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Stevens WR, Anable NR, Barrett C, Jeans KA, Podeszwa DA. Investigating the association between self-reported physical function, temporo-spatial parameters, walking kinematics and community-based ambulatory activity: Analysis of post-operative hip preservation patients. Gait Posture 2024; 113:53-57. [PMID: 38843707 DOI: 10.1016/j.gaitpost.2024.05.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 05/24/2024] [Accepted: 05/25/2024] [Indexed: 09/09/2024]
Abstract
INTRODUCTION Wearable sensors provide the ability to assess ambulatory activity in the community after hip preservation surgery (HPS). In combination with gait analysis and patient reported outcomes, more perspective on post-operative function is gained. The purpose of this study was to assess the relationship between self-reported function/activity, temporo-spatial parameters and walking kinematics to objectively measured ambulatory activity. METHODS Forty-nine participants (38 Females; age range 16-38 years) who were five years or more post-surgery and the following diagnoses were included: Acetabular Dysplasia (n=34), Femoroacetabular Impingement (n=12) and Legg-Calvé Perthes disease (n=3). Participants underwent 3D gait analysis and gait deviations were quantified using the Gait Deviation Index (GDI) and Gait Profile Score (GPS). Temporo-spatial parameters were also calculated. Self-reported pain/function and activity level were assessed via the Harris Hip Score (HHS) and UCLA Activity Scale (UCLA). Participants wore a StepWatch Activity Monitor in their community and the Intensity/Duration of ambulatory bouts were analyzed. Spearman correlation coefficients were run to assess the following relationships: in-lab walking measures, self-reported function/activity vs.community ambulatory activity. RESULTS There were no statistically significant correlations between HHS, UCLA or temporospatial parameters with ambulatory activity (p>0.05). Worsening gait deviations (GDI/GPS scores) correlated with daily total ambulatory time (ρ=0.284/-0.284, p<0.05), time spent in Short duration ambulatory bouts (ρ=-0.321/0.321, p<0.05) and the amount of time in Long duration ambulatory bouts (ρ=0.366/-0.366, p<0.05). The amount of time spent in Easy intensity/Short duration and Easy intensity/Long duration ambulatory bouts did have a weak correlation with the GDI and GPS (p<0.05). CONCLUSIONS In HPS patients after long-term follow up, ambulatory activity in the community did not correlate with patient reported outcomes but there was a weak correlation with the presence of gait deviations. Incorporating wearable sensors to assess community ambulatory bout intensity/duration, provides additional quantifiable measures into the overall function of patients following HPS.
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VanNostrand M, Bae M, Ramsdell JC, Kasser SL. Information processing speed and disease severity predict real-world ambulation in persons with multiple sclerosis. Gait Posture 2024; 111:99-104. [PMID: 38657478 DOI: 10.1016/j.gaitpost.2024.04.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 04/17/2024] [Accepted: 04/18/2024] [Indexed: 04/26/2024]
Abstract
BACKGROUND Impairments in real-world gait quality and quantity are multifaceted for individuals with multiple sclerosis (MS), encompassing mobility, cognition, and fear of falling. However, these factors are often examined independently, limiting insights into the combined contributions they make to real-world ambulation. RESEARCH QUESTION How do mobility, cognition, and fear of falling contribute to real-world gait quality and quantity in individuals with MS? METHODS Twenty individuals with MS underwent a series of cognitive assessments, including the Paced Auditory Serial Addition Test (PASAT), Symbol Digits Modalities Test (SDMT), Stroop Test, and the Selective Reminding Test (SRT). Participants also completed the Falls Efficacy Scale - International (FES-I) and walking impairment using the Patient Determined Disease Steps (PDDS). Following the in-lab session, participants wore an inertial sensor on their lower back and asked to go about their typical daily routines for three days. Metrics of gait speed, stride regularity, time spent walking, and total bouts were extracted from the real-world data. RESULTS Significant correlations were found between both real-world gait speed and stride regularity and the SDMT, FES-I, and PDDS. Backward linear regression analysis was conducted for gait speed and stride regularity, with PDDS and SDMT included in the final model for both metrics. These variables explained 63% of the variance in gait speed and 69% of the variance in stride regularity. Results were not significant for gait quantity after adjusting for age and sex. SIGNIFICANCE The study's results provide insight regarding the roles of cognition, walking impairment, and fear of falling on real-world ambulation. Deeper understanding of these contributions can inform the development of targeted interventions that aim to improve walking. Additionally, the absence of significant correlations between gait metrics, cognition, and fear of falling with gait quantity underscores the need for further research to identify factors that increased walking in this population.
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Affiliation(s)
- Michael VanNostrand
- University of Vermont, Rehabilitation and Movement Science, Burlington, VT, USA.
| | - Myeongjin Bae
- University of Vermont, Rehabilitation and Movement Science, Burlington, VT, USA
| | - John C Ramsdell
- University of Vermont, Electrical and Biomedical Engineering, Burlington, VT, USA
| | - Susan L Kasser
- University of Vermont, Rehabilitation and Movement Science, Burlington, VT, USA
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Schalkamp AK, Harrison NA, Peall KJ, Sandor C. Digital outcome measures from smartwatch data relate to non-motor features of Parkinson's disease. NPJ Parkinsons Dis 2024; 10:110. [PMID: 38811633 PMCID: PMC11137004 DOI: 10.1038/s41531-024-00719-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 05/08/2024] [Indexed: 05/31/2024] Open
Abstract
Monitoring of Parkinson's disease (PD) has seen substantial improvement over recent years as digital sensors enable a passive and continuous collection of information in the home environment. However, the primary focus of this work has been motor symptoms, with little focus on the non-motor aspects of the disease. To address this, we combined longitudinal clinical non-motor assessment data and digital multi-sensor data from the Verily Study Watch for 149 participants from the Parkinson's Progression Monitoring Initiative (PPMI) cohort with a diagnosis of PD. We show that digitally collected physical activity and sleep measures significantly relate to clinical non-motor assessments of cognitive, autonomic, and daily living impairment. However, the poor predictive performance we observed, highlights the need for better targeted digital outcome measures to enable monitoring of non-motor symptoms.
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Affiliation(s)
- Ann-Kathrin Schalkamp
- Division of Psychological Medicine and Clinical Neuroscience, School of Medicine, Cardiff University, Cardiff, United Kingdom
- UK Dementia Research Institute, Cardiff University, Cardiff, United Kingdom
- Division of Brain Sciences, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Neil A Harrison
- Division of Psychological Medicine and Clinical Neurosciences, Neuroscience and Mental Health Innovation Institute, Cardiff, United Kingdom
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff, United Kingdom
| | - Kathryn J Peall
- Division of Psychological Medicine and Clinical Neurosciences, Neuroscience and Mental Health Innovation Institute, Cardiff, United Kingdom.
- Neuroscience and Mental Health Innovation Institute, Cardiff University, Cardiff, United Kingdom.
| | - Cynthia Sandor
- Division of Psychological Medicine and Clinical Neuroscience, School of Medicine, Cardiff University, Cardiff, United Kingdom.
- UK Dementia Research Institute, Cardiff University, Cardiff, United Kingdom.
- Division of Brain Sciences, Faculty of Medicine, Imperial College London, London, United Kingdom.
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Cai W, Young CB, Yuan R, Lee B, Ryman S, Kim J, Yang L, Levine TF, Henderson VW, Poston KL, Menon V. Subthalamic nucleus-language network connectivity predicts dopaminergic modulation of speech function in Parkinson's disease. Proc Natl Acad Sci U S A 2024; 121:e2316149121. [PMID: 38768342 PMCID: PMC11145286 DOI: 10.1073/pnas.2316149121] [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: 09/18/2023] [Accepted: 04/15/2024] [Indexed: 05/22/2024] Open
Abstract
Speech impediments are a prominent yet understudied symptom of Parkinson's disease (PD). While the subthalamic nucleus (STN) is an established clinical target for treating motor symptoms, these interventions can lead to further worsening of speech. The interplay between dopaminergic medication, STN circuitry, and their downstream effects on speech in PD is not yet fully understood. Here, we investigate the effect of dopaminergic medication on STN circuitry and probe its association with speech and cognitive functions in PD patients. We found that changes in intrinsic functional connectivity of the STN were associated with alterations in speech functions in PD. Interestingly, this relationship was characterized by altered functional connectivity of the dorsolateral and ventromedial subdivisions of the STN with the language network. Crucially, medication-induced changes in functional connectivity between the STN's dorsolateral subdivision and key regions in the language network, including the left inferior frontal cortex and the left superior temporal gyrus, correlated with alterations on a standardized neuropsychological test requiring oral responses. This relation was not observed in the written version of the same test. Furthermore, changes in functional connectivity between STN and language regions predicted the medication's downstream effects on speech-related cognitive performance. These findings reveal a previously unidentified brain mechanism through which dopaminergic medication influences speech function in PD. Our study sheds light into the subcortical-cortical circuit mechanisms underlying impaired speech control in PD. The insights gained here could inform treatment strategies aimed at mitigating speech deficits in PD and enhancing the quality of life for affected individuals.
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Affiliation(s)
- Weidong Cai
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA94305
- Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, CA94305
| | - Christina B. Young
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA94305
| | - Rui Yuan
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA94305
| | - Byeongwook Lee
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA94305
| | - Sephira Ryman
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA94305
| | - Jeehyun Kim
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA94305
| | - Laurice Yang
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA94305
| | - Taylor F. Levine
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA94305
| | - Victor W. Henderson
- Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, CA94305
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA94305
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA94305
| | - Kathleen L. Poston
- Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, CA94305
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA94305
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA94305
| | - Vinod Menon
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA94305
- Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, CA94305
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA94305
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Boborzi L, Decker J, Rezaei R, Schniepp R, Wuehr M. Human Activity Recognition in a Free-Living Environment Using an Ear-Worn Motion Sensor. SENSORS (BASEL, SWITZERLAND) 2024; 24:2665. [PMID: 38732771 PMCID: PMC11085719 DOI: 10.3390/s24092665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 04/16/2024] [Accepted: 04/20/2024] [Indexed: 05/13/2024]
Abstract
Human activity recognition (HAR) technology enables continuous behavior monitoring, which is particularly valuable in healthcare. This study investigates the viability of using an ear-worn motion sensor for classifying daily activities, including lying, sitting/standing, walking, ascending stairs, descending stairs, and running. Fifty healthy participants (between 20 and 47 years old) engaged in these activities while under monitoring. Various machine learning algorithms, ranging from interpretable shallow models to state-of-the-art deep learning approaches designed for HAR (i.e., DeepConvLSTM and ConvTransformer), were employed for classification. The results demonstrate the ear sensor's efficacy, with deep learning models achieving a 98% accuracy rate of classification. The obtained classification models are agnostic regarding which ear the sensor is worn and robust against moderate variations in sensor orientation (e.g., due to differences in auricle anatomy), meaning no initial calibration of the sensor orientation is required. The study underscores the ear's efficacy as a suitable site for monitoring human daily activity and suggests its potential for combining HAR with in-ear vital sign monitoring. This approach offers a practical method for comprehensive health monitoring by integrating sensors in a single anatomical location. This integration facilitates individualized health assessments, with potential applications in tele-monitoring, personalized health insights, and optimizing athletic training regimes.
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Affiliation(s)
- Lukas Boborzi
- German Center for Vertigo and Balance Disorders (DSGZ), Ludwig-Maximilians-University of Munich, 81377 Munich, Germany
| | - Julian Decker
- German Center for Vertigo and Balance Disorders (DSGZ), Ludwig-Maximilians-University of Munich, 81377 Munich, Germany
| | - Razieh Rezaei
- German Center for Vertigo and Balance Disorders (DSGZ), Ludwig-Maximilians-University of Munich, 81377 Munich, Germany
| | - Roman Schniepp
- Institute for Emergency Medicine and Medical Management, Ludwig-Maximilians-University of Munich, 80336 Munich, Germany
| | - Max Wuehr
- German Center for Vertigo and Balance Disorders (DSGZ), Ludwig-Maximilians-University of Munich, 81377 Munich, Germany
- Department of Neurology, Ludwig-Maximilians-University of Munich, 81377 Munich, Germany
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Mirelman A, Volkov J, Salomon A, Gazit E, Nieuwboer A, Rochester L, Del Din S, Avanzino L, Pelosin E, Bloem BR, Della Croce U, Cereatti A, Thaler A, Roggen D, Mazza C, Shirvan J, Cedarbaum JM, Giladi N, Hausdorff JM. Digital Mobility Measures: A Window into Real-World Severity and Progression of Parkinson's Disease. Mov Disord 2024; 39:328-338. [PMID: 38151859 DOI: 10.1002/mds.29689] [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: 09/05/2023] [Revised: 11/20/2023] [Accepted: 11/27/2023] [Indexed: 12/29/2023] Open
Abstract
BACKGROUND Real-world monitoring using wearable sensors has enormous potential for assessing disease severity and symptoms among persons with Parkinson's disease (PD). Many distinct features can be extracted, reflecting multiple mobility domains. However, it is unclear which digital measures are related to PD severity and are sensitive to disease progression. OBJECTIVES The aim was to identify real-world mobility measures that reflect PD severity and show discriminant ability and sensitivity to disease progression, compared to the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) scale. METHODS Multicenter real-world continuous (24/7) digital mobility data from 587 persons with PD and 68 matched healthy controls were collected using an accelerometer adhered to the lower back. Machine learning feature selection and regression algorithms evaluated associations of the digital measures using the MDS-UPDRS (I-III). Binary logistic regression assessed discriminatory value using controls, and longitudinal observational data from a subgroup (n = 33) evaluated sensitivity to change over time. RESULTS Digital measures were only moderately correlated with the MDS-UPDRS (part II-r = 0.60 and parts I and III-r = 0.50). Most associated measures reflected activity quantity and distribution patterns. A model with 14 digital measures accurately distinguished recently diagnosed persons with PD from healthy controls (81.1%, area under the curve: 0.87); digital measures showed larger effect sizes (Cohen's d: [0.19-0.66]), for change over time than any of the MDS-UPDRS parts (Cohen's d: [0.04-0.12]). CONCLUSIONS Real-world mobility measures are moderately associated with clinical assessments, suggesting that they capture different aspects of motor capacity and function. Digital mobility measures are sensitive to early-stage disease and to disease progression, to a larger degree than conventional clinical assessments, demonstrating their utility, primarily for clinical trials but ultimately also for clinical care. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Anat Mirelman
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
- Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Jana Volkov
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Amit Salomon
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Eran Gazit
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Alice Nieuwboer
- Department of Rehabilitation Science, KU Leuven, Neuromotor Rehabilitation Research Group, Leuven, Belgium
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, United Kingdom
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, United Kingdom
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Laura Avanzino
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal Child Health (DINOGMI), University of Genoa, Genoa, Italy
- Department of Experimental Medicine, Section of Human Physiology, University of Genoa, Genoa, Italy
| | - Elisa Pelosin
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal Child Health (DINOGMI), University of Genoa, Genoa, Italy
- IRCCS Policlinico San Martino Teaching Hospital, Genoa, Italy
| | - Bastiaan R Bloem
- Department of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands
| | - Ugo Della Croce
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Andrea Cereatti
- Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Avner Thaler
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
- Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | | | | | | | - Jesse M Cedarbaum
- Coeruleus Clinical Sciences, Woodbridge, Connecticut, USA
- Yale University School of Medicine, New Haven, Connecticut, USA
| | - Nir Giladi
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
- Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Jeffrey M Hausdorff
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
- Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Department of Physical Therapy, Tel Aviv University, Tel Aviv, Israel
- Department of Orthopedic Surgery, Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA
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7
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Zheng P, Jeng B, Huynh TLT, Aguiar EJ, Motl RW. Free-Living Peak Cadence in Multiple Sclerosis: A New Measure of Real-World Walking? Neurorehabil Neural Repair 2023; 37:716-726. [PMID: 37864454 DOI: 10.1177/15459683231206741] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2023]
Abstract
BACKGROUND Physical function and walking performance have become important outcomes in clinical trials and rehabilitation involving persons with multiple sclerosis (MS). However, assessments conducted in controlled settings may not reflect real-world capacity and movement in a natural environment. Peak cadence via accelerometry might represent a novel measure of walking intensity and prolonged natural effort under free-living conditions. OBJECTIVE We compared peak 30-minute cadence, peak 1-minute cadence, and time spent in incremental cadence bands between persons with MS and healthy controls, and examined the associations between peak cadence and laboratory-assessed physical function and walking performance. METHODS Participants (147 MS and 54 healthy controls) completed questionnaires on disability status and self-reported physical activity, underwent the Short Physical Performance Battery, Timed 25-Foot Walk, Timed Up and Go, and 6-Minute Walk, and wore an accelerometer for 7 days. We performed independent samples t-tests and Spearman bivariate and partial correlations adjusting for daily steps. RESULTS The MS sample demonstrated lower physical function and walking performance scores, daily steps, and peak cadence (P < .001), and spent less time in purposeful steps and slow-to-brisk walking (40-119 steps/minutes), but accumulated more incidental movement (1-19 steps/minutes) than healthy controls. The associations between peak cadence and performance outcomes were strong in MS (|rs| = 0.59-0.68) and remained significant after controlling for daily steps (|prs| = 0.22-0.44), P-values < .01. Peak cadence was inversely correlated with age and disability, regardless of daily steps (P < .01). CONCLUSIONS Our findings provide preliminary evidence for the potential use of peak cadence with step-based metrics for comprehensively evaluating free-living walking performance in MS.
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Affiliation(s)
- Peixuan Zheng
- Department of Kinesiology and Nutrition, University of Illinois Chicago, Chicago, IL, USA
| | - Brenda Jeng
- Department of Kinesiology and Nutrition, University of Illinois Chicago, Chicago, IL, USA
| | - Trinh L T Huynh
- Department of Physical Therapy, School of Health Professions, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Elroy J Aguiar
- Department of Kinesiology, The University of Alabama, Tuscaloosa, AL, USA
| | - Robert W Motl
- Department of Kinesiology and Nutrition, University of Illinois Chicago, Chicago, IL, USA
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Tam W, Alajlani M, Abd-Alrazaq A. An Exploration of Wearable Device Features Used in UK Hospital Parkinson Disease Care: Scoping Review. J Med Internet Res 2023; 25:e42950. [PMID: 37594791 PMCID: PMC10474516 DOI: 10.2196/42950] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 03/13/2023] [Accepted: 04/14/2023] [Indexed: 08/19/2023] Open
Abstract
BACKGROUND The prevalence of Parkinson disease (PD) is becoming an increasing concern owing to the aging population in the United Kingdom. Wearable devices have the potential to improve the clinical care of patients with PD while reducing health care costs. Consequently, exploring the features of these wearable devices is important to identify the limitations and further areas of investigation of how wearable devices are currently used in clinical care in the United Kingdom. OBJECTIVE In this scoping review, we aimed to explore the features of wearable devices used for PD in hospitals in the United Kingdom. METHODS A scoping review of the current research was undertaken and reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The literature search was undertaken on June 6, 2022, and publications were obtained from MEDLINE or PubMed, Embase, and the Cochrane Library. Eligible publications were initially screened by their titles and abstracts. Publications that passed the initial screening underwent a full review. The study characteristics were extracted from the final publications, and the evidence was synthesized using a narrative approach. Any queries were reviewed by the first and second authors. RESULTS Of the 4543 publications identified, 39 (0.86%) publications underwent a full review, and 20 (0.44%) publications were included in the scoping review. Most studies (11/20, 55%) were conducted at the Newcastle upon Tyne Hospitals NHS Foundation Trust, with sample sizes ranging from 10 to 418. Most study participants were male individuals with a mean age ranging from 57.7 to 78.0 years. The AX3 was the most popular device brand used, and it was commercially manufactured by Axivity. Common wearable device types included body-worn sensors, inertial measurement units, and smartwatches that used accelerometers and gyroscopes to measure the clinical features of PD. Most wearable device primary measures involved the measured gait, bradykinesia, and dyskinesia. The most common wearable device placements were the lumbar region, head, and wrist. Furthermore, 65% (13/20) of the studies used artificial intelligence or machine learning to support PD data analysis. CONCLUSIONS This study demonstrated that wearable devices could help provide a more detailed analysis of PD symptoms during the assessment phase and personalize treatment. Using machine learning, wearable devices could differentiate PD from other neurodegenerative diseases. The identified evidence gaps include the lack of analysis of wearable device cybersecurity and data management. The lack of cost-effectiveness analysis and large-scale participation in studies resulted in uncertainty regarding the feasibility of the widespread use of wearable devices. The uncertainty around the identified research gaps was further exacerbated by the lack of medical regulation of wearable devices for PD, particularly in the United Kingdom where regulations were changing due to the political landscape.
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Affiliation(s)
- William Tam
- Insitute of Digital Healthcare, Warwick Manufacturing Group, University of Warwick, Coventry, United Kingdom
| | - Mohannad Alajlani
- Insitute of Digital Healthcare, Warwick Manufacturing Group, University of Warwick, Coventry, United Kingdom
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Kretch KS, Koziol NA, Marcinowski EC, Hsu LY, Harbourne RT, Lobo MA, McCoy SW, Willett SL, Dusing SC. Sitting Capacity and Performance in Infants with Typical Development and Infants with Motor Delay. Phys Occup Ther Pediatr 2023; 44:164-179. [PMID: 37550959 DOI: 10.1080/01942638.2023.2241537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 07/19/2023] [Accepted: 07/21/2023] [Indexed: 08/09/2023]
Abstract
AIMS Infants with neuromotor disorders demonstrate delays in sitting skills (decreased capacity) and are less likely to maintain independent sitting during play than their peers with typical development (decreased performance). This study aimed to quantify developmental trajectories of sitting capacity and sitting performance in infants with typical development and infants with significant motor delay and to assess whether the relationship between capacity and performance differs between the groups. METHODS Typically developing infants (n = 35) and infants with significant motor delay (n = 31) were assessed longitudinally over a year following early sitting readiness. The Gross Motor Function Measure (GMFM) Sitting Dimension was used to assess sitting capacity, and a 5-min free play observation was used to assess sitting performance. RESULTS Both capacity and performance increased at a faster rate initially, with more deceleration across time, in infants with typical development compared to infants with motor delay. At lower GMFM scores, changes in GMFM sitting were associated with larger changes in independent sitting for infants with typical development, and the association between GMFM sitting and independent sitting varied more across GMFM scores for typically developing infants. CONCLUSIONS Intervention and assessment for infants with motor delay should target both sitting capacity and sitting performance.
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Affiliation(s)
- Kari S Kretch
- Division of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA, United States
| | - Natalie A Koziol
- Nebraska Center for Research on Children, Youth, Families and Schools, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Emily C Marcinowski
- School of Kinesiology, Louisiana State University, Baton Rouge, LA, United States
| | - Lin-Ya Hsu
- Division of Physical Therapy, University of Washington, Seattle, WA, United States
| | - Regina T Harbourne
- Department of Physical Therapy, Duquesne University, Pittsburgh PA, United States
| | - Michele A Lobo
- Department of Physical Therapy, University of Delaware, Newark, DE, United States
| | - Sarah W McCoy
- Division of Physical Therapy, University of Washington, Seattle, WA, United States
| | - Sandra L Willett
- Munroe-Meyer Institute, University of Nebraska Medical Center, Omaha, NE, United States
| | - Stacey C Dusing
- Division of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA, United States
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ZhuParris A, de Goede AA, Yocarini IE, Kraaij W, Groeneveld GJ, Doll RJ. Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115243. [PMID: 37299969 DOI: 10.3390/s23115243] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 05/23/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023]
Abstract
BACKGROUND Central nervous system (CNS) disorders benefit from ongoing monitoring to assess disease progression and treatment efficacy. Mobile health (mHealth) technologies offer a means for the remote and continuous symptom monitoring of patients. Machine Learning (ML) techniques can process and engineer mHealth data into a precise and multidimensional biomarker of disease activity. OBJECTIVE This narrative literature review aims to provide an overview of the current landscape of biomarker development using mHealth technologies and ML. Additionally, it proposes recommendations to ensure the accuracy, reliability, and interpretability of these biomarkers. METHODS This review extracted relevant publications from databases such as PubMed, IEEE, and CTTI. The ML methods employed across the selected publications were then extracted, aggregated, and reviewed. RESULTS This review synthesized and presented the diverse approaches of 66 publications that address creating mHealth-based biomarkers using ML. The reviewed publications provide a foundation for effective biomarker development and offer recommendations for creating representative, reproducible, and interpretable biomarkers for future clinical trials. CONCLUSION mHealth-based and ML-derived biomarkers have great potential for the remote monitoring of CNS disorders. However, further research and standardization of study designs are needed to advance this field. With continued innovation, mHealth-based biomarkers hold promise for improving the monitoring of CNS disorders.
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Affiliation(s)
- Ahnjili ZhuParris
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
- Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA Leiden, The Netherlands
| | - Annika A de Goede
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
| | - Iris E Yocarini
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
| | - Wessel Kraaij
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
- The Netherlands Organisation for Applied Scientific Research (TNO), Anna van Buerenplein 1, 2595 DA, Den Haag, The Netherlands
| | - Geert Jan Groeneveld
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
| | - Robert Jan Doll
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
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11
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Salis F, Bertuletti S, Bonci T, Caruso M, Scott K, Alcock L, Buckley E, Gazit E, Hansen C, Schwickert L, Aminian K, Becker C, Brown P, Carsin AE, Caulfield B, Chiari L, D’Ascanio I, Del Din S, Eskofier BM, Garcia-Aymerich J, Hausdorff JM, Hume EC, Kirk C, Kluge F, Koch S, Kuederle A, Maetzler W, Micó-Amigo EM, Mueller A, Neatrour I, Paraschiv-Ionescu A, Palmerini L, Yarnall AJ, Rochester L, Sharrack B, Singleton D, Vereijken B, Vogiatzis I, Della Croce U, Mazzà C, Cereatti A, for the Mobilise-D consortium. A multi-sensor wearable system for the assessment of diseased gait in real-world conditions. Front Bioeng Biotechnol 2023; 11:1143248. [PMID: 37214281 PMCID: PMC10194657 DOI: 10.3389/fbioe.2023.1143248] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 03/30/2023] [Indexed: 05/24/2023] Open
Abstract
Introduction: Accurately assessing people's gait, especially in real-world conditions and in case of impaired mobility, is still a challenge due to intrinsic and extrinsic factors resulting in gait complexity. To improve the estimation of gait-related digital mobility outcomes (DMOs) in real-world scenarios, this study presents a wearable multi-sensor system (INDIP), integrating complementary sensing approaches (two plantar pressure insoles, three inertial units and two distance sensors). Methods: The INDIP technical validity was assessed against stereophotogrammetry during a laboratory experimental protocol comprising structured tests (including continuous curvilinear and rectilinear walking and steps) and a simulation of daily-life activities (including intermittent gait and short walking bouts). To evaluate its performance on various gait patterns, data were collected on 128 participants from seven cohorts: healthy young and older adults, patients with Parkinson's disease, multiple sclerosis, chronic obstructive pulmonary disease, congestive heart failure, and proximal femur fracture. Moreover, INDIP usability was evaluated by recording 2.5-h of real-world unsupervised activity. Results and discussion: Excellent absolute agreement (ICC >0.95) and very limited mean absolute errors were observed for all cohorts and digital mobility outcomes (cadence ≤0.61 steps/min, stride length ≤0.02 m, walking speed ≤0.02 m/s) in the structured tests. Larger, but limited, errors were observed during the daily-life simulation (cadence 2.72-4.87 steps/min, stride length 0.04-0.06 m, walking speed 0.03-0.05 m/s). Neither major technical nor usability issues were declared during the 2.5-h acquisitions. Therefore, the INDIP system can be considered a valid and feasible solution to collect reference data for analyzing gait in real-world conditions.
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Affiliation(s)
- Francesca Salis
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System (IuC BoHNes), Sassari, Italy
| | - Stefano Bertuletti
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System (IuC BoHNes), Sassari, Italy
| | - Tecla Bonci
- Department of Mechanical Engineering, Insigneo Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
| | - Marco Caruso
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System (IuC BoHNes), Sassari, Italy
- Department of Electronics and Telecommunications, Politecnico Di Torino, Torino, Italy
| | - Kirsty Scott
- Department of Mechanical Engineering, Insigneo Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
| | - Lisa Alcock
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Ellen Buckley
- Department of Mechanical Engineering, Insigneo Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
| | - Eran Gazit
- Centre for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Centre, Tel Aviv, Israel
| | - Clint Hansen
- Department of Neurology, University Medical Centre Schleswig-Holstein Campus Kiel and Kiel University, Kiel, Germany
| | - Lars Schwickert
- Department for Geriatric Rehabilitation, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Kamiar Aminian
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Clemens Becker
- Department for Geriatric Rehabilitation, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Philip Brown
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, United Kingdom
| | - Anne-Elie Carsin
- Instituto de Salud Global Barcelona, Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Brian Caulfield
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
| | - Lorenzo Chiari
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, Bologna, Italy
- Health Sciences and Technologies-Interdepartmental Centre for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
| | - Ilaria D’Ascanio
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, Bologna, Italy
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Bjoern M. Eskofier
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Judith Garcia-Aymerich
- Instituto de Salud Global Barcelona, Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Jeffrey M. Hausdorff
- Centre for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Centre, Tel Aviv, Israel
| | - Emily C. Hume
- Department of Sport, Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University, Northumbia, United Kingdom
| | - Cameron Kirk
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Felix Kluge
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Novartis Institutes of Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Sarah Koch
- Instituto de Salud Global Barcelona, Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Arne Kuederle
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Walter Maetzler
- Department of Neurology, University Medical Centre Schleswig-Holstein Campus Kiel and Kiel University, Kiel, Germany
| | - Encarna M. Micó-Amigo
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Arne Mueller
- Novartis Institutes of Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Isabel Neatrour
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Anisoara Paraschiv-Ionescu
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Luca Palmerini
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, Bologna, Italy
- Health Sciences and Technologies-Interdepartmental Centre for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
| | - Alison J. Yarnall
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University, Newcastle Upon Tyne, United Kingdom
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, United Kingdom
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University, Newcastle Upon Tyne, United Kingdom
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, United Kingdom
| | - Basil Sharrack
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - David Singleton
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
| | - Beatrix Vereijken
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ioannis Vogiatzis
- Department of Sport, Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University, Northumbia, United Kingdom
| | - Ugo Della Croce
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System (IuC BoHNes), Sassari, Italy
| | - Claudia Mazzà
- Department of Mechanical Engineering, Insigneo Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
| | - Andrea Cereatti
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System (IuC BoHNes), Sassari, Italy
- Department of Electronics and Telecommunications, Politecnico Di Torino, Torino, Italy
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12
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Shah VV, Jagodinsky A, McNames J, Carlson-Kuhta P, Nutt JG, El-Gohary M, Sowalsky K, Harker G, Mancini M, Horak FB. Gait and turning characteristics from daily life increase ability to predict future falls in people with Parkinson's disease. Front Neurol 2023; 14:1096401. [PMID: 36937534 PMCID: PMC10015637 DOI: 10.3389/fneur.2023.1096401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 02/02/2023] [Indexed: 03/05/2023] Open
Abstract
Objectives To investigate if digital measures of gait (walking and turning) collected passively over a week of daily activities in people with Parkinson's disease (PD) increases the discriminative ability to predict future falls compared to fall history alone. Methods We recruited 34 individuals with PD (17 with history of falls and 17 non-fallers), age: 68 ± 6 years, MDS-UPDRS III ON: 31 ± 9. Participants were classified as fallers (at least one fall) or non-fallers based on self-reported falls in past 6 months. Eighty digital measures of gait were derived from 3 inertial sensors (Opal® V2 System) placed on the feet and lower back for a week of passive gait monitoring. Logistic regression employing a "best subsets selection strategy" was used to find combinations of measures that discriminated future fallers from non-fallers, and the Area Under Curve (AUC). Participants were followed via email every 2 weeks over the year after the study for self-reported falls. Results Twenty-five subjects reported falls in the follow-up year. Quantity of gait and turning measures (e.g., number of gait bouts and turns per hour) were similar in future fallers and non-fallers. The AUC to discriminate future fallers from non-fallers using fall history alone was 0.77 (95% CI: [0.50-1.00]). In contrast, the highest AUC for gait and turning digital measures with 4 combinations was 0.94 [0.84-1.00]. From the top 10 models (all AUCs>0.90) via the best subsets strategy, the most consistently selected measures were variability of toe-out angle of the foot (9 out of 10), pitch angle of the foot during mid-swing (8 out of 10), and peak turn velocity (7 out of 10). Conclusions These findings highlight the importance of considering precise digital measures, captured via sensors strategically placed on the feet and low back, to quantify several different aspects of gait (walking and turning) during daily life to improve the classification of future fallers in PD.
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Affiliation(s)
- Vrutangkumar V. Shah
- Department of Neurology, Oregon Health & Science University, Portland, OR, United States
- APDM Wearable Technologies, A Clario Company, Portland, OR, United States
| | - Adam Jagodinsky
- APDM Wearable Technologies, A Clario Company, Portland, OR, United States
| | - James McNames
- APDM Wearable Technologies, A Clario Company, Portland, OR, United States
- Department of Electrical and Computer Engineering, Portland State University, Portland, OR, United States
| | - Patricia Carlson-Kuhta
- Department of Neurology, Oregon Health & Science University, Portland, OR, United States
| | - John G. Nutt
- Department of Neurology, Oregon Health & Science University, Portland, OR, United States
| | - Mahmoud El-Gohary
- APDM Wearable Technologies, A Clario Company, Portland, OR, United States
| | - Kristen Sowalsky
- APDM Wearable Technologies, A Clario Company, Portland, OR, United States
| | - Graham Harker
- Department of Neurology, Oregon Health & Science University, Portland, OR, United States
| | - Martina Mancini
- Department of Neurology, Oregon Health & Science University, Portland, OR, United States
| | - Fay B. Horak
- Department of Neurology, Oregon Health & Science University, Portland, OR, United States
- APDM Wearable Technologies, A Clario Company, Portland, OR, United States
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13
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Cohen M, Herman T, Ganz N, Badichi I, Gurevich T, Hausdorff JM. Multidisciplinary Intensive Rehabilitation Program for People with Parkinson's Disease: Gaps between the Clinic and Real-World Mobility. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:3806. [PMID: 36900826 PMCID: PMC10001519 DOI: 10.3390/ijerph20053806] [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: 12/05/2022] [Revised: 02/12/2023] [Accepted: 02/15/2023] [Indexed: 06/18/2023]
Abstract
Intensive rehabilitation programs improve motor and non-motor symptoms in people with Parkinson's disease (PD), however, it is not known whether transfer to daily-living walking occurs. The effects of multidisciplinary-intensive-outpatient rehabilitation (MIOR) on gait and balance in the clinic and on everyday walking were examined. Forty-six (46) people with PD were evaluated before and after the intensive program. A 3D accelerometer placed on the lower back measured daily-living walking during the week before and after the intervention. Participants were also stratified into "responders" and "non-responders" based on daily-living-step-counts. After the intervention, gait and balance significantly improved, e.g., MiniBest scores (p < 0.001), dual-task gait speed increased (p = 0.016) and 6-minute walk distance increased (p < 0.001). Many improvements persisted after 3 months. In contrast, daily-living number of steps and gait quality features did not change in response to the intervention (p > 0.1). Only among the "responders", a significant increase in daily-living number of steps was found (p < 0.001). These findings demonstrate that in people with PD improvements in the clinic do not necessarily carry over to daily-living walking. In a select group of people with PD, it is possible to ameliorate daily-living walking quality, potentially also reducing fall risk. Nevertheless, we speculate that self-management in people with PD is relatively poor; therefore, to maintain health and everyday walking abilities, actions such as long-term engaging in physical activity and preserving mobility may be needed.
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Affiliation(s)
- Moriya Cohen
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492416, Israel
- Ezra Lemarpeh Center, Bnei Brak 5111501, Israel
| | - Talia Herman
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492416, Israel
| | - Natalie Ganz
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492416, Israel
| | | | - Tanya Gurevich
- Movement Disorders Unit, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492416, Israel
- Sagol School of Neuroscience, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Department of Neurology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Jeffrey M. Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492416, Israel
- Sagol School of Neuroscience, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Rush Alzheimer’s Disease Center and Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL 60612, USA
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14
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Shah VV, McNames J, Carlson‐Kuhta P, Nutt JG, El‐Gohary M, Sowalsky K, Mancini M, Horak FB. Effect of Levodopa and Environmental Setting on Gait and Turning Digital Markers Related to Falls in People with Parkinson's Disease. Mov Disord Clin Pract 2023; 10:223-230. [PMID: 36825056 PMCID: PMC9941945 DOI: 10.1002/mdc3.13601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/04/2022] [Accepted: 10/08/2022] [Indexed: 11/11/2022] Open
Abstract
Background It is unknown whether medication status (off and on levodopa) or laboratory versus home settings plays a role in discriminating fallers and non-fallers in people with Parkinson's disease (PD). Objectives To investigate which specific digital gait and turning measures, obtained with body-worn sensors, best discriminated fallers from non-fallers with PD in the clinic and during daily life. Methods We recruited 34 subjects with PD (17 fallers and 17 non-fallers based on the past 6 month's falls). Subjects wore three inertial sensors attached to both feet and the lumbar region in the laboratory for a 3-minute walking task (both off and on levodopa) and during daily life activities for a week. We derived 24 digital (18 gait and 6 turn) measures from the 3-minute walk and from daily life. Results In clinic, none of the gait and turning measures collected during on levodopa state were significantly different between fallers and non-fallers. In contrast, digital measures collected in the off levodopa state were significantly different between groups, (average turn velocity, average number of steps to complete a turn, and variability of gait speed, P < 0.03). During daily life, the variability of average turn velocity (P = 0.023) was significantly different in fallers than non-fallers. Last, the average number of steps to complete a turn was significantly correlated with the patient-reported outcomes. Conclusions Digital measures of turning, but not gait, were different in fallers compared to non-fallers with PD, in the laboratory when off medication and during a daily life.
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Affiliation(s)
- Vrutangkumar V. Shah
- Department of NeurologyOregon Health & Science UniversityPortlandOregonUSA
- APDM Wearable Technologies, a Clario companyPortlandOregonUSA
| | - James McNames
- APDM Wearable Technologies, a Clario companyPortlandOregonUSA
- Department of Electrical and Computer EngineeringPortland State UniversityPortlandOregonUSA
| | | | - John G. Nutt
- Department of NeurologyOregon Health & Science UniversityPortlandOregonUSA
| | | | | | - Martina Mancini
- Department of NeurologyOregon Health & Science UniversityPortlandOregonUSA
| | - Fay B. Horak
- Department of NeurologyOregon Health & Science UniversityPortlandOregonUSA
- APDM Wearable Technologies, a Clario companyPortlandOregonUSA
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Kirk C, Zia Ur Rehman R, Galna B, Alcock L, Ranciati S, Palmerini L, Garcia-Aymerich J, Hansen C, Schaeffer E, Berg D, Maetzler W, Rochester L, Del Din S, Yarnall AJ. Can Digital Mobility Assessment Enhance the Clinical Assessment of Disease Severity in Parkinson's Disease? JOURNAL OF PARKINSON'S DISEASE 2023; 13:999-1009. [PMID: 37545259 PMCID: PMC10578274 DOI: 10.3233/jpd-230044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/03/2023] [Indexed: 08/08/2023]
Abstract
BACKGROUND Real-world walking speed (RWS) measured using wearable devices has the potential to complement the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS III) for motor assessment in Parkinson's disease (PD). OBJECTIVE Explore cross-sectional and longitudinal differences in RWS between PD and older adults (OAs), and whether RWS was related to motor disease severity cross-sectionally, and if MDS-UPDRS III was related to RWS, longitudinally. METHODS 88 PD and 111 OA participants from ICICLE-GAIT (UK) were included. RWS was evaluated using an accelerometer at four time points. RWS was aggregated within walking bout (WB) duration thresholds. Between-group-comparisons in RWS between PD and OAs were conducted cross-sectionally, and longitudinally with mixed effects models (MEMs). Cross-sectional association between RWS and MDS-UPDRS III was explored using linear regression, and longitudinal association explored with MEMs. RESULTS RWS was significantly lower in PD (1.04 m/s) in comparison to OAs (1.10 m/s) cross-sectionally. RWS significantly decreased over time for both cohorts and decline was more rapid in PD by 0.02 m/s per year. Significant negative relationship between RWS and the MDS-UPDRS III only existed at a specific WB threshold (30 to 60 s, β= - 3.94 points, p = 0.047). MDS-UPDRS III increased significantly by 1.84 points per year, which was not related to change in RWS. CONCLUSION Digital mobility assessment of gait may add unique information to quantify disease progression remotely, but further validation in research and clinical settings is needed.
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Affiliation(s)
- Cameron Kirk
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Rana Zia Ur Rehman
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- Janssen Research & Development, High Wycombe, UK
| | - Brook Galna
- School of Allied Health (Exercise Science) / Health Futures Institute, Murdoch University, Perth, Australia
| | - Lisa Alcock
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- National Institute for Healthand Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle upon Tyne, UK
| | - Saverio Ranciati
- Department of Statistical Science “Paolo Fortunati”, University of Bologna, Bologna, Italy
| | - Luca Palmerini
- Department of Electrical, Electronic and Information Engineering, “Guglielmo Marconi”, University of Bologna, Bologna, Italy
- Health Sciences and Technologies—Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
| | - Judith Garcia-Aymerich
- ISGlobal, Barcelona, Spain
- University Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiologica y Salud Publica (CIBERESP), Barcelona, Spain
| | - Clint Hansen
- Department of Neurology, Christian-Albrecht-University Kiel, Kiel, Germany
| | - Eva Schaeffer
- Department of Neurology, Christian-Albrecht-University Kiel, Kiel, Germany
| | - Daniela Berg
- Department of Neurology, Christian-Albrecht-University Kiel, Kiel, Germany
- German Centre of Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Walter Maetzler
- Department of Neurology, Christian-Albrecht-University Kiel, Kiel, Germany
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- National Institute for Healthand Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle upon Tyne, UK
- Newcastle upon Tyne Hospitals NHS Foundations Trust, Newcastle upon Tyne, UK
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- National Institute for Healthand Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle upon Tyne, UK
| | - Alison J. Yarnall
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- National Institute for Healthand Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle upon Tyne, UK
- Newcastle upon Tyne Hospitals NHS Foundations Trust, Newcastle upon Tyne, UK
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16
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Multimodal Mobility Assessment Predicts Fall Frequency and Severity in Cerebellar Ataxia. CEREBELLUM (LONDON, ENGLAND) 2023; 22:85-95. [PMID: 35122222 PMCID: PMC9883327 DOI: 10.1007/s12311-021-01365-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 12/29/2021] [Indexed: 02/01/2023]
Abstract
This cohort study aims to evaluate the predictive validity of multimodal clinical assessment and quantitative measures of in- and off-laboratory mobility for fall-risk estimation in patients with cerebellar ataxia (CA).Occurrence, severity, and consequences of falling were prospectively assessed for 6 months in 93 patients with hereditary (N = 36) and sporadic or secondary (N = 57) forms of CA and 63 healthy controls. Participants completed a multimodal clinical and functional fall risk assessment, in-laboratory gait examination, and a 2-week inertial sensor-based daily mobility monitoring. Multivariate logistic regression analyses were performed to evaluate the predictive capacity of all clinical and in- and off-laboratory mobility measures with respect to fall (1) status (non-faller vs. faller), (2) frequency (occasional vs. frequent falls), and (3) severity (benign vs. injurious fall) of patients. 64% of patients experienced one or recurrent falls and 65% of these severe fall-related injuries during prospective assessment. Mobility impairments in patients corresponded to a mild-to-moderate ataxic gait disorder. Patients' fall status and frequency could be reliably predicted (78% and 81% accuracy, respectively), primarily based on their retrospective fall status. Clinical scoring of ataxic symptoms and in- and off-laboratory gait and mobility measures improved classification and provided unique information for the prediction of fall severity (84% accuracy).These results encourage a stepwise approach for fall risk assessment in patients with CA: fall history-taking readily and reliably informs the clinician about patients' general fall risk. Clinical scoring and instrument-based mobility measures provide further in-depth information on the risk of recurrent and injurious falling.
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17
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Fragmentation, circadian amplitude, and fractal pattern of daily-living physical activity in people with multiple sclerosis: Is there relevant information beyond the total amount of physical activity? Mult Scler Relat Disord 2022; 68:104108. [PMID: 36063732 DOI: 10.1016/j.msard.2022.104108] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 08/12/2022] [Indexed: 12/15/2022]
Abstract
BACKGROUND Physical activity is lower in people with multiple sclerosis (pwMS) compared to healthy controls. Previous work focused on studying activity levels or activity volume, but studies of daily-living rest-activity fragmentation patterns, circadian rhythms, and fractal regulation in pwMS are limited. Based on findings in other cohorts, one could suggest that these aspects of daily-living physical activity will provide additional information about the health and well-being of pwMS. Therefore, here, we aimed to (1) identify which fragmentation, fractal, and circadian amplitude measures differ between pwMS and healthy controls, (2) evaluate the relationship between fragmentation, fractal, and circadian amplitude measures and disease severity, and (3) begin to evaluate the added value of those measures, as compared to more conventional measures of physical activity (e.g., mean signal vector magnitude (SVM). A global measure of the overall volume of physical activity). METHODS 132 people with relapsing-remitting MS (47±11 yrs, 69.7% female, Expanded Disability Status Scale, EDSS, median (IQR): 3 (2-4)) and 90 healthy controls (46±11 yrs, 47.8% female) were asked to wear a 3D accelerometer on their lower back for 7 days. Rest-activity fragmentation, circadian amplitude, fractal regulation, and mean SVM metrics were extracted. PwMS and healthy controls were compared using independent samples t-tests and linear regression, including comparisons adjusted for mean SVM to control for the effect of physical activity volume. Spearman correlations between measures and logistic regressions were used to identify the clinical condition based on the measures that differed significantly after adjusting for SVM. All analyses included adjustments for demographic and clinical parameters (e.g., age, sex). RESULTS Multiple measures of activity fragmentation significantly differed between pwMS and healthy controls, reflecting a more fragmented active behavior in pwMS. PwMS had a lower circadian rhythm amplitude, indicating a smaller amplitude in the circadian changes of daily activity, and weaker temporal correlations as based on the fractal analysis. When taking into account physical activity volume, one circadian amplitude measure and one fractal measure remained significantly different in pwMS and controls. Fragmentation measures and circadian amplitude measures were significantly associated with disability level as measured by the EDSS; the association with circadian amplitude remained significant, even after adjusting for the mean SVM. CONCLUSION The physical activity patterns of pwMS differ from those of healthy individuals in rest-activity fragmentation, the amplitude of the circadian rhythm, and fractal regulation. Measures describing these aspects of activity provide information that is not captured in the total volume of physical activity and could, perhaps, augment the monitoring of disease progression and evaluation of the response to interventions.
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18
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Zajac JA, Cavanaugh JT, Baker T, Duncan RP, Fulford D, Girnis J, LaValley M, Nordahl T, Porciuncula F, Rawson KS, Saint-Hilaire M, Thomas CA, Earhart GM, Ellis TD. Does clinically measured walking capacity contribute to real-world walking performance in Parkinson's disease? Parkinsonism Relat Disord 2022; 105:123-127. [PMID: 36423521 PMCID: PMC9722599 DOI: 10.1016/j.parkreldis.2022.11.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 11/05/2022] [Accepted: 11/14/2022] [Indexed: 11/17/2022]
Abstract
OBJECTIVE The study examined how clinically measured walking capacity contributes to real-world walking performance in persons with Parkinson's disease (PD). METHODS Cross-sectional baseline data (n = 82) from a PD clinical trial were analyzed. The 6-Minute Walk Test (6MWT) and 10-Meter Walk Test (10MWT) were used to generate capacity metrics of walking endurance and fast gait speed, respectively. An activity monitor worn for seven days was used to generate performance metrics of mean daily steps and weekly moderate intensity walking minutes. Univariate linear regression analyses were used to examine associations between each capacity and performance measure in the full sample and less and more active subgroups. RESULTS Walking capacity significantly contributed to daily steps in the full sample (endurance: R2=.13, p < .001; fast gait speed: R2=.07, p = .017) and in the less active subgroup (endurance: R2 =.09, p = .045). Similarly, walking capacity significantly contributed to weekly moderate intensity minutes in the full sample (endurance: R2=.13, p < .001; fast gait speed: R2=.09, p = .007) and less active subgroup (endurance: R2 = .25, p < .001; fast gait speed: R2 =.21, p = .007). Walking capacity did not significantly contribute to daily steps or moderate intensity minutes in the more active subgroup. CONCLUSIONS Walking capacity contributed to, but explained a relatively small portion of the variance in, real-world walking performance. The contribution was somewhat greater in less active individuals. The study adds support to the idea that clinically measured walking capacity may have limited benefit for understanding real-world walking performance in PD. Factors beyond walking capacity may better account for actual walking behavior.
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Affiliation(s)
- Jenna A Zajac
- Department of Physical Therapy and Athletic Training, Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA, USA.
| | - James T Cavanaugh
- Department of Physical Therapy, University of New England, Portland, ME, USA
| | - Teresa Baker
- Department of Physical Therapy and Athletic Training, Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA, USA
| | - Ryan P Duncan
- Program in Physical Therapy, Washington University in St Louis School of Medicine, St Louis, MO, USA; Department of Neurology, Washington University in St Louis School of Medicine, St Louis, MO, USA
| | - Daniel Fulford
- Department of Occupational Therapy, Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA, USA
| | - Jaimie Girnis
- Department of Physical Therapy and Athletic Training, Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA, USA
| | | | - Timothy Nordahl
- Department of Physical Therapy and Athletic Training, Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA, USA
| | - Franchino Porciuncula
- Department of Physical Therapy and Athletic Training, Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA, USA
| | - Kerri S Rawson
- Program in Physical Therapy, Washington University in St Louis School of Medicine, St Louis, MO, USA
| | - Marie Saint-Hilaire
- Department of Neurology, Parkinson's Disease and Movement Disorders Center, Boston University, Boston, MA, USA
| | - Cathi A Thomas
- Department of Neurology, Parkinson's Disease and Movement Disorders Center, Boston University, Boston, MA, USA
| | - Gammon M Earhart
- Program in Physical Therapy, Washington University in St Louis School of Medicine, St Louis, MO, USA; Department of Neurology, Washington University in St Louis School of Medicine, St Louis, MO, USA; Department of Neuroscience, Washington University in St Louis School of Medicine, St Louis, MO, USA
| | - Terry D Ellis
- Department of Physical Therapy and Athletic Training, Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA, USA
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Tam W, Alajlani M, Abd-alrazaq A. An Exploration of Wearable Device Features Used in UK Hospital Parkinson Disease Care: Scoping Review (Preprint).. [DOI: 10.2196/preprints.42950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
BACKGROUND
The prevalence of Parkinson disease (PD) is becoming an increasing concern owing to the aging population in the United Kingdom. Wearable devices have the potential to improve the clinical care of patients with PD while reducing health care costs. Consequently, exploring the features of these wearable devices is important to identify the limitations and further areas of investigation of how wearable devices are currently used in clinical care in the United Kingdom.
OBJECTIVE
In this scoping review, we aimed to explore the features of wearable devices used for PD in hospitals in the United Kingdom.
METHODS
A scoping review of the current research was undertaken and reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The literature search was undertaken on June 6, 2022, and publications were obtained from MEDLINE or PubMed, Embase, and the Cochrane Library. Eligible publications were initially screened by their titles and abstracts. Publications that passed the initial screening underwent a full review. The study characteristics were extracted from the final publications, and the evidence was synthesized using a narrative approach. Any queries were reviewed by the first and second authors.
RESULTS
Of the 4543 publications identified, 39 (0.86%) publications underwent a full review, and 20 (0.44%) publications were included in the scoping review. Most studies (11/20, 55%) were conducted at the Newcastle upon Tyne Hospitals NHS Foundation Trust, with sample sizes ranging from 10 to 418. Most study participants were male individuals with a mean age ranging from 57.7 to 78.0 years. The AX3 was the most popular device brand used, and it was commercially manufactured by Axivity. Common wearable device types included body-worn sensors, inertial measurement units, and smartwatches that used accelerometers and gyroscopes to measure the clinical features of PD. Most wearable device primary measures involved the measured gait, bradykinesia, and dyskinesia. The most common wearable device placements were the lumbar region, head, and wrist. Furthermore, 65% (13/20) of the studies used artificial intelligence or machine learning to support PD data analysis.
CONCLUSIONS
This study demonstrated that wearable devices could help provide a more detailed analysis of PD symptoms during the assessment phase and personalize treatment. Using machine learning, wearable devices could differentiate PD from other neurodegenerative diseases. The identified evidence gaps include the lack of analysis of wearable device cybersecurity and data management. The lack of cost-effectiveness analysis and large-scale participation in studies resulted in uncertainty regarding the feasibility of the widespread use of wearable devices. The uncertainty around the identified research gaps was further exacerbated by the lack of medical regulation of wearable devices for PD, particularly in the United Kingdom where regulations were changing due to the political landscape.
CLINICALTRIAL
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20
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Liu Y, Zhang G, Tarolli CG, Hristov R, Jensen-Roberts S, Waddell EM, Myers TL, Pawlik ME, Soto JM, Wilson RM, Yang Y, Nordahl T, Lizarraga KJ, Adams JL, Schneider RB, Kieburtz K, Ellis T, Dorsey ER, Katabi D. Monitoring gait at home with radio waves in Parkinson's disease: A marker of severity, progression, and medication response. Sci Transl Med 2022; 14:eadc9669. [PMID: 36130014 DOI: 10.1126/scitranslmed.adc9669] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Parkinson's disease (PD) is the fastest-growing neurological disease in the world. A key challenge in PD is tracking disease severity, progression, and medication response. Existing methods are semisubjective and require visiting the clinic. In this work, we demonstrate an effective approach for assessing PD severity, progression, and medication response at home, in an objective manner. We used a radio device located in the background of the home. The device detected and analyzed the radio waves that bounce off people's bodies and inferred their movements and gait speed. We continuously monitored 50 participants, with and without PD, in their homes for up to 1 year. We collected over 200,000 gait speed measurements. Cross-sectional analysis of the data shows that at-home gait speed strongly correlates with gold-standard PD assessments, as evaluated by the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III subscore and total score. At-home gait speed also provides a more sensitive marker for tracking disease progression over time than the widely used MDS-UPDRS. Further, the monitored gait speed was able to capture symptom fluctuations in response to medications and their impact on patients' daily functioning. Our study shows the feasibility of continuous, objective, sensitive, and passive assessment of PD at home and hence has the potential of improving clinical care and drug clinical trials.
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Affiliation(s)
- Yingcheng Liu
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Guo Zhang
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Christopher G Tarolli
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | | | - Stella Jensen-Roberts
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Emma M Waddell
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Taylor L Myers
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Meghan E Pawlik
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Julia M Soto
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Renee M Wilson
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Yuzhe Yang
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Timothy Nordahl
- Department of Physical Therapy & Athletic Training, Center for Neurorehabilitation, Boston University College of Health and Rehabilitation: Sargent College, Boston, MA 02215, USA
| | - Karlo J Lizarraga
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Jamie L Adams
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Ruth B Schneider
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Karl Kieburtz
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Terry Ellis
- Department of Physical Therapy & Athletic Training, Center for Neurorehabilitation, Boston University College of Health and Rehabilitation: Sargent College, Boston, MA 02215, USA
| | - E Ray Dorsey
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.,Center for Health + Technology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Dina Katabi
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Emerald Innovations Inc., Cambridge, MA 02142, USA
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Brand YE, Schwartz D, Gazit E, Buchman AS, Gilad-Bachrach R, Hausdorff JM. Gait Detection from a Wrist-Worn Sensor Using Machine Learning Methods: A Daily Living Study in Older Adults and People with Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22187094. [PMID: 36146441 PMCID: PMC9502704 DOI: 10.3390/s22187094] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 08/31/2022] [Accepted: 09/12/2022] [Indexed: 05/14/2023]
Abstract
Remote assessment of the gait of older adults (OAs) during daily living using wrist-worn sensors has the potential to augment clinical care and mobility research. However, hand movements can degrade gait detection from wrist-sensor recordings. To address this challenge, we developed an anomaly detection algorithm and compared its performance to four previously published gait detection algorithms. Multiday accelerometer recordings from a wrist-worn and lower-back sensor (i.e., the “gold-standard” reference) were obtained in 30 OAs, 60% with Parkinson’s disease (PD). The area under the receiver operator curve (AUC) and the area under the precision−recall curve (AUPRC) were used to evaluate the performance of the algorithms. The anomaly detection algorithm obtained AUCs of 0.80 and 0.74 for OAs and PD, respectively, but AUPRCs of 0.23 and 0.31 for OAs and PD, respectively. The best performing detection algorithm, a deep convolutional neural network (DCNN), exhibited high AUCs (i.e., 0.94 for OAs and 0.89 for PD) but lower AUPRCs (i.e., 0.66 for OAs and 0.60 for PD), indicating trade-offs between precision and recall. When choosing a classification threshold of 0.9 (i.e., opting for high precision) for the DCNN algorithm, strong correlations (r > 0.8) were observed between daily living walking time estimates based on the lower-back (reference) sensor and the wrist sensor. Further, gait quality measures were significantly different in OAs and PD compared to healthy adults. These results demonstrate that daily living gait can be quantified using a wrist-worn sensor.
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Affiliation(s)
- Yonatan E. Brand
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492416, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Dafna Schwartz
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492416, Israel
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Eran Gazit
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492416, Israel
| | - Aron S. Buchman
- Rush Alzheimer’s Disease Center, Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Ran Gilad-Bachrach
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
- Edmond J. Safra Center for Bioinformatics, Tel-Aviv University, Tel Aviv 6997801, Israel
| | - Jeffrey M. Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492416, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
- Rush Alzheimer’s Disease Center and Department of Orthopedic Surgery, Rush University, Chicago, IL 60612, USA
- Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Correspondence:
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22
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Inskip MJ, Mavros Y, Sachdev PS, Hausdorff JM, Hillel I, Singh MAF. Promoting independence in Lewy body dementia through exercise: the PRIDE study. BMC Geriatr 2022; 22:650. [PMID: 35945508 PMCID: PMC9361699 DOI: 10.1186/s12877-022-03347-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 07/28/2022] [Indexed: 11/24/2022] Open
Abstract
Background Lewy body dementia (LBD) is an aggressive type of dementia of rapid, fluctuating disease trajectory, higher incidence of adverse events, and poorer functional independence than observed in Alzheimer’s disease dementia. Non-pharmacological treatments such as progressive, high-intensity exercise are effective in other neurological cohorts but have been scarcely evaluated in LBD. Methods The Promoting Independence in Lewy Body Dementia through Exercise (PRIDE) trial was a non-randomised, non-blinded, crossover pilot trial involving older adults with LBD consisting of a baseline assessment, an 8-week wait-list, and an 8-week exercise intervention. The aims of this study were to evaluate the determinants of the primary outcome functional independence, as measured by the Movement Disorder Society Unified Parkinson’s Disease Rating Scale, and the feasibility and preliminary efficacy of an exercise intervention on this outcome. Additionally, important clinical characteristics were evaluated to explore associations and treatment targets. The exercise intervention was supervised, clinic-based, high-intensity progressive resistance training (PRT), challenging balance, and functional exercises, 3 days/week. Results Nine participants completed the baseline cross-sectional study, of which five had a diagnosis of Parkinson’s disease dementia (PDD), and four dementia with Lewy Bodies (DLB). Six completed the exercise intervention (three PDD, three DLB). The cohort was diverse, ranging from mild to severe dementia and living in various residential settings. Greater functional independence at baseline was significantly associated with better physical function, balance, cognition, quality of life, muscle mass ratio, walking endurance, faster walking speed and cadence, and lower dementia severity (p < 0.05). Participants declined by clinically meaningful amounts in functional independence, cognition, physical function, muscle mass, and weight over the wait-list period (p < 0.05). Following exercise, participants improved by clinically meaningful amounts in functional independence, cognition, physical function, and strength (p < 0.05). Progressive, high intensity exercise was well-tolerated (> 80% adherence), and only one minor exercise-related adverse event occurred. Conclusions PRIDE is the first exercise trial conducted specifically within individuals diagnosed with LBD, and provides important insight for the design of larger, randomized trials for further evaluation of progressive, high-intensity exercise as a valuable treatment in LBD. Trial registration The PRIDE trial protocol has previously been prospectively registered (08/04/2016, ANZCTR: ACTRN12616000466448). Supplementary Information The online version contains supplementary material available at 10.1186/s12877-022-03347-2.
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Affiliation(s)
- Michael J Inskip
- Sport and Exercise Science, College of Healthcare Sciences, James Cook University, Townsville, QLD, Australia. .,Exercise and Sport Science, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, 2006, Australia.
| | - Yorgi Mavros
- Exercise and Sport Science, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, 2006, Australia
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry, University of New South Wales, Sydney, NSW, Australia.,Neuropsychiatric Institute, The Prince of Wales Hospital, Sydney, NSW, Australia
| | - Jeffrey M Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,Sagol School of Neuroscience and Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,Rush Alzheimer's Disease Center and Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, Illinois, USA
| | - Inbar Hillel
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Maria A Fiatarone Singh
- Exercise and Sport Science, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, 2006, Australia.,Sydney Medical School, The University of Sydney, Camperdown, NSW, Australia.,Hebrew SeniorLife, Boston, MA, USA
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23
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Correno MB, Hansen C, Carlin T, Vuillerme N. Objective Measurement of Walking Activity Using Wearable Technologies in People with Parkinson Disease: A Systematic Review. SENSORS 2022; 22:s22124551. [PMID: 35746329 PMCID: PMC9229799 DOI: 10.3390/s22124551] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 06/04/2022] [Accepted: 06/09/2022] [Indexed: 12/10/2022]
Abstract
Parkinson’s disease (PD) is a complex neurodegenerative disease with a multitude of disease variations including motor and non-motor symptoms. Quality of life and symptom management may be improved with physical activity. Due to technological advancement, development of small new wearable devices recently emerged and allowed objective measurement of walking activity in daily life. This review was specifically designed to synthesize literature on objective walking activity measurements using wearable devices of patients with PD. Inclusion criteria included patients with a diagnosis of PD and exclusion criteria included studies using animal models or mixed syndromes. Participants were not required to undergo any type of intervention and the studies must have reported at least one output that quantifies daily walking activity. Three databases were systematically searched with no limitation on publication date. Twenty-six studies were eligible and included in the systematic review. The most frequently used device was the ActiGraph GT3X which was used in 10 studies. Duration of monitoring presented a range from 8 h to one year. Nevertheless, 11 studies measured walking activity during a 7-day period. On-body sensor wearing location differed throughout the included studies showing eight positions, with the waist, ankle, and wrist being the most frequently used locations. The main procedures consisted of measurement of walking hours during a 2-day period or more, equipped with a triaxial accelerometer at the dominant hip or ankle. It is also important for further research to take care of different factors such as the population, their pathology, the period, and the environment.
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Affiliation(s)
- Mathias Baptiste Correno
- Laboratory AGEIS, Université Grenoble Alpes, 38000 Grenoble, France; (M.B.C.); (T.C.); (N.V.)
- LabCom Telecom4Health, Orange Labs, Université Grenoble Alpes, CNRS, Inria, Grenoble INP-UGA, 38000 Grenoble, France
- Department of Neurology, Universitätsklinikum Schleswig-Holstein, 24105 Kiel, Germany
| | - Clint Hansen
- Department of Neurology, Universitätsklinikum Schleswig-Holstein, 24105 Kiel, Germany
- Correspondence:
| | - Thomas Carlin
- Laboratory AGEIS, Université Grenoble Alpes, 38000 Grenoble, France; (M.B.C.); (T.C.); (N.V.)
- LabCom Telecom4Health, Orange Labs, Université Grenoble Alpes, CNRS, Inria, Grenoble INP-UGA, 38000 Grenoble, France
| | - Nicolas Vuillerme
- Laboratory AGEIS, Université Grenoble Alpes, 38000 Grenoble, France; (M.B.C.); (T.C.); (N.V.)
- LabCom Telecom4Health, Orange Labs, Université Grenoble Alpes, CNRS, Inria, Grenoble INP-UGA, 38000 Grenoble, France
- Institut Universitaire de France, 75231 Paris, France
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24
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A Single Wearable Sensor for Gait Analysis in Parkinson’s Disease: A Preliminary Study. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12115486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Movement monitoring in patients with Parkinson’s disease (PD) is critical for quantifying disease progression and assessing how a subject responds to medication administration over time. In this work, we propose a continuous monitoring system based on a single wearable sensor placed on the lower back and an algorithm for gait parameters evaluation. In order to preliminarily validate the proposed system, seven PD subjects took part in an experimental protocol in preparation for a larger randomized controlled study. We validated the feasibility of our algorithm in a constrained environment through a laboratory scenario. Successively, it was tested in an unsupervised environment, such as the home scenario, for a total of almost 12 h of daily living activity data. During all phases of the experimental protocol, videos were shot to document the tasks. The obtained results showed a good accuracy of the proposed algorithm. For all PD subjects in the laboratory scenario, the algorithm for step identification reached a percentage error low of 2%, 99.13% of sensitivity and 100% of specificity. In the home scenario the Bland–Altman plot showed a mean difference of −3.29 and −1 between the algorithm and the video recording for walking bout detection and steps identification, respectively.
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25
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Smid A, Elting JWJ, van Dijk JMC, Otten B, Oterdoom DLM, Tamasi K, Heida T, van Laar T, Drost G. Intraoperative Quantification of MDS-UPDRS Tremor Measurements Using 3D Accelerometry: A Pilot Study. J Clin Med 2022; 11:jcm11092275. [PMID: 35566401 PMCID: PMC9104023 DOI: 10.3390/jcm11092275] [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: 03/18/2022] [Revised: 04/10/2022] [Accepted: 04/16/2022] [Indexed: 02/05/2023] Open
Abstract
The most frequently used method for evaluating tremor in Parkinson’s disease (PD) is currently the internationally standardized Movement Disorder Society—Unified PD Rating Scale (MDS-UPDRS). However, the MDS-UPDRS is associated with limitations, such as its inherent subjectivity and reliance on experienced raters. Objective motor measurements using accelerometry may overcome the shortcomings of visually scored scales. Therefore, the current study focuses on translating the MDS-UPDRS tremor tests into an objective scoring method using 3D accelerometry. An algorithm to measure and classify tremor according to MDS-UPDRS criteria is proposed. For this study, 28 PD patients undergoing neurosurgical treatment and 26 healthy control subjects were included. Both groups underwent MDS-UPDRS tests to rate tremor severity, while accelerometric measurements were performed at the index fingers. All measurements were performed in an off-medication state. Quantitative measures were calculated from the 3D acceleration data, such as tremor amplitude and area-under-the-curve of power in the 4−6 Hz range. Agreement between MDS-UPDRS tremor scores and objective accelerometric scores was investigated. The trends were consistent with the logarithmic relationship between tremor amplitude and MDS-UPDRS score reported in previous studies. The accelerometric scores showed a substantial concordance (>69.6%) with the MDS-UPDRS ratings. However, accelerometric kinetic tremor measures poorly associated with the given MDS-UPDRS scores (R2 < 0.3), mainly due to the noise between 4 and 6 Hz found in the healthy controls. This study shows that MDS-UDPRS tremor tests can be translated to objective accelerometric measurements. However, discrepancies were found between accelerometric kinetic tremor measures and MDS-UDPRS ratings. This technology has the potential to reduce rater dependency of MDS-UPDRS measurements and allow more objective intraoperative monitoring of tremor.
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Affiliation(s)
- Annemarie Smid
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (J.M.C.v.D.); (D.L.M.O.); (K.T.); (G.D.)
- Correspondence:
| | - Jan Willem J. Elting
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (J.W.J.E.); (T.v.L.)
- Expertise Center Movement Disorders Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - J. Marc C. van Dijk
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (J.M.C.v.D.); (D.L.M.O.); (K.T.); (G.D.)
| | - Bert Otten
- Center for Human Movement Sciences, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands;
| | - D. L. Marinus Oterdoom
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (J.M.C.v.D.); (D.L.M.O.); (K.T.); (G.D.)
| | - Katalin Tamasi
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (J.M.C.v.D.); (D.L.M.O.); (K.T.); (G.D.)
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Tjitske Heida
- Department of Biomedical Signals and Systems, Faculty EEMCS, TechMed Centre, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands;
| | - Teus van Laar
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (J.W.J.E.); (T.v.L.)
- Expertise Center Movement Disorders Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Gea Drost
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (J.M.C.v.D.); (D.L.M.O.); (K.T.); (G.D.)
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (J.W.J.E.); (T.v.L.)
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26
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Bastos P, Meira B, Mendonça M, Barbosa R. Distinct gait dimensions are modulated by physical activity in Parkinson's disease patients. J Neural Transm (Vienna) 2022; 129:879-887. [PMID: 35426538 PMCID: PMC9011371 DOI: 10.1007/s00702-022-02501-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 04/06/2022] [Indexed: 11/27/2022]
Abstract
Parkinson’s disease (PD) is the fastest growing neurodegenerative disease, but disease-modifying or preventive treatments are lacking. Physical activity is a modifiable factor that decreases the PD risk and improves motor symptoms in PD. Understanding which dimensions of gait performance correlate with physical activity in PD can have important pathophysiological and therapeutic implications. Clinical/demographic data together with physical activity levels were collected from thirty-nine PD patients. Gait analysis was performed wearing seven inertial measurement units on the lower body, reconstructing the subjects’ lower body motion using 3D kinematic biomechanical models. Higher physical activity scores were significantly correlated with MDS-UPDRS part III scores (r = − 0.58, p value = 9.2 × 10−5), age (r = − 0.39, p value = 1.5 × 10−2) and quality-of-life (r = − 0.47, p value = 5.9 × 10−3). Physical activity was negatively associated with MDS-UPDRS part III scores after adjusting for age and disease duration (β = − 0.08530, p value = 0.0010). The effect of physical activity on quality-of-life was mediated by the MDS-UPDRS part III (62.10%, 95% CI = 0.0758–1.78, p value = 0.022). The level of physical activity was correlated primarily with spatiotemporal performance. While spatiotemporal performance displays the strongest association with physical activity, other quality-of-movement dimensions of clinical relevance (e.g., smoothness, rhythmicity) fail to do so. Interventions targeting these ought to be leveraged for performance enhancement in PD through neuroprotective and brain network connectivity strengthening. It remains to be ascertained to which extent these are amenable to modulation.
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Affiliation(s)
- Paulo Bastos
- CEDOC, Chronic Diseases Research Centre, NOVA Medical School, Lisbon, Portugal
| | - Bruna Meira
- Neurology Department, Hospital de Egas Moniz, Centro Hospitalar de Lisboa Ocidental, Lisbon, Portugal
| | - Marcelo Mendonça
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
- NOVA Medical School, Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Lisbon, Portugal
| | - Raquel Barbosa
- Neurology Department, Hospital de Egas Moniz, Centro Hospitalar de Lisboa Ocidental, Lisbon, Portugal.
- NOVA Medical School, Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Lisbon, Portugal.
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27
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Rehman RZU, Guan Y, Shi JQ, Alcock L, Yarnall AJ, Rochester L, Del Din S. Investigating the Impact of Environment and Data Aggregation by Walking Bout Duration on Parkinson's Disease Classification Using Machine Learning. Front Aging Neurosci 2022; 14:808518. [PMID: 35391750 PMCID: PMC8981298 DOI: 10.3389/fnagi.2022.808518] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 02/14/2022] [Indexed: 12/14/2022] Open
Abstract
Parkinson's disease (PD) is a common neurodegenerative disease. PD misdiagnosis can occur in early stages. Gait impairment in PD is typical and is linked with an increased fall risk and poorer quality of life. Applying machine learning (ML) models to real-world gait has the potential to be more sensitive to classify PD compared to laboratory data. Real-world gait yields multiple walking bouts (WBs), and selecting the optimal method to aggregate the data (e.g., different WB durations) is essential as this may influence classification performance. The objective of this study was to investigate the impact of environment (laboratory vs. real world) and data aggregation on ML performance for optimizing sensitivity of PD classification. Gait assessment was performed on 47 people with PD (age: 68 ± 9 years) and 52 controls [Healthy controls (HCs), age: 70 ± 7 years]. In the laboratory, participants walked at their normal pace for 2 min, while in the real world, participants were assessed over 7 days. In both environments, 14 gait characteristics were evaluated from one tri-axial accelerometer attached to the lower back. The ability of individual gait characteristics to differentiate PD from HC was evaluated using the Area Under the Curve (AUC). ML models (i.e., support vector machine, random forest, and ensemble models) applied to real-world gait showed better classification performance compared to laboratory data. Real-world gait characteristics aggregated over longer WBs (WB 30-60 s, WB > 60 s, WB > 120 s) resulted in superior discriminative performance (PD vs. HC) compared to laboratory gait characteristics (0.51 ≤ AUC ≤ 0.77). Real-world gait speed showed the highest AUC of 0.77. Overall, random forest trained on 14 gait characteristics aggregated over WBs > 60 s gave better performance (F1 score = 77.20 ± 5.51%) as compared to laboratory results (F1 Score = 68.75 ± 12.80%). Findings from this study suggest that the choice of environment and data aggregation are important to achieve maximum discrimination performance and have direct impact on ML performance for PD classification. This study highlights the importance of a harmonized approach to data analysis in order to drive future implementation and clinical use. Clinical Trial Registration [09/H0906/82].
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Affiliation(s)
- Rana Zia Ur Rehman
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Yu Guan
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Jian Qing Shi
- School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne, United Kingdom
- Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, China
| | - Lisa Alcock
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Alison J. Yarnall
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
- The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Lynn Rochester
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
- The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Silvia Del Din
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
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28
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Safarpour D, Dale ML, Shah VV, Talman L, Carlson-Kuhta P, Horak FB, Mancini M. Surrogates for rigidity and PIGD MDS-UPDRS subscores using wearable sensors. Gait Posture 2022; 91:186-191. [PMID: 34736096 PMCID: PMC8671321 DOI: 10.1016/j.gaitpost.2021.10.029] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 10/03/2021] [Accepted: 10/13/2021] [Indexed: 02/02/2023]
Abstract
BACKGROUND Telemedicine has the advantage of expanding access to care for patients with Parkinson's Disease (PD). However, rigidity and postural instability in PD are difficult to measure remotely, and are important measures of functional impairment and fall risk. RESEARCH QUESTION Can measures from wearable sensors be used as future surrogates for the MDS-UPDRS rigidity and Postural Instability and Gait Difficulty (PIGD) subscores? METHODS Thirty-one individuals with mild to moderate PD wore 3 inertial sensors at home for one week to measure quantity and quality of gait and turning in daily life. Separately, we performed a clinical assessment and balance characterization of postural sway with the same wearable sensors in the laboratory (On medication). We then first performed a traditional correlation analysis between clinical scores and objective measures of gait and balance followed by multivariable linear regression employing a best subset selection strategy. RESULTS The number of walking bouts and turns correlated significantly with the rigidity subscore, while the number of turns, foot pitch angle, and sway area while standing correlated significantly with the PIGD subscore (p < 0.05). The multivariable linear regression showed that rigidity subscore was best predicted by the number of walking bouts while the PIGD subscore was best predicted by a combination of number of walking bouts, gait speed, and postural sway. SIGNIFICANCE The correlation between objective sensor data and MDS-UPDRS rigidity and PIGD scores paves the way for future larger studies that evaluate use of objective sensor data to supplement remote MDS-UPDRS assessment.
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Affiliation(s)
- Delaram Safarpour
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA
| | - Marian L. Dale
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA
| | | | - Lauren Talman
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA
| | - Patty Carlson-Kuhta
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA
| | - Fay B. Horak
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA
| | - Martina Mancini
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA
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29
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Nouredanesh M, Ojeda L, Alexander NB, Godfrey A, Schwenk M, Melek W, Tung J. Automated Detection of Older Adults’ Naturally-Occurring Compensatory Balance Reactions: Translation From Laboratory to Free-Living Conditions. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2022. [DOI: 10.1109/jtehm.2022.3163967] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Mina Nouredanesh
- Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Lauro Ojeda
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Neil B. Alexander
- Department of Internal Medicine, Division of Geriatric and Palliative Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Alan Godfrey
- Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, U.K
| | - Michael Schwenk
- Network Aging Research (NAR), Heidelberg University, Heidelberg, Germany
| | - William Melek
- Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON, Canada
| | - James Tung
- Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON, Canada
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30
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Atrsaei A, Hansen C, Elshehabi M, Solbrig S, Berg D, Liepelt-Scarfone I, Maetzler W, Aminian K. Effect of Fear of Falling on Mobility Measured During Lab and Daily Activity Assessments in Parkinson's Disease. Front Aging Neurosci 2021; 13:722830. [PMID: 34916920 PMCID: PMC8669821 DOI: 10.3389/fnagi.2021.722830] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 10/21/2021] [Indexed: 12/24/2022] Open
Abstract
In chronic disorders such as Parkinson’s disease (PD), fear of falling (FOF) is associated with falls and reduced quality of life. With inertial measurement units (IMUs) and dedicated algorithms, different aspects of mobility can be obtained during supervised tests in the lab and also during daily activities. To our best knowledge, the effect of FOF on mobility has not been investigated in both of these settings simultaneously. Our goal was to evaluate the effect of FOF on the mobility of 26 patients with PD during clinical assessments and 14 days of daily activity monitoring. Parameters related to gait, sit-to-stand transitions, and turns were extracted from IMU signals on the lower back. Fear of falling was assessed using the Falls Efficacy Scale-International (FES-I) and the patients were grouped as with (PD-FOF+) and without FOF (PD-FOF−). Mobility parameters between groups were compared using logistic regression as well as the effect size values obtained using the Wilcoxon rank-sum test. The peak angular velocity of the turn-to-sit transition of the timed-up-and-go (TUG) test had the highest discriminative power between PD-FOF+ and PD-FOF− (r-value of effect size = 0.61). Moreover, PD-FOF+ had a tendency toward lower gait speed at home and a lower amount of walking bouts, especially for shorter walking bouts. The combination of lab and daily activity parameters reached a higher discriminative power [area under the curve (AUC) = 0.75] than each setting alone (AUC = 0.68 in the lab, AUC = 0.54 at home). Comparing the gait speed between the two assessments, the PD-FOF+ showed higher gait speeds in the capacity area compared with their TUG test in the lab. The mobility parameters extracted from both lab and home-based assessments contribute to the detection of FOF in PD. This study adds further evidence to the usefulness of mobility assessments that include different environments and assessment strategies. Although this study was limited in the sample size, it still provides a helpful method to consider the daily activity measurement of the patients with PD into clinical evaluation. The obtained results can help the clinicians with a more accurate prevention and treatment strategy.
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Affiliation(s)
- Arash Atrsaei
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Clint Hansen
- Department of Neurology, UKSH, Christian-Albrechts-University, Kiel, Germany
| | - Morad Elshehabi
- Department of Neurology, UKSH, Christian-Albrechts-University, Kiel, Germany
| | - Susanne Solbrig
- Department of Neurodegeneration, Center for Neurology and Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.,German Center for Neurodegenerative Diseases, Tübingen, Germany
| | - Daniela Berg
- Department of Neurology, UKSH, Christian-Albrechts-University, Kiel, Germany.,Department of Neurodegeneration, Center for Neurology and Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.,German Center for Neurodegenerative Diseases, Tübingen, Germany
| | - Inga Liepelt-Scarfone
- Department of Neurodegeneration, Center for Neurology and Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.,German Center for Neurodegenerative Diseases, Tübingen, Germany.,IB-Hochschule, Stuttgart, Germany
| | - Walter Maetzler
- Department of Neurology, UKSH, Christian-Albrechts-University, Kiel, Germany
| | - Kamiar Aminian
- Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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31
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Salis F, Bertuletti S, Scott K, Caruso M, Bonci T, Buckley E, Croce UD, Mazza C, Cereatti A. A wearable multi-sensor system for real world gait analysis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:7020-7023. [PMID: 34892719 DOI: 10.1109/embc46164.2021.9630392] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Gait analysis is commonly performed in standardized environments, but there is a growing interest in assessing gait also in ecological conditions. In this regard, an important limitation is the lack of an accurate mobile gold standard for validating any wearable system, such as continuous monitoring devices mounted on the trunk or wrist. This study therefore deals with the development and validation of a new wearable multi-sensor-based system for digital gait assessment in free-living conditions. In particular, results obtained from five healthy subjects during lab-based and real-world experiments were presented and discussed. The in-lab validation, which assessed the accuracy and reliability of the proposed system, shows median percentage errors smaller than 2% in the estimation of spatio-temporal parameters. The system also proved to be easy to use, comfortable to wear and robust during the out-of-lab acquisitions, showing its feasibility for free-living applications.
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32
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Polhemus A, Delgado-Ortiz L, Brittain G, Chynkiamis N, Salis F, Gaßner H, Gross M, Kirk C, Rossanigo R, Taraldsen K, Balta D, Breuls S, Buttery S, Cardenas G, Endress C, Gugenhan J, Keogh A, Kluge F, Koch S, Micó-Amigo ME, Nerz C, Sieber C, Williams P, Bergquist R, Bosch de Basea M, Buckley E, Hansen C, Mikolaizak AS, Schwickert L, Scott K, Stallforth S, van Uem J, Vereijken B, Cereatti A, Demeyer H, Hopkinson N, Maetzler W, Troosters T, Vogiatzis I, Yarnall A, Becker C, Garcia-Aymerich J, Leocani L, Mazzà C, Rochester L, Sharrack B, Frei A, Puhan M. Walking on common ground: a cross-disciplinary scoping review on the clinical utility of digital mobility outcomes. NPJ Digit Med 2021; 4:149. [PMID: 34650191 PMCID: PMC8516969 DOI: 10.1038/s41746-021-00513-5] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 08/09/2021] [Indexed: 02/08/2023] Open
Abstract
Physical mobility is essential to health, and patients often rate it as a high-priority clinical outcome. Digital mobility outcomes (DMOs), such as real-world gait speed or step count, show promise as clinical measures in many medical conditions. However, current research is nascent and fragmented by discipline. This scoping review maps existing evidence on the clinical utility of DMOs, identifying commonalities across traditional disciplinary divides. In November 2019, 11 databases were searched for records investigating the validity and responsiveness of 34 DMOs in four diverse medical conditions (Parkinson's disease, multiple sclerosis, chronic obstructive pulmonary disease, hip fracture). Searches yielded 19,672 unique records. After screening, 855 records representing 775 studies were included and charted in systematic maps. Studies frequently investigated gait speed (70.4% of studies), step length (30.7%), cadence (21.4%), and daily step count (20.7%). They studied differences between healthy and pathological gait (36.4%), associations between DMOs and clinical measures (48.8%) or outcomes (4.3%), and responsiveness to interventions (26.8%). Gait speed, step length, cadence, step time and step count exhibited consistent evidence of validity and responsiveness in multiple conditions, although the evidence was inconsistent or lacking for other DMOs. If DMOs are to be adopted as mainstream tools, further work is needed to establish their predictive validity, responsiveness, and ecological validity. Cross-disciplinary efforts to align methodology and validate DMOs may facilitate their adoption into clinical practice.
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Affiliation(s)
- Ashley Polhemus
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.
| | - Laura Delgado-Ortiz
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Gavin Brittain
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust & University of Sheffield, Sheffield, England
| | - Nikolaos Chynkiamis
- Department of Sport, Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University Newcastle, Newcastle, UK
| | - Francesca Salis
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Heiko Gaßner
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Michaela Gross
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Cameron Kirk
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Rachele Rossanigo
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Kristin Taraldsen
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Diletta Balta
- Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy
| | - Sofie Breuls
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
- Department of Respiratory Diseases, University hospitals Leuven, Leuven, Belgium
| | - Sara Buttery
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Gabriela Cardenas
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Christoph Endress
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Julia Gugenhan
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Alison Keogh
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
| | - Felix Kluge
- Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Sarah Koch
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - M Encarna Micó-Amigo
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Corinna Nerz
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Chloé Sieber
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Parris Williams
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Ronny Bergquist
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Magda Bosch de Basea
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Ellen Buckley
- Insigneo Institute, Department of Mechanical Engineering, University of Sheffield, Sheffield, UK
| | - Clint Hansen
- Department of Neurology, University Medical Center Schleswig-Holstein, Kiel, Germany
| | | | - Lars Schwickert
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Kirsty Scott
- Insigneo Institute, Department of Mechanical Engineering, University of Sheffield, Sheffield, UK
| | - Sabine Stallforth
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Janet van Uem
- Department of Neurology, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Beatrix Vereijken
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Andrea Cereatti
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
- Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy
| | - Heleen Demeyer
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
- Department of Respiratory Diseases, University hospitals Leuven, Leuven, Belgium
- Department of Rehabilitation Sciences, Ghent University, Ghent, Belgium
| | | | - Walter Maetzler
- Department of Neurology, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Thierry Troosters
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
- Department of Respiratory Diseases, University hospitals Leuven, Leuven, Belgium
| | - Ioannis Vogiatzis
- Department of Sport, Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University Newcastle, Newcastle, UK
| | - Alison Yarnall
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Clemens Becker
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Judith Garcia-Aymerich
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Letizia Leocani
- Department of Neurology, San Raffaele University, Milan, Italy
| | - Claudia Mazzà
- Insigneo Institute, Department of Mechanical Engineering, University of Sheffield, Sheffield, UK
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Basil Sharrack
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust & University of Sheffield, Sheffield, England
| | - Anja Frei
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Milo Puhan
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
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Walha R, Lebel K, Gaudreault N, Dagenais P, Cereatti A, Della Croce U, Boissy P. The Accuracy and Precision of Gait Spatio-Temporal Parameters Extracted from an Instrumented Sock during Treadmill and Overground Walking in Healthy Subjects and Patients with a Foot Impairment Secondary to Psoriatic Arthritis. SENSORS 2021; 21:s21186179. [PMID: 34577387 PMCID: PMC8472002 DOI: 10.3390/s21186179] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 08/31/2021] [Accepted: 09/10/2021] [Indexed: 11/16/2022]
Abstract
The objectives of this study were to assess the accuracy and precision of a system combining an IMU-instrumented sock and a validated algorithm for the estimation of the spatio-temporal parameters of gait. A total of 25 healthy participants (HP) and 21 patients with foot impairments secondary to psoriatic arthritis (PsA) performed treadmill walking at three different speeds and overground walking at a comfortable speed. HP performed the assessment over two sessions. The proposed system's estimations of cadence (CAD), gait cycle duration (GCD), gait speed (GS), and stride length (SL) obtained for treadmill walking were validated versus those estimated with a motion capture system. The system was also compared with a well-established multi-IMU-based system for treadmill and overground walking. The results showed a good agreement between the motion capture system and the IMU-instrumented sock in estimating the spatio-temporal parameters during the treadmill walking at normal and fast speeds for both HP and PsA participants. The accuracy of GS and SL obtained from the IMU-instrumented sock was better compared to the established multi-IMU-based system in both groups. The precision (inter-session reliability) of the gait parameter estimations obtained from the IMU-instrumented sock was good to excellent for overground walking and treadmill walking at fast speeds, but moderate-to-good for slow and normal treadmill walking. The proposed IMU-instrumented sock offers a novel form factor addressing the wearability issues of IMUs and could potentially be used to measure spatio-temporal parameters under clinical conditions and free-living conditions.
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Affiliation(s)
- Roua Walha
- Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada; (R.W.); (N.G.); (P.D.)
| | - Karina Lebel
- Research Center on Aging, CIUSSS Estrie CHUS, Sherbrooke, QC J1H 4C4, Canada;
- Faculty of Engineering, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
| | - Nathaly Gaudreault
- Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada; (R.W.); (N.G.); (P.D.)
| | - Pierre Dagenais
- Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada; (R.W.); (N.G.); (P.D.)
| | - Andrea Cereatti
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy;
| | - Ugo Della Croce
- Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy;
- Biomedical Engineering Department, Catholic University of America, Washington, DC 20064, USA
| | - Patrick Boissy
- Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada; (R.W.); (N.G.); (P.D.)
- Research Center on Aging, CIUSSS Estrie CHUS, Sherbrooke, QC J1H 4C4, Canada;
- Correspondence: ; Tel.: +1-819-780-2220 (ext. 45628)
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Kluge F, Del Din S, Cereatti A, Gaßner H, Hansen C, Helbostad JL, Klucken J, Küderle A, Müller A, Rochester L, Ullrich M, Eskofier BM, Mazzà C. Consensus based framework for digital mobility monitoring. PLoS One 2021; 16:e0256541. [PMID: 34415959 PMCID: PMC8378707 DOI: 10.1371/journal.pone.0256541] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 08/09/2021] [Indexed: 12/31/2022] Open
Abstract
Digital mobility assessment using wearable sensor systems has the potential to capture walking performance in a patient's natural environment. It enables monitoring of health status and disease progression and evaluation of interventions in real-world situations. In contrast to laboratory settings, real-world walking occurs in non-conventional environments and under unconstrained and uncontrolled conditions. Despite the general understanding, there is a lack of agreed definitions about what constitutes real-world walking, impeding the comparison and interpretation of the acquired data across systems and studies. The goal of this study was to obtain expert-based consensus on specific aspects of real-world walking and to provide respective definitions in a common terminological framework. An adapted Delphi method was used to obtain agreed definitions related to real-world walking. In an online survey, 162 participants from a panel of academic, clinical and industrial experts with experience in the field of gait analysis were asked for agreement on previously specified definitions. Descriptive statistics was used to evaluate whether consent (> 75% agreement as defined a priori) was reached. Of 162 experts invited to participate, 51 completed all rounds (31.5% response rate). We obtained consensus on all definitions ("Walking" > 90%, "Purposeful" > 75%, "Real-world" > 90%, "Walking bout" > 80%, "Walking speed" > 75%, "Turning" > 90% agreement) after two rounds. The identification of a consented set of real-world walking definitions has important implications for the development of assessment and analysis protocols, as well as for the reporting and comparison of digital mobility outcomes across studies and systems. The definitions will serve as a common framework for implementing digital and mobile technologies for gait assessment and are an important link for the transition from supervised to unsupervised gait assessment.
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Affiliation(s)
- Felix Kluge
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Andrea Cereatti
- Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy
| | - Heiko Gaßner
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Clint Hansen
- Department of Neurology, University of Kiel, Kiel, Germany
| | - Jorunn L. Helbostad
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Jochen Klucken
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Arne Küderle
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | | | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- The Newcastle upon Tyne NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Martin Ullrich
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Bjoern M. Eskofier
- Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Claudia Mazzà
- Department of Mechanical Engineering & Insigneo Institute for in Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
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Corrà MF, Atrsaei A, Sardoreira A, Hansen C, Aminian K, Correia M, Vila-Chã N, Maetzler W, Maia L. Comparison of Laboratory and Daily-Life Gait Speed Assessment during ON and OFF States in Parkinson's Disease. SENSORS 2021; 21:s21123974. [PMID: 34207565 PMCID: PMC8229328 DOI: 10.3390/s21123974] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 05/31/2021] [Accepted: 06/07/2021] [Indexed: 11/16/2022]
Abstract
Accurate assessment of Parkinson's disease (PD) ON and OFF states in the usual environment is essential for tailoring optimal treatments. Wearables facilitate measurements of gait in novel and unsupervised environments; however, differences between unsupervised and in-laboratory measures have been reported in PD. We aimed to investigate whether unsupervised gait speed discriminates medication states and which supervised tests most accurately represent home performance. In-lab gait speeds from different gait tasks were compared to home speeds of 27 PD patients at ON and OFF states using inertial sensors. Daily gait speed distribution was expressed in percentiles and walking bout (WB) length. Gait speeds differentiated ON and OFF states in the lab and the home. When comparing lab with home performance, ON assessments in the lab showed moderate-to-high correlations with faster gait speeds in unsupervised environment (r = 0.69; p < 0.001), associated with long WB. OFF gait assessments in the lab showed moderate correlation values with slow gait speeds during OFF state at home (r = 0.56; p = 0.004), associated with short WB. In-lab and daily assessments of gait speed with wearables capture additional integrative aspects of PD, reflecting different aspects of mobility. Unsupervised assessment using wearables adds complementary information to the clinical assessment of motor fluctuations in PD.
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Affiliation(s)
- Marta Francisca Corrà
- Abel Salazar Biomedical Sciences Institute (ICBAS), University of Porto, 4050-313 Porto, Portugal; (M.C.); (L.M.)
- University Hospital Santo Antonio of Porto (CHUP), 4099-001 Porto, Portugal; (A.S.); (N.V.-C.)
- Correspondence:
| | - Arash Atrsaei
- Laboratory of Movement Analysis and Measurement, Swiss Federal Institute of Technology in Lausanne (EPFL), 1015 Lausanne, Switzerland; (A.A.); (K.A.)
| | - Ana Sardoreira
- University Hospital Santo Antonio of Porto (CHUP), 4099-001 Porto, Portugal; (A.S.); (N.V.-C.)
| | - Clint Hansen
- Department of Neurology, Christian-Albrechts-University, 24118 Kiel, Germany; (C.H.); (W.M.)
| | - Kamiar Aminian
- Laboratory of Movement Analysis and Measurement, Swiss Federal Institute of Technology in Lausanne (EPFL), 1015 Lausanne, Switzerland; (A.A.); (K.A.)
| | - Manuel Correia
- Abel Salazar Biomedical Sciences Institute (ICBAS), University of Porto, 4050-313 Porto, Portugal; (M.C.); (L.M.)
- University Hospital Santo Antonio of Porto (CHUP), 4099-001 Porto, Portugal; (A.S.); (N.V.-C.)
| | - Nuno Vila-Chã
- University Hospital Santo Antonio of Porto (CHUP), 4099-001 Porto, Portugal; (A.S.); (N.V.-C.)
| | - Walter Maetzler
- Department of Neurology, Christian-Albrechts-University, 24118 Kiel, Germany; (C.H.); (W.M.)
| | - Luís Maia
- Abel Salazar Biomedical Sciences Institute (ICBAS), University of Porto, 4050-313 Porto, Portugal; (M.C.); (L.M.)
- University Hospital Santo Antonio of Porto (CHUP), 4099-001 Porto, Portugal; (A.S.); (N.V.-C.)
- Institute for Research and Innovation in Health (i3s), University of Porto, 4200-135 Porto, Portugal
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Mirelman A, Ben Or Frank M, Melamed M, Granovsky L, Nieuwboer A, Rochester L, Del Din S, Avanzino L, Pelosin E, Bloem BR, Della Croce U, Cereatti A, Bonato P, Camicioli R, Ellis T, Hamilton JL, Hass CJ, Almeida QJ, Inbal M, Thaler A, Shirvan J, Cedarbaum JM, Giladi N, Hausdorff JM. Detecting Sensitive Mobility Features for Parkinson's Disease Stages Via Machine Learning. Mov Disord 2021; 36:2144-2155. [PMID: 33955603 DOI: 10.1002/mds.28631] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 04/10/2021] [Accepted: 04/12/2021] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND It is not clear how specific gait measures reflect disease severity across the disease spectrum in Parkinson's disease (PD). OBJECTIVE To identify the gait and mobility measures that are most sensitive and reflective of PD motor stages and determine the optimal sensor location in each disease stage. METHODS Cross-sectional wearable-sensor records were collected in 332 patients with PD (Hoehn and Yahr scale I-III) and 100 age-matched healthy controls. Sensors were adhered to the participant's lower back, bilateral ankles, and wrists. Study participants walked in a ~15-meter corridor for 1 minute under two walking conditions: (1) preferred, usual walking speed and (2) walking while engaging in a cognitive task (dual-task). A subgroup (n = 303, 67% PD) also performed the Timed Up and Go test. Multiple machine-learning feature selection and classification algorithms were applied to discriminate between controls and PD and between the different PD severity stages. RESULTS High discriminatory values were found between motor disease stages with mean sensitivity in the range 72%-83%, specificity 69%-80%, and area under the curve (AUC) 0.76-0.90. Measures from upper-limb sensors best discriminated controls from early PD, turning measures obtained from the trunk sensor were prominent in mid-stage PD, and stride timing and regularity were discriminative in more advanced stages. CONCLUSIONS Applying machine-learning to multiple, wearable-derived features reveals that different measures of gait and mobility are associated with and discriminate distinct stages of PD. These disparate feature sets can augment the objective monitoring of disease progression and may be useful for cohort selection and power analyses in clinical trials of PD. © 2021 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Anat Mirelman
- Laboratory for Early Markers Of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Mor Ben Or Frank
- Laboratory for Early Markers Of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
| | | | | | - Alice Nieuwboer
- Department of Rehabilitation Science, KU Leuven, Neuromotor Rehabilitation Research Group, Leuven, Belgium
| | - Lynn Rochester
- Institute of Neuroscience, Newcastle University Institute for Ageing, Newcastle University, Newcastle upon Tyne, UK
| | - Silvia Del Din
- Institute of Neuroscience, Newcastle University Institute for Ageing, Newcastle University, Newcastle upon Tyne, UK
| | - Laura Avanzino
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal Child Health (DINOGMI), University of Genoa, Genoa, Italy.,IRCCS Policlinico San Martino Teaching Hospital, Genoa, Italy
| | - Elisa Pelosin
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal Child Health (DINOGMI), University of Genoa, Genoa, Italy.,IRCCS Policlinico San Martino Teaching Hospital, Genoa, Italy
| | - Bastiaan R Bloem
- Department of Neurology, Radboud University Medical Center; Donders Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands
| | - Ugo Della Croce
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Andrea Cereatti
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy.,Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Paolo Bonato
- Department of Physical Medicine & Rehabilitation, Harvard Medical School, Boston, Massachusetts, USA
| | - Richard Camicioli
- Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - Theresa Ellis
- Department of Physical Therapy & Athletic Training, Boston University, Boston, Massachusetts, USA
| | - Jamie L Hamilton
- Michael J. Fox Foundation for Parkinson's Research, New York, New York, USA
| | - Chris J Hass
- College of Health & Human Performance, Applied Physiology and Kinesiology, University of Florida, Gainesville, Florida, USA
| | - Quincy J Almeida
- Movement Disorders Research & Rehabilitation Centre, Wilfrid Laurier University, Waterloo, Canada
| | - Maidan Inbal
- Laboratory for Early Markers Of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Avner Thaler
- Laboratory for Early Markers Of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | | | - Jesse M Cedarbaum
- Coeruleus Clinical Sciences, Woodbridge, Connecticut, USA.,Yale University School of Medicine, New Haven, Connecticut, USA
| | - Nir Giladi
- Laboratory for Early Markers Of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Jeffrey M Hausdorff
- Laboratory for Early Markers Of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.,Department of Physical Therapy, Tel Aviv University, Tel Aviv, Israel.,Department of Orthopedic Surgery, Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA
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Carlin T, Hansen C, Vuillerme N. Objective Measurement of Walking Activity Using Wearable Technologies in People with Parkinson Disease: A Systematic Review Protocol. Biomed Hub 2021; 6:64-68. [PMID: 34616747 PMCID: PMC8460917 DOI: 10.1159/000516819] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 04/23/2021] [Indexed: 11/19/2022] Open
Abstract
INTRODUCTION Parkinson's disease (PD) is a complex neurodegenerative disease with motor and nonmotor symptoms with a multitude of disease variations and severity. Physical activity can improve the management of disease symptoms and increase patients' quality of life. Technological development of small wearable devices allows objective activity measurement such as daily step count. OBJECTIVE To synthesize ongoing and past research on objective walking activity measurements using wearable devices in patients with PD. METHODS PubMed, Cochrane, Web of Science, and PEDro database are systematically searched with no limitation on publication date. Keywords are relative to (1) the population, (2) the measurement tool, and (3) the measured outcomes. Only full-text English articles published in a peer-reviewed journal will be included. Participants do not have to undergo any type of intervention. Included studies must report an objective measurement of walking activity using wearable devices in PD patients. After an independent screening process done by 2 reviewers, data will be extracted from the articles according to the following 5 set of data: (1) the study metrics, (2) the population characteristics, (3) the measurement tools, (4) the experimental procedure, and (5) the reported outcomes. RESULTS The results will contain inter alia summaries of the wearables' specifications, wearing location, and recommendations for feasible methodologies to capture daily walking activity. DISCUSSION This review aims to synthesize the evidence of objective walking activity assessment with wearable devices in patients with PD. It will also provide recommendations with regard to device selection and suggest key points when monitoring walking activity in this specific population.
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Affiliation(s)
- Thomas Carlin
- Laboratory AGEIS, Universitaire Grenoble Alpes, Grenoble, France
- LabCom Telecom4Health, Universitaire Grenoble Alpes, Grenoble, France
| | - Clint Hansen
- Department of Neurology, Universitätsklinikum Schleswig-Holstein, Kiel, Germany
| | - Nicolas Vuillerme
- Laboratory AGEIS, Universitaire Grenoble Alpes, Grenoble, France
- LabCom Telecom4Health, Universitaire Grenoble Alpes, Grenoble, France
- Institut Universitaire de France, Paris, France
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38
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Gait speed in clinical and daily living assessments in Parkinson's disease patients: performance versus capacity. NPJ Parkinsons Dis 2021; 7:24. [PMID: 33674597 PMCID: PMC7935857 DOI: 10.1038/s41531-021-00171-0] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 01/25/2021] [Indexed: 01/31/2023] Open
Abstract
Gait speed often referred as the sixth vital sign is the most powerful biomarker of mobility. While a clinical setting allows the estimation of gait speed under controlled conditions that present functional capacity, gait speed in real-life conditions provides the actual performance of the patient. The goal of this study was to investigate objectively under what conditions during daily activities, patients perform as well as or better than in the clinic. To this end, we recruited 27 Parkinson's disease (PD) patients and measured their gait speed by inertial measurement units through several walking tests in the clinic as well as their daily activities at home. By fitting a bimodal Gaussian model to their gait speed distribution, we found that on average, patients had similar modes in the clinic and during daily activities. Furthermore, we observed that the number of medication doses taken throughout the day had a moderate correlation with the difference between clinic and home. Performing a cycle-by-cycle analysis on gait speed during the home assessment, overall only about 3% of the strides had equal or greater gait speeds than the patients' capacity in the clinic. These strides were during long walking bouts (>1 min) and happened before noon, around 26 min after medication intake, reaching their maximum occurrence probability 3 h after Levodopa intake. These results open the possibility of better control of medication intake in PD by considering both functional capacity and continuous monitoring of gait speed during real-life conditions.
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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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A Long-Term, Real-Life Parkinson Monitoring Database Combining Unscripted Objective and Subjective Recordings. DATA 2021. [DOI: 10.3390/data6020022] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Accurate real-life monitoring of motor and non-motor symptoms is a challenge in Parkinson’s disease (PD). The unobtrusive capturing of symptoms and their naturalistic fluctuations within or between days can improve evaluation and titration of therapy. First-generation commercial PD motion sensors are promising to augment clinical decision-making in general neurological consultation, but concerns remain regarding their short-term validity, and long-term real-life usability. In addition, tools monitoring real-life subjective experiences of motor and non-motor symptoms are lacking. The dataset presented in this paper constitutes a combination of objective kinematic data and subjective experiential data, recorded parallel to each other in a naturalistic, long-term real-life setting. The objective data consists of accelerometer and gyroscope data, and the subjective data consists of data from ecological momentary assessments. Twenty PD patients were monitored without daily life restrictions for fourteen consecutive days. The two types of data can be used to address hypotheses on naturalistic motor and/or non-motor symptomatology in PD.
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41
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Mirelman A, Dorsey ER, Brundin P, Bloem BR. Using Technology to Reshape Clinical Care and Research in Parkinson's Disease. JOURNAL OF PARKINSONS DISEASE 2021; 11:S1-S3. [PMID: 33612498 DOI: 10.3233/jpd-219002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Anat Mirelman
- Laboratory for Early Markers Of Neurodegeneration, Center for The Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv Israel.,Sackler Faculty of Medicine, and Sagol School of Neuroscience, Tel Aviv University, Israel
| | - E Ray Dorsey
- Department of Neurology, Centre for Health + Technology, University of Rochester Medical Centre, Rochester, New York, USA
| | - Patrik Brundin
- Laboratory of Translational Parkinson's Disease Research, Center for Neurodegenerative Science, Van Andel Research Institute, Grand Rapids, MI, USA
| | - Bastiaan R Bloem
- Department of Neurology, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands.,Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
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Chen PW, Baune NA, Zwir I, Wang J, Swamidass V, Wong AW. Measuring Activities of Daily Living in Stroke Patients with Motion Machine Learning Algorithms: A Pilot Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18041634. [PMID: 33572116 PMCID: PMC7915561 DOI: 10.3390/ijerph18041634] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 02/04/2021] [Accepted: 02/05/2021] [Indexed: 11/20/2022]
Abstract
Measuring activities of daily living (ADLs) using wearable technologies may offer higher precision and granularity than the current clinical assessments for patients after stroke. This study aimed to develop and determine the accuracy of detecting different ADLs using machine-learning (ML) algorithms and wearable sensors. Eleven post-stroke patients participated in this pilot study at an ADL Simulation Lab across two study visits. We collected blocks of repeated activity (“atomic” activity) performance data to train our ML algorithms during one visit. We evaluated our ML algorithms using independent semi-naturalistic activity data collected at a separate session. We tested Decision Tree, Random Forest, Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost) for model development. XGBoost was the best classification model. We achieved 82% accuracy based on ten ADL tasks. With a model including seven tasks, accuracy improved to 90%. ADL tasks included chopping food, vacuuming, sweeping, spreading jam or butter, folding laundry, eating, brushing teeth, taking off/putting on a shirt, wiping a cupboard, and buttoning a shirt. Results provide preliminary evidence that ADL functioning can be predicted with adequate accuracy using wearable sensors and ML. The use of external validation (independent training and testing data sets) and semi-naturalistic testing data is a major strength of the study and a step closer to the long-term goal of ADL monitoring in real-world settings. Further investigation is needed to improve the ADL prediction accuracy, increase the number of tasks monitored, and test the model outside of a laboratory setting.
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Affiliation(s)
- Pin-Wei Chen
- PlatformSTL, St. Louis, MO 63110, USA; (P.-W.C.); (N.A.B.); (V.S.)
- Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Nathan A. Baune
- PlatformSTL, St. Louis, MO 63110, USA; (P.-W.C.); (N.A.B.); (V.S.)
- Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Igor Zwir
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA; (I.Z.); (J.W.)
- Department of Computer Science and Artificial Intelligence, University of Granada, 18010 Granada, Spain
| | - Jiayu Wang
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA; (I.Z.); (J.W.)
| | | | - Alex W.K. Wong
- Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO 63108, USA
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA; (I.Z.); (J.W.)
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Center for Rehabilitation Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL 60611, USA
- Correspondence:
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Varma VR, Ghosal R, Hillel I, Volfson D, Weiss J, Urbanek J, Hausdorff JM, Zipunnikov V, Watts A. Continuous gait monitoring discriminates community-dwelling mild Alzheimer's disease from cognitively normal controls. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2021; 7:e12131. [PMID: 33598530 PMCID: PMC7864220 DOI: 10.1002/trc2.12131] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 11/25/2020] [Indexed: 01/12/2023]
Abstract
INTRODUCTION Few studies have explored whether gait measured continuously within a community setting can identify individuals with Alzheimer's disease (AD). This study tests the feasibility of this method to identify individuals at the earliest stage of AD. METHODS Mild AD (n = 38) and cognitively normal control (CNC; n = 48) participants from the University of Kansas Alzheimer's Disease Center Registry wore a GT3x+ accelerometer continuously for 7 days to assess gait. Penalized logistic regression with repeated five-fold cross-validation followed by adjusted logistic regression was used to identify gait metrics with the highest predictive performance in discriminating mild AD from CNC. RESULTS Variability in step velocity and cadence had the highest predictive utility in identifying individuals with mild AD. Metrics were also associated with cognitive domains impacted in early AD. DISCUSSION Continuous gait monitoring may be a scalable method to identify individuals at-risk for developing dementia within large, population-based studies.
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Affiliation(s)
- Vijay R. Varma
- Clinical and Translational Neuroscience SectionLaboratory of Behavioral NeuroscienceNational Institute on Aging (NIA)National Institutes of Health (NIH)BaltimoreMarylandUSA
| | - Rahul Ghosal
- Department of BiostatisticsJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
| | - Inbar Hillel
- Center for the Study of Movement, Cognition and MobilityTel Aviv Sourasky Medical Center, Neurological InstituteTel AvivIsrael
| | - Dmitri Volfson
- Neuroscience AnalyticsComputational Biology, TakedaCambridgeMassachusettsUSA
| | - Jordan Weiss
- Department of DemographyUniversity of California, BerkeleyBerkeleyCaliforniaUSA
| | - Jacek Urbanek
- Department of MedicineJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Jeffrey M. Hausdorff
- Center for the Study of Movement, Cognition and MobilityTel Aviv Sourasky Medical Center, Neurological InstituteTel AvivIsrael
- Sagol School of NeuroscienceTel Aviv UniversityTel AvivIsrael
- Rush Alzheimer's Disease Center and Department of Orthopaedic SurgeryRush University Medical CenterChicagoUSA
- Department of Physical Therapy, Sackler Faculty of MedicineTel Aviv UniversityTel AvivIsrael
| | - Vadim Zipunnikov
- Department of BiostatisticsJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
| | - Amber Watts
- Department of PsychologyUniversity of KansasLawrenceKansasUSA
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Sica M, Tedesco S, Crowe C, Kenny L, Moore K, Timmons S, Barton J, O’Flynn B, Komaris DS. Continuous home monitoring of Parkinson's disease using inertial sensors: A systematic review. PLoS One 2021; 16:e0246528. [PMID: 33539481 PMCID: PMC7861548 DOI: 10.1371/journal.pone.0246528] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 01/20/2021] [Indexed: 02/01/2023] Open
Abstract
Parkinson’s disease (PD) is a progressive neurological disorder of the central nervous system that deteriorates motor functions, while it is also accompanied by a large diversity of non-motor symptoms such as cognitive impairment and mood changes, hallucinations, and sleep disturbance. Parkinsonism is evaluated during clinical examinations and appropriate medical treatments are directed towards alleviating symptoms. Tri-axial accelerometers, gyroscopes, and magnetometers could be adopted to support clinicians in the decision-making process by objectively quantifying the patient’s condition. In this context, at-home data collections aim to capture motor function during daily living and unobstructedly assess the patients’ status and the disease’s symptoms for prolonged time periods. This review aims to collate existing literature on PD monitoring using inertial sensors while it focuses on papers with at least one free-living data capture unsupervised either directly or via videotapes. Twenty-four papers were selected at the end of the process: fourteen investigated gait impairments, eight of which focused on walking, three on turning, two on falls, and one on physical activity; ten articles on the other hand examined symptoms, including bradykinesia, tremor, dyskinesia, and motor state fluctuations in the on/off phenomenon. In summary, inertial sensors are capable of gathering data over a long period of time and have the potential to facilitate the monitoring of people with Parkinson’s, providing relevant information about their motor status. Concerning gait impairments, kinematic parameters (such as duration of gait cycle, step length, and velocity) were typically used to discern PD from healthy subjects, whereas for symptoms’ assessment, researchers were capable of achieving accuracies of over 90% in a free-living environment. Further investigations should be focused on the development of ad-hoc hardware and software capable of providing real-time feedback to clinicians and patients. In addition, features such as the wearability of the system and user comfort, set-up process, and instructions for use, need to be strongly considered in the development of wearable sensors for PD monitoring.
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Affiliation(s)
- Marco Sica
- Tyndall National Institute, University College Cork, Cork, Ireland
- * E-mail:
| | | | - Colum Crowe
- Tyndall National Institute, University College Cork, Cork, Ireland
| | - Lorna Kenny
- Centre for Gerontology and Rehabilitation, University College Cork, Cork, Ireland
| | - Kevin Moore
- Centre for Gerontology and Rehabilitation, University College Cork, Cork, Ireland
| | - Suzanne Timmons
- Centre for Gerontology and Rehabilitation, University College Cork, Cork, Ireland
| | - John Barton
- Tyndall National Institute, University College Cork, Cork, Ireland
| | - Brendan O’Flynn
- Tyndall National Institute, University College Cork, Cork, Ireland
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Del Din S, Kirk C, Yarnall AJ, Rochester L, Hausdorff JM. Body-Worn Sensors for Remote Monitoring of Parkinson's Disease Motor Symptoms: Vision, State of the Art, and Challenges Ahead. JOURNAL OF PARKINSON'S DISEASE 2021; 11:S35-S47. [PMID: 33523020 PMCID: PMC8385520 DOI: 10.3233/jpd-202471] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 01/05/2021] [Indexed: 12/15/2022]
Abstract
The increasing prevalence of neurodegenerative conditions such as Parkinson's disease (PD) and related mobility issues places a serious burden on healthcare systems. The COVID-19 pandemic has reinforced the urgent need for better tools to manage chronic conditions remotely, as regular access to clinics may be problematic. Digital health technology in the form of remote monitoring with body-worn sensors offers significant opportunities for transforming research and revolutionizing the clinical management of PD. Significant efforts are being invested in the development and validation of digital outcomes to support diagnosis and track motor and mobility impairments "off-line". Imagine being able to remotely assess your patient, understand how well they are functioning, evaluate the impact of any recent medication/intervention, and identify the need for urgent follow-up before overt, irreparable change takes place? This could offer new pragmatic solutions for personalized care and clinical research. So the question remains: how close are we to achieving this? Here, we describe the state-of-the-art based on representative papers published between 2017 and 2020. We focus on remote (i.e., real-world, daily-living) monitoring of PD using body-worn sensors (e.g., accelerometers, inertial measurement units) for assessing motor symptoms and their complications. Despite the tremendous potential, existing challenges exist (e.g., validity, regulatory) that are preventing the widespread clinical adoption of body-worn sensors as a digital outcome. We propose a roadmap with clear recommendations for addressing these challenges and future directions to bring us closer to the implementation and widespread adoption of this important way of improving the clinical care, evaluation, and monitoring of PD.
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Affiliation(s)
- Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Cameron Kirk
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Alison J. Yarnall
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Jeffrey M. Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv Israel
- Department of Physical Therapy, Sackler School of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Rush Alzheimer’s Disease Center and Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
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Skeleton avatar technology as a way to measure physical activity in healthy older adults. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100609] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
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Galperin I, Herman T, Assad M, Ganz N, Mirelman A, Giladi N, Hausdorff JM. Sensor-Based and Patient-Based Assessment of Daily-Living Physical Activity in People with Parkinson's Disease: Do Motor Subtypes Play a Role? SENSORS (BASEL, SWITZERLAND) 2020; 20:E7015. [PMID: 33302434 PMCID: PMC7762555 DOI: 10.3390/s20247015] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 12/01/2020] [Accepted: 12/05/2020] [Indexed: 11/25/2022]
Abstract
The benefits of daily-living physical activity are clear. Nonetheless, the relationship between physical activity levels and motor subtypes of Parkinson's disease (PD), i.e., tremor dominant (TD) and postural instability gait difficulty (PIGD), have not been well-studied. It is also unclear if patient perspectives and motor symptom severity are related to objective, sensor-based assessment of daily-living activity in those subtypes. To address these questions, total daily-living physical activity was quantified in 73 patients with PD and 29 healthy controls using a 3D-accelerometer worn on the lower back for at least three days. We found that individuals with the PIGD subtype were significantly less active than healthy older adults (p = 0.007), unlike individuals with the TD subtype. Among the PIGD subtype, higher daily physical activity was negatively associated with more severe ON bradykinesia (rS = -0.499, p = 0.002), motor symptoms (higher ON MDS-UPDRS (Unified Parkinson's Disease Rating Scale motor examination)-III scores), gait difficulties (rS = -0.502, p = 0.002), motor complications (rS = 0.466, p = 0.004), and balance (rS = 0.519, p = 0.001). In contrast, among the TD subtype, disease-related characteristics were not related to daily-living physical activity. Intriguingly, physical activity was not related to self-report of ADL difficulties (scores of the MDS-UPDRS Parts I or II) in both motor subtypes. These findings highlight the importance of objective daily-living physical activity monitoring and suggest that self-report does not necessarily reflect objective physical activity levels. Furthermore, the results point to important differences in factors related to physical activity in PD motor subtypes, setting the stage for personalized treatment programs.
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Affiliation(s)
- Irina Galperin
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492415, Israel; (I.G.); (T.H.); (M.A.); (N.G.); (A.M.); (N.G.)
| | - Talia Herman
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492415, Israel; (I.G.); (T.H.); (M.A.); (N.G.); (A.M.); (N.G.)
| | - Mira Assad
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492415, Israel; (I.G.); (T.H.); (M.A.); (N.G.); (A.M.); (N.G.)
- Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel-Aviv 69978, Israel
| | - Natalie Ganz
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492415, Israel; (I.G.); (T.H.); (M.A.); (N.G.); (A.M.); (N.G.)
| | - Anat Mirelman
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492415, Israel; (I.G.); (T.H.); (M.A.); (N.G.); (A.M.); (N.G.)
- Sagol School of Neuroscience, Tel Aviv University, Tel-Aviv 69978, Israel
- Department of Neurology and Neurosurgery, Sackler School of Medicine, Tel Aviv University, Tel-Aviv 69978, Israel
| | - Nir Giladi
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492415, Israel; (I.G.); (T.H.); (M.A.); (N.G.); (A.M.); (N.G.)
- Sagol School of Neuroscience, Tel Aviv University, Tel-Aviv 69978, Israel
- Department of Neurology and Neurosurgery, Sackler School of Medicine, Tel Aviv University, Tel-Aviv 69978, Israel
| | - Jeffrey M. Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492415, Israel; (I.G.); (T.H.); (M.A.); (N.G.); (A.M.); (N.G.)
- Sagol School of Neuroscience, Tel Aviv University, Tel-Aviv 69978, Israel
- Department of Physical Therapy, Sackler Faculty of Medicine, Tel-Aviv 69978, Israel
- Rush Alzheimer’s Disease Center and Department of Orthopedic Surgery, Rush University Medical Center, Chicago 60612, IL, USA
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Shah VV, McNames J, Mancini M, Carlson-Kuhta P, Spain RI, Nutt JG, El-Gohary M, Curtze C, Horak FB. Laboratory versus daily life gait characteristics in patients with multiple sclerosis, Parkinson's disease, and matched controls. J Neuroeng Rehabil 2020; 17:159. [PMID: 33261625 PMCID: PMC7708140 DOI: 10.1186/s12984-020-00781-4] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 10/25/2020] [Indexed: 01/30/2023] Open
Abstract
BACKGROUND AND PURPOSE Recent findings suggest that a gait assessment at a discrete moment in a clinic or laboratory setting may not reflect functional, everyday mobility. As a step towards better understanding gait during daily life in neurological populations, we compared gait measures that best discriminated people with multiple sclerosis (MS) and people with Parkinson's Disease (PD) from their respective, age-matched, healthy control subjects (MS-Ctl, PD-Ctl) in laboratory tests versus a week of daily life monitoring. METHODS We recruited 15 people with MS (age mean ± SD: 49 ± 10 years), 16 MS-Ctl (45 ± 11 years), 16 people with idiopathic PD (71 ± 5 years), and 15 PD-Ctl (69 ± 7 years). Subjects wore 3 inertial sensors (one each foot and lower back) in the laboratory followed by 7 days during daily life. Mann-Whitney U test and area under the curve (AUC) compared differences between PD and PD-Ctl, and between MS and MS-Ctl in the laboratory and in daily life. RESULTS Participants wore sensors for 60-68 h in daily life. Measures that best discriminated gait characteristics in people with MS and PD from their respective control groups were different between the laboratory gait test and a week of daily life. Specifically, the toe-off angle best discriminated MS versus MS-Ctl in the laboratory (AUC [95% CI] = 0.80 [0.63-0.96]) whereas gait speed in daily life (AUC = 0.84 [0.69-1.00]). In contrast, the lumbar coronal range of motion best discriminated PD versus PD-Ctl in the laboratory (AUC = 0.78 [0.59-0.96]) whereas foot-strike angle in daily life (AUC = 0.84 [0.70-0.98]). AUCs were larger in daily life compared to the laboratory. CONCLUSIONS Larger AUC for daily life gait measures compared to the laboratory gait measures suggest that daily life monitoring may be more sensitive to impairments from neurological disease, but each neurological disease may require different gait outcome measures.
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Affiliation(s)
- Vrutangkumar V Shah
- Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA.
| | - James McNames
- Department of Electrical and Computer Engineering, Portland State University, Portland, OR, USA
- APDM Wearable Technologies, Portland, OR, USA
| | - Martina Mancini
- Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA
| | - Patricia Carlson-Kuhta
- Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA
| | - Rebecca I Spain
- Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA
- Veterans Affairs Portland Health Care System, Portland, OR, USA
| | - John G Nutt
- Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA
| | | | - Carolin Curtze
- Department of Biomechanics, University of Nebraska At Omaha, Omaha, NE, USA
| | - Fay B Horak
- Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA
- APDM Wearable Technologies, Portland, OR, USA
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An Objective Methodology for the Selection of a Device for Continuous Mobility Assessment. SENSORS 2020; 20:s20226509. [PMID: 33202608 PMCID: PMC7696193 DOI: 10.3390/s20226509] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 11/05/2020] [Accepted: 11/10/2020] [Indexed: 01/11/2023]
Abstract
Continuous monitoring by wearable technology is ideal for quantifying mobility outcomes in “real-world” conditions. Concurrent factors such as validity, usability, and acceptability of such technology need to be accounted for when choosing a monitoring device. This study proposes a bespoke methodology focused on defining a decision matrix to allow for effective decision making. A weighting system based on responses (n = 69) from a purpose-built questionnaire circulated within the IMI Mobilise-D consortium and its external collaborators was established, accounting for respondents’ background and level of expertise in using wearables in clinical practice. Four domains (concurrent validity, CV; human factors, HF; wearability and usability, WU; and data capture process, CP), associated evaluation criteria, and scores were established through literature research and group discussions. While the CV was perceived as the most relevant domain (37%), the others were also considered highly relevant (WU: 30%, HF: 17%, CP: 16%). Respondents (~90%) preferred a hidden fixation and identified the lower back as an ideal sensor location for mobility outcomes. Overall, this study provides a novel, holistic, objective, as well as a standardized approach accounting for complementary aspects that should be considered by professionals and researchers when selecting a solution for continuous mobility monitoring.
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Bhidayasiri R, Mari Z. Digital phenotyping in Parkinson's disease: Empowering neurologists for measurement-based care. Parkinsonism Relat Disord 2020; 80:35-40. [DOI: 10.1016/j.parkreldis.2020.08.038] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 08/26/2020] [Accepted: 08/28/2020] [Indexed: 12/24/2022]
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