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Anderson E, Lennon M, Kavanagh K, Weir N, Kernaghan D, Roper M, Dunlop E, Lapp L. Predictive Data Analytics in Telecare and Telehealth: Systematic Scoping Review. Online J Public Health Inform 2024; 16:e57618. [PMID: 39110501 PMCID: PMC11339581 DOI: 10.2196/57618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 05/15/2024] [Accepted: 06/11/2024] [Indexed: 08/24/2024] Open
Abstract
BACKGROUND Telecare and telehealth are important care-at-home services used to support individuals to live more independently at home. Historically, these technologies have reactively responded to issues. However, there has been a recent drive to make better use of the data from these services to facilitate more proactive and predictive care. OBJECTIVE This review seeks to explore the ways in which predictive data analytics techniques have been applied in telecare and telehealth in at-home settings. METHODS The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist was adhered to alongside Arksey and O'Malley's methodological framework. English language papers published in MEDLINE, Embase, and Social Science Premium Collection between 2012 and 2022 were considered and results were screened against inclusion or exclusion criteria. RESULTS In total, 86 papers were included in this review. The types of analytics featuring in this review can be categorized as anomaly detection (n=21), diagnosis (n=32), prediction (n=22), and activity recognition (n=11). The most common health conditions represented were Parkinson disease (n=12) and cardiovascular conditions (n=11). The main findings include: a lack of use of routinely collected data; a dominance of diagnostic tools; and barriers and opportunities that exist, such as including patient-reported outcomes, for future predictive analytics in telecare and telehealth. CONCLUSIONS All papers in this review were small-scale pilots and, as such, future research should seek to apply these predictive techniques into larger trials. Additionally, further integration of routinely collected care data and patient-reported outcomes into predictive models in telecare and telehealth offer significant opportunities to improve the analytics being performed and should be explored further. Data sets used must be of suitable size and diversity, ensuring that models are generalizable to a wider population and can be appropriately trained, validated, and tested.
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Affiliation(s)
- Euan Anderson
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow, United Kingdom
| | - Marilyn Lennon
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow, United Kingdom
| | - Kimberley Kavanagh
- Department of Mathematics and Statistics, University of Strathclyde, Glasgow, United Kingdom
| | - Natalie Weir
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, United Kingdom
| | - David Kernaghan
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, United Kingdom
| | - Marc Roper
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow, United Kingdom
| | - Emma Dunlop
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, United Kingdom
| | - Linda Lapp
- Centre for Heart Lung Innovation, University of British Columbia, Vancouver, BC, Canada
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Paolillo EW, Casaletto KB, Clark AL, Taylor JC, Heuer HW, Wise AB, Dhanam S, Sanderson-Cimino M, Saloner R, Kramer JH, Kornak J, Kremers W, Forsberg L, Appleby B, Bayram E, Bozoki A, Brushaber D, Darby RR, Day GS, Dickerson BC, Domoto-Reilly K, Elahi F, Fields JA, Ghoshal N, Graff-Radford N, G H Hall M, Honig LS, Huey ED, Lapid MI, Litvan I, Mackenzie IR, Masdeu JC, Mendez MF, Mester C, Miyagawa T, Naasan G, Pascual B, Pressman P, Ramos EM, Rankin KP, Rexach J, Rojas JC, VandeVrede L, Wong B, Wszolek ZK, Boeve BF, Rosen HJ, Boxer AL, Staffaroni AM. Examining Associations Between Smartphone Use and Clinical Severity in Frontotemporal Dementia: Proof-of-Concept Study. JMIR Aging 2024; 7:e52831. [PMID: 38922667 PMCID: PMC11237775 DOI: 10.2196/52831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 02/09/2024] [Accepted: 03/07/2024] [Indexed: 06/27/2024] Open
Abstract
BACKGROUND Frontotemporal lobar degeneration (FTLD) is a leading cause of dementia in individuals aged <65 years. Several challenges to conducting in-person evaluations in FTLD illustrate an urgent need to develop remote, accessible, and low-burden assessment techniques. Studies of unobtrusive monitoring of at-home computer use in older adults with mild cognitive impairment show that declining function is reflected in reduced computer use; however, associations with smartphone use are unknown. OBJECTIVE This study aims to characterize daily trajectories in smartphone battery use, a proxy for smartphone use, and examine relationships with clinical indicators of severity in FTLD. METHODS Participants were 231 adults (mean age 52.5, SD 14.9 years; n=94, 40.7% men; n=223, 96.5% non-Hispanic White) enrolled in the Advancing Research and Treatment of Frontotemporal Lobar Degeneration (ARTFL study) and Longitudinal Evaluation of Familial Frontotemporal Dementia Subjects (LEFFTDS study) Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) Mobile App study, including 49 (21.2%) with mild neurobehavioral changes and no functional impairment (ie, prodromal FTLD), 43 (18.6%) with neurobehavioral changes and functional impairment (ie, symptomatic FTLD), and 139 (60.2%) clinically normal adults, of whom 55 (39.6%) harbored heterozygous pathogenic or likely pathogenic variants in an autosomal dominant FTLD gene. Participants completed the Clinical Dementia Rating plus National Alzheimer's Coordinating Center Frontotemporal Lobar Degeneration Behavior and Language Domains (CDR+NACC FTLD) scale, a neuropsychological battery; the Neuropsychiatric Inventory; and brain magnetic resonance imaging. The ALLFTD Mobile App was installed on participants' smartphones for remote, passive, and continuous monitoring of smartphone use. Battery percentage was collected every 15 minutes over an average of 28 (SD 4.2; range 14-30) days. To determine whether temporal patterns of battery percentage varied as a function of disease severity, linear mixed effects models examined linear, quadratic, and cubic effects of the time of day and their interactions with each measure of disease severity on battery percentage. Models covaried for age, sex, smartphone type, and estimated smartphone age. RESULTS The CDR+NACC FTLD global score interacted with time on battery percentage such that participants with prodromal or symptomatic FTLD demonstrated less change in battery percentage throughout the day (a proxy for less smartphone use) than clinically normal participants (P<.001 in both cases). Additional models showed that worse performance in all cognitive domains assessed (ie, executive functioning, memory, language, and visuospatial skills), more neuropsychiatric symptoms, and smaller brain volumes also associated with less battery use throughout the day (P<.001 in all cases). CONCLUSIONS These findings support a proof of concept that passively collected data about smartphone use behaviors associate with clinical impairment in FTLD. This work underscores the need for future studies to develop and validate passive digital markers sensitive to longitudinal clinical decline across neurodegenerative diseases, with potential to enhance real-world monitoring of neurobehavioral change.
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Affiliation(s)
- Emily W Paolillo
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Kaitlin B Casaletto
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Annie L Clark
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Jack C Taylor
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Hilary W Heuer
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Amy B Wise
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Sreya Dhanam
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Mark Sanderson-Cimino
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Rowan Saloner
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Joel H Kramer
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - John Kornak
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, United States
| | - Walter Kremers
- Department of Quantitative Health Sciences, Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, MN, United States
| | - Leah Forsberg
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Brian Appleby
- Department of Neurology, Case Western Reserve University, Cleveland, OH, United States
| | - Ece Bayram
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, United States
| | - Andrea Bozoki
- Department of Neurology, University of North Carolina, Chapel Hill, NC, United States
| | - Danielle Brushaber
- Department of Quantitative Health Sciences, Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, MN, United States
| | - R Ryan Darby
- Department of Neurology, Vanderbilt University, Nashville, TN, United States
| | - Gregory S Day
- Department of Neurology, Mayo Clinic, Jacksonville, FL, United States
| | - Bradford C Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | | | - Fanny Elahi
- Department of Neurology, The Deane Center for Wellness and Cognitive Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- James J. Peters Veterans Affairs Medical Center, New York, NY, United States
| | - Julie A Fields
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Nupur Ghoshal
- Department of Neurology, Knight Alzheimer's Disease Research Center, Washington University, St. Louis, MO, United States
| | | | - Matthew G H Hall
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Lawrence S Honig
- Department of Neurology, Columbia University, New York, NY, United States
| | - Edward D Huey
- Department of Psychiatry and Human Behavior, Brown University, Providence, RI, United States
| | - Maria I Lapid
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Irene Litvan
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, United States
| | - Ian R Mackenzie
- Department of Pathology, University of British Columbia, Vancouver, BC, Canada
| | - Joseph C Masdeu
- Stanley H. Appel Department of Neurology, Nantz National Alzheimer Center, Houston Methodist Research Institute, Weill Cornell Medicine, Houston, TX, United States
| | - Mario F Mendez
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Carly Mester
- Department of Quantitative Health Sciences, Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, MN, United States
| | - Toji Miyagawa
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Georges Naasan
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Belen Pascual
- Stanley H. Appel Department of Neurology, Nantz National Alzheimer Center, Houston Methodist Research Institute, Weill Cornell Medicine, Houston, TX, United States
| | - Peter Pressman
- Department of Neurology, University of Colorado, Aurora, CO, United States
| | - Eliana Marisa Ramos
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Katherine P Rankin
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Jessica Rexach
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Julio C Rojas
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Lawren VandeVrede
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Bonnie Wong
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | | | - Bradley F Boeve
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Howard J Rosen
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Adam L Boxer
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Adam M Staffaroni
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
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Chico-Garcia JL, Sainz-Amo R, Monreal E, Rodriguez-Jorge F, Sainz de la Maza S, Masjuan J, Villar LM, Costa-Frossard França L. Passive assessment of tapping speed through smartphone is useful for monitoring multiple sclerosis. Mult Scler Relat Disord 2024; 86:105595. [PMID: 38598952 DOI: 10.1016/j.msard.2024.105595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 03/06/2024] [Accepted: 03/27/2024] [Indexed: 04/12/2024]
Abstract
INTRODUCTION Continuously acquired smartphone keyboard interactions may be useful to monitor progression in multiple sclerosis (MS). We aimed to study the correlation between tapping speed (TS), measured as keys/s, and baseline disability scales in patients with MS. METHODS Single-center prospective study in patients with MS. We passively assessed TS during first week, measured by an "in house" smartphone application. Reliability was assessed by intraclass correlation coefficient (ICC). Correlations between median and maximum keys/s of first week of assessment and baseline disability measures were explored. RESULTS One-hundred three patients were included: 62.1 % women, with a median (IQR) age of 47 (40.4-54.8) years-old and an EDSS score of 3.0 (2.0-4.0). Distribution by MS subtypes was: 77.7 % relapsing-remitting MS (RRMS), 17.5 % secondary-progressive MS (SPMS) and 4.9 % primary-progressive MS (PPMS). ICC during first week was 0.714 (p < 0.00001). Both median and maximum keys/s showed a negative correlation with Expanded Disability Status Score, 9-hole peg test and timed 25-foot walk and a positive correlation with Processing Speed Test CogEval® raw and Z-score. Median and maximum keys/s were lower in patients diagnosed with SPMS than in RRMS. Both measures of tapping speed were associated with MS phenotype independently of age. CONCLUSION TS measured through our application is reliable and correlates with baseline disability scales.
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Affiliation(s)
- Juan Luis Chico-Garcia
- Department of Neurology, University Hospital Ramon y Cajal, IRYCIS, Madrid, Spain
- Alcala University, Alcalá de Henares, Spain
| | - Raquel Sainz-Amo
- Department of Neurology, University Hospital Ramon y Cajal, IRYCIS, Madrid, Spain
- Alcala University, Alcalá de Henares, Spain
| | - Enric Monreal
- Department of Neurology, University Hospital Ramon y Cajal, IRYCIS, Madrid, Spain
- Alcala University, Alcalá de Henares, Spain
| | - Fernando Rodriguez-Jorge
- Department of Neurology, University Hospital Ramon y Cajal, IRYCIS, Madrid, Spain
- Alcala University, Alcalá de Henares, Spain
| | - Susana Sainz de la Maza
- Department of Neurology, University Hospital Ramon y Cajal, IRYCIS, Madrid, Spain
- Alcala University, Alcalá de Henares, Spain
| | - Jaime Masjuan
- Department of Neurology, University Hospital Ramon y Cajal, IRYCIS, Madrid, Spain
- Alcala University, Alcalá de Henares, Spain
| | - Luisa María Villar
- Alcala University, Alcalá de Henares, Spain
- Department of Immunology, University Hospital Ramon y Cajal, IRYCIS, Madrid, Spain
| | - Lucienne Costa-Frossard França
- Department of Neurology, University Hospital Ramon y Cajal, IRYCIS, Madrid, Spain
- Alcala University, Alcalá de Henares, Spain
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Tripathi S, Acien A, Holmes AA, Arroyo-Gallego T, Giancardo L. Generalizing Parkinson's disease detection using keystroke dynamics: a self-supervised approach. J Am Med Inform Assoc 2024; 31:1239-1246. [PMID: 38497957 PMCID: PMC11105137 DOI: 10.1093/jamia/ocae050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 02/19/2024] [Accepted: 02/27/2024] [Indexed: 03/19/2024] Open
Abstract
OBJECTIVE Passive monitoring of touchscreen interactions generates keystroke dynamic signals that can be used to detect and track neurological conditions such as Parkinson's disease (PD) and psychomotor impairment with minimal burden on the user. However, this typically requires datasets with clinically confirmed labels collected in standardized environments, which is challenging, especially for a large subject pool. This study validates the efficacy of a self-supervised learning method in reducing the reliance on labels and evaluates its generalizability. MATERIALS AND METHODS We propose a new type of self-supervised loss combining Barlow Twins loss, which attempts to create similar feature representations with reduced feature redundancy for samples coming from the same subject, and a Dissimilarity loss, which promotes uncorrelated features for samples generated by different subjects. An encoder is first pre-trained using this loss on unlabeled data from an uncontrolled setting, then fine-tuned with clinically validated data. Our experiments test the model generalizability with controls and subjects with PD on 2 independent datasets. RESULTS Our approach showed better generalization compared to previous methods, including a feature engineering strategy, a deep learning model pre-trained on Parkinsonian signs, and a traditional supervised model. DISCUSSION The absence of standardized data acquisition protocols and the limited availability of annotated datasets compromise the generalizability of supervised models. In these contexts, self-supervised models offer the advantage of learning more robust patterns from the data, bypassing the need for ground truth labels. CONCLUSION This approach has the potential to accelerate the clinical validation of touchscreen typing software for neurodegenerative diseases.
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Affiliation(s)
- Shikha Tripathi
- D. Bradley McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, United States
| | | | | | | | - Luca Giancardo
- D. Bradley McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, United States
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Demir B, Ulukaya S, Erdem O. Detection of Parkinson's disease with keystroke data. Comput Methods Biomech Biomed Engin 2023; 26:1653-1667. [PMID: 37599616 DOI: 10.1080/10255842.2023.2245516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 03/21/2023] [Accepted: 07/16/2023] [Indexed: 08/22/2023]
Abstract
Parkinson's disease (PD) is one of the most widespread neurological disorders associated with nerve damage without definitive treatment. Impairments, such as trembling and slowing down in hand movements are among the first symptoms. For this purpose, in this study, machine learning (ML)-based models were developed by using keyboard keystroke dynamics. According to patients' drug use status, disease severity, and gender, we created 14 different sub-datasets and extracted 378 features using raw keystroke data. We developed alternative models with Support Vector Machines (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) algorithms. We further used Minimum Redundancy Maximum Relevance (mRmR), RELIEF, sequential forward selection (SFS), and RF feature selection methods to investigate prominent features in distinguishing PD. We developed ML models that jointly use the most popular features of selection algorithms (feature ensemble [FE]) and an ensemble classifier by combining multiple ML algorithms utilizing majority vote (model ensemble [ME]). With 14 different sets, FE and ME models provided accuracy (Acc.) in the range of 91.73 - 100% and 81.08 - 100%, respectively.
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Affiliation(s)
- Bahar Demir
- Department of Computational Science, Trakya University, Edirne, Turkey
| | - Sezer Ulukaya
- Department of Electrical and Electronics Engineering, Trakya University, Edirne, Turkey
| | - Oğuzhan Erdem
- Department of Electrical and Electronics Engineering, Trakya University, Edirne, Turkey
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Holmes AA, Matarazzo M, Mondesire‐Crump I, Katz E, Mahajan R, Arroyo‐Gallego T. Exploring Asymmetric Fine Motor Impairment Trends in Early Parkinson's Disease via Keystroke Typing. Mov Disord Clin Pract 2023; 10:1530-1535. [PMID: 37868929 PMCID: PMC10585965 DOI: 10.1002/mdc3.13864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 07/10/2023] [Accepted: 08/06/2023] [Indexed: 10/24/2023] Open
Abstract
Background The nQiMechPD algorithm transforms natural typing data into a numerical index that characterizes motor impairment in people with Parkinson's Disease (PwPD). Objectives Use nQiMechPD to compare asymmetrical progression of PD-related impairment in dominant (D-PD) versus non-dominant side onset (ND-PD) de-novo patients. Methods Keystroke data were collected from 53 right-handed participants (15 D-PD, 13 ND-PD, 25 controls). We apply linear mixed effects modeling to evaluate participants' right, left, and both hands nQiMechPD relative change by group. Results The 6-month nQiMechPD trajectories of right (**P = 0.002) and both (*P = 0.043) hands showed a significant difference in nQiMechPD trends between D-PD and ND-PD participants. Left side trends were not significantly different between these two groups (P = 0.328). Conclusions Significant differences between D-PD and ND-PD groups were observed, likely driven by contrasting dominant hand trends. Our findings suggest disease onset side may influence motor impairment progression, medication response, and functional outcomes in PwPD.
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Affiliation(s)
| | - Michele Matarazzo
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Fundación Hospitales de MadridHospital Universitario HM Puerta del Sur, HM HospitalesMadridSpain
| | | | | | - Rahul Mahajan
- nQ MedicalCambridgeMassachusettsUSA
- Division of Neurocritical Care, Department of NeurologyBrigham & Women's HospitalBostonMassachusettsUSA
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Dotov D, Cochen de Cock V, Driss V, Bardy B, Dalla Bella S. Coordination Rigidity in the Gait, Posture, and Speech of Persons with Parkinson's Disease. J Mot Behav 2023; 55:394-409. [PMID: 37257844 DOI: 10.1080/00222895.2023.2217100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Revised: 04/04/2023] [Accepted: 05/18/2023] [Indexed: 06/02/2023]
Abstract
Parkinson's disease (PD) is associated with reduced coordination abilities. These can result either in random or rigid patterns of movement. The latter, described here as coordination rigidity (CR), have been studied less often. We explored whether CR was present in gait, quiet stance, and speech-tasks involving coordination among multiple joints and muscles. Kinematic and voice recordings were used to compute measures describing the dynamics of systems with multiple degrees of freedom and nonlinear interactions. After clinical evaluation, patients with moderate stage PD were compared against matched healthy participants. In the PD group, gait dynamics was associated with decreased dynamic divergence-lower instability-in the vertical axis. Postural fluctuations were associated with increased regularity in the anterior-posterior axis, and voice dynamics with increased predictability, all consistent with CR. The clinical relevance of CR was confirmed by showing that some of those features contribute to disease classification with supervised machine learning (82/81/85% accuracy/sensitivity/specificity).
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Affiliation(s)
- Dobromir Dotov
- Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Canada
| | - Valérie Cochen de Cock
- Clinique Beau Soleil and CHU, Hôpital St Eloi, Montpellier, France
- EuroMov Digital Health in Motion, Université de Montpellier, Montpellier, France
| | - Valérie Driss
- Clinical Investigation Centre (CIC) 1411, University Hospital of Montpellier & Inserm, Montpellier, France
| | - Benoît Bardy
- EuroMov Digital Health in Motion, Université de Montpellier, Montpellier, France
- Institut Universitaire de France (IUF), Paris, France
| | - Simone Dalla Bella
- EuroMov Digital Health in Motion, Université de Montpellier, Montpellier, France
- International Laboratory for Brain, Music, and Sound Research (BRAMS) and Department of Psychology, University of Montreal, Montreal, Canada
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Alfalahi H, Dias SB, Khandoker AH, Chaudhuri KR, Hadjileontiadis LJ. A scoping review of neurodegenerative manifestations in explainable digital phenotyping. NPJ Parkinsons Dis 2023; 9:49. [PMID: 36997573 PMCID: PMC10063633 DOI: 10.1038/s41531-023-00494-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 03/16/2023] [Indexed: 04/03/2023] Open
Abstract
Neurologists nowadays no longer view neurodegenerative diseases, like Parkinson's and Alzheimer's disease, as single entities, but rather as a spectrum of multifaceted symptoms with heterogeneous progression courses and treatment responses. The definition of the naturalistic behavioral repertoire of early neurodegenerative manifestations is still elusive, impeding early diagnosis and intervention. Central to this view is the role of artificial intelligence (AI) in reinforcing the depth of phenotypic information, thereby supporting the paradigm shift to precision medicine and personalized healthcare. This suggestion advocates the definition of disease subtypes in a new biomarker-supported nosology framework, yet without empirical consensus on standardization, reliability and interpretability. Although the well-defined neurodegenerative processes, linked to a triad of motor and non-motor preclinical symptoms, are detected by clinical intuition, we undertake an unbiased data-driven approach to identify different patterns of neuropathology distribution based on the naturalistic behavior data inherent to populations in-the-wild. We appraise the role of remote technologies in the definition of digital phenotyping specific to brain-, body- and social-level neurodegenerative subtle symptoms, emphasizing inter- and intra-patient variability powered by deep learning. As such, the present review endeavors to exploit digital technologies and AI to create disease-specific phenotypic explanations, facilitating the understanding of neurodegenerative diseases as "bio-psycho-social" conditions. Not only does this translational effort within explainable digital phenotyping foster the understanding of disease-induced traits, but it also enhances diagnostic and, eventually, treatment personalization.
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Affiliation(s)
- Hessa Alfalahi
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.
| | - Sofia B Dias
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- CIPER, Faculdade de Motricidade Humana, University of Lisbon, Lisbon, Portugal
| | - Ahsan H Khandoker
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Kallol Ray Chaudhuri
- Parkinson Foundation, International Center of Excellence, King's College London, Denmark Hills, London, UK
- Department of Basic and Clinical Neurosciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London, UK
| | - Leontios J Hadjileontiadis
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
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Yoon S, Kim M, Lee WW. Long Short-Term Memory-Based Deep Learning Models for Screening Parkinson's Disease Using Sequential Diagnostic Codes. J Clin Neurol 2023; 19:270-279. [PMID: 36647230 PMCID: PMC10169913 DOI: 10.3988/jcn.2022.0160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 10/24/2022] [Accepted: 10/26/2022] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND AND PURPOSE It is challenging to detect Parkinson's disease (PD) in its early stages, which has prompted researchers to develop techniques based on machine learning methods for detecting PD. However, previous studies did not fully incorporate the slow progression of PD over a long period of time nor consider that its symptoms occur in a time-sequential manner. Contributing to the literature on PD, which has relied heavily on cross-sectional data, this study aimed to develop a method for detecting PD early that can process time-series information using the long short-term memory (LSTM) algorithm. METHODS We sampled 926 patients with PD and 9,260 subjects without PD using medical-claims data. The LSTM algorithm was tested using diagnostic histories, which contained the diagnostic codes and their respective time information. We compared the prediction power of the 12-month diagnostic codes under two different settings over the 4 years prior to the first PD diagnosis. RESULTS The model that was trained using the most-recent 12-month diagnostic codes had the best performance, with an accuracy of 94.25%, a sensitivity of 82.91%, and a specificity of 95.26%. The other three models (12-month codes from 2, 3, and 4 years prior) were found to have comparable performances, with accuracies of 92.27%, 91.86%, and 91.81%, respectively. The areas under the curve from our data settings ranged from 0.839 to 0.923. CONCLUSIONS We explored the possibility that PD specialists could benefit from our proposed machine learning method as an early detection method for PD.
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Affiliation(s)
- Seokjoon Yoon
- College of Business, Korea Advanced Institute of Science and Technology, Seoul, Korea
| | - Minki Kim
- College of Business, Korea Advanced Institute of Science and Technology, Seoul, Korea
| | - Woong-Woo Lee
- Department of Neurology, Nowon Eulji Medical Center, Eulji University, Seoul, Korea.,Department of Neurology, Eulji University College of Medicine, Daejeon, Korea.
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10
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Tripathi S, Arroyo-Gallego T, Giancardo L. Keystroke-Dynamics for Parkinson's Disease Signs Detection in an At-Home Uncontrolled Population: A New Benchmark and Method. IEEE Trans Biomed Eng 2023; 70:182-192. [PMID: 35767495 PMCID: PMC9904385 DOI: 10.1109/tbme.2022.3187309] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Parkinson's disease (PD) is the second most prevalent neurodegenerative disease disorder in the world. A prompt diagnosis would enable clinical trials for disease-modifying neuroprotective therapies. Recent research efforts have unveiled imaging and blood markers that have the potential to be used to identify PD patients promptly, however, the idiopathic nature of PD makes these tests very hard to scale to the general population. To this end, we need an easily deployable tool that would enable screening for PD signs in the general population. In this work, we propose a new set of features based on keystroke dynamics, i.e., the time required to press and release keyboard keys during typing, and used to detect PD in an ecologically valid data acquisition setup at the subject's homes, without requiring any pre-defined task. We compare and contrast existing models presented in the literature and present a new model that combines a new type of keystroke dynamics signal representation using hold time and flight time series as a function of key types and asymmetry in the time series using a convolutional neural network. We show how this model achieves an Area Under the Receiving Operating Characteristic curve ranging from 0.80 to 0.83 on a dataset of subjects who actively interacted with their computers for at least 5 months and positively compares against other state-of-the-art approaches previously tested on keystroke dynamics data acquired with mechanical keyboards.
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11
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Bocu R, Bocu D, Iavich M. An Extended Review Concerning the Relevance of Deep Learning and Privacy Techniques for Data-Driven Soft Sensors. SENSORS (BASEL, SWITZERLAND) 2022; 23:294. [PMID: 36616892 PMCID: PMC9824402 DOI: 10.3390/s23010294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 12/19/2022] [Accepted: 12/20/2022] [Indexed: 06/17/2023]
Abstract
The continuously increasing number of mobile devices actively being used in the world amounted to approximately 6.8 billion by 2022. Consequently, this implies a substantial increase in the amount of personal data collected, transported, processed, and stored. The authors of this paper designed and implemented an integrated personal health data management system, which considers data-driven software and hardware sensors, comprehensive data privacy techniques, and machine-learning-based algorithmic models. It was determined that there are very few relevant and complete surveys concerning this specific problem. Therefore, the current scientific research was considered, and this paper comprehensively analyzes the importance of deep learning techniques that are applied to the overall management of data collected by data-driven soft sensors. This survey considers aspects that are related to demographics, health and body parameters, and human activity and behaviour pattern detection. Additionally, the relatively complex problem of designing and implementing data privacy mechanisms, while ensuring efficient data access, is also discussed, and the relevant metrics are presented. The paper concludes by presenting the most important open research questions and challenges. The paper provides a comprehensive and thorough scientific literature survey, which is useful for any researcher or practitioner in the scope of data-driven soft sensors and privacy techniques, in relation to the relevant machine-learning-based models.
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Affiliation(s)
- Razvan Bocu
- Department of Mathematics and Computer Science, Transilvania University of Brasov, 500036 Brașov, Romania
- Department of Research and Technology, Siemens Industry Software, 500203 Brașov, Romania
| | - Dorin Bocu
- Department of Mathematics and Computer Science, Transilvania University of Brasov, 500036 Brașov, Romania
| | - Maksim Iavich
- Department of Computer Science, Caucasus University, Tbilisi 0102, Georgia
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12
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Guo CC, Chiesa PA, de Moor C, Fazeli MS, Schofield T, Hofer K, Belachew S, Scotland A. Digital Devices for Assessing Motor Functions in Mobility-Impaired and Healthy Populations: Systematic Literature Review. J Med Internet Res 2022; 24:e37683. [DOI: 10.2196/37683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 07/18/2022] [Accepted: 10/11/2022] [Indexed: 11/22/2022] Open
Abstract
Background
With the advent of smart sensing technology, mobile and wearable devices can provide continuous and objective monitoring and assessment of motor function outcomes.
Objective
We aimed to describe the existing scientific literature on wearable and mobile technologies that are being used or tested for assessing motor functions in mobility-impaired and healthy adults and to evaluate the degree to which these devices provide clinically valid measures of motor function in these populations.
Methods
A systematic literature review was conducted by searching Embase, MEDLINE, CENTRAL (January 1, 2015, to June 24, 2020), the United States and European Union clinical trial registries, and the United States Food and Drug Administration website using predefined study selection criteria. Study selection, data extraction, and quality assessment were performed by 2 independent reviewers.
Results
A total of 91 publications representing 87 unique studies were included. The most represented clinical conditions were Parkinson disease (n=51 studies), followed by stroke (n=5), Huntington disease (n=5), and multiple sclerosis (n=2). A total of 42 motion-detecting devices were identified, and the majority (n=27, 64%) were created for the purpose of health care–related data collection, although approximately 25% were personal electronic devices (eg, smartphones and watches) and 11% were entertainment consoles (eg, Microsoft Kinect or Xbox and Nintendo Wii). The primary motion outcomes were related to gait (n=30), gross motor movements (n=25), and fine motor movements (n=23). As a group, sensor-derived motion data showed a mean sensitivity of 0.83 (SD 7.27), a mean specificity of 0.84 (SD 15.40), a mean accuracy of 0.90 (SD 5.87) in discriminating between diseased individuals and healthy controls, and a mean Pearson r validity coefficient of 0.52 (SD 0.22) relative to clinical measures. We did not find significant differences in the degree of validity between in-laboratory and at-home sensor-based assessments nor between device class (ie, health care–related device, personal electronic devices, and entertainment consoles).
Conclusions
Sensor-derived motion data can be leveraged to classify and quantify disease status for a variety of neurological conditions. However, most of the recent research on digital clinical measures is derived from proof-of-concept studies with considerable variation in methodological approaches, and much of the reviewed literature has focused on clinical validation, with less than one-quarter of the studies performing analytical validation. Overall, future research is crucially needed to further consolidate that sensor-derived motion data may lead to the development of robust and transformative digital measurements intended to predict, diagnose, and quantify neurological disease state and its longitudinal change.
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Lam KH, Twose J, Lissenberg-Witte B, Licitra G, Meijer K, Uitdehaag B, De Groot V, Killestein J. The Use of Smartphone Keystroke Dynamics to Passively Monitor Upper Limb and Cognitive Function in Multiple Sclerosis: Longitudinal Analysis. J Med Internet Res 2022; 24:e37614. [PMID: 36342763 PMCID: PMC9679948 DOI: 10.2196/37614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 07/31/2022] [Accepted: 08/22/2022] [Indexed: 11/09/2022] Open
Abstract
Background Typing on smartphones, which has become a near daily activity, requires both upper limb and cognitive function. Analysis of keyboard interactions during regular typing, that is, keystroke dynamics, could therefore potentially be utilized for passive and continuous monitoring of function in patients with multiple sclerosis. Objective To determine whether passively acquired smartphone keystroke dynamics correspond to multiple sclerosis outcomes, we investigated the association between keystroke dynamics and clinical outcomes (upper limb and cognitive function). This association was investigated longitudinally in order to study within-patient changes independently of between-patient differences. Methods During a 1-year follow-up, arm function and information processing speed were assessed every 3 months in 102 patients with multiple sclerosis with the Nine-Hole Peg Test and Symbol Digit Modalities Test, respectively. Keystroke-dynamics data were continuously obtained from regular typing on the participants’ own smartphones. Press-and-release latency of the alphanumeric keys constituted the fine motor score cluster, while latency of the punctuation and backspace keys constituted the cognition score cluster. The association over time between keystroke clusters and the corresponding clinical outcomes was assessed with linear mixed models with subjects as random intercepts. By centering around the mean and calculating deviation scores within subjects, between-subject and within-subject effects were distinguished. Results Mean (SD) scores for the fine motor score cluster and cognition score cluster were 0.43 (0.16) and 0.94 (0.41) seconds, respectively. The fine motor score cluster was significantly associated with the Nine-Hole Peg Test: between-subject β was 15.9 (95% CI 12.2-19.6) and within-subject β was 6.9 (95% CI 2.0-11.9). The cognition score cluster was significantly associated with the Symbol Digit Modalities Test between subjects (between-subject β –11.2, 95% CI –17.3 to –5.2) but not within subjects (within-subject β –0.4, 95% CI –5.6 to 4.9). Conclusions Smartphone keystroke dynamics were longitudinally associated with multiple sclerosis outcomes. Worse arm function corresponded with longer latency in typing both across and within patients. Worse processing speed corresponded with higher latency in using punctuation and backspace keys across subjects. Hence, keystroke dynamics are a potential digital biomarker for remote monitoring and predicting clinical outcomes in patients with multiple sclerosis. Trial Registration Netherlands Trial Register NTR7268; https://trialsearch.who.int/Trial2.aspx?TrialID=NTR7268
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Affiliation(s)
- Ka-Hoo Lam
- Department of Neurology, Amsterdam University Medical Centers (VU University Medical Center location), Amsterdam, Netherlands
| | | | - Birgit Lissenberg-Witte
- Department of Epidemiology and Data Science, Amsterdam University Medical Centers (VU University Medical Center location), Amsterdam, Netherlands
| | | | | | - Bernard Uitdehaag
- Department of Neurology, Amsterdam University Medical Centers (VU University Medical Center location), Amsterdam, Netherlands
| | - Vincent De Groot
- Department of Rehabilitation Medicine, Amsterdam University Medical Centers (VU University Medical Center location), Amsterdam, Netherlands
| | - Joep Killestein
- Department of Neurology, Amsterdam University Medical Centers (VU University Medical Center location), Amsterdam, Netherlands
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Milne-Ives M, Carroll C, Meinert E. Self-management Interventions for People With Parkinson Disease: Scoping Review. J Med Internet Res 2022; 24:e40181. [PMID: 35930315 PMCID: PMC9391969 DOI: 10.2196/40181] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 07/07/2022] [Accepted: 07/20/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Parkinson disease can impose substantial distress and costs on patients, their families and caregivers, and health care systems. To address these burdens for families and health care systems, there is a need to better support patient self-management. To achieve this, an overview of the current state of the literature on self-management is needed to identify what is being done, how well it is working, and what might be missing. OBJECTIVE The aim of this scoping review was to provide an overview of the current body of research on self-management interventions for people with Parkinson disease and identify any knowledge gaps. METHODS The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) and Population, Intervention, Comparator, Outcome, and Study type frameworks were used to structure the methodology of the review. Due to time and resource constraints, 1 reviewer systematically searched 4 databases (PubMed, Ovid, Scopus, and Web of Science) for the evaluations of self-management interventions for Parkinson disease published in English. The references were screened using the EndNote X9 citation management software, titles and abstracts were manually reviewed, and studies were selected for inclusion based on the eligibility criteria. Data were extracted into a pre-established form and synthesized in a descriptive analysis. RESULTS There was variation among the studies on study design, sample size, intervention type, and outcomes measured. The randomized controlled trials had the strongest evidence of effectiveness: 5 out of 8 randomized controlled trials found a significant difference between groups favoring the intervention on their primary outcome, and the remaining 3 had significant effects on at least some of the secondary outcomes. The 2 interventions included in the review that targeted mental health outcomes both found significant changes over time, and the 3 algorithms evaluated performed well. The remaining studies examined patient perceptions, acceptability, and cost-effectiveness and found generally positive results. CONCLUSIONS This scoping review identified a wide variety of interventions designed to support various aspects of self-management for people with Parkinson disease. The studies all generally reported positive results, and although the strength of the evidence varied, it suggests that self-management interventions are promising for improving the care and outcomes of people with Parkinson disease. However, the research tended to focus on the motor aspects of Parkinson disease, with few nonmotor or holistic interventions, and there was a lack of evaluation of cost-effectiveness. This research will be important to providing self-management interventions that meet the varied and diverse needs of people with Parkinson disease and determining which interventions are worth promoting for widespread adoption.
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Affiliation(s)
- Madison Milne-Ives
- Centre for Health Technology, University of Plymouth, Plymouth, United Kingdom
| | - Camille Carroll
- Peninsula Medical School, Faculty of Health, University of Plymouth, Plymouth, United Kingdom
| | - Edward Meinert
- Centre for Health Technology, University of Plymouth, Plymouth, United Kingdom
- Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, United Kingdom
- Harvard TH Chan School of Public Health, Harvard University, Boston, MA, United States
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15
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Holmes AA, Tripathi S, Katz E, Mondesire-Crump I, Mahajan R, Ritter A, Arroyo-Gallego T, Giancardo L. A novel framework to estimate cognitive impairment via finger interaction with digital devices. Brain Commun 2022; 4:fcac194. [PMID: 35950091 PMCID: PMC9356723 DOI: 10.1093/braincomms/fcac194] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 05/11/2022] [Accepted: 07/25/2022] [Indexed: 11/13/2022] Open
Abstract
Measuring cognitive function is essential for characterizing brain health and tracking cognitive decline in Alzheimer’s Disease and other neurodegenerative conditions. Current tools to accurately evaluate cognitive impairment typically rely on a battery of questionnaires administered during clinical visits which is essential for the acquisition of repeated measurements in longitudinal studies. Previous studies have shown that the remote data collection of passively monitored daily interaction with personal digital devices can measure motor signs in the early stages of synucleinopathies, as well as facilitate longitudinal patient assessment in the real-world scenario with high patient compliance. This was achieved by the automatic discovery of patterns in the time series of keystroke dynamics, i.e. the time required to press and release keys, by machine learning algorithms. In this work, our hypothesis is that the typing patterns generated from user-device interaction may reflect relevant features of the effects of cognitive impairment caused by neurodegeneration. We use machine learning algorithms to estimate cognitive performance through the analysis of keystroke dynamic patterns that were extracted from mechanical and touchscreen keyboard use in a dataset of cognitively normal (n = 39, 51% male) and cognitively impaired subjects (n = 38, 60% male). These algorithms are trained and evaluated using a novel framework that integrates items from multiple neuropsychological and clinical scales into cognitive subdomains to generate a more holistic representation of multifaceted clinical signs. In our results, we see that these models based on typing input achieve moderate correlations with verbal memory, non-verbal memory and executive function subdomains [Spearman’s ρ between 0.54 (P < 0.001) and 0.42 (P < 0.001)] and a weak correlation with language/verbal skills [Spearman’s ρ 0.30 (P < 0.05)]. In addition, we observe a moderate correlation between our typing-based approach and the Total Montreal Cognitive Assessment score [Spearman’s ρ 0.48 (P < 0.001)]. Finally, we show that these machine learning models can perform better by using our subdomain framework that integrates the information from multiple neuropsychological scales as opposed to using the individual items that make up these scales. Our results support our hypothesis that typing patterns are able to reflect the effects of neurodegeneration in mild cognitive impairment and Alzheimer’s disease and that this new subdomain framework both helps the development of machine learning models and improves their interpretability.
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Affiliation(s)
| | - Shikha Tripathi
- Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston , Houston, TX 77030 , USA
| | | | | | - Rahul Mahajan
- nQ Medical , Cambridge, MA 02142 , USA
- Division of Neurocritical Care, Department of Neurology, Brigham & Women’s Hospital , Boston, MA 02115 , USA
| | - Aaron Ritter
- Cleveland Clinic Lou Ruvo Center for Brain Health, Cleveland Clinic , Las Vegas, NV 89106 , USA
| | | | - Luca Giancardo
- Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston , Houston, TX 77030 , USA
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Alfalahi H, Khandoker AH, Chowdhury N, Iakovakis D, Dias SB, Chaudhuri KR, Hadjileontiadis LJ. Diagnostic accuracy of keystroke dynamics as digital biomarkers for fine motor decline in neuropsychiatric disorders: a systematic review and meta-analysis. Sci Rep 2022; 12:7690. [PMID: 35546606 PMCID: PMC9095860 DOI: 10.1038/s41598-022-11865-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 04/25/2022] [Indexed: 12/12/2022] Open
Abstract
The unmet timely diagnosis requirements, that take place years after substantial neural loss and neuroperturbations in neuropsychiatric disorders, affirm the dire need for biomarkers with proven efficacy. In Parkinson's disease (PD), Mild Cognitive impairment (MCI), Alzheimers disease (AD) and psychiatric disorders, it is difficult to detect early symptoms given their mild nature. We hypothesize that employing fine motor patterns, derived from natural interactions with keyboards, also knwon as keystroke dynamics, could translate classic finger dexterity tests from clinics to populations in-the-wild for timely diagnosis, yet, further evidence is required to prove this efficiency. We have searched PubMED, Medline, IEEEXplore, EBSCO and Web of Science for eligible diagnostic accuracy studies employing keystroke dynamics as an index test for the detection of neuropsychiatric disorders as the main target condition. We evaluated the diagnostic performance of keystroke dynamics across 41 studies published between 2014 and March 2022, comprising 3791 PD patients, 254 MCI patients, and 374 psychiatric disease patients. Of these, 25 studies were included in univariate random-effect meta-analysis models for diagnostic performance assessment. Pooled sensitivity and specificity are 0.86 (95% Confidence Interval (CI) 0.82-0.90, I2 = 79.49%) and 0.83 (CI 0.79-0.87, I2 = 83.45%) for PD, 0.83 (95% CI 0.65-1.00, I2 = 79.10%) and 0.87 (95% CI 0.80-0.93, I2 = 0%) for psychomotor impairment, and 0.85 (95% CI 0.74-0.96, I2 = 50.39%) and 0.82 (95% CI 0.70-0.94, I2 = 87.73%) for MCI and early AD, respectively. Our subgroup analyses conveyed the diagnosis efficiency of keystroke dynamics for naturalistic self-reported data, and the promising performance of multimodal analysis of naturalistic behavioral data and deep learning methods in detecting disease-induced phenotypes. The meta-regression models showed the increase in diagnostic accuracy and fine motor impairment severity index with age and disease duration for PD and MCI. The risk of bias, based on the QUADAS-2 tool, is deemed low to moderate and overall, we rated the quality of evidence to be moderate. We conveyed the feasibility of keystroke dynamics as digital biomarkers for fine motor decline in naturalistic environments. Future work to evaluate their performance for longitudinal disease monitoring and therapeutic implications is yet to be performed. We eventually propose a partnership strategy based on a "co-creation" approach that stems from mechanistic explanations of patients' characteristics derived from data obtained in-clinics and under ecologically valid settings. The protocol of this systematic review and meta-analysis is registered in PROSPERO; identifier CRD42021278707. The presented work is supported by the KU-KAIST joint research center.
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Affiliation(s)
- Hessa Alfalahi
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates.
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates.
| | - Ahsan H Khandoker
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
| | - Nayeefa Chowdhury
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
| | - Dimitrios Iakovakis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece
| | - Sofia B Dias
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
- CIPER, Faculdade de Motricidade Humana, Universidade de Lisboa, Cruz Quebrada, 1499-002, Lisbon, Portugal
| | - K Ray Chaudhuri
- Parkinson's Foundation Centre of Excellence, King's College Hospital NHS Foundation Trust, Denmark Hill, London, SE5 9RS, United Kingdom
- Department of Basic and Clinical Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, SE5 8AF, United Kingdom
| | - Leontios J Hadjileontiadis
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece
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Gopal A, Hsu WY, Allen DD, Bove R. Remote Assessments of Hand Function in Neurological Disorders: Systematic Review. JMIR Rehabil Assist Technol 2022; 9:e33157. [PMID: 35262502 PMCID: PMC8943610 DOI: 10.2196/33157] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 01/17/2022] [Accepted: 01/26/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Loss of fine motor skills is observed in many neurological diseases, and remote monitoring assessments can aid in early diagnosis and intervention. Hand function can be regularly assessed to monitor loss of fine motor skills in people with central nervous system disorders; however, there are challenges to in-clinic assessments. Remotely assessing hand function could facilitate monitoring and supporting of early diagnosis and intervention when warranted. OBJECTIVE Remote assessments can facilitate the tracking of limitations, aiding in early diagnosis and intervention. This study aims to systematically review existing evidence regarding the remote assessment of hand function in populations with chronic neurological dysfunction. METHODS PubMed and MEDLINE, CINAHL, Web of Science, and Embase were searched for studies that reported remote assessment of hand function (ie, outside of traditional in-person clinical settings) in adults with chronic central nervous system disorders. We excluded studies that included participants with orthopedic upper limb dysfunction or used tools for intervention and treatment. We extracted data on the evaluated hand function domains, validity and reliability, feasibility, and stage of development. RESULTS In total, 74 studies met the inclusion criteria for Parkinson disease (n=57, 77% studies), stroke (n=9, 12%), multiple sclerosis (n=6, 8%), spinal cord injury (n=1, 1%), and amyotrophic lateral sclerosis (n=1, 1%). Three assessment modalities were identified: external device (eg, wrist-worn accelerometer), smartphone or tablet, and telerehabilitation. The feasibility and overall participant acceptability were high. The most common hand function domains assessed included finger tapping speed (fine motor control and rigidity), hand tremor (pharmacological and rehabilitation efficacy), and finger dexterity (manipulation of small objects required for daily tasks) and handwriting (coordination). Although validity and reliability data were heterogeneous across studies, statistically significant correlations with traditional in-clinic metrics were most commonly reported for telerehabilitation and smartphone or tablet apps. The most readily implementable assessments were smartphone or tablet-based. CONCLUSIONS The findings show that remote assessment of hand function is feasible in neurological disorders. Although varied, the assessments allow clinicians to objectively record performance in multiple hand function domains, improving the reliability of traditional in-clinic assessments. Remote assessments, particularly via telerehabilitation and smartphone- or tablet-based apps that align with in-clinic metrics, facilitate clinic to home transitions, have few barriers to implementation, and prompt remote identification and treatment of hand function impairments.
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Affiliation(s)
- Arpita Gopal
- Weill Institute of Neurosciences, University of California San Francisco, San Francisco, CA, United States
| | - Wan-Yu Hsu
- Weill Institute of Neurosciences, University of California San Francisco, San Francisco, CA, United States
| | - Diane D Allen
- Department of Physical Therapy and Rehabilitation Science, University of California San Francisco/San Francisco State University, San Francisco, CA, United States
| | - Riley Bove
- Weill Institute of Neurosciences, University of California San Francisco, San Francisco, CA, United States
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18
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Parkinson's disease severity clustering based on tapping activity on mobile device. Sci Rep 2022; 12:3142. [PMID: 35210451 PMCID: PMC8873556 DOI: 10.1038/s41598-022-06572-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 02/01/2022] [Indexed: 11/09/2022] Open
Abstract
In this study, we investigated the relationship between finger tapping tasks on the smartphone and the MDS-UPDRS I–II and PDQ-8 using the mPower dataset. mPower is a mobile application-based study for monitoring key indicators of PD progression and diagnosis. Currently, it is one of the largest, open access, mobile Parkinson’s Disease studies. Data from seven modules with a total of 8,320 participants who provided the data of at least one task were released to the public researcher. The modules comprise demographics, MDS-UPDRS I–II, PDQ-8, memory, tapping, voice, and walking. Finger-tapping is one of the tasks that easy to perform and has been analyzed for the quantitative measurement of PD. Therefore, participants who performed both the tapping activity and MDS-UPDRS I–II rating scale were selected for our analysis. Note that the MDS-UPDRS mPower Survey only contains parts of the original scale and has not been clinimetrically tested for validity and reliability. We obtained a total of 1851 samples that contained the tapping activity and MDS-UPDRS I–II for the analysis. Nine features were selected to represent tapping activity. K-mean was applied as an unsupervised clustering algorithm in our study. For determining the number of clusters, the elbow method, Sihouette score, and Davies–Bouldin index, were employed as supporting evaluation metrics. Based on these metrics and expert opinion, we decide that three clusters were appropriate for our study. The statistical analysis found that the tapping features could separate participants into three severity groups. Each group has different characteristics and could represent different PD severity based on the MDS-UPDRS I–II and PDQ-8 scores. Currently, the severity assessment of a movement disorder is based on clinical observation. Therefore, it is highly dependant on the skills and experiences of the trained movement disorder specialist who performs the procedure. We believe that any additional methods that could potentially assist with quantitative assessment of disease severity, without the need for a clinical visit would be beneficial to both the healthcare professionals and patients.
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Abstract
Internet-connected devices, including personal computers, smartphones, smartwatches, and voice assistants, have evolved into powerful multisensor technologies that billions of people interact with daily to connect with friends and colleagues, access and share information, purchase goods, play games, and navigate their environment. Digital phenotyping taps into the data streams captured by these devices to characterize and understand health and disease. The purpose of this article is to summarize opportunities for digital phenotyping in neurology, review studies using everyday technologies to obtain motor and cognitive information, and provide a perspective on how neurologists can embrace and accelerate progress in this emerging field.
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Affiliation(s)
- Anoopum S. Gupta
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
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20
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Paul S, Maindarkar M, Saxena S, Saba L, Turk M, Kalra M, Krishnan PR, Suri JS. Bias Investigation in Artificial Intelligence Systems for Early Detection of Parkinson's Disease: A Narrative Review. Diagnostics (Basel) 2022; 12:166. [PMID: 35054333 PMCID: PMC8774851 DOI: 10.3390/diagnostics12010166] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 12/27/2021] [Accepted: 01/01/2022] [Indexed: 12/13/2022] Open
Abstract
Background and Motivation: Diagnosis of Parkinson's disease (PD) is often based on medical attention and clinical signs. It is subjective and does not have a good prognosis. Artificial Intelligence (AI) has played a promising role in the diagnosis of PD. However, it introduces bias due to lack of sample size, poor validation, clinical evaluation, and lack of big data configuration. The purpose of this study is to compute the risk of bias (RoB) automatically. METHOD The PRISMA search strategy was adopted to select the best 39 AI studies out of 85 PD studies closely associated with early diagnosis PD. The studies were used to compute 30 AI attributes (based on 6 AI clusters), using AP(ai)Bias 1.0 (AtheroPointTM, Roseville, CA, USA), and the mean aggregate score was computed. The studies were ranked and two cutoffs (Moderate-Low (ML) and High-Moderate (MH)) were determined to segregate the studies into three bins: low-, moderate-, and high-bias. RESULT The ML and HM cutoffs were 3.50 and 2.33, respectively, which constituted 7, 13, and 6 for low-, moderate-, and high-bias studies. The best and worst architectures were "deep learning with sketches as outcomes" and "machine learning with Electroencephalography," respectively. We recommend (i) the usage of power analysis in big data framework, (ii) that it must undergo scientific validation using unseen AI models, and (iii) that it should be taken towards clinical evaluation for reliability and stability tests. CONCLUSION The AI is a vital component for the diagnosis of early PD and the recommendations must be followed to lower the RoB.
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Affiliation(s)
- Sudip Paul
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India
| | - Maheshrao Maindarkar
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India
| | - Sanjay Saxena
- Department of CSE, International Institute of Information Technology, Bhuneshwar 751003, India
| | - Luca Saba
- Department of Radiology, University of Cagliari, 09121 Cagliari, Italy
| | - Monika Turk
- Department of Neurology, University Medical Centre Maribor, 1262 Maribor, Slovenia
| | - Manudeep Kalra
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
| | | | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
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21
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Zhang D, Lim J, Zhou L, Dahl AA. Breaking the Data Value-Privacy Paradox in Mobile Mental Health Systems Through User-Centered Privacy Protection: A Web-Based Survey Study. JMIR Ment Health 2021; 8:e31633. [PMID: 34951604 PMCID: PMC8742208 DOI: 10.2196/31633] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/06/2021] [Accepted: 10/15/2021] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND Mobile mental health systems (MMHS) have been increasingly developed and deployed in support of monitoring, management, and intervention with regard to patients with mental disorders. However, many of these systems rely on patient data collected by smartphones or other wearable devices to infer patients' mental status, which raises privacy concerns. Such a value-privacy paradox poses significant challenges to patients' adoption and use of MMHS; yet, there has been limited understanding of it. OBJECTIVE To address the significant literature gap, this research aims to investigate both the antecedents of patients' privacy concerns and the effects of privacy concerns on their continuous usage intention with regard to MMHS. METHODS Using a web-based survey, this research collected data from 170 participants with MMHS experience recruited from online mental health communities and a university community. The data analyses used both repeated analysis of variance and partial least squares regression. RESULTS The results showed that data type (P=.003), data stage (P<.001), privacy victimization experience (P=.01), and privacy awareness (P=.08) have positive effects on privacy concerns. Specifically, users report higher privacy concerns for social interaction data (P=.007) and self-reported data (P=.001) than for biometrics data; privacy concerns are higher for data transmission (P=.01) and data sharing (P<.001) than for data collection. Our results also reveal that privacy concerns have an effect on attitude toward privacy protection (P=.001), which in turn affects continuous usage intention with regard to MMHS. CONCLUSIONS This study contributes to the literature by deepening our understanding of the data value-privacy paradox in MMHS research. The findings offer practical guidelines for breaking the paradox through the design of user-centered and privacy-preserving MMHS.
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Affiliation(s)
- Dongsong Zhang
- The University of North Carolina at Charlotte, Charlotte, NC, United States
| | - Jaewan Lim
- The University of North Carolina at Charlotte, Charlotte, NC, United States
| | - Lina Zhou
- The University of North Carolina at Charlotte, Charlotte, NC, United States
| | - Alicia A Dahl
- The University of North Carolina at Charlotte, Charlotte, NC, United States
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22
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Touchscreen-based finger tapping: Repeatability and configuration effects on tapping performance. PLoS One 2021; 16:e0260783. [PMID: 34874977 PMCID: PMC8651103 DOI: 10.1371/journal.pone.0260783] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 11/16/2021] [Indexed: 11/30/2022] Open
Abstract
Parkinson’s disease (PD) is a progressive neurodegenerative disease that affects almost 2% of the population above the age of 65. To better quantify the effects of new medications, fast and objective methods are needed. Touchscreen-based tapping tasks are simple yet effective tools for quantifying drug effects on PD-related motor symptoms, especially bradykinesia. However, there is no consensus on the optimal task set-up. The present study compares four tapping tasks in 14 healthy participants. In alternate finger tapping (AFT), tapping occurred with the index and middle finger with 2.5 cm between targets, whereas in alternate side tapping (AST) the index finger with 20 cm between targets was used. Both configurations were tested with or without the presence of a visual cue. Moreover, for each tapping task, within- and between-day repeatability and (potential) sensitivity of the calculated parameters were assessed. Visual cueing reduced tapping speed and rhythm, and improved accuracy. This effect was most pronounced for AST. On average, AST had a lower tapping speed with impaired accuracy and improved rhythm compared to AFT. Of all parameters, the total number of taps and mean spatial error had the highest repeatability and sensitivity. The findings suggest against the use of visual cueing because it is crucial that parameters can vary freely to accurately capture medication effects. The choice for AFT or AST depends on the research question, as these tasks assess different aspects of movement. These results encourage further validation of non-cued AFT and AST in PD patients.
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23
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López‐Blanco R, Llamas‐Velasco S, Velasco MA. Touchscreen Smartphone Interaction in Parkinson's Disease and Healthy Subjects in Outpatient Clinics. Mov Disord Clin Pract 2021; 8:1278-1280. [PMID: 34765695 PMCID: PMC8564826 DOI: 10.1002/mdc3.13341] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 07/09/2021] [Accepted: 07/14/2021] [Indexed: 01/23/2023] Open
Affiliation(s)
- Roberto López‐Blanco
- Integrated Neurology DepartmentHospital Universitario Rey Juan Carlos (Móstoles), Hospital General de Villalba, Hospital Infanta ElenaValdemoroSpain
- Healthcare Research Institute (i+12)Hospital Universitario 12 de OctubreMadridSpain
| | - Sara Llamas‐Velasco
- Healthcare Research Institute (i+12)Hospital Universitario 12 de OctubreMadridSpain
- Neurology DepartmentHospital UniversitarioMadridSpain
| | - Miguel A. Velasco
- Centro de Automática y Robótica.Consejo Superior Investigaciones Científicas ‐ Universidad Politécnica de Madrid (CSIC‐UPM)Arganda del Rey (Madrid)Spain
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24
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Vellata C, Belli S, Balsamo F, Giordano A, Colombo R, Maggioni G. Effectiveness of Telerehabilitation on Motor Impairments, Non-motor Symptoms and Compliance in Patients With Parkinson's Disease: A Systematic Review. Front Neurol 2021; 12:627999. [PMID: 34512495 PMCID: PMC8427282 DOI: 10.3389/fneur.2021.627999] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 07/19/2021] [Indexed: 12/17/2022] Open
Abstract
Introduction: Parkinson's disease (PD) is a chronic neurodegenerative disease involving a progressive alteration of the motor and non-motor function. PD influences the patient's daily living and reduces participation and quality of life in all phases of the disease. Early physical exercise can mitigate the effects of symptoms but access to specialist care is difficult. With current technological progress, telemedicine, and telerehabilitation is now a viable option for managing patients, although few studies have investigated the use of telerehabilitation in PD. In this systematic review, was investigated whether telerehabilitation leads to improvements in global or specific motor tasks (gait and balance, hand function) and non-motor dysfunction (motor speech disorder, dysphagia). The impact of TR on quality of life and patient satisfaction, were also assessed. The usage of telerehabilitation technologies in the management of cognitive impairment was not addressed. Method: An electronic database search was performed using the following databases: PubMed/MEDLINE, COCHRANE Library, PEDro, and SCOPUS for data published between January 2005 and December 2019 on the effects of telerehabilitation systems in managing motor and non-motor symptoms. This systematic review was conducted in accordance with the PRISMA guideline and was registered in the PROSPERO database (CRD42020141300). Results: A total of 15 articles involving 421 patients affected by PD were analyzed. The articles were divided into two categories based on their topic of interest or outcome. The first category consisted of the effects of telerehabilitation on gait and balance (3), dexterity of the upper limbs (3), and bradykinesia (0); the second category regarded non-motor symptoms such as speech disorders (8) and dysphagia (0). Quality of life (7) and patient satisfaction (8) following telerehabilitation programs were also analyzed, as well as feasibility and costs. Conclusion: Telerehabilitation is feasible in people affected by PD. Our analysis of the available data highlighted that telerehabilitation systems are effective in maintaining and/or improving some clinical and non-clinical aspects of PD (balance and gait, speech and voice, quality of life, patient satisfaction). Systematic Review Registration:https://www.crd.york.ac.uk/prospero/, identifier: CRD42020141300.
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Affiliation(s)
- Chiara Vellata
- Istituti Clinici Scientifici Maugeri Spa - Società Benefit, Neurologic Rehabilitation Unit of Veruno Institute, Veruno, Italy
| | - Stefano Belli
- Istituti Clinici Scientifici Maugeri Spa - Società Benefit, Neurologic Rehabilitation Unit of Veruno Institute, Veruno, Italy
| | - Francesca Balsamo
- Istituti Clinici Scientifici Maugeri Spa - Società Benefit, Neurologic Rehabilitation Unit of Veruno Institute, Veruno, Italy
| | - Andrea Giordano
- Istituti Clinici Scientifici Maugeri Spa - Società Benefit, Bioengineering Service, Veruno, Italy
| | - Roberto Colombo
- Istituti Clinici Scientifici Maugeri Spa - Società Benefit, Bioengineering Service, Veruno, Italy
| | - Giorgio Maggioni
- Istituti Clinici Scientifici Maugeri Spa - Società Benefit, Neurologic Rehabilitation Unit of Veruno Institute, Veruno, Italy
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25
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Digital Technology in Movement Disorders: Updates, Applications, and Challenges. Curr Neurol Neurosci Rep 2021; 21:16. [PMID: 33660110 PMCID: PMC7928701 DOI: 10.1007/s11910-021-01101-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/21/2021] [Indexed: 12/14/2022]
Abstract
Purpose of Review Digital technology affords the opportunity to provide objective, frequent, and sensitive assessment of disease outside of the clinic environment. This article reviews recent literature on the application of digital technology in movement disorders, with a focus on Parkinson’s disease (PD) and Huntington’s disease. Recent Findings Recent research has demonstrated the ability for digital technology to discriminate between individuals with and without PD, identify those at high risk for PD, quantify specific motor features, predict clinical events in PD, inform clinical management, and generate novel insights. Summary Digital technology has enormous potential to transform clinical research and care in movement disorders. However, more work is needed to better validate existing digital measures, including in new populations, and to develop new more holistic digital measures that move beyond motor features.
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26
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Chen OY, Lipsmeier F, Phan H, Prince J, Taylor KI, Gossens C, Lindemann M, Vos MD. Building a Machine-Learning Framework to Remotely Assess Parkinson's Disease Using Smartphones. IEEE Trans Biomed Eng 2020; 67:3491-3500. [PMID: 32324537 DOI: 10.1109/tbme.2020.2988942] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Parkinson's disease (PD) is a neurodegenerative disorder that affects multiple neurological systems. Traditional PD assessment is conducted by a physician during infrequent clinic visits. Using smartphones, remote patient monitoring has the potential to obtain objective behavioral data semi-continuously, track disease fluctuations, and avoid rater dependency. METHODS Smartphones collect sensor data during various active tests and passive monitoring, including balance (postural instability), dexterity (skill in performing tasks using hands), gait (the pattern of walking), tremor (involuntary muscle contraction and relaxation), and voice. Some of the features extracted from smartphone data are potentially associated with specific PD symptoms identified by physicians. To leverage large-scale cross-modality smartphone features, we propose a machine-learning framework for performing automated disease assessment. The framework consists of a two-step feature selection procedure and a generic model based on the elastic-net regularization. RESULTS Using this framework, we map the PD-specific architecture of behaviors using data obtained from both PD participants and healthy controls (HCs). Utilizing these atlases of features, the framework shows promises to (a) discriminate PD participants from HCs, and (b) estimate the disease severity of individuals with PD. SIGNIFICANCE Data analysis results from 437 behavioral features obtained from 72 subjects (37 PD and 35 HC) sampled from 17 separate days during a period of up to six months suggest that this framework is potentially useful for the analysis of remotely collected smartphone sensor data in individuals with PD.
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27
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Sanderson JB, Yu JH, Liu DD, Amaya D, Lauro PM, D'Abreu A, Akbar U, Lee S, Asaad WF. Multi-Dimensional, Short-Timescale Quantification of Parkinson's Disease and Essential Tremor Motor Dysfunction. Front Neurol 2020; 11:886. [PMID: 33071924 PMCID: PMC7530842 DOI: 10.3389/fneur.2020.00886] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 07/10/2020] [Indexed: 12/18/2022] Open
Abstract
Introduction: Parkinson's disease (PD) is a progressive movement disorder characterized by heterogenous motor dysfunction with fluctuations in severity. Objective, short-timescale characterization of this dysfunction is necessary as therapies become increasingly adaptive. Objectives: This study aims to characterize a novel, naturalistic, and goal-directed tablet-based task and complementary analysis protocol designed to characterize the motor features of PD. Methods: A total of 26 patients with PD and without deep brain stimulation (DBS), 20 control subjects, and eight patients with PD and with DBS completed the task. Eight metrics, each designed to capture an aspect of motor dysfunction in PD, were calculated from 1-second, non-overlapping epochs of the raw positional and pressure data captured during task completion. These metrics were used to generate a classifier using a support vector machine (SVM) model to produce a unifying, scalar “motor error score” (MES). The data generated from these patients with PD were compared to same-day standard clinical assessments. Additionally, these data were compared to analogous data generated from a separate group of 12 patients with essential tremor (ET) to assess the task's specificity for different movement disorders. Finally, an SVM model was generated for each of the eight patients with PD and with DBS to differentiate between their motor dysfunction in the “DBS On” and “DBS Off” stimulation states. Results: The eight metrics calculated from the raw positional and force data captured during task completion were non-redundant. MES generated by the SVM analysis protocol showed a strong correlation with MDS-UPDRS-III scores assigned by movement disorder specialists. Analysis of the relative contributions of each of the eight metrics showed a significant difference between the motor dysfunction of PD and ET. Much of this difference was attributable to the homogenous, tremor-dominant phenotype of ET motor dysfunction. Finally, in individual patients with PD with DBS, task performance and subsequent SVM classification effectively differentiated between the “DBS On” and “DBS Off” stimulation states. Conclusion: This tablet-based task and analysis protocol correlated strongly with expert clinical assessments of PD motor dysfunction. Additionally, the task showed specificity for PD when compared to ET, another common movement disorder. This specificity was driven by the relative heterogeneity of motor dysfunction of PD compared to ET. Finally, the task was able to distinguish between the “DBS On” and “DBS Off” states within single patients with PD. This task provides temporally-precise and specific information about motor dysfunction in at least two movement disorders that could feasibly correlate to neural activity.
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Affiliation(s)
- John B Sanderson
- The Warren Alpert Medical School, Brown University, Providence, RI, United States
| | - James H Yu
- The Warren Alpert Medical School, Brown University, Providence, RI, United States
| | - David D Liu
- The Warren Alpert Medical School, Brown University, Providence, RI, United States.,Department of Neurosurgery, Rhode Island Hospital, Providence, RI, United States
| | - Daniel Amaya
- Department of Neuroscience, Brown University, Providence, RI, United States.,Carney Institute for Brain Science, Brown University, Providence, RI, United States.,Norman Prince Neurosciences Institute, Rhode Island Hospital, Providence, RI, United States
| | - Peter M Lauro
- The Warren Alpert Medical School, Brown University, Providence, RI, United States.,Department of Neuroscience, Brown University, Providence, RI, United States.,Carney Institute for Brain Science, Brown University, Providence, RI, United States.,Norman Prince Neurosciences Institute, Rhode Island Hospital, Providence, RI, United States
| | - Anelyssa D'Abreu
- The Warren Alpert Medical School, Brown University, Providence, RI, United States.,Norman Prince Neurosciences Institute, Rhode Island Hospital, Providence, RI, United States.,Department of Neurology, Rhode Island Hospital, Providence, RI, United States
| | - Umer Akbar
- The Warren Alpert Medical School, Brown University, Providence, RI, United States.,Norman Prince Neurosciences Institute, Rhode Island Hospital, Providence, RI, United States.,Department of Neurology, Rhode Island Hospital, Providence, RI, United States
| | - Shane Lee
- Department of Neuroscience, Brown University, Providence, RI, United States.,Carney Institute for Brain Science, Brown University, Providence, RI, United States.,Norman Prince Neurosciences Institute, Rhode Island Hospital, Providence, RI, United States
| | - Wael F Asaad
- The Warren Alpert Medical School, Brown University, Providence, RI, United States.,Department of Neurosurgery, Rhode Island Hospital, Providence, RI, United States.,Department of Neuroscience, Brown University, Providence, RI, United States.,Carney Institute for Brain Science, Brown University, Providence, RI, United States.,Norman Prince Neurosciences Institute, Rhode Island Hospital, Providence, RI, United States
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28
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Monje MHG, Foffani G, Obeso J, Sánchez-Ferro Á. New Sensor and Wearable Technologies to Aid in the Diagnosis and Treatment Monitoring of Parkinson's Disease. Annu Rev Biomed Eng 2020; 21:111-143. [PMID: 31167102 DOI: 10.1146/annurev-bioeng-062117-121036] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Parkinson's disease (PD) is a degenerative disorder of the brain characterized by the impairment of the nigrostriatal system. This impairment leads to specific motor manifestations (i.e., bradykinesia, tremor, and rigidity) that are assessed through clinical examination, scales, and patient-reported outcomes. New sensor-based and wearable technologies are progressively revolutionizing PD care by objectively measuring these manifestations and improving PD diagnosis and treatment monitoring. However, their use is still limited in clinical practice, perhaps because of the absence of external validation and standards for their continuous use at home. In the near future, these systems will progressively complement traditional tools and revolutionize the way we diagnose and monitor patients with PD.
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Affiliation(s)
- Mariana H G Monje
- HM CINAC, Hospital Universitario HM Puerta del Sur, Universidad CEU-San Pablo, 28938 Móstoles, Madrid, Spain; , , , .,Department of Anatomy, Histology and Neuroscience, School of Medicine, Universidad Autónoma de Madrid, 28029 Madrid, Spain
| | - Guglielmo Foffani
- HM CINAC, Hospital Universitario HM Puerta del Sur, Universidad CEU-San Pablo, 28938 Móstoles, Madrid, Spain; , , , .,Hospital Nacional de Parapléjicos, Servicio de Salud de Castilla La Mancha, 45071 Toledo, Spain
| | - José Obeso
- HM CINAC, Hospital Universitario HM Puerta del Sur, Universidad CEU-San Pablo, 28938 Móstoles, Madrid, Spain; , , , .,Centro de Investigación Biomédica en Red, Enfermedades Neurodegenerativas, 28031 Madrid, Spain
| | - Álvaro Sánchez-Ferro
- HM CINAC, Hospital Universitario HM Puerta del Sur, Universidad CEU-San Pablo, 28938 Móstoles, Madrid, Spain; , , , .,Centro de Investigación Biomédica en Red, Enfermedades Neurodegenerativas, 28031 Madrid, Spain.,Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
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29
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Laine CM, Valero-Cuevas FJ. Parkinson's Disease Exhibits Amplified Intermuscular Coherence During Dynamic Voluntary Action. Front Neurol 2020; 11:204. [PMID: 32308641 PMCID: PMC7145888 DOI: 10.3389/fneur.2020.00204] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 03/09/2020] [Indexed: 12/29/2022] Open
Abstract
Parkinson's disease (PD) is typically diagnosed and evaluated on the basis of overt motor dysfunction, however, subtle changes in the frequency spectrum of neural drive to muscles have been reported as well. During dynamic actions, coactive muscles of healthy adults often share a common source of 6-15 Hz (alpha-band) neural drive, creating synchronous alpha-band activity in their EMG signals. Individuals with PD commonly exhibit kinetic action tremor at similar frequencies, but the potential relationship between the intermuscular alpha-band neural drive seen in healthy adults and the action tremor associated with PD is not well-understood. A close relationship is most tenable during voluntary dynamic tasks where alpha-band neural drive is strongest in healthy adults, and where neural circuits affected by PD are most engaged. In this study, we characterized the frequency spectrum of EMG synchronization (intermuscular coherence) in 16 participants with PD and 15 age-matched controls during two dynamic motor tasks: (1) rotation of a dial between the thumb and index finger, and (2) dynamic scaling of isometric precision pinch force. These tasks produce different profiles of coherence between the first dorsal interosseous and abductor pollicis brevis muscles. We sought to determine if alpha-band intermuscular coherence would be amplified in participants with PD relative to controls, if such differences would be task-specific, and if they would correlate with symptom severity. We found that relative to controls, the PD group displayed amplified, but similarly task-dependent, coherence in the alpha-band. The magnitude of coherence during the rotation task correlated with overall symptom severity as per the UPDRS rating scale. Finally, we explored the potential for our coherence measures, with no additional information, to discriminate individuals with PD from controls. The area under the Receiver Operating Characteristic curve (AUC) indicated a clear separation between groups (AUC = 0.96), even though participants with PD were on their typical medication and displayed only mild-moderate symptoms. We conclude that a task-dependent, intermuscular neural drive within the alpha-band is amplified in PD. Its quantification via intermuscular coherence analysis may provide a useful tool for detecting the presence of PD, or assessing its progression.
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Affiliation(s)
- Christopher M Laine
- Division of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA, United States
| | - Francisco J Valero-Cuevas
- Division of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA, United States.,Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States
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30
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Youngmann B, Allerhand L, Paltiel O, Yom-Tov E, Arkadir D. A machine learning algorithm successfully screens for Parkinson's in web users. Ann Clin Transl Neurol 2019; 6:2503-2509. [PMID: 31714022 PMCID: PMC6917308 DOI: 10.1002/acn3.50945] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Accepted: 10/21/2019] [Indexed: 12/17/2022] Open
Abstract
Objective To develop, apply, and evaluate, a novel web‐based classifier for screening for Parkinson disease among a large cohort of search engine users. Methods A supervised machine learning classifier learned to distinguish web users with self‐reported Parkinson's disease from controls based on their interactions with a search engine (Bing, Microsoft). It was then applied to groups of web users with low or high risk for actual Parkinson's disease. Textual content of web queries was used to sort surfers into the different risk groups, but not for classifying users as negative or positive for Parkinson's disease. Disease detection was unsolicited. Researchers did not have access to any identifying data on users. Results Applying the classifier (with an estimated positive predictive value of 25%) resulted in 17,843/1,490,987 (1.2%) web users over the age of 40 years screened positive for Parkinson's disease. This percentile was higher in at‐risk groups (Fisher exact P < 0.00001), including users who searched for information regarding the disease (518/804, 64.4%), and users with non‐motor Parkinson's symptom or with an affected relative (57/1064, 5.3%). Longitudinal follow‐up revealed that in all studied groups individuals classified as having the disease showed a higher mean rate of progression in disease‐related features (t‐test P < 0.05). Interpretation An automatic classifier, based on mouse and keyboard interactions with a search engine, is able to reliably trace individuals at high risk for actual Parkinson's disease as well as to demonstrate more rapid progression of disease‐related signs in those who screened positive. This ability raises novel ethical issues.
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Affiliation(s)
| | | | - Ora Paltiel
- Braun School of Public Health and Community Medicine, Hadassah-Hebrew University, Jerusalem, Israel
| | - Elad Yom-Tov
- Microsoft Research, Herzliya, Israel.,Faculty of Industrial Engineering and Management, Technion, Haifa, Israel
| | - David Arkadir
- Department of Neurology, Hadassah Hebrew University, Jerusalem, Israel
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31
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Mastoras RE, Iakovakis D, Hadjidimitriou S, Charisis V, Kassie S, Alsaadi T, Khandoker A, Hadjileontiadis LJ. Touchscreen typing pattern analysis for remote detection of the depressive tendency. Sci Rep 2019; 9:13414. [PMID: 31527640 PMCID: PMC6746713 DOI: 10.1038/s41598-019-50002-9] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Accepted: 09/04/2019] [Indexed: 11/08/2022] Open
Abstract
Depressive disorder (DD) is a mental illness affecting more than 300 million people worldwide, whereas social stigma and subtle, variant symptoms impede diagnosis. Psychomotor retardation is a common component of DD with a negative impact on motor function, usually reflected on patients' routine activities, including, nowadays, their interaction with mobile devices. Therefore, such interactions constitute an enticing source of information towards unsupervised screening for DD symptoms in daily life. In this vein, this paper proposes a machine learning-based method for discriminating between subjects with depressive tendency and healthy controls, as denoted by self-reported Patient Health Questionnaire-9 (PHQ-9) compound scores, based on typing patterns captured in-the-wild. The latter consisted of keystroke timing sequences and typing metadata, passively collected during natural typing on touchscreen smartphones by 11/14 subjects with/without depressive tendency. Statistical features were extracted and tested in univariate and multivariate classification pipelines to reach a decision on subjects' status. The best-performing pipeline achieved an AUC = 0.89 (0.72-1.00; 95% Confidence Interval) and 0.82/0.86 sensitivity/specificity, with the outputted probabilities significantly correlating (>0.60) with the respective PHQ-9 scores. This work adds to the findings of previous research associating typing patterns with psycho-motor impairment and contributes to the development of an unobtrusive, high-frequency monitoring of depressive tendency in everyday living.
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Affiliation(s)
- Rafail-Evangelos Mastoras
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Dimitrios Iakovakis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Stelios Hadjidimitriou
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vasileios Charisis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Seada Kassie
- American Center for Psychiatry and Neurology, Abu Dhabi, UAE
| | - Taoufik Alsaadi
- American Center for Psychiatry and Neurology, Abu Dhabi, UAE
| | - Ahsan Khandoker
- Healthcare Engineering Innovation Center, Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, UAE
| | - Leontios J Hadjileontiadis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece.
- Healthcare Engineering Innovation Center, Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, UAE.
- Department of Electrical and Computer Engineering, Khalifa University of Science and Technology, Abu Dhabi, UAE.
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Matarazzo M, Arroyo-Gallego T, Montero P, Puertas-Martín V, Butterworth I, Mendoza CS, Ledesma-Carbayo MJ, Catalán MJ, Molina JA, Bermejo-Pareja F, Martínez-Castrillo JC, López-Manzanares L, Alonso-Cánovas A, Rodríguez JH, Obeso I, Martínez-Martín P, Martínez-Ávila JC, de la Cámara AG, Gray M, Obeso JA, Giancardo L, Sánchez-Ferro Á. Remote Monitoring of Treatment Response in Parkinson's Disease: The Habit of Typing on a Computer. Mov Disord 2019; 34:1488-1495. [PMID: 31211469 DOI: 10.1002/mds.27772] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 05/03/2019] [Accepted: 05/13/2019] [Indexed: 11/08/2022] Open
Abstract
OBJECTIVE The recent advances in technology are opening a new opportunity to remotely evaluate motor features in people with Parkinson's disease (PD). We hypothesized that typing on an electronic device, a habitual behavior facilitated by the nigrostriatal dopaminergic pathway, could allow for objectively and nonobtrusively monitoring parkinsonian features and response to medication in an at-home setting. METHODS We enrolled 31 participants recently diagnosed with PD who were due to start dopaminergic treatment and 30 age-matched controls. We remotely monitored their typing pattern during a 6-month (24 weeks) follow-up period before and while dopaminergic medications were being titrated. The typing data were used to develop a novel algorithm based on recursive neural networks and detect participants' responses to medication. The latter were defined by the Unified Parkinson's Disease Rating Scale-III (UPDRS-III) minimal clinically important difference. Furthermore, we tested the accuracy of the algorithm to predict the final response to medication as early as 21 weeks prior to the final 6-month clinical outcome. RESULTS The score on the novel algorithm based on recursive neural networks had an overall moderate kappa agreement and fair area under the receiver operating characteristic (ROC) curve with the time-coincident UPDRS-III minimal clinically important difference. The participants classified as responders at the final visit (based on the UPDRS-III minimal clinically important difference) had higher scores on the novel algorithm based on recursive neural networks when compared with the participants with stable UPDRS-III, from the third week of the study onward. CONCLUSIONS This preliminary study suggests that remotely gathered unsupervised typing data allows for the accurate detection and prediction of drug response in PD. © 2019 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Michele Matarazzo
- HM-CINAC, Hospital Universitario HM Puerta del Sur, Móstoles and Medical School, CEU-San Pablo University, Madrid, Spain.,Neurology Department, Instituto de Investigación del Hospital 12 de Octubre, Madrid, Spain.,Centro de Investigación Biomédica en Red, Enfermedades Neurodegenerativas, Madrid, Spain.,Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Teresa Arroyo-Gallego
- Biomedical Image Technologies, Universidad Politécnica de Madrid and CIBERBBN, Madrid, Spain.,Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,nQ Medical Inc., Cambridge, Massachusetts, USA
| | - Paloma Montero
- Neurology Department, Instituto de Investigación del Hospital 12 de Octubre, Madrid, Spain.,Movement Disorders Unit, Hospital Clínico San Carlos, Madrid, Spain
| | | | - Ian Butterworth
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Carlos S Mendoza
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - María J Ledesma-Carbayo
- Biomedical Image Technologies, Universidad Politécnica de Madrid and CIBERBBN, Madrid, Spain
| | | | - José Antonio Molina
- Neurology Department, Instituto de Investigación del Hospital 12 de Octubre, Madrid, Spain
| | - Félix Bermejo-Pareja
- Neurology Department, Instituto de Investigación del Hospital 12 de Octubre, Madrid, Spain
| | | | | | | | | | - Ignacio Obeso
- HM-CINAC, Hospital Universitario HM Puerta del Sur, Móstoles and Medical School, CEU-San Pablo University, Madrid, Spain.,Centro de Investigación Biomédica en Red, Enfermedades Neurodegenerativas, Madrid, Spain
| | - Pablo Martínez-Martín
- Centro de Investigación Biomédica en Red, Enfermedades Neurodegenerativas, Madrid, Spain.,Area of Applied Epidemiology, National Centre of Epidemiology, Carlos III Institute of Health, Madrid, Spain
| | - José Carlos Martínez-Ávila
- Clinical Research Unit, Instituto de Investigación Hospital 12 de Octubre, Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - Agustín Gómez de la Cámara
- Clinical Research Unit, Instituto de Investigación Hospital 12 de Octubre, Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - Martha Gray
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - José A Obeso
- HM-CINAC, Hospital Universitario HM Puerta del Sur, Móstoles and Medical School, CEU-San Pablo University, Madrid, Spain.,Centro de Investigación Biomédica en Red, Enfermedades Neurodegenerativas, Madrid, Spain
| | - Luca Giancardo
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Álvaro Sánchez-Ferro
- HM-CINAC, Hospital Universitario HM Puerta del Sur, Móstoles and Medical School, CEU-San Pablo University, Madrid, Spain.,Neurology Department, Instituto de Investigación del Hospital 12 de Octubre, Madrid, Spain.,Centro de Investigación Biomédica en Red, Enfermedades Neurodegenerativas, Madrid, Spain.,Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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33
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Bannard C, Leriche M, Bandmann O, Brown CH, Ferracane E, Sánchez-Ferro Á, Obeso J, Redgrave P, Stafford T. Reduced habit-driven errors in Parkinson's Disease. Sci Rep 2019; 9:3423. [PMID: 30833640 PMCID: PMC6399280 DOI: 10.1038/s41598-019-39294-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Accepted: 12/11/2018] [Indexed: 11/16/2022] Open
Abstract
Parkinson’s Disease can be understood as a disorder of motor habits. A prediction of this theory is that early stage Parkinson’s patients will display fewer errors caused by interference from previously over-learned behaviours. We test this prediction in the domain of skilled typing, where actions are easy to record and errors easy to identify. We describe a method for categorizing errors as simple motor errors or habit-driven errors. We test Spanish and English participants with and without Parkinson’s, and show that indeed patients make fewer habit errors than healthy controls, and, further, that classification of error type increases the accuracy of discriminating between patients and healthy controls. As well as being a validation of a theory-led prediction, these results offer promise for automated, enhanced and early diagnosis of Parkinson’s Disease.
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Affiliation(s)
- Colin Bannard
- Department of Psychological Sciences, University of Liverpool, Liverpool, UK.
| | - Mariana Leriche
- Department of Anatomy, University of Otago, Dunedin, New Zealand
| | - Oliver Bandmann
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | | | - Elisa Ferracane
- Department of Linguistics, University of Texas at Austin, Austin, USA
| | - Álvaro Sánchez-Ferro
- HM Hospitales, Centre for Integrative Neuroscience AC, Hospital Universitario HM Puerta del Sur, Mostoles and CEU San Pablo University. Center for Networked Biomedical Research on Neurodegenerative Diseases, Institute Carlos III, Madrid, Spain
| | - José Obeso
- HM Hospitales, Centre for Integrative Neuroscience AC, Hospital Universitario HM Puerta del Sur, Mostoles and CEU San Pablo University. Center for Networked Biomedical Research on Neurodegenerative Diseases, Institute Carlos III, Madrid, Spain
| | - Peter Redgrave
- Department of Psychology, University of Sheffield, Sheffield, UK
| | - Tom Stafford
- Department of Psychology, University of Sheffield, Sheffield, UK
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34
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Smith DG. Digital phenotyping approaches and mobile devices enhance CNS biopharmaceutical research and development. Neuropsychopharmacology 2018; 43:2504-2505. [PMID: 30267015 PMCID: PMC6224520 DOI: 10.1038/s41386-018-0222-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Revised: 09/06/2018] [Accepted: 09/11/2018] [Indexed: 11/09/2022]
Affiliation(s)
- Daniel G Smith
- Department of Clinical Research, Alkermes Incorporated, Waltham, MA, 02451, USA.
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35
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Phan D, Horne M, Pathirana PN, Farzanehfar P. Effect of Parkinsonism on Proximal Unstructured Movement Captured by Inertial Sensors. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:5507-5510. [PMID: 30441584 DOI: 10.1109/embc.2018.8513510] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
In this study, we endeavour to measure characteristic movements of patients with Parkinson's disease (PD). Our eventual aim is to obtain the severity of these exhibited movements entirely based on measurements conducted in un-clinical environments. Indeed, we investigate the feasibility of capturing such un-structured movements using wearable sensors. In particular, as Bradykinesia and axial Bradykinesia are vital characteristics yet challenging to measure, we design a test system of Inertial Measurement (IM) based wearable sensors in order to capture the affected movements of the back. The study evaluated the characteristics of PD patients during the unstructured activities. Our analysis captured back flexibility based on frequency information of the sensors attached to the human back. Satisfactory classification in each test confirms that this testing system can identify as well as evaluate PD patients using a minimal number of sensors during these unstructured movements. Our objective is to enhance the uptake and promote the use of wearable sensors in longer term monitoring scenarios relevant to non-clinical environments. Thus, we envisage clinicians monitoring the progress due to the treatment of patients residing in their homes assisted by sensors with enhanced wearability.
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36
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Iakovakis D, Hadjidimitriou S, Charisis V, Bostantjopoulou S, Katsarou Z, Klingelhoefer L, Reichmann H, Dias SB, Diniz JA, Trivedi D, Chaudhuri KR, Hadjileontiadis LJ. Motor Impairment Estimates via Touchscreen Typing Dynamics Toward Parkinson's Disease Detection From Data Harvested In-the-Wild. ACTA ACUST UNITED AC 2018. [DOI: 10.3389/fict.2018.00028] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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37
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Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt‐Eriksen J, Cheng W, Fernandez‐Garcia I, Siebourg‐Polster J, Jin L, Soto J, Verselis L, Boess F, Koller M, Grundman M, Monsch AU, Postuma RB, Ghosh A, Kremer T, Czech C, Gossens C, Lindemann M. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord 2018; 33:1287-1297. [PMID: 29701258 PMCID: PMC6175318 DOI: 10.1002/mds.27376] [Citation(s) in RCA: 156] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Revised: 02/15/2018] [Accepted: 02/16/2018] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Ubiquitous digital technologies such as smartphone sensors promise to fundamentally change biomedical research and treatment monitoring in neurological diseases such as PD, creating a new domain of digital biomarkers. OBJECTIVES The present study assessed the feasibility, reliability, and validity of smartphone-based digital biomarkers of PD in a clinical trial setting. METHODS During a 6-month, phase 1b clinical trial with 44 Parkinson participants, and an independent, 45-day study in 35 age-matched healthy controls, participants completed six daily motor active tests (sustained phonation, rest tremor, postural tremor, finger-tapping, balance, and gait), then carried the smartphone during the day (passive monitoring), enabling assessment of, for example, time spent walking and sit-to-stand transitions by gyroscopic and accelerometer data. RESULTS Adherence was acceptable: Patients completed active testing on average 3.5 of 7 times/week. Sensor-based features showed moderate-to-excellent test-retest reliability (average intraclass correlation coefficient = 0.84). All active and passive features significantly differentiated PD from controls with P < 0.005. All active test features except sustained phonation were significantly related to corresponding International Parkinson and Movement Disorder Society-Sponsored UPRDS clinical severity ratings. On passive monitoring, time spent walking had a significant (P = 0.005) relationship with average postural instability and gait disturbance scores. Of note, for all smartphone active and passive features except postural tremor, the monitoring procedure detected abnormalities even in those Parkinson participants scored as having no signs in the corresponding International Parkinson and Movement Disorder Society-Sponsored UPRDS items at the site visit. CONCLUSIONS These findings demonstrate the feasibility of smartphone-based digital biomarkers and indicate that smartphone-sensor technologies provide reliable, valid, clinically meaningful, and highly sensitive phenotypic data in Parkinson's disease. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Florian Lipsmeier
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Kirsten I. Taylor
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Timothy Kilchenmann
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Detlef Wolf
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Alf Scotland
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Jens Schjodt‐Eriksen
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Wei‐Yi Cheng
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Ignacio Fernandez‐Garcia
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Juliane Siebourg‐Polster
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Liping Jin
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Jay Soto
- Prothena Biosciences Inc.South San FranciscoCaliforniaUSA
| | - Lynne Verselis
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Frank Boess
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Martin Koller
- Prothena Biosciences Inc.South San FranciscoCaliforniaUSA
| | - Michael Grundman
- Prothena Biosciences Inc.South San FranciscoCaliforniaUSA
- Global R&D Partners, LLCSan DiegoCaliforniaUSA
| | - Andreas U. Monsch
- Felix Platter Hospital, University Center for Medicine of Aging, Memory Clinic, Basel, Switzerland; University of Basel, Faculty of PsychologyBaselSwitzerland
| | - Ronald B. Postuma
- Department of NeurologyMcGill University, Montreal General HospitalMontrealQuebecCanada
| | - Anirvan Ghosh
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Thomas Kremer
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Christian Czech
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Christian Gossens
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Michael Lindemann
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann‐La Roche Ltd.BaselSwitzerland
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38
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Iakovakis D, Hadjidimitriou S, Charisis V, Bostantzopoulou S, Katsarou Z, Hadjileontiadis LJ. Touchscreen typing-pattern analysis for detecting fine motor skills decline in early-stage Parkinson's disease. Sci Rep 2018; 8:7663. [PMID: 29769594 PMCID: PMC5955899 DOI: 10.1038/s41598-018-25999-0] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2017] [Accepted: 05/02/2018] [Indexed: 11/25/2022] Open
Abstract
Parkinson’s disease (PD) is a degenerative movement disorder causing progressive disability that severely affects patients’ quality of life. While early treatment can produce significant benefits for patients, the mildness of many early signs combined with the lack of accessible high-frequency monitoring tools may delay clinical diagnosis. To meet this need, user interaction data from consumer technologies have recently been exploited towards unsupervised screening for PD symptoms in daily life. Similarly, this work proposes a method for detecting fine motor skills decline in early PD patients via analysis of patterns emerging from finger interaction with touchscreen smartphones during natural typing. Our approach relies on low-/higher-order statistical features of keystrokes timing and pressure variables, computed from short typing sessions. Features are fed into a two-stage multi-model classification pipeline that reaches a decision on the subject’s status (PD patient/control) by gradually fusing prediction probabilities obtained for individual typing sessions and keystroke variables. This method achieved an AUC = 0.92 and 0.82/0.81 sensitivity/specificity (matched groups of 18 early PD patients/15 controls) with discriminant features plausibly correlating with clinical scores of relevant PD motor symptoms. These findings suggest an improvement over similar approaches, thereby constituting a further step towards unobtrusive early PD detection from routine activities.
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Affiliation(s)
- Dimitrios Iakovakis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Stelios Hadjidimitriou
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vasileios Charisis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | | | - Zoe Katsarou
- Department of Neurology, Hippokration Hospital, Thessaloniki, Greece
| | - Leontios J Hadjileontiadis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece. .,Department of Electrical and Computer Engineering, Khalifa University of Science and Technology, Abu Dhabi, UAE.
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39
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Arroyo-Gallego T, Ledesma-Carbayo MJ, Butterworth I, Matarazzo M, Montero-Escribano P, Puertas-Martín V, Gray ML, Giancardo L, Sánchez-Ferro Á. Detecting Motor Impairment in Early Parkinson's Disease via Natural Typing Interaction With Keyboards: Validation of the neuroQWERTY Approach in an Uncontrolled At-Home Setting. J Med Internet Res 2018; 20:e89. [PMID: 29581092 PMCID: PMC5891671 DOI: 10.2196/jmir.9462] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Revised: 12/24/2017] [Accepted: 12/24/2017] [Indexed: 11/16/2022] Open
Abstract
Background Parkinson’s disease (PD) is the second most prevalent neurodegenerative disease and one of the most common forms of movement disorder. Although there is no known cure for PD, existing therapies can provide effective symptomatic relief. However, optimal titration is crucial to avoid adverse effects. Today, decision making for PD management is challenging because it relies on subjective clinical evaluations that require a visit to the clinic. This challenge has motivated recent research initiatives to develop tools that can be used by nonspecialists to assess psychomotor impairment. Among these emerging solutions, we recently reported the neuroQWERTY index, a new digital marker able to detect motor impairment in an early PD cohort through the analysis of the key press and release timing data collected during a controlled in-clinic typing task. Objective The aim of this study was to extend the in-clinic implementation to an at-home implementation by validating the applicability of the neuroQWERTY approach in an uncontrolled at-home setting, using the typing data from subjects’ natural interaction with their laptop to enable remote and unobtrusive assessment of PD signs. Methods We implemented the data-collection platform and software to enable access and storage of the typing data generated by users while using their computer at home. We recruited a total of 60 participants; of these participants 52 (25 people with Parkinson’s and 27 healthy controls) provided enough data to complete the analysis. Finally, to evaluate whether our in-clinic-built algorithm could be used in an uncontrolled at-home setting, we compared its performance on the data collected during the controlled typing task in the clinic and the results of our method using the data passively collected at home. Results Despite the randomness and sparsity introduced by the uncontrolled setting, our algorithm performed nearly as well in the at-home data (area under the receiver operating characteristic curve [AUC] of 0.76 and sensitivity/specificity of 0.73/0.69) as it did when used to evaluate the in-clinic data (AUC 0.83 and sensitivity/specificity of 0.77/0.72). Moreover, the keystroke metrics presented a strong correlation between the 2 typing settings, which suggests a minimal influence of the in-clinic typing task in users’ normal typing. Conclusions The finding that an algorithm trained on data from an in-clinic setting has comparable performance with that tested on data collected through naturalistic at-home computer use reinforces the hypothesis that subtle differences in motor function can be detected from typing behavior. This work represents another step toward an objective, user-convenient, and quasi-continuous monitoring tool for PD.
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Affiliation(s)
- Teresa Arroyo-Gallego
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States.,Biomedical Image Technologies, Universidad Politécnica de Madrid, Madrid, Spain.,Biomedical Research Networking Centre thematic area of Bioengineering, Biomaterials and Nanomedicine, Madrid, Spain.,nQ Medical Inc, Cambridge, MA, United States
| | - María J Ledesma-Carbayo
- Biomedical Image Technologies, Universidad Politécnica de Madrid, Madrid, Spain.,Biomedical Research Networking Centre thematic area of Bioengineering, Biomaterials and Nanomedicine, Madrid, Spain
| | - Ian Butterworth
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Michele Matarazzo
- Centro Integral de Neurociencias A.C., Hospital Universitario HM Puerta del Sur, Móstoles, Spain.,Neurology Department, Instituto de Investigación del Hospital 12 de Octubre, Madrid, Spain.,Enfermedades Neurodegenerativas, Centro de Investigación Biomédica en Red, Madrid, Spain.,Pacific Parkinson's Research Centre, The University of British Columbia, Vancouver, BC, Canada
| | | | | | - Martha L Gray
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States.,Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Luca Giancardo
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, United States.,Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Álvaro Sánchez-Ferro
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, United States.,Centro Integral de Neurociencias A.C., Hospital Universitario HM Puerta del Sur, Móstoles, Spain.,Neurology Department, Instituto de Investigación del Hospital 12 de Octubre, Madrid, Spain.,Enfermedades Neurodegenerativas, Centro de Investigación Biomédica en Red, Madrid, Spain.,Medical School, CEU-San Pablo University, Madrid, Spain
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