; Zhou et al, 2016 ), and subjects with tremors in their hands were expected to have higher power in the high-frequency components (Alhamid et al, 2010 ). Although most of the works used signals from accelerometers to calculate features for tremor assessment, Surangsrirat et al ( 2016 ) and Thanawattano et al ( 2015 ) proposed the use of angular velocities to calculate the ratio of temporal fluctuations of tremor signal during resting tasks and kinetic tasks.…”

Section: Resultsmentioning

confidence: 99%

“…On this topic, the solution proposed by Cavallo et al ( 2013 ) seems to be promising in terms of wearability, portability, light weight, performance, and ease of use. In addition, the wrist-worn Parkinson's Kinetigraph System can accurately detect tremor over an extended time (Braybrook et al, 2016 ). Also, smartphone-based solutions (Daneault et al, 2012 ; Bazgir et al, 2015 ; Kostikis et al, 2015 ) could be an alternative for the measurement of tremor, even if the need to have a custom-made glove-case makes the device usable for only a short time.…”

Section: Resultsmentioning

confidence: 99%

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How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review

Maremmani²,

2017

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Background: Parkinson's disease (PD) is a common and disabling pathology that is characterized by both motor and non-motor symptoms and affects millions of people worldwide. The disease significantly affects quality of life of those affected. Many works in literature discuss the effects of the disease. The most promising trends involve sensor devices, which are low cost, low power, unobtrusive, and accurate in the measurements, for monitoring and managing the pathology. Objectives: This review focuses on wearable devices for PD applications and identifies five main fields: early diagnosis, tremor, body motion analysis, motor fluctuations (ON–OFF phases), and home and long-term monitoring. The concept is to obtain an overview of the pathology at each stage of development, from the beginning of the disease to consider early symptoms, during disease progression with analysis of the most common disorders, and including management of the most complicated situations (i.e., motor fluctuations and long-term remote monitoring). Data sources: The research was conducted within three databases: IEEE Xplore®, Science Direct®, and PubMed Central®, between January 2006 and December 2016. Study eligibility criteria: Since 1,429 articles were found, accurate definition of the exclusion criteria and selection strategy allowed identification of the most relevant papers. Results: Finally, 136 papers were fully evaluated and included in this review, allowing a wide overview of wearable devices for the management of Parkinson's disease.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review

Maremmani²,

2017

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“…Tremor is usually assessed by visual observation of the patient, which is not a clear reference standard. In one study, a BioMeT's measurement of Percent of Time that Tremor is Present in Parkinson's patients was assessed against visual observation to generate an accuracy score 40 .…”

Section: How Much Validation Is "Enough"?mentioning

confidence: 99%

Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs)

Goldsack¹,

et al. 2020

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Digital medicine is an interdisciplinary field, drawing together stakeholders with expertize in engineering, manufacturing, clinical science, data science, biostatistics, regulatory science, ethics, patient advocacy, and healthcare policy, to name a few. Although this diversity is undoubtedly valuable, it can lead to confusion regarding terminology and best practices. There are many instances, as we detail in this paper, where a single term is used by different groups to mean different things, as well as cases where multiple terms are used to describe essentially the same concept. Our intent is to clarify core terminology and best practices for the evaluation of Biometric Monitoring Technologies (BioMeTs), without unnecessarily introducing new terms. We focus on the evaluation of BioMeTs as fit-for-purpose for use in clinical trials. However, our intent is for this framework to be instructional to all users of digital measurement tools, regardless of setting or intended use. We propose and describe a three-component framework intended to provide a foundational evaluation framework for BioMeTs. This framework includes (1) verification, (2) analytical validation, and (3) clinical validation. We aim for this common vocabulary to enable more effective communication and collaboration, generate a common and meaningful evidence base for BioMeTs, and improve the accessibility of the digital medicine field.

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“…These targets were established by consensus of a panel of four neurologists experienced in treating PD and interpreting the PKG prior to commencing the study. 2 The targets were based on the bradykinesia score at which most PwP switch from tremor to non-tremor 24 and the level that approximated 2 h/day of “OFF” time for bradykinesia and 2 h/day with dyskinesia. These PKG criteria target ranges are described below and the reader is referred to the other studies that describe how the PKG scores (BKS, DKS, FDS) are derived and their relationship to other clinical rating scales.…”

Section: Methodsmentioning

confidence: 99%

Objective measurement in routine care of people with Parkinson’s disease improves outcomes

Farzanehfar

Woodrow

Braybrook

et al. 2018

npj Parkinson's Disease

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It is common in medicine to titrate therapy according to target ranges of objectively measured parameters. Objective measurement of motor function is available for Parkinson’s Disease (PD), making it possible to optimise therapy and clinical outcomes. In this study, an accelerometry based measurement and predefined target ranges were used to assess motor function in a Northern Tasmania PD cohort managed by a Movement Disorder clinic. Approximately 40% (n = 103) of the total PD population participated in this study and motor scores were within target in 22%. In the 78% above target, changes in oral therapy were recommended in 74%, Advanced Therapy in 12% and treatment was contraindicated in 9%. Following changes in oral therapy, there was a further objective measurement and clinical consultation to establish whether scores had reached target range: if so subjects left the study, otherwise further changes of therapy were recommended (unless contraindications were present). Seventy-seven cases completed the study, with 48% achieving target (including 22% at outset), Advanced Therapy recommended in 19% and contraindications preventing any change in therapy in 17%. In the 43% of cases in whom oral therapy was changed, total UPDRS improved significantly (effect size = 8) as did the PDQ39 in cases reaching target. NMS Quest and MOCA scores also improved significantly. This study shows that many people in a representative cohort of PD would benefit from objective assessment and treatment of their PD features against a target.

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An Ambulatory Tremor Score for Parkinson’s Disease

Abstract: We propose this as a means of assessing the presence of tremor and its relationship to bradykinesia.

Cited by 70 publications

References 21 publications

How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review

How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review

Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs)

Objective measurement in routine care of people with Parkinson’s disease improves outcomes

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