Publications by authors named "Max A Little"

50 Publications

Detecting Parkinson Disease Using a Web-Based Speech Task: Observational Study.

J Med Internet Res 2021 Oct 19;23(10):e26305. Epub 2021 Oct 19.

Department of Computer Science, University of Rochester, Rochester, NY, United States.

Background: Access to neurological care for Parkinson disease (PD) is a rare privilege for millions of people worldwide, especially in resource-limited countries. In 2013, there were just 1200 neurologists in India for a population of 1.3 billion people; in Africa, the average population per neurologist exceeds 3.3 million people. In contrast, 60,000 people receive a diagnosis of PD every year in the United States alone, and similar patterns of rising PD cases-fueled mostly by environmental pollution and an aging population-can be seen worldwide. The current projection of more than 12 million patients with PD worldwide by 2040 is only part of the picture given that more than 20% of patients with PD remain undiagnosed. Timely diagnosis and frequent assessment are key to ensure timely and appropriate medical intervention, thus improving the quality of life of patients with PD.

Objective: In this paper, we propose a web-based framework that can help anyone anywhere around the world record a short speech task and analyze the recorded data to screen for PD.

Methods: We collected data from 726 unique participants (PD: 262/726, 36.1% were women; non-PD: 464/726, 63.9% were women; average age 61 years) from all over the United States and beyond. A small portion of the data (approximately 54/726, 7.4%) was collected in a laboratory setting to compare the performance of the models trained with noisy home environment data against high-quality laboratory-environment data. The participants were instructed to utter a popular pangram containing all the letters in the English alphabet, "the quick brown fox jumps over the lazy dog." We extracted both standard acoustic features (mel-frequency cepstral coefficients and jitter and shimmer variants) and deep learning-based embedding features from the speech data. Using these features, we trained several machine learning algorithms. We also applied model interpretation techniques such as Shapley additive explanations to ascertain the importance of each feature in determining the model's output.

Results: We achieved an area under the curve of 0.753 for determining the presence of self-reported PD by modeling the standard acoustic features through the XGBoost-a gradient-boosted decision tree model. Further analysis revealed that the widely used mel-frequency cepstral coefficient features and a subset of previously validated dysphonia features designed for detecting PD from a verbal phonation task (pronouncing "ahh") influence the model's decision the most.

Conclusions: Our model performed equally well on data collected in a controlled laboratory environment and in the wild across different gender and age groups. Using this tool, we can collect data from almost anyone anywhere with an audio-enabled device and help the participants screen for PD remotely, contributing to equity and access in neurological care.
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http://dx.doi.org/10.2196/26305DOI Listing
October 2021

Remote smartphone monitoring of Parkinson's disease and individual response to therapy.

Nat Biotechnol 2021 Aug 9. Epub 2021 Aug 9.

Sage Bionetworks, Seattle, WA, USA.

Remote health assessments that gather real-world data (RWD) outside clinic settings require a clear understanding of appropriate methods for data collection, quality assessment, analysis and interpretation. Here we examine the performance and limitations of smartphones in collecting RWD in the remote mPower observational study of Parkinson's disease (PD). Within the first 6 months of study commencement, 960 participants had enrolled and performed at least five self-administered active PD symptom assessments (speeded tapping, gait/balance, phonation or memory). Task performance, especially speeded tapping, was predictive of self-reported PD status (area under the receiver operating characteristic curve (AUC) = 0.8) and correlated with in-clinic evaluation of disease severity (r = 0.71; P < 1.8 × 10) when compared with motor Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Although remote assessment requires careful consideration for accurate interpretation of RWD, our results support the use of smartphones and wearables in objective and personalized disease assessments.
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http://dx.doi.org/10.1038/s41587-021-00974-9DOI Listing
August 2021

Smartphones for Remote Symptom Monitoring of Parkinson's Disease.

Authors:
Max A Little

J Parkinsons Dis 2021;11(s1):S49-S53

University of Birmingham, Edgbaston, Birmingham, UK.

Parkinson's disease is a complex and heterogeneous condition, and there are many gaps in the medical community's scientific and practical understanding of the disease. Closing these gaps relies on objective data about symptoms and signs, collected over long durations. Smartphones contain sensor devices which can be used to remotely capture behavioral signals. From these signals, computational algorithms can distill metrics of symptom severity and progression. This brief review introduces the main concepts of the discipline, addressing the experimental, hardware and software logistics, and computational analysis. The article finishes with an exploration of future prospects for the technology.
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http://dx.doi.org/10.3233/JPD-202453DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8385528PMC
January 2021

Remote Assessment of Parkinson's Disease Symptom Severity Using the Simulated Cellular Mobile Telephone Network.

IEEE Access 2021 11;9:11024-11036. Epub 2021 Jan 11.

Department of Speech, Language, and Hearing ScienceUniversity of Colorado BoulderBoulderCO80309USA.

Telemonitoring of Parkinson's Disease (PD) has attracted considerable research interest because of its potential to make a lasting, positive impact on the life of patients and their carers. Purpose-built devices have been developed that record various signals which can be associated with average PD symptom severity, as quantified on standard clinical metrics such as the Unified Parkinson's Disease Rating Scale (UPDRS). Speech signals are particularly promising in this regard, because they can be easily recorded without the use of expensive, dedicated hardware. Previous studies have demonstrated replication of UPDRS to within less than 2 points of a clinical raters' assessment of symptom severity, using high-quality speech signals collected using dedicated telemonitoring hardware. Here, we investigate the potential of using the standard voice-over-GSM (2G) or UMTS (3G) cellular mobile telephone networks for PD telemonitoring, networks that, together, have greater than 5 billion subscribers worldwide. We test the robustness of this approach using a simulated noisy mobile communication network over which speech signals are transmitted, and approximately 6000 recordings from 42 PD subjects. We show that UPDRS can be estimated to within less than 3.5 points difference from the clinical raters' assessment, which is clinically useful given that the inter-rater variability for UPDRS can be as high as 4-5 UPDRS points. This provides compelling evidence that the existing voice telephone network has potential towards facilitating inexpensive, mass-scale PD symptom telemonitoring applications.
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http://dx.doi.org/10.1109/ACCESS.2021.3050524DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7821632PMC
January 2021

Probabilistic Modelling of Gait for Robust Passive Monitoring in Daily Life.

IEEE J Biomed Health Inform 2021 06 3;25(6):2293-2304. Epub 2021 Jun 3.

Passive monitoring in daily life may provide valuable insights into a person's health throughout the day. Wearable sensor devices play a key role in enabling such monitoring in a non-obtrusive fashion. However, sensor data collected in daily life reflect multiple health and behavior-related factors together. This creates the need for a structured principled analysis to produce reliable and interpretable predictions that can be used to support clinical diagnosis and treatment. In this work we develop a principled modelling approach for free-living gait (walking) analysis. Gait is a promising target for non-obtrusive monitoring because it is common and indicative of many different movement disorders such as Parkinson's disease (PD), yet its analysis has largely been limited to experimentally controlled lab settings. To locate and characterize stationary gait segments in free-living using accelerometers, we present an unsupervised probabilistic framework designed to segment signals into differing gait and non-gait patterns. We evaluate the approach using a new video-referenced dataset including 25 PD patients with motor fluctuations and 25 age-matched controls, performing unscripted daily living activities in and around their own houses. Using this dataset, we demonstrate the framework's ability to detect gait and predict medication induced fluctuations in PD patients based on free-living gait. We show that our approach is robust to varying sensor locations, including the wrist, ankle, trouser pocket and lower back.
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http://dx.doi.org/10.1109/JBHI.2020.3037857DOI Listing
June 2021

Phenotype-Agnostic Molecular Subtyping of Neurodegenerative Disorders: The Cincinnati Cohort Biomarker Program (CCBP).

Front Aging Neurosci 2020 8;12:553635. Epub 2020 Oct 8.

James J. and Joan A. Gardner Family Center for Parkinson's disease and Movement Disorders, Department of Neurology, University of Cincinnati, Cincinnati, OH, United States.

Ongoing biomarker development programs have been designed to identify serologic or imaging signatures of clinico-pathologic entities, assuming distinct biological boundaries between them. Identified putative biomarkers have exhibited large variability and inconsistency between cohorts, and remain inadequate for selecting suitable recipients for potential disease-modifying interventions. We launched the Cincinnati Cohort Biomarker Program (CCBP) as a population-based, phenotype-agnostic longitudinal study. While patients affected by a wide range of neurodegenerative disorders will be deeply phenotyped using clinical, imaging, and mobile health technologies, analyses will not be anchored on phenotypic clusters but on bioassays of to-be-repurposed medications as well as on genomics, transcriptomics, proteomics, metabolomics, epigenomics, microbiomics, and pharmacogenomics analyses blinded to phenotypic data. Unique features of this cohort study include (1) a reverse biology-to-phenotype direction of biomarker development in which clinical, imaging, and mobile health technologies are subordinate to biological signals of interest; (2) hypothesis free, causally- and data driven-based analyses; (3) inclusive recruitment of patients with neurodegenerative disorders beyond clinical criteria-meeting patients with Parkinson's and Alzheimer's diseases, and (4) a large number of longitudinally followed participants. The parallel development of serum bioassays will be aimed at linking biologically suitable subjects to already available drugs with repurposing potential in future proof-of-concept adaptive clinical trials. Although many challenges are anticipated, including the unclear pathogenic relevance of identifiable biological signals and the possibility that some signals of importance may not yet be measurable with current technologies, this cohort study abandons the anchoring role of clinico-pathologic criteria in favor of biomarker-driven disease subtyping to facilitate future biosubtype-specific disease-modifying therapeutic efforts.
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http://dx.doi.org/10.3389/fnagi.2020.553635DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7578373PMC
October 2020

Real-Life Gait Performance as a Digital Biomarker for Motor Fluctuations: The [email protected] Validation Study.

J Med Internet Res 2020 10 9;22(10):e19068. Epub 2020 Oct 9.

Center of Expertise for Parkinson and Movement Disorders, department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands.

Background: Wearable sensors have been used successfully to characterize bradykinetic gait in patients with Parkinson disease (PD), but most studies to date have been conducted in highly controlled laboratory environments.

Objective: This paper aims to assess whether sensor-based analysis of real-life gait can be used to objectively and remotely monitor motor fluctuations in PD.

Methods: The [email protected] validation study provides a new reference data set for the development of digital biomarkers to monitor persons with PD in daily life. Specifically, a group of 25 patients with PD with motor fluctuations and 25 age-matched controls performed unscripted daily activities in and around their homes for at least one hour while being recorded on video. Patients with PD did this twice: once after overnight withdrawal of dopaminergic medication and again 1 hour after medication intake. Participants wore sensors on both wrists and ankles, on the lower back, and in the front pants pocket, capturing movement and contextual data. Gait segments of 25 seconds were extracted from accelerometer signals based on manual video annotations. The power spectral density of each segment and device was estimated using Welch's method, from which the total power in the 0.5- to 10-Hz band, width of the dominant frequency, and cadence were derived. The ability to discriminate between before and after medication intake and between patients with PD and controls was evaluated using leave-one-subject-out nested cross-validation.

Results: From 18 patients with PD (11 men; median age 65 years) and 24 controls (13 men; median age 68 years), ≥10 gait segments were available. Using logistic LASSO (least absolute shrinkage and selection operator) regression, we classified whether the unscripted gait segments occurred before or after medication intake, with mean area under the receiver operator curves (AUCs) varying between 0.70 (ankle of least affected side, 95% CI 0.60-0.81) and 0.82 (ankle of most affected side, 95% CI 0.72-0.92) across sensor locations. Combining all sensor locations did not significantly improve classification (AUC 0.84, 95% CI 0.75-0.93). Of all signal properties, the total power in the 0.5- to 10-Hz band was most responsive to dopaminergic medication. Discriminating between patients with PD and controls was generally more difficult (AUC of all sensor locations combined: 0.76, 95% CI 0.62-0.90). The video recordings revealed that the positioning of the hands during real-life gait had a substantial impact on the power spectral density of both the wrist and pants pocket sensor.

Conclusions: We present a new video-referenced data set that includes unscripted activities in and around the participants' homes. Using this data set, we show the feasibility of using sensor-based analysis of real-life gait to monitor motor fluctuations with a single sensor location. Future work may assess the value of contextual sensors to control for real-world confounders.
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http://dx.doi.org/10.2196/19068DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7584982PMC
October 2020

Deep Phenotyping of Parkinson's Disease.

J Parkinsons Dis 2020 ;10(3):855-873

Department of Computer Science, University of Rochester, Rochester, NY, USA.

Phenotype is the set of observable traits of an organism or condition. While advances in genetics, imaging, and molecular biology have improved our understanding of the underlying biology of Parkinson's disease (PD), clinical phenotyping of PD still relies primarily on history and physical examination. These subjective, episodic, categorical assessments are valuable for diagnosis and care but have left gaps in our understanding of the PD phenotype. Sensors can provide objective, continuous, real-world data about the PD clinical phenotype, increase our knowledge of its pathology, enhance evaluation of therapies, and ultimately, improve patient care. In this paper, we explore the concept of deep phenotyping-the comprehensive assessment of a condition using multiple clinical, biological, genetic, imaging, and sensor-based tools-for PD. We discuss the rationale for, outline current approaches to, identify benefits and limitations of, and consider future directions for deep clinical phenotyping.
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http://dx.doi.org/10.3233/JPD-202006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7458535PMC
August 2021

Metadata Concepts for Advancing the Use of Digital Health Technologies in Clinical Research.

Digit Biomark 2019 Sep-Dec;3(3):116-132. Epub 2019 Oct 7.

Tufts University School of Medicine, Boston, Massachusetts, USA.

Digital health technologies (smartphones, smartwatches, and other body-worn sensors) can act as novel tools to aid in the diagnosis and remote objective monitoring of an individual's disease symptoms, both in clinical care and in research. Nonetheless, such digital health technologies have yet to widely demonstrate value in clinical research due to insufficient data interpretability and lack of regulatory acceptance. Metadata, i.e., data that accompany and describe the primary data, can be utilized to better understand the context of the sensor data and can assist in data management, data sharing, and subsequent data analysis. The need for data and metadata standards for digital health technologies has been raised in academic and industry research communities and has also been noted by regulatory authorities. Therefore, to address this unmet need, we here propose a metadata set that reflects regulatory guidelines and that can serve as a conceptual map to (1) inform researchers on the metadata they should collect in digital health studies, aiming to increase the interpretability and exchangeability of their data, and (2) direct standard development organizations on how to extend their existing standards to incorporate digital health technologies. The proposed metadata set is informed by existing standards pertaining to clinical trials and medical devices, in addition to existing schemas that have supported digital health technology studies. We illustrate this specifically in the context of Parkinson's disease, as a model for a wide range of other chronic conditions for which remote monitoring would be useful in both care and science. We invite the scientific and clinical research communities to apply the proposed metadata set to ongoing and planned research. Where the proposed metadata fall short, we ask users to contribute to its ongoing revision so that an adequate degree of consensus can be maintained in a rapidly evolving technology landscape.
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http://dx.doi.org/10.1159/000502951DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7046173PMC
October 2019

Probabilistic Modelling for Unsupervised Analysis of Human Behaviour in Smart Cities.

Sensors (Basel) 2020 Jan 31;20(3). Epub 2020 Jan 31.

Department of Computer Science, University of Birmingham, Birmingham B15 2TT, UK.

The growth of urban areas in recent years has motivated a large amount of new sensor applications in smart cities. At the centre of many new applications stands the goal of gaining insights into human activity. Scalable monitoring of urban environments can facilitate better informed city planning, efficient security, regular transport and commerce. A large part of monitoring capabilities have already been deployed; however, most rely on expensive motion imagery and privacy invading video cameras. It is possible to use a low-cost sensor alternative, which enables deep understanding of population behaviour such as the Global Positioning System (GPS) data. However, the automated analysis of such low dimensional sensor data, requires new flexible and structured techniques that can describe the generative distribution and time dynamics of the observation data, while accounting for external contextual influences such as time of day or the difference between weekend/weekday trends. In this paper, we propose a novel time series analysis technique that allows for multiple different transition matrices depending on the data's contextual realisations all following shared adaptive observational models that govern the global distribution of the data given a latent sequence. The proposed approach, which we name Adaptive Input Hidden Markov model (AI-HMM) is tested on two datasets from different sensor types: GPS trajectories of taxis and derived vehicle counts in populated areas. We demonstrate that our model can group different categories of behavioural trends and identify time specific anomalies.
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http://dx.doi.org/10.3390/s20030784DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7038491PMC
January 2020

A review of accelerometer-derived physical activity in the idiopathic inflammatory myopathies.

BMC Rheumatol 2019 21;3:41. Epub 2019 Oct 21.

1Centre for Epidemiology Versus Arthritis, The University of Manchester, Stopford Building, Oxford Road, Manchester, M13 9PT UK.

Background: The idiopathic inflammatory myopathies (IIMs) are a group of rare conditions characterised by muscle inflammation (myositis). Accurate disease activity assessment is vital in both clinical and research settings, however, current available methods lack ability to quantify associated variation of physical activity, an important consequence of myositis.This study aims to review studies that have collected accelerometer-derived physical activity data in IIM populations, and to investigate if these studies identified associations between physical and myositis disease activity.

Methods: A narrative review was conducted to identify original articles that have collected accelerometer-derived physical activity data in IIM populations. The following databases were searched from February 2000 until February 2019: Medline via PubMed, Embase via OVID and Scopus.

Results: Of the 297 publications screened, eight studies describing accelerometer use in 181 IIM cases were identified. Seven out of the eight studies investigated juvenile dermatomyositis (JDM) populations and only one reported on an adult-onset population. Population sizes, disease duration, accelerometer devices used, body placement sites, and study duration varied between each study.Accelerometer-derived physical activity levels were reduced in IIM cohorts, compared to healthy controls, and studies reported improvement of physical activity levels following exercise programme interventions, thus demonstrating efficacy.Higher levels of accelerometer-derived physical activity measurements were associated with shorter JDM disease duration, current glucocorticoid use and lower serum creatine kinase. However, no clear association between muscle strength and accelerometer-derived physical activity measures was identified.

Conclusions: The use of accelerometer-derived physical activity in IIM research is in its infancy. Whilst knowledge is currently limited to small studies, the opportunities are promising and future research in this area has the potential to improve disease activity assessment for clinical and research applications.
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http://dx.doi.org/10.1186/s41927-019-0088-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6805320PMC
October 2019

Genome-wide association analysis of self-reported daytime sleepiness identifies 42 loci that suggest biological subtypes.

Nat Commun 2019 08 13;10(1):3503. Epub 2019 Aug 13.

Institute for Molecular Medicine FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.

Excessive daytime sleepiness (EDS) affects 10-20% of the population and is associated with substantial functional deficits. Here, we identify 42 loci for self-reported daytime sleepiness in GWAS of 452,071 individuals from the UK Biobank, with enrichment for genes expressed in brain tissues and in neuronal transmission pathways. We confirm the aggregate effect of a genetic risk score of 42 SNPs on daytime sleepiness in independent Scandinavian cohorts and on other sleep disorders (restless legs syndrome, insomnia) and sleep traits (duration, chronotype, accelerometer-derived sleep efficiency and daytime naps or inactivity). However, individual daytime sleepiness signals vary in their associations with objective short vs long sleep, and with markers of sleep continuity. The 42 sleepiness variants primarily cluster into two predominant composite biological subtypes - sleep propensity and sleep fragmentation. Shared genetic links are also seen with obesity, coronary heart disease, psychiatric diseases, cognitive traits and reproductive ageing.
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http://dx.doi.org/10.1038/s41467-019-11456-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6692391PMC
August 2019

Genome-wide association study identifies genetic loci for self-reported habitual sleep duration supported by accelerometer-derived estimates.

Nat Commun 2019 03 7;10(1):1100. Epub 2019 Mar 7.

Division of Endocrinology, Diabetes & Gastroenterology, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PL, UK.

Sleep is an essential state of decreased activity and alertness but molecular factors regulating sleep duration remain unknown. Through genome-wide association analysis in 446,118 adults of European ancestry from the UK Biobank, we identify 78 loci for self-reported habitual sleep duration (p < 5 × 10; 43 loci at p < 6 × 10). Replication is observed for PAX8, VRK2, and FBXL12/UBL5/PIN1 loci in the CHARGE study (n = 47,180; p < 6.3 × 10), and 55 signals show sign-concordant effects. The 78 loci further associate with accelerometer-derived sleep duration, daytime inactivity, sleep efficiency and number of sleep bouts in secondary analysis (n = 85,499). Loci are enriched for pathways including striatum and subpallium development, mechanosensory response, dopamine binding, synaptic neurotransmission and plasticity, among others. Genetic correlation indicates shared links with anthropometric, cognitive, metabolic, and psychiatric traits and two-sample Mendelian randomization highlights a bidirectional causal link with schizophrenia. This work provides insights into the genetic basis for inter-individual variation in sleep duration implicating multiple biological pathways.
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http://dx.doi.org/10.1038/s41467-019-08917-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6405943PMC
March 2019

Biological and clinical insights from genetics of insomnia symptoms.

Nat Genet 2019 03 25;51(3):387-393. Epub 2019 Feb 25.

K.G. Jebsen Centre for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.

Insomnia is a common disorder linked with adverse long-term medical and psychiatric outcomes. The underlying pathophysiological processes and causal relationships of insomnia with disease are poorly understood. Here we identified 57 loci for self-reported insomnia symptoms in the UK Biobank (n = 453,379) and confirmed their effects on self-reported insomnia symptoms in the HUNT Study (n = 14,923 cases and 47,610 controls), physician-diagnosed insomnia in the Partners Biobank (n = 2,217 cases and 14,240 controls), and accelerometer-derived measures of sleep efficiency and sleep duration in the UK Biobank (n = 83,726). Our results suggest enrichment of genes involved in ubiquitin-mediated proteolysis and of genes expressed in multiple brain regions, skeletal muscle, and adrenal glands. Evidence of shared genetic factors was found between frequent insomnia symptoms and restless legs syndrome, aging, and cardiometabolic, behavioral, psychiatric, and reproductive traits. Evidence was found for a possible causal link between insomnia symptoms and coronary artery disease, depressive symptoms, and subjective well-being.
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http://dx.doi.org/10.1038/s41588-019-0361-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6415688PMC
March 2019

Collecting Symptoms and Sensor Data With Consumer Smartwatches (the Knee OsteoArthritis, Linking Activity and Pain Study): Protocol for a Longitudinal, Observational Feasibility Study.

JMIR Res Protoc 2019 Jan 23;8(1):e10238. Epub 2019 Jan 23.

Arthritis Research United Kingdom Centre for Epidemiology, Centre for Musculoskeletal Research, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom.

Background: The Knee OsteoArthritis, Linking Activity and Pain (KOALAP) study is the first to test the feasibility of using consumer-grade cellular smartwatches for health care research.

Objective: The overall aim was to investigate the feasibility of using consumer-grade cellular smartwatches as a novel tool to capture data on pain (multiple times a day) and physical activity (continuously) in patients with knee osteoarthritis. Additionally, KOALAP aimed to investigate smartwatch sensor data quality and assess whether engagement, acceptability, and user experience are sufficient for future large-scale observational and interventional studies.

Methods: A total of 26 participants with self-diagnosed knee osteoarthritis were recruited in September 2017. All participants were aged 50 years or over and either lived in or were willing to travel to the Greater Manchester area. Participants received a smartwatch (Huawei Watch 2) with a bespoke app that collected patient-reported outcomes via questionnaires and continuous watch sensor data. All data were collected daily for 90 days. Additional data were collected through interviews (at baseline and follow-up) and baseline and end-of-study questionnaires. This study underwent full review by the University of Manchester Research Ethics Committee (#0165) and University Information Governance (#IGRR000060). For qualitative data analysis, a system-level security policy was developed in collaboration with the University Information Governance Office. Additionally, the project underwent an internal review process at Google, including separate reviews of accessibility, product engineering, privacy, security, legal, and protection regulation compliance.

Results: Participants were recruited in September 2017. Data collection via the watches was completed in January 2018. Collection of qualitative data through patient interviews is still ongoing. Data analysis will commence when all data are collected; results are expected in 2019.

Conclusions: KOALAP is the first health study to use consumer cellular smartwatches to collect self-reported symptoms alongside sensor data for musculoskeletal disorders. The results of this study will be used to inform the design of future mobile health studies. Results for feasibility and participant motivations will inform future researchers whether or under which conditions cellular smartwatches are a useful tool to collect patient-reported outcomes alongside passively measured patient behavior. The exploration of associations between self-reported symptoms at different moments will contribute to our understanding of whether it may be valuable to collect symptom data more frequently. Sensor data-quality measurements will indicate whether cellular smartwatch usage is feasible for obtaining sensor data. Methods for data-quality assessment and data-processing methods may be reusable, although generalizability to other clinical areas should be further investigated.

International Registered Report Identifier (irrid): DERR1-10.2196/10238.
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http://dx.doi.org/10.2196/10238DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6366393PMC
January 2019

Investigating Voice as a Biomarker for Leucine-Rich Repeat Kinase 2-Associated Parkinson's Disease.

J Parkinsons Dis 2018 ;8(4):503-510

The Edmond J. Safra Program in Parkinson's Disease and the Morton and Gloria Shulman Movement Disorders Centre and, Toronto Western Hospital, Toronto, ON, Canada.

We investigate the potential association between leucine-rich repeat kinase 2 (LRRK2) mutations and voice. Sustained phonations ('aaah' sounds) were recorded from 7 individuals with LRRK2-associated Parkinson's disease (PD), 17 participants with idiopathic PD (iPD), 20 non-manifesting LRRK2-mutation carriers, 25 related non-carriers, and 26 controls. In distinguishing LRRK2-associated PD and iPD, the mean sensitivity was 95.4% (SD 17.8%) and mean specificity was 89.6% (SD 26.5%). Voice features for non-manifesting carriers, related non-carriers, and controls were much less discriminatory. Vocal deficits in LRRK2-associated PD may be different than those in iPD. These preliminary results warrant longitudinal analyses and replication in larger cohorts.
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http://dx.doi.org/10.3233/JPD-181389DOI Listing
October 2019

Smartphone motor testing to distinguish idiopathic REM sleep behavior disorder, controls, and PD.

Neurology 2018 10 19;91(16):e1528-e1538. Epub 2018 Sep 19.

From the Oxford Parkinson's Disease Centre (OPDC) (S.A., F.B., C.L., T.R.B., M.R., C.R., J.C.K., J.R., A.L., R.W.-M, M.T.H.), University of Oxford, UK; Engineering and Applied Science (S.A., M.A.L.), Aston University, Birmingham, UK; Somerville College (S.A.), University of Oxford, UK; Nuffield Department of Clinical Neurosciences (F.B., C.L., T.R.B., M.A.L., M.T.H.), University of Oxford, UK; Population Health Sciences (M.A.L.), University of Bristol, UK; andDepartment of Computer Science (A.Z.), Johns Hopkins University, Baltimore; Department of Neurology and Neurophysiology (Z.Z., G.L., M.T.H.), Oxford University Hospitals NHS Trust, UK; Respiratory Support and Sleep Centre (T.Q.), Papworth Hospital, Cambridge, UK; Department of Neurology (G.D.), Royal Hallamshire Hospital, Sheffield, UK; and Media Lab (M.A.L.), Massachusetts Institute of Technology, Cambridge, MA.

Objective: We sought to identify motor features that would allow the delineation of individuals with sleep study-confirmed idiopathic REM sleep behavior disorder (iRBD) from controls and Parkinson disease (PD) using a customized smartphone application.

Methods: A total of 334 PD, 104 iRBD, and 84 control participants performed 7 tasks to evaluate voice, balance, gait, finger tapping, reaction time, rest tremor, and postural tremor. Smartphone recordings were collected both in clinic and at home under noncontrolled conditions over several days. All participants underwent detailed parallel in-clinic assessments. Using only the smartphone sensor recordings, we sought to (1) discriminate whether the participant had iRBD or PD and (2) identify which of the above 7 motor tasks were most salient in distinguishing groups.

Results: Statistically significant differences based on these 7 tasks were observed between the 3 groups. For the 3 pairwise discriminatory comparisons, (1) controls vs iRBD, (2) controls vs PD, and (3) iRBD vs PD, the mean sensitivity and specificity values ranged from 84.6% to 91.9%. Postural tremor, rest tremor, and voice were the most discriminatory tasks overall, whereas the reaction time was least discriminatory.

Conclusions: Prodromal forms of PD include the sleep disorder iRBD, where subtle motor impairment can be detected using clinician-based rating scales (e.g., Unified Parkinson's Disease Rating Scale), which may lack the sensitivity to detect and track granular change. Consumer grade smartphones can be used to accurately separate not only iRBD from controls but also iRBD from PD participants, providing a growing consensus for the utility of digital biomarkers in early and prodromal PD.
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http://dx.doi.org/10.1212/WNL.0000000000006366DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6202945PMC
October 2018

Consumer Smartwatches for Collecting Self-Report and Sensor Data: App Design and Engagement.

Stud Health Technol Inform 2018 ;247:291-295

ARUK Centre for Epidemiology, University of Manchester, Manchester, UK.

Longitudinal data from patients' natural environments would benefit chronic disease care, yet most devices cannot collect sensor data alongside patient-reported outcomes. Here we describe Koalap, a consumer cellular smartwatch application that collects patient-reported outcomes alongside physical activity data from various sensors. Additionally, we show preliminary results indicating high engagement of our 26 participants with knee osteoarthritis. Our future work will show whether data collection with consumer smartwatches is feasible in terms of user engagement, acceptability, data quality and consistency.
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June 2018

Automated Quality Control for Sensor Based Symptom Measurement Performed Outside the Lab.

Sensors (Basel) 2018 Apr 16;18(4). Epub 2018 Apr 16.

School of Engineering and Applied Sciences, Aston University, Birmingham B4 7ET, UK.

The use of wearable sensing technology for objective, non-invasive and remote clinimetric testing of symptoms has considerable potential. However, the accuracy achievable with such technology is highly reliant on separating the useful from irrelevant sensor data. Monitoring patient symptoms using digital sensors outside of controlled, clinical lab settings creates a variety of practical challenges, such as recording unexpected user behaviors. These behaviors often violate the assumptions of clinimetric testing protocols, where these protocols are designed to probe for specific symptoms. Such violations are frequent outside the lab and affect the accuracy of the subsequent data analysis and scientific conclusions. To address these problems, we report on a unified algorithmic framework for automated sensor data quality control, which can identify those parts of the sensor data that are sufficiently reliable for further analysis. Combining both parametric and nonparametric signal processing and machine learning techniques, we demonstrate that across 100 subjects and 300 clinimetric tests from three different types of behavioral clinimetric protocols, the system shows an average segmentation accuracy of around 90%. By extracting reliable sensor data, it is possible to strip the data of confounding factors in the environment that may threaten reproducibility and replicability.
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http://dx.doi.org/10.3390/s18041215DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948536PMC
April 2018

Impact of motor fluctuations on real-life gait in Parkinson's patients.

Gait Posture 2018 05 28;62:388-394. Epub 2018 Mar 28.

Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Nijmegen, The Netherlands; Radboud University Medical Center, Radboud Institute for Health Sciences, Scientific Center for Quality of Healthcare, Nijmegen, The Netherlands.

Background: People with PD (PWP) have an increased risk of becoming inactive. Wearable sensors can provide insights into daily physical activity and walking patterns.

Research Questions: (1) Is the severity of motor fluctuations associated with sensor-derived average daily walking quantity? (2) Is the severity of motor fluctuations associated with the amount of change in sensor-derived walking quantity after levodopa intake?

Methods: 304 Dutch PWP from the [email protected] study were included. At baseline, all participants received a clinical examination. During the follow-up period (median: 97 days; 25-Interquartile range-IQR: 91 days, 75-IQR: 188 days), participants used the Fox Wearable Companion app and streamed smartwatch accelerometer data to a cloud platform. The first research question was assessed by linear regression on the sensor-derived mean time spent walking/day with the severity of fluctuations (MDS-UPDRS item 4.4) as independent variable, controlled for age and MDS-UPDRS part-III score. The second research question was assessed by linear regression on the sensor-derived mean post-levodopa walking quantity, with the sensor-derived mean pre-levodopa walking quantity and severity of fluctuations as independent variables, controlled for mean time spent walking per day, age and MDS-UPDRS part-III score.

Results: PWP spent most time walking between 8am and 1pm, summing up to 72 ± 39 (mean ± standard deviation) minutes of walking/day. The severity of motor fluctuations did not influence the mean time spent walking (B = 2.4 ± 1.9, p = 0.20), but higher age (B = -1.3 ± 0.3, p = < 0.001) and greater severity of motor symptoms (B = -0.6 ± 0.2, p < 0.001) was associated with less time spent walking (F(3216) = 14.6, p < .001, R = .17). The severity of fluctuations was not associated with the amount of change in time spent walking in relation to levodopa intake in any part of the day.

Significance: Analysis of sensor-derived gait quantity suggests that the severity of motor fluctuations is not associated with changes in real-life walking patterns in mildly to moderate affected PWP.
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http://dx.doi.org/10.1016/j.gaitpost.2018.03.045DOI Listing
May 2018

Using Smartphones and Machine Learning to Quantify Parkinson Disease Severity: The Mobile Parkinson Disease Score.

JAMA Neurol 2018 07;75(7):876-880

Department of Computer Science, Johns Hopkins University, Baltimore, Maryland.

Importance: Current Parkinson disease (PD) measures are subjective, rater-dependent, and assessed in clinic. Smartphones can measure PD features, yet no smartphone-derived rating score exists to assess motor symptom severity in real-world settings.

Objectives: To develop an objective measure of PD severity and test construct validity by evaluating the ability of the measure to capture intraday symptom fluctuations, correlate with current standard PD outcome measures, and respond to dopaminergic therapy.

Design, Setting, And Participants: This observational study assessed individuals with PD who remotely completed 5 tasks (voice, finger tapping, gait, balance, and reaction time) on the smartphone application. We used a novel machine-learning-based approach to generate a mobile Parkinson disease score (mPDS) that objectively weighs features derived from each smartphone activity (eg, stride length from the gait activity) and is scaled from 0 to 100 (where higher scores indicate greater severity). Individuals with and without PD additionally completed standard in-person assessments of PD with smartphone assessments during a period of 6 months.

Main Outcomes And Measures: Ability of the mPDS to detect intraday symptom fluctuations, the correlation between the mPDS and standard measures, and the ability of the mPDS to respond to dopaminergic medication.

Results: The mPDS was derived from 6148 smartphone activity assessments from 129 individuals (mean [SD] age, 58.7 [8.6] years; 56 [43.4%] women). Gait features contributed most to the total mPDS (33.4%). In addition, 23 individuals with PD (mean [SD] age, 64.6 [11.5] years; 11 [48%] women) and 17 without PD (mean [SD] age 54.2 [16.5] years; 12 [71%] women) completed in-clinic assessments. The mPDS detected symptom fluctuations with a mean (SD) intraday change of 13.9 (10.3) points on a scale of 0 to 100. The measure correlated well with the Movement Disorder Society Unified Parkinson Disease's Rating Scale total (r = 0.81; P < .001) and part III only (r = 0.88; P < .001), the Timed Up and Go assessment (r = 0.72; P = .002), and the Hoehn and Yahr stage (r = 0.91; P < .001). The mPDS improved by a mean (SD) of 16.3 (5.6) points in response to dopaminergic therapy.

Conclusions And Relevance: Using a novel machine-learning approach, we created and demonstrated construct validity of an objective PD severity score derived from smartphone assessments. This score complements standard PD measures by providing frequent, objective, real-world assessments that could enhance clinical care and evaluation of novel therapeutics.
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http://dx.doi.org/10.1001/jamaneurol.2018.0809DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5885192PMC
July 2018

Feasibility of large-scale deployment of multiple wearable sensors in Parkinson's disease.

PLoS One 2017 20;12(12):e0189161. Epub 2017 Dec 20.

Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.

Wearable devices can capture objective day-to-day data about Parkinson's Disease (PD). This study aims to assess the feasibility of implementing wearable technology to collect data from multiple sensors during the daily lives of PD patients. The [email protected] study is an observational, two-cohort (North America, NAM; The Netherlands, NL) study. To recruit participants, different strategies were used between sites. Main enrolment criteria were self-reported diagnosis of PD, possession of a smartphone and age≥18 years. Participants used the Fox Wearable Companion app on a smartwatch and smartphone for a minimum of 6 weeks (NAM) or 13 weeks (NL). Sensor-derived measures estimated information about movement. Additionally, medication intake and symptoms were collected via self-reports in the app. A total of 953 participants were included (NL: 304, NAM: 649). Enrolment rate was 88% in the NL (n = 304) and 51% (n = 649) in NAM. Overall, 84% (n = 805) of participants contributed sensor data. Participants were compliant for 68% (16.3 hours/participant/day) of the study period in NL and for 62% (14.8 hours/participant/day) in NAM. Daily accelerometer data collection decreased 23% in the NL after 13 weeks, and 27% in NAM after 6 weeks. Data contribution was not affected by demographics, clinical characteristics or attitude towards technology, but was by the platform usability score in the NL (χ2 (2) = 32.014, p<0.001), and self-reported depression in NAM (χ2(2) = 6.397, p = .04). The [email protected] study shows that it is feasible to collect objective data using multiple wearable sensors in PD during daily life in a large cohort.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0189161PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5738046PMC
January 2018

Sleep and cognitive performance: cross-sectional associations in the UK Biobank.

Sleep Med 2017 Oct 14;38:85-91. Epub 2017 Jul 14.

Clinic for Psychiatry and Psychotherapy, Medical Centre - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany.

Objective: The relationship between insomnia symptoms and cognitive performance is unclear, particularly at the population level. We conducted the largest examination of this association to date through analysis of the UK Biobank, a large population-based sample of adults aged 40-69 years. We also sought to determine associations between cognitive performance and self-reported chronotype, sleep medication use and sleep duration.

Methods: This cross-sectional, population-based study involved 477,529 participants, comprising 133,314 patients with frequent insomnia symptoms (age: 57.4 ± 7.7 years; 62.1% female) and 344,215 controls without insomnia symptoms (age: 56.1 ± 8.2 years; 52.0% female). Cognitive performance was assessed by a touchscreen test battery probing reasoning, basic reaction time, numeric memory, visual memory, and prospective memory. Adjusted models included relevant demographic, clinical, and sleep variables.

Results: Frequent insomnia symptoms were associated with cognitive impairment in unadjusted models; however, these effects were reversed after full adjustment, leaving those with frequent insomnia symptoms showing statistically better cognitive performance over those without. Relative to intermediate chronotype, evening chronotype was associated with superior task performance, while morning chronotype was associated with the poorest performance. Sleep medication use and both long (>9 h) and short (<7 h) sleep durations were associated with impaired performance.

Conclusions: Our results suggest that after adjustment for potential confounding variables, frequent insomnia symptoms may be associated with a small statistical advantage, which is unlikely to be clinically meaningful, on simple neurocognitive tasks. Further work is required to examine the mechanistic underpinnings of an apparent evening chronotype advantage in cognitive performance and the impairment associated with morning chronotype, sleep medication use, and sleep duration extremes.
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http://dx.doi.org/10.1016/j.sleep.2017.07.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5930168PMC
October 2017

Using and understanding cross-validation strategies. Perspectives on Saeb et al.

Gigascience 2017 05;6(5):1-6

Department of Preventive Medicine, Northwestern University, 750 N Lake Shore Dr, 60611, Chicago, USA.

This three-part review takes a detailed look at the complexities of cross-validation, fostered by the peer review of Saeb et al.'s paper entitled "The need to approximate the use-case in clinical machine learning." It contains perspectives by reviewers and by the original authors that touch upon cross-validation: the suitability of different strategies and their interpretation.
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http://dx.doi.org/10.1093/gigascience/gix020DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5441396PMC
May 2017

Freezing of gait and fall detection in Parkinson's disease using wearable sensors: a systematic review.

J Neurol 2017 Aug 1;264(8):1642-1654. Epub 2017 Mar 1.

Department of Neurology, Radboud university medical center, Nijmegen, The Netherlands.

Despite the large number of studies that have investigated the use of wearable sensors to detect gait disturbances such as Freezing of gait (FOG) and falls, there is little consensus regarding appropriate methodologies for how to optimally apply such devices. Here, an overview of the use of wearable systems to assess FOG and falls in Parkinson's disease (PD) and validation performance is presented. A systematic search in the PubMed and Web of Science databases was performed using a group of concept key words. The final search was performed in January 2017, and articles were selected based upon a set of eligibility criteria. In total, 27 articles were selected. Of those, 23 related to FOG and 4 to falls. FOG studies were performed in either laboratory or home settings, with sample sizes ranging from 1 PD up to 48 PD presenting Hoehn and Yahr stage from 2 to 4. The shin was the most common sensor location and accelerometer was the most frequently used sensor type. Validity measures ranged from 73-100% for sensitivity and 67-100% for specificity. Falls and fall risk studies were all home-based, including samples sizes of 1 PD up to 107 PD, mostly using one sensor containing accelerometers, worn at various body locations. Despite the promising validation initiatives reported in these studies, they were all performed in relatively small sample sizes, and there was a significant variability in outcomes measured and results reported. Given these limitations, the validation of sensor-derived assessments of PD features would benefit from more focused research efforts, increased collaboration among researchers, aligning data collection protocols, and sharing data sets.
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http://dx.doi.org/10.1007/s00415-017-8424-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5533840PMC
August 2017

Genome-wide association analyses of sleep disturbance traits identify new loci and highlight shared genetics with neuropsychiatric and metabolic traits.

Nat Genet 2017 Feb 19;49(2):274-281. Epub 2016 Dec 19.

Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, USA.

Chronic sleep disturbances, associated with cardiometabolic diseases, psychiatric disorders and all-cause mortality, affect 25-30% of adults worldwide. Although environmental factors contribute substantially to self-reported habitual sleep duration and disruption, these traits are heritable and identification of the genes involved should improve understanding of sleep, mechanisms linking sleep to disease and development of new therapies. We report single- and multiple-trait genome-wide association analyses of self-reported sleep duration, insomnia symptoms and excessive daytime sleepiness in the UK Biobank (n = 112,586). We discover loci associated with insomnia symptoms (near MEIS1, TMEM132E, CYCL1 and TGFBI in females and WDR27 in males), excessive daytime sleepiness (near AR-OPHN1) and a composite sleep trait (near PATJ (INADL) and HCRTR2) and replicate a locus associated with sleep duration (at PAX8). We also observe genetic correlation between longer sleep duration and schizophrenia risk (r = 0.29, P = 1.90 × 10) and between increased levels of excessive daytime sleepiness and increased measures for adiposity traits (body mass index (BMI): r = 0.20, P = 3.12 × 10; waist circumference: r = 0.20, P = 2.12 × 10).
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http://dx.doi.org/10.1038/ng.3749DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5491693PMC
February 2017

What to Do When K-Means Clustering Fails: A Simple yet Principled Alternative Algorithm.

PLoS One 2016;11(9):e0162259. Epub 2016 Sep 26.

School of Mathematics, Aston University, Birmingham, United Kingdom.

The K-means algorithm is one of the most popular clustering algorithms in current use as it is relatively fast yet simple to understand and deploy in practice. Nevertheless, its use entails certain restrictive assumptions about the data, the negative consequences of which are not always immediately apparent, as we demonstrate. While more flexible algorithms have been developed, their widespread use has been hindered by their computational and technical complexity. Motivated by these considerations, we present a flexible alternative to K-means that relaxes most of the assumptions, whilst remaining almost as fast and simple. This novel algorithm which we call MAP-DP (maximum a-posteriori Dirichlet process mixtures), is statistically rigorous as it is based on nonparametric Bayesian Dirichlet process mixture modeling. This approach allows us to overcome most of the limitations imposed by K-means. The number of clusters K is estimated from the data instead of being fixed a-priori as in K-means. In addition, while K-means is restricted to continuous data, the MAP-DP framework can be applied to many kinds of data, for example, binary, count or ordinal data. Also, it can efficiently separate outliers from the data. This additional flexibility does not incur a significant computational overhead compared to K-means with MAP-DP convergence typically achieved in the order of seconds for many practical problems. Finally, in contrast to K-means, since the algorithm is based on an underlying statistical model, the MAP-DP framework can deal with missing data and enables model testing such as cross validation in a principled way. We demonstrate the simplicity and effectiveness of this algorithm on the health informatics problem of clinical sub-typing in a cluster of diseases known as parkinsonism.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0162259PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5036949PMC
September 2016

Large-Scale Wearable Sensor Deployment in Parkinson's Patients: The [email protected] Study Protocol.

JMIR Res Protoc 2016 Aug 26;5(3):e172. Epub 2016 Aug 26.

Donders Institute for Brain, Cognition and Behavior, Radboud university medical center, Nijmegen, Netherlands.

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 [email protected] 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 [email protected] 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 [email protected] 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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http://dx.doi.org/10.2196/resprot.5990DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5018102PMC
August 2016

Machine learning for large-scale wearable sensor data in Parkinson's disease: Concepts, promises, pitfalls, and futures.

Mov Disord 2016 09 8;31(9):1314-26. Epub 2016 Aug 8.

Aston University, Aston Triangle, Birmingham, United Kingdom.

For the treatment and monitoring of Parkinson's disease (PD) to be scientific, a key requirement is that measurement of disease stages and severity is quantitative, reliable, and repeatable. The last 50 years in PD research have been dominated by qualitative, subjective ratings obtained by human interpretation of the presentation of disease signs and symptoms at clinical visits. More recently, "wearable," sensor-based, quantitative, objective, and easy-to-use systems for quantifying PD signs for large numbers of participants over extended durations have been developed. This technology has the potential to significantly improve both clinical diagnosis and management in PD and the conduct of clinical studies. However, the large-scale, high-dimensional character of the data captured by these wearable sensors requires sophisticated signal processing and machine-learning algorithms to transform it into scientifically and clinically meaningful information. Such algorithms that "learn" from data have shown remarkable success in making accurate predictions for complex problems in which human skill has been required to date, but they are challenging to evaluate and apply without a basic understanding of the underlying logic on which they are based. This article contains a nontechnical tutorial review of relevant machine-learning algorithms, also describing their limitations and how these can be overcome. It discusses implications of this technology and a practical road map for realizing the full potential of this technology in PD research and practice. © 2016 International Parkinson and Movement Disorder Society.
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http://dx.doi.org/10.1002/mds.26693DOI Listing
September 2016

Wearable Sensors in Huntington Disease: A Pilot Study.

J Huntingtons Dis 2016 06;5(2):199-206

Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA.

Background: The Unified Huntington's Disease Rating Scale (UHDRS) is the principal means of assessing motor impairment in Huntington disease but is subjective and generally limited to in-clinic assessments.

Objective: To evaluate the feasibility and ability of wearable sensors to measure motor impairment in individuals with Huntington disease in the clinic and at home.

Methods: Participants with Huntington disease and controls were asked to wear five accelerometer-based sensors attached to the chest and each limb for standardized, in-clinic assessments and for one day at home. A second chest sensor was worn for six additional days at home. Gait measures were compared between controls, participants with Huntington disease, and participants with Huntington disease grouped by UHDRS total motor score using Cohen's d values.

Results: Fifteen individuals with Huntington disease and five controls completed the study. Sensor data were successfully captured from 18 of the 20 participants at home. In the clinic, the standard deviation of step time (time between consecutive steps) was increased in Huntington disease (p < 0.0001; Cohen's d = 2.61) compared to controls. At home with additional observations, significant differences were observed in seven additional gait measures. The gait of individuals with higher total motor scores (50 or more) differed significantly from those with lower total motor scores (below 50) on multiple measures at home.

Conclusions: In this pilot study, the use of wearable sensors in clinic and at home was feasible and demonstrated gait differences between controls, participants with Huntington disease, and participants with Huntington disease grouped by motor impairment.
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http://dx.doi.org/10.3233/JHD-160197DOI Listing
June 2016
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