Predicting motor, cognitive &amp; functional impairment in Parkinson's

Lo, Christine; Arora, Siddharth; Baig, Fahd; Lawton, Michael; Mouden, Claire El; Barber, Thomas R; Ruffmann, Claudio; Klein, Johannes; Brown, Peter; Ben‐Shlomo, Yoav; Vos, Maarten De; Hu, Michele T.M.

doi:10.1002/acn3.50853

Cited by 42 publications

(51 citation statements)

References 34 publications

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“…Voice recordings and clinical data were collected from participants enrolled in the Oxford Discovery Cohort (further details are discussed in Barber et al [32]; Baig et al [33]; Lo et al [13]). PWP met the United Kingdom PD Brain Bank criteria for probable PD [34].…”

Section: Datamentioning

confidence: 99%

“…Briefly, these acoustic measures aimed to quantify the deviation from vocal fold periodicity (in terms of frequency the jitter variants and in terms of amplitude the shimmer variants), acoustic/turbulent noise, and articulator placement. For the physiological background, rationale, and detailed algorithmic expressions for the computation of the acoustic measures please refer to our previous studies [6], [13][14][15]. The MATLAB source code for the computation of the acoustic measures is freely available on the author's (AT) website: https://www.darth-group.com/software.…”

Section: B Feature Extractionmentioning

confidence: 99%

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Smartphone Speech Testing for Symptom Assessment in Rapid Eye Movement Sleep Behavior Disorder and Parkinson’s Disease

Arora

et al. 2021

IEEE Access

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Speech impairment in Parkinson's Disease (PD) has been extensively studied. Our understanding of speech in people who are at an increased risk of developing PD is, however, rather limited. It is known that isolated Rapid Eye Movement (REM) sleep Behavior Disorder (RBD) is associated with a high risk of developing PD. The aim of this study is to investigate smartphone speech testing to: (1) distinguish participants with RBD from controls and PD, and (2) predict a range of self-or researcheradministered clinical scores that quantify participants' motor symptoms, cognition, daytime sleepiness, depression, and the overall state of health. The rationale of our analyses is to test an initial hypothesis that speech can be used to detect and quantify the symptoms associated with RBD and PD. We analyzed 4242 smartphone voice recordings collected in clinic and at home from 92 Controls, 112 RBD and 335 PD participants. We used acoustic signal analysis and machine learning, employing 337 features that quantify different properties of speech impairment. Using a leave-one-subject-out cross-validation scheme, we were able to distinguish RBD from controls (sensitivity 60.7%, specificity 69.6%) and RBD from PD participants (sensitivity 74.9%, specificity 73.2%), and predict clinical assessments with clinically useful accuracy. These promising findings warrant further investigation in using speech as a digital biomarker for PD and RBD to facilitate intervention in the early and prodromal stages of PD.

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Section: Datamentioning

confidence: 99%

Section: B Feature Extractionmentioning

confidence: 99%

Smartphone Speech Testing for Symptom Assessment in Rapid Eye Movement Sleep Behavior Disorder and Parkinson’s Disease

Arora

et al. 2021

IEEE Access

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“…Digital health technology tools (DHTTs) are digital tools that remotely and frequently acquire health and disease-related data from individuals (see Table 1 for a list of terms and definitions used here). DHTTs are becoming a standard part of clinical research studies in which patients collect two basic modalities of sensor data [1][2][3][4][5][6][7] : (1) sensor data can be captured while patients perform specific tasks designed to measure key symptoms ("active tests") and (2) sensors may monitor human behavior passively ("passive monitoring"). Active tests can be administered, e.g., using tablets, smartphones, and to a limited extent also smartwatches.…”

Section: Introductionmentioning

confidence: 99%

Outcome measures based on digital health technology sensor data: data- and patient-centric approaches

et al. 2020

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Digital health technology tools (DHTT) are technologies such as apps, smartphones, and wearables that remotely acquire healthrelated information from individuals. They have the potential advantages of objectivity and sensitivity of measurement, richness of high-frequency sensor data, and opportunity for passive collection of health-related data. Thus, DHTTs promise to provide patient phenotyping at an order of granularity several times greater than is possible with traditional clinical research tools. While the conceptual development of novel DHTTs is keeping pace with technological and analytical advancements, an as yet unaddressed gap is how to develop robust and meaningful outcome measures based on sensor data. Here, we describe two roadmaps which were developed to generate outcome measures based on DHTT data: one using a data-centric approach and the second a patientcentric approach. The data-centric approach to develop digital outcome measures summarizes those sensor features maximally sensitive to the concept of interest, exemplified with the quantification of disease progression. The patient-centric approach summarizes those sensor features that are optimally relevant to patients' functioning in everyday life. Both roadmaps are exemplified for use in tracking disease progression in observational and clinical interventional studies, and with a DHTT designed to evaluate motor symptom severity and symptom experience in Parkinson's disease. Use cases other than disease progression (e.g., case-finding) are considered summarily. DHTT research requires methods to summarize sensor data into meaningful outcome measures. It is hoped that the concepts outlined here will encourage a scientific discourse and eventual consensus on the creation of novel digital outcome measures for both basic clinical research and clinical drug development.

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“…Successful deployment of remote monitoring technologies for identification of those who are likely to develop synucleinopathies has true potential for enabling early intervention strategies. The OPDC Discovery Cohort also used smartphones to predict the onset of clinically relevant endpoints in early PD subjects, including new-onset falls, freezing, postural instability, and cognitive and functional impairment at 18 months [33].…”

Section: Oxford Parkinson's Disease Centre Discovery Cohortmentioning

confidence: 99%

Precompetitive Consensus Building to Facilitate the Use of Digital Health Technologies to Support Parkinson Disease Drug Development through Regulatory Science

et al. 2020

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Innovative tools are urgently needed to accelerate the evaluation and subsequent approval of novel treatments that may slow, halt, or reverse the relentless progression of Parkinson disease (PD). Therapies that intervene early in the disease continuum are a priority for the many candidates in the drug development pipeline. There is a paucity of sensitive and objective, yet clinically interpretable, measures that can capture meaningful aspects of the disease. This poses a major challenge for the development of new therapies and is compounded by the considerable heterogeneity in clinical manifestations across patients and the fluctuating nature of many signs and symptoms of PD. Digital health technologies (DHT), such as smartphone applications, wearable sensors, and digital diaries, have the potential to address many of these gaps by enabling the objective, remote, and frequent measurement of PD signs and symptoms in natural living environments. The current climate of the COVID-19 pandemic creates a heightened sense of urgency for effective implementation of such strategies. In order for these technologies to be adopted in drug development studies, a regulatory-aligned consensus on best practices in implementing appropriate technologies, including the collection, processing, and interpretation of digital sensor data, is required. A growing number of collaborative initiatives are being launched to identify effective ways to advance the use of DHT in PD clinical trials. The Critical Path for Parkinson’s Consortium of the Critical Path Institute is highlighted as a case example where stakeholders collectively engaged regulatory agencies on the effective use of DHT in PD clinical trials. Global regulatory agencies, including the US Food and Drug Administration and the European Medicines Agency, are encouraging the efficiencies of data-driven engagements through multistakeholder consortia. To this end, we review how the advancement of DHT can be most effectively achieved by aligning knowledge, expertise, and data sharing in ways that maximize efficiencies.

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Predicting motor, cognitive & functional impairment in Parkinson's

Cited by 42 publications

References 34 publications

Smartphone Speech Testing for Symptom Assessment in Rapid Eye Movement Sleep Behavior Disorder and Parkinson’s Disease

Smartphone Speech Testing for Symptom Assessment in Rapid Eye Movement Sleep Behavior Disorder and Parkinson’s Disease

Outcome measures based on digital health technology sensor data: data- and patient-centric approaches

Precompetitive Consensus Building to Facilitate the Use of Digital Health Technologies to Support Parkinson Disease Drug Development through Regulatory Science

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