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Marom P, Brik M, Agay N, Dankner R, Katzir Z, Keshet N, Doron D. The Reliability and Validity of the OneStep Smartphone Application for Gait Analysis among Patients Undergoing Rehabilitation for Unilateral Lower Limb Disability. SENSORS (BASEL, SWITZERLAND) 2024; 24:3594. [PMID: 38894386 PMCID: PMC11175355 DOI: 10.3390/s24113594] [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: 04/04/2024] [Revised: 05/20/2024] [Accepted: 05/28/2024] [Indexed: 06/21/2024]
Abstract
An easy-to-use and reliable tool is essential for gait assessment of people with gait pathologies. This study aimed to assess the reliability and validity of the OneStep smartphone application compared to the C-Mill-VR+ treadmill (Motek, Nederlands), among patients undergoing rehabilitation for unilateral lower extremity disability. Spatiotemporal gait parameters were extracted from the treadmill and from two smartphones, one on each leg. Inter-device reliability was evaluated using Pearson correlation, intra-cluster correlation coefficient (ICC), and Cohen's d, comparing the application's readings from the two phones. Validity was assessed by comparing readings from each phone to the treadmill. Twenty-eight patients completed the study; the median age was 45.5 years, and 61% were males. The ICC between the phones showed a high correlation (r = 0.89-1) and good-to-excellent reliability (ICC range, 0.77-1) for all the gait parameters examined. The correlations between the phones and the treadmill were mostly above 0.8. The ICC between each phone and the treadmill demonstrated moderate-to-excellent validity for all the gait parameters (range, 0.58-1). Only 'step length of the impaired leg' showed poor-to-good validity (range, 0.37-0.84). Cohen's d effect size was small (d < 0.5) for all the parameters. The studied application demonstrated good reliability and validity for spatiotemporal gait assessment in patients with unilateral lower limb disability.
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Affiliation(s)
- Pnina Marom
- Reuth Research and Development Institute, Reuth Rehabilitation Hospital, Tel Aviv 6772830, Israel; (M.B.); (R.D.); (Z.K.)
- Department of Health Promotion, School of Public Health, Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Michael Brik
- Reuth Research and Development Institute, Reuth Rehabilitation Hospital, Tel Aviv 6772830, Israel; (M.B.); (R.D.); (Z.K.)
| | - Nirit Agay
- Unit for Cardiovascular Epidemiology, The Gertner Institute for Epidemiology and Health Policy Research, Sheba Medical Center, Tel Hashomer, Ramat Gan 5262000, Israel;
| | - Rachel Dankner
- Reuth Research and Development Institute, Reuth Rehabilitation Hospital, Tel Aviv 6772830, Israel; (M.B.); (R.D.); (Z.K.)
- Unit for Cardiovascular Epidemiology, The Gertner Institute for Epidemiology and Health Policy Research, Sheba Medical Center, Tel Hashomer, Ramat Gan 5262000, Israel;
- Department of Epidemiology and Preventive Medicine, School of Public Health, Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Zoya Katzir
- Reuth Research and Development Institute, Reuth Rehabilitation Hospital, Tel Aviv 6772830, Israel; (M.B.); (R.D.); (Z.K.)
- Department of General Medicine, School of Medicine, Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Naama Keshet
- Department of Physical Therapy, Reuth Rehabilitation Hospital, Tel Aviv 6772830, Israel;
| | - Dana Doron
- Ambulatory Day Care, Reuth Rehabilitation Hospital, Tel Aviv 6772830, Israel
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Gogoi N, Zhu Y, Kirchner J, Fischer G. Choice of Piezoelectric Element over Accelerometer for an Energy-Autonomous Shoe-Based System. SENSORS (BASEL, SWITZERLAND) 2024; 24:2549. [PMID: 38676166 PMCID: PMC11055156 DOI: 10.3390/s24082549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 04/05/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024]
Abstract
Shoe-based wearable sensor systems are a growing research area in health monitoring, disease diagnosis, rehabilitation, and sports training. These systems-equipped with one or more sensors, either of the same or different types-capture information related to foot movement or pressure maps beneath the foot. This captured information offers an overview of the subject's overall movement, known as the human gait. Beyond sensing, these systems also provide a platform for hosting ambient energy harvesters. They hold the potential to harvest energy from foot movements and operate related low-power devices sustainably. This article proposes two types of strategies (Strategy 1 and Strategy 2) for an energy-autonomous shoe-based system. Strategy 1 uses an accelerometer as a sensor for gait acquisition, which reflects the classical choice. Strategy 2 uses a piezoelectric element for the same, which opens up a new perspective in its implementation. In both strategies, the piezoelectric elements are used to harvest energy from foot activities and operate the system. The article presents a fair comparison between both strategies in terms of power consumption, accuracy, and the extent to which piezoelectric energy harvesters can contribute to overall power management. Moreover, Strategy 2, which uses piezoelectric elements for simultaneous sensing and energy harvesting, is a power-optimized method for an energy-autonomous shoe system.
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Affiliation(s)
- Niharika Gogoi
- Department of Computer Science, Durham University, Upper Mountjoy Campus, Stockton Road, Durham DH13LE, UK;
- Institute of Technical Electronics, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany; (Y.Z.); (J.K.)
| | - Yuanjia Zhu
- Institute of Technical Electronics, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany; (Y.Z.); (J.K.)
| | - Jens Kirchner
- Institute of Technical Electronics, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany; (Y.Z.); (J.K.)
- Faculty of Information Technology, University of Applied Sciences and Arts, 44227 Dortmund, Germany
| | - Georg Fischer
- Institute of Technical Electronics, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany; (Y.Z.); (J.K.)
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3
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Esper CD, Valdovinos BY, Schneider RB. The Importance of Digital Health Literacy in an Evolving Parkinson's Disease Care System. JOURNAL OF PARKINSON'S DISEASE 2024:JPD230229. [PMID: 38250786 DOI: 10.3233/jpd-230229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
Digital health technologies are growing at a rapid pace and changing the healthcare landscape. Our current understanding of digital health literacy in Parkinson's disease (PD) is limited. In this review, we discuss the potential challenges of low digital health literacy in PD with particular attention to telehealth, deep brain stimulation, wearable sensors, and smartphone applications. We also highlight inequities in access to digital health technologies. Future research is needed to better understand digital health literacy among individuals with PD and to develop effective solutions. We must invest resources to evaluate, understand, and enhance digital health literacy for individuals with PD.
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Affiliation(s)
| | | | - Ruth B Schneider
- Department of Neurology, University of Rochester, Rochester, NY, USA
- Center for Health + Technology, University of Rochester, Rochester, NY, USA
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Fay-Karmon T, Galor N, Heimler B, Zilka A, Bartsch RP, Plotnik M, Hassin-Baer S. Home-based monitoring of persons with advanced Parkinson's disease using smartwatch-smartphone technology. Sci Rep 2024; 14:9. [PMID: 38167434 PMCID: PMC10761812 DOI: 10.1038/s41598-023-48209-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Accepted: 11/23/2023] [Indexed: 01/05/2024] Open
Abstract
Movement deterioration is the hallmark of Parkinson's disease (PD), characterized by levodopa-induced motor-fluctuations (i.e., symptoms' variability related to the medication cycle) in advanced stages. However, motor symptoms are typically too sporadically and/or subjectively assessed, ultimately preventing the effective monitoring of their progression, and thus leading to suboptimal treatment/therapeutic choices. Smartwatches (SW) enable a quantitative-oriented approach to motor-symptoms evaluation, namely home-based monitoring (HBM) using an embedded inertial measurement unit. Studies validated such approach against in-clinic evaluations. In this work, we aimed at delineating personalized motor-fluctuations' profiles, thus capturing individual differences. 21 advanced PD patients with motor fluctuations were monitored for 2 weeks using a SW and a smartphone-dedicated app (Intel Pharma Analytics Platform). The SW continuously collected passive data (tremor, dyskinesia, level of activity using dedicated algorithms) and active data, i.e., time-up-and-go, finger tapping, hand tremor and hand rotation carried out daily, once in OFF and once in ON levodopa periods. We observed overall high compliance with the protocol. Furthermore, we observed striking differences among the individual patterns of symptoms' levodopa-related variations across the HBM, allowing to divide our participants among four data-driven, motor-fluctuations' profiles. This highlights the potential of HBM using SW technology for revolutionizing clinical practices.
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Affiliation(s)
- Tsviya Fay-Karmon
- Movement Disorders Institute, Department of Neurology, Sheba Medical Center, Ramat Gan, Israel
| | - Noam Galor
- Center of Advanced Technologies in Rehabilitation, Sheba Medical Center, Ramat Gan, Israel
| | - Benedetta Heimler
- Center of Advanced Technologies in Rehabilitation, Sheba Medical Center, Ramat Gan, Israel
| | - Asaf Zilka
- Movement Disorders Institute, Department of Neurology, Sheba Medical Center, Ramat Gan, Israel
| | - Ronny P Bartsch
- Department of Physics, Bar-Ilan University, Ramat Gan, Israel
| | - Meir Plotnik
- Center of Advanced Technologies in Rehabilitation, Sheba Medical Center, Ramat Gan, Israel
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Sharon Hassin-Baer
- Movement Disorders Institute, Department of Neurology, Sheba Medical Center, Ramat Gan, Israel.
- Department of Neurology and Neurosurgery, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
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Sjaelland NS, Gramkow MH, Hasselbalch SG, Frederiksen KS. Digital Biomarkers for the Assessment of Non-Cognitive Symptoms in Patients with Dementia with Lewy Bodies: A Systematic Review. J Alzheimers Dis 2024; 100:431-451. [PMID: 38943394 DOI: 10.3233/jad-240327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2024]
Abstract
Background Portable digital health technologies (DHTs) could help evaluate non-cognitive symptoms, but evidence to support their use in patients with dementia with Lewy bodies (DLB) is uncertain. Objective 1) To describe portable or wearable DHTs used to obtain digital biomarkers in patients with DLB, 2) to assess the digital biomarkers' ability to evaluate non-cognitive symptoms, and 3) to assess the feasibility of applying digital biomarkers in patients with DLB. Methods We systematically searched databases MEDLINE, Embase, and Web of Science from inception through February 28, 2023. Studies assessing digital biomarkers obtained by portable or wearable DHTs and related to non-cognitive symptoms were eligible if including patients with DLB. The quality of studies was assessed using a modified check list based on the NIH Quality assessment tool for Observational Cohort and Cross-sectional Studies. A narrative synthesis of data was carried out. Results We screened 4,295 records and included 20 studies. Seventeen different DHTs were identified for assessment of most non-cognitive symptoms related to DLB. No thorough validation of digital biomarkers for measurement of non-cognitive symptoms in DLB was reported. Studies did not report on aspects of feasibility in a systematic way. Conclusions Knowledge about feasibility and validity of individual digital biomarkers remains extremely limited. Study heterogeneity is a barrier for establishing a broad evidence base for application of digital biomarkers in DLB. Researchers should conform to recommended standards for systematic evaluation of digital biomarkers.
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Affiliation(s)
- Nikolai S Sjaelland
- Danish Dementia Research Centre, Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Mathias H Gramkow
- Danish Dementia Research Centre, Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Steen G Hasselbalch
- Danish Dementia Research Centre, Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Kristian Steen Frederiksen
- Danish Dementia Research Centre, Department of Neurology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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Strongman C, Cavallerio F, Timmis MA, Morrison A. A Scoping Review of the Validity and Reliability of Smartphone Accelerometers When Collecting Kinematic Gait Data. SENSORS (BASEL, SWITZERLAND) 2023; 23:8615. [PMID: 37896708 PMCID: PMC10611257 DOI: 10.3390/s23208615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 10/18/2023] [Accepted: 10/19/2023] [Indexed: 10/29/2023]
Abstract
The aim of this scoping review is to evaluate and summarize the existing literature that considers the validity and/or reliability of smartphone accelerometer applications when compared to 'gold standard' kinematic data collection (for example, motion capture). An electronic keyword search was performed on three databases to identify appropriate research. This research was then examined for details of measures and methodology and general study characteristics to identify related themes. No restrictions were placed on the date of publication, type of smartphone, or participant demographics. In total, 21 papers were reviewed to synthesize themes and approaches used and to identify future research priorities. The validity and reliability of smartphone-based accelerometry data have been assessed against motion capture, pressure walkways, and IMUs as 'gold standard' technology and they have been found to be accurate and reliable. This suggests that smartphone accelerometers can provide a cheap and accurate alternative to gather kinematic data, which can be used in ecologically valid environments to potentially increase diversity in research participation. However, some studies suggest that body placement may affect the accuracy of the result, and that position data correlate better than actual acceleration values, which should be considered in any future implementation of smartphone technology. Future research comparing different capture frequencies and resulting noise, and different walking surfaces, would be useful.
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Affiliation(s)
- Clare Strongman
- Cambridge Centre for Sport and Exercise Sciences, Anglia Ruskin University, East Road, Cambridge CB1 1PT, UK; (F.C.); (M.A.T.); (A.M.)
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Beigi OM, Nóbrega LR, Houghten S, Alves Pereira A, de Oliveira Andrade A. Freezing of gait in Parkinson's disease: Classification using computational intelligence. Biosystems 2023; 232:105006. [PMID: 37634658 DOI: 10.1016/j.biosystems.2023.105006] [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: 03/31/2023] [Revised: 08/20/2023] [Accepted: 08/20/2023] [Indexed: 08/29/2023]
Abstract
Parkinson's disease (PD) is a neurodegenerative disease represented by the progressive loss of dopamine producing neurons, with motor and non-motor symptoms that may be hard to distinguish from other disorders. Affecting millions of people across the world, its symptoms include bradykinesia, tremors, depression, rigidity, postural instability, cognitive decline, and falls. Furthermore, changes in gait can be used as a primary diagnosis factor. A dataset is described that records data on healthy individuals and on PD patients, including those who experience freezing of gait, in both the ON and OFF-medication states. The dataset is comprised of data for four separate tasks: voluntary stop, timed up and go, simple motor task, and dual motor and cognitive task. Seven different classifiers are applied to two problems relating to this data. The first problem is to distinguish PD patients from healthy individuals, both overall and per task. The second problem is to determine the effectiveness of medication. A thorough analysis on the classifiers and their results is performed. Overall, multilayer perceptron and decision tree provide the most consistent results.
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Affiliation(s)
- Omid Mohamad Beigi
- Computer Science Department, Brock University, St. Catharines, Ontario, Canada
| | - Lígia Reis Nóbrega
- Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia, Brazil
| | - Sheridan Houghten
- Computer Science Department, Brock University, St. Catharines, Ontario, Canada.
| | - Adriano Alves Pereira
- Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia, Brazil
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Alfalahi H, Dias SB, Khandoker AH, Chaudhuri KR, Hadjileontiadis LJ. A scoping review of neurodegenerative manifestations in explainable digital phenotyping. NPJ Parkinsons Dis 2023; 9:49. [PMID: 36997573 PMCID: PMC10063633 DOI: 10.1038/s41531-023-00494-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 03/16/2023] [Indexed: 04/03/2023] Open
Abstract
Neurologists nowadays no longer view neurodegenerative diseases, like Parkinson's and Alzheimer's disease, as single entities, but rather as a spectrum of multifaceted symptoms with heterogeneous progression courses and treatment responses. The definition of the naturalistic behavioral repertoire of early neurodegenerative manifestations is still elusive, impeding early diagnosis and intervention. Central to this view is the role of artificial intelligence (AI) in reinforcing the depth of phenotypic information, thereby supporting the paradigm shift to precision medicine and personalized healthcare. This suggestion advocates the definition of disease subtypes in a new biomarker-supported nosology framework, yet without empirical consensus on standardization, reliability and interpretability. Although the well-defined neurodegenerative processes, linked to a triad of motor and non-motor preclinical symptoms, are detected by clinical intuition, we undertake an unbiased data-driven approach to identify different patterns of neuropathology distribution based on the naturalistic behavior data inherent to populations in-the-wild. We appraise the role of remote technologies in the definition of digital phenotyping specific to brain-, body- and social-level neurodegenerative subtle symptoms, emphasizing inter- and intra-patient variability powered by deep learning. As such, the present review endeavors to exploit digital technologies and AI to create disease-specific phenotypic explanations, facilitating the understanding of neurodegenerative diseases as "bio-psycho-social" conditions. Not only does this translational effort within explainable digital phenotyping foster the understanding of disease-induced traits, but it also enhances diagnostic and, eventually, treatment personalization.
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Affiliation(s)
- Hessa Alfalahi
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.
| | - Sofia B Dias
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- CIPER, Faculdade de Motricidade Humana, University of Lisbon, Lisbon, Portugal
| | - Ahsan H Khandoker
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Kallol Ray Chaudhuri
- Parkinson Foundation, International Center of Excellence, King's College London, Denmark Hills, London, UK
- Department of Basic and Clinical Neurosciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London, UK
| | - Leontios J Hadjileontiadis
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
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Mavragani A, Bruhin LC, Schütz N, Naef AC, Hegi H, Reuse P, Schindler KA, Krack P, Wiest R, Chan A, Nef T, Gerber SM. Development of an Open-source and Lightweight Sensor Recording Software System for Conducting Biomedical Research: Technical Report. JMIR Form Res 2023; 7:e43092. [PMID: 36800219 PMCID: PMC9985000 DOI: 10.2196/43092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 11/28/2022] [Accepted: 01/03/2023] [Indexed: 02/18/2023] Open
Abstract
BACKGROUND Digital sensing devices have become an increasingly important component of modern biomedical research, as they help provide objective insights into individuals' everyday behavior in terms of changes in motor and nonmotor symptoms. However, there are significant barriers to the adoption of sensor-enhanced biomedical solutions in terms of both technical expertise and associated costs. The currently available solutions neither allow easy integration of custom sensing devices nor offer a practicable methodology in cases of limited resources. This has become particularly relevant, given the need for real-time sensor data that could help lower health care costs by reducing the frequency of clinical assessments performed by specialists and improve access to health assessments (eg, for people living in remote areas or older adults living at home). OBJECTIVE The objective of this paper is to detail the end-to-end development of a novel sensor recording software system that supports the integration of heterogeneous sensor technologies, runs as an on-demand service on consumer-grade hardware to build sensor systems, and can be easily used to reliably record longitudinal sensor measurements in research settings. METHODS The proposed software system is based on a server-client architecture, consisting of multiple self-contained microservices that communicated with each other (eg, the web server transfers data to a database instance) and were implemented as Docker containers. The design of the software is based on state-of-the-art open-source technologies (eg, Node.js or MongoDB), which fulfill nonfunctional requirements and reduce associated costs. A series of programs to facilitate the use of the software were documented. To demonstrate performance, the software was tested in 3 studies (2 gait studies and 1 behavioral study assessing activities of daily living) that ran between 2 and 225 days, with a total of 114 participants. We used descriptive statistics to evaluate longitudinal measurements for reliability, error rates, throughput rates, latency, and usability (with the System Usability Scale [SUS] and the Post-Study System Usability Questionnaire [PSSUQ]). RESULTS Three qualitative features (event annotation program, sample delay analysis program, and monitoring dashboard) were elaborated and realized as integrated programs. Our quantitative findings demonstrate that the system operates reliably on consumer-grade hardware, even across multiple months (>420 days), providing high throughput (2000 requests per second) with a low latency and error rate (<0.002%). In addition, the results of the usability tests indicate that the system is effective, efficient, and satisfactory to use (mean usability ratings for the SUS and PSSUQ were 89.5 and 1.62, respectively). CONCLUSIONS Overall, this sensor recording software could be leveraged to test sensor devices, as well as to develop and validate algorithms that are able to extract digital measures (eg, gait parameters or actigraphy). The proposed software could help significantly reduce barriers related to sensor-enhanced biomedical research and allow researchers to focus on the research questions at hand rather than on developing recording technologies.
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Affiliation(s)
| | - Lena C Bruhin
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Narayan Schütz
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.,DomoHealth SA, Lausanne, Switzerland
| | - Aileen C Naef
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Heinz Hegi
- Department of Sport Science, University of Bern, Bern, Switzerland
| | - Pascal Reuse
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Kaspar A Schindler
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Paul Krack
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Andrew Chan
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Tobias Nef
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.,Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Stephan M Gerber
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
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Gait Analysis in Orthopaedic Surgery: History, Limitations, and Future Directions. J Am Acad Orthop Surg 2022; 30:e1366-e1373. [PMID: 36026713 DOI: 10.5435/jaaos-d-21-00785] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 06/19/2022] [Indexed: 02/01/2023] Open
Abstract
Gait analysis has expanding indications in orthopaedic surgery, both for clinical and research applications. Early work has been particularly helpful for understanding pathologic gait deviations in neuromuscular disorders and biomechanical imbalances that contribute to injury. Notable advances in image acquisition, health-related wearable devices, and computational capabilities for big data sets have led to a rapid expansion of gait analysis tools, enabling novel research in all orthopaedic subspecialties. Given the lower cost and increased accessibility, new gait analysis tools will surely affect the next generation of objective patient outcome data. This article reviews the basic principles of gait analysis, modern tools available to the common surgeon, and future directions in this space.
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11
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Uribe P, Fuentes N, Álvarez-Ruf J, Cornejo I, Mariman JJ. Differentiation of the motor cost associated with cognitive tasks in Parkinson's disease: a dual-task study. Eur J Neurosci 2022; 56:5106-5115. [PMID: 35962541 DOI: 10.1111/ejn.15792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 07/25/2022] [Accepted: 08/10/2022] [Indexed: 11/28/2022]
Abstract
Parkinson's disease is a neurodegenerative condition associated with motor and cognitive impairments. While the execution of dual cognitive-motor tasks imposes a cost on gait velocity, it has been barely determined if the gait deterioration depends on the specific cognitive domain involved in the dual-task. Twenty-four subjects (twelve patients with Parkinson's disease and twelve healthy subjects) carried out a single task (gait alone) and several dual tasks where the concurrent second task was the Trail Making Test (Part A) and the six tasks of the Frontal Assessment Battery. Gait variables were measured by accelerometry via smartphone. Data analysis included analysis of variance and exploratory factorial analysis. Both groups showed a similar gait performance, except for velocity, where patients exhibited a bradykinetic profile. The dual-task during the Trail Making Test showed the highest motor cost. Frontal Assessment Battery's tasks as conceptualization, mental flexibility, and motor programming showed a higher motor cost than the other tasks (sensibility to interference, inhibitory control, and environmental autonomy). The factorial analysis applied to the motor costs confirmed two profiles, grouping those related to the dorsolateral prefrontal cortex (mental flexibility and motor programming tasks) in an independent factor. Among cognitive functions, attention is critical for gait control in Parkinson's disease and healthy elderly people. The interference posed by several executive operations suggests a specific competition in prefrontal regions that support dual tasks. Moreover, the higher cost for Parkinson's disease patients emphasizes the cognitive decline and compensatory cognitive strategy for gait control related to attention and executive functions.
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Affiliation(s)
- Paula Uribe
- Laboratorio de Cognición y Comportamiento Sensoriomotor, Departamento de Kinesiología, Facultad de Artes y Educación Física, Universidad Metropolitana de Ciencias de la Educación, Santiago, Chile
| | - Natalia Fuentes
- Laboratorio de Cognición y Comportamiento Sensoriomotor, Departamento de Kinesiología, Facultad de Artes y Educación Física, Universidad Metropolitana de Ciencias de la Educación, Santiago, Chile
| | - Joel Álvarez-Ruf
- Laboratorio de Cognición y Comportamiento Sensoriomotor, Departamento de Kinesiología, Facultad de Artes y Educación Física, Universidad Metropolitana de Ciencias de la Educación, Santiago, Chile.,Laboratorio de Biomecánica Clínica, Facultad de Medicina Clínica Alemana, Universidad del Desarrollo, Carrera de Kinesiología, Santiago, Chile
| | - Isabel Cornejo
- Laboratorio de Cognición y Comportamiento Sensoriomotor, Departamento de Kinesiología, Facultad de Artes y Educación Física, Universidad Metropolitana de Ciencias de la Educación, Santiago, Chile.,Liga Chilena contra el Mal de Parkinson, Santiago, Chile
| | - Juan J Mariman
- Laboratorio de Cognición y Comportamiento Sensoriomotor, Departamento de Kinesiología, Facultad de Artes y Educación Física, Universidad Metropolitana de Ciencias de la Educación, Santiago, Chile.,Departamento de Kinesiología, Facultad de Medicina, Universidad de Chile, Santiago, Chile
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12
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Tripathi S, Malhotra A, Qazi M, Chou J, Wang F, Barkan S, Hellmers N, Henchcliffe C, Sarva H. Clinical Review of Smartphone Applications in Parkinson's Disease. Neurologist 2022; 27:183-193. [PMID: 35051970 DOI: 10.1097/nrl.0000000000000413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
BACKGROUND Parkinson's disease (PD) is the second leading neurodegenerative disease worldwide. Important advances in monitoring and treatment have been made in recent years. This article reviews literature on utility of smartphone applications in monitoring PD symptoms that may ultimately facilitate improved patient care, and on movement modulation as a potential therapeutic. REVIEW SUMMARY Novel mobile phone applications can provide one-time and/or continuous data to monitor PD motor symptoms in person or remotely, that may support precise therapeutic adjustments and management decisions. Apps have also been developed for medication management and treatment. CONCLUSIONS Smartphone applications provide a wide array of platforms allowing for meaningful short-term and long-term data collection and are also being tested for intervention. However, the variability of the applications and the need to translate complicated sensor data may hinder immediate clinical applicability. Future studies should involve stake-holders early in the design process to promote usability and streamline the interface between patients, clinicians, and PD apps.
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Affiliation(s)
- Susmit Tripathi
- Department of Neurology, New York-Presbyterian Hospital/Weill Cornell Medical Center
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Ashwin Malhotra
- Department of Neurology, New York-Presbyterian Hospital/Weill Cornell Medical Center
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Murtaza Qazi
- Weill Cornell Medicine Qatar, Education City, Qatar
| | - Jingyuan Chou
- Department of Neurology, New York-Presbyterian Hospital/Weill Cornell Medical Center
| | - Fei Wang
- Department of Neurology, New York-Presbyterian Hospital/Weill Cornell Medical Center
| | - Samantha Barkan
- Department of Neurology, New York-Presbyterian Hospital/Weill Cornell Medical Center
| | - Natalie Hellmers
- Department of Neurology, New York-Presbyterian Hospital/Weill Cornell Medical Center
| | - Claire Henchcliffe
- Department of Neurology, New York-Presbyterian Hospital/Weill Cornell Medical Center
- Department of Neurology, University of California, Irvine, Irvine, CA
| | - Harini Sarva
- Department of Neurology, New York-Presbyterian Hospital/Weill Cornell Medical Center
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13
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Rentz C, Far MS, Boltes M, Schnitzler A, Amunts K, Dukart J, Minnerop M. System Comparison for Gait and Balance Monitoring Used for the Evaluation of a Home-Based Training. SENSORS (BASEL, SWITZERLAND) 2022; 22:4975. [PMID: 35808470 PMCID: PMC9269735 DOI: 10.3390/s22134975] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 06/24/2022] [Accepted: 06/28/2022] [Indexed: 06/15/2023]
Abstract
There are currently no standard methods for evaluating gait and balance performance at home. Smartphones include acceleration sensors and may represent a promising and easily accessible tool for this purpose. We performed an interventional feasibility study and compared a smartphone-based approach with two standard gait analysis systems (force plate and motion capturing systems). Healthy adults (n = 25, 44.1 ± 18.4 years) completed two laboratory evaluations before and after a three-week gait and balance training at home. There was an excellent agreement between all systems for stride time and cadence during normal, tandem and backward gait, whereas correlations for gait velocity were lower. Balance variables of both standard systems were moderately intercorrelated across all stance tasks, but only few correlated with the corresponding smartphone measures. Significant differences over time were found for several force plate and mocap system-obtained gait variables of normal, backward and tandem gait. Changes in balance variables over time were more heterogeneous and not significant for any system. The smartphone seems to be a suitable method to measure cadence and stride time of different gait, but not balance, tasks in healthy adults. Additional optimizations in data evaluation and processing may further improve the agreement between the analysis systems.
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Affiliation(s)
- Clara Rentz
- Institute of Neuroscience and Medicine (INM-1), Research Centre Juelich, 52428 Juelich, Germany; (K.A.); (M.M.)
| | - Mehran Sahandi Far
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Juelich, 52428 Juelich, Germany; (M.S.F.); (J.D.)
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Duesseldorf, 40225 Duesseldorf, Germany
| | - Maik Boltes
- Institute for Advanced Simulation (IAS-7), Research Centre Juelich, 52428 Juelich, Germany;
| | - Alfons Schnitzler
- Department of Neurology, Center for Movement Disorders and Neuromodulation, Medical Faculty, Heinrich-Heine University Duesseldorf, 40225 Duesseldorf, Germany;
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine University Duesseldorf, 40225 Duesseldorf, Germany
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Research Centre Juelich, 52428 Juelich, Germany; (K.A.); (M.M.)
- C. and O. Vogt Institute for Brain Research, Medical Faculty, University Hospital Duesseldorf, Heinrich-Heine University Duesseldorf, 40225 Duesseldorf, Germany
| | - Juergen Dukart
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Juelich, 52428 Juelich, Germany; (M.S.F.); (J.D.)
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Duesseldorf, 40225 Duesseldorf, Germany
| | - Martina Minnerop
- Institute of Neuroscience and Medicine (INM-1), Research Centre Juelich, 52428 Juelich, Germany; (K.A.); (M.M.)
- Department of Neurology, Center for Movement Disorders and Neuromodulation, Medical Faculty, Heinrich-Heine University Duesseldorf, 40225 Duesseldorf, Germany;
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine University Duesseldorf, 40225 Duesseldorf, Germany
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14
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Vila-Viçosa D, Leitão M, Bouça-Machado R, Pona-Ferreira F, Alberto S, Ferreira JJ, Matias R. Smartphone-Based Body Location-Independent Functional Mobility Analysis in Patients with Parkinson’s Disease: A Step towards Precise Medicine. J Pers Med 2022; 12:jpm12050826. [PMID: 35629247 PMCID: PMC9143184 DOI: 10.3390/jpm12050826] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 05/13/2022] [Accepted: 05/16/2022] [Indexed: 01/27/2023] Open
Abstract
Ecological evaluation of gait using mobile technologies provides crucial information regarding the evolution of symptoms in Parkinson’s disease (PD). However, the reliability and validity of such information may be influenced by the smartphone’s location on the body. This study analyzed how the smartphone location affects the assessment of PD patients’ gait in a free-living environment. Twenty PD patients (mean ± SD age, 64.3 ± 10.6 years; 9 women (45%) performed 3 trials of a 250 m outdoor walk using smartphones in 5 different body locations (pants pocket, belt, hand, shirt pocket, and a shoulder bag). A method to derive gait-related metrics from smartphone sensors is presented, and its reliability is evaluated between different trials as well as its concurrent validity against optoelectronic and smartphone criteria. Excellent relative reliability was found with all intraclass correlation coefficient values above or equal to 0.85. High absolute reliability was observed in 21 out of 30 comparisons. Bland-Altman analysis revealed a high level of agreement (LoA between 4.4 and 17.5%), supporting the use of the presented method. This study advances the use of mobile technology to accurately and reliably quantify gait-related metrics from PD patients in free-living walking regardless of the smartphone’s location on the body.
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Affiliation(s)
| | - Mariana Leitão
- CNS—Campus Neurológico Sénior, 2560-280 Torres Vedras, Portugal; (M.L.); (R.B.-M.); (F.P.-F.); (J.J.F.)
| | - Raquel Bouça-Machado
- CNS—Campus Neurológico Sénior, 2560-280 Torres Vedras, Portugal; (M.L.); (R.B.-M.); (F.P.-F.); (J.J.F.)
- Instituto de Medicina Molecular João Lobo Antunes, 1649-028 Lisbon, Portugal
| | - Filipa Pona-Ferreira
- CNS—Campus Neurológico Sénior, 2560-280 Torres Vedras, Portugal; (M.L.); (R.B.-M.); (F.P.-F.); (J.J.F.)
| | - Sara Alberto
- Kinetikos, 3030-199 Coimbra, Portugal; (D.V.-V.); (S.A.)
| | - Joaquim J. Ferreira
- CNS—Campus Neurológico Sénior, 2560-280 Torres Vedras, Portugal; (M.L.); (R.B.-M.); (F.P.-F.); (J.J.F.)
- Instituto de Medicina Molecular João Lobo Antunes, 1649-028 Lisbon, Portugal
- Laboratory of Clinical Pharmacology and Therapeutics, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisbon, Portugal
| | - Ricardo Matias
- Kinetikos, 3030-199 Coimbra, Portugal; (D.V.-V.); (S.A.)
- Physics Department & Institute of Biophysics and Biomedical Engineering (IBEB), Faculty of Sciences, University of Lisbon, Campo Grande, 1749-016 Lisbon, Portugal
- Correspondence:
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15
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An Activity Recognition Framework for Continuous Monitoring of Non-Steady-State Locomotion of Individuals with Parkinson’s Disease. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12094682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Fundamental knowledge in activity recognition of individuals with motor disorders such as Parkinson’s disease (PD) has been primarily limited to detection of steady-state/static tasks (e.g., sitting, standing, walking). To date, identification of non-steady-state locomotion on uneven terrains (stairs, ramps) has not received much attention. Furthermore, previous research has mainly relied on data from a large number of body locations which could adversely affect user convenience and system performance. Here, individuals with mild stages of PD and healthy subjects performed non-steady-state circuit trials comprising stairs, ramp, and changes of direction. An offline analysis using a linear discriminant analysis (LDA) classifier and a Long-Short Term Memory (LSTM) neural network was performed for task recognition. The performance of accelerographic and gyroscopic information from varied lower/upper-body segments were tested across a set of user-independent and user-dependent training paradigms. Comparing the F1 score of a given signal across classifiers showed improved performance using LSTM compared to LDA. Using LSTM, even a subset of information (e.g., feet data) in subject-independent training appeared to provide F1 score > 0.8. However, employing LDA was shown to be at the expense of being limited to using a subject-dependent training and/or biomechanical data from multiple body locations. The findings could inform a number of applications in the field of healthcare monitoring and developing advanced lower-limb assistive devices by providing insights into classification schemes capable of handling non-steady-state and unstructured locomotion in individuals with mild Parkinson’s disease.
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16
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Patel G, Mullerpatan R, Agarwal B, Shetty T, Ojha R, Shaikh-Mohammed J, Sujatha S. Validation of wearable inertial sensor-based gait analysis system for measurement of spatiotemporal parameters and lower extremity joint kinematics in sagittal plane. Proc Inst Mech Eng H 2022; 236:686-696. [DOI: 10.1177/09544119211072971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Wearable inertial sensor-based motion analysis systems are promising alternatives to standard camera-based motion capture systems for the measurement of gait parameters and joint kinematics. These wearable sensors, unlike camera-based gold standard systems, find usefulness in outdoor natural environment along with confined indoor laboratory-based environment due to miniature size and wireless data transmission. This study reports validation of our developed (i-Sens) wearable motion analysis system against standard motion capture system. Gait analysis was performed at self-selected speed on non-disabled volunteers in indoor ( n = 15) and outdoor ( n = 8) environments. Two i-Sens units were placed at the level of knee and hip along with passive markers (for indoor study only) for simultaneous 3D motion capture using a motion capture system. Mean absolute percentage error (MAPE) was computed for spatiotemporal parameters from the i-Sens system versus the motion capture system as a true reference. Mean and standard deviation of kinematic data for a gait cycle were plotted for both systems against normative data. Joint kinematics data were analyzed to compute the root mean squared error (RMSE) and Pearson’s correlation coefficient. Kinematic plots indicate a high degree of accuracy of the i-Sens system with the reference system. Excellent positive correlation was observed between the two systems in terms of hip and knee joint angles (Indoor: hip 3.98° ± 1.03°, knee 6.48° ± 1.91°, Outdoor: hip 3.94° ± 0.78°, knee 5.82° ± 0.99°) with low RMSE. Reliability characteristics (defined using standard statistical thresholds of MAPE) of stride length, cadence, walking speed in both outdoor and indoor environment were well within the “Good” category. The i-Sens system has emerged as a potentially cost-effective, valid, accurate, and reliable alternative to expensive, standard motion capture systems for gait analysis. Further clinical trials using the i-Sens system are warranted on participants across different age groups.
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Affiliation(s)
- Gunjan Patel
- Department of Mechanical Engineering, TTK Center for Rehabilitation Research and Device Development (R2D2), IIT Madras, Chennai, India
- Biodesign Medical Technology, Synersense Private Limited, Ahmedabad, India
| | - Rajani Mullerpatan
- MGM School of Physiotherapy, MGM Institute of Health Sciences, Navi Mumbai, India
| | - Bela Agarwal
- MGM School of Physiotherapy, MGM Institute of Health Sciences, Navi Mumbai, India
| | - Triveni Shetty
- MGM School of Physiotherapy, MGM Institute of Health Sciences, Navi Mumbai, India
| | - Rajdeep Ojha
- Movement Analysis and Rehab Research Laboratories, Department of Physical Medicine and Rehabilitation, Christian Medical College, Vellore, India
| | - Javeed Shaikh-Mohammed
- Department of Mechanical Engineering, TTK Center for Rehabilitation Research and Device Development (R2D2), IIT Madras, Chennai, India
| | - S Sujatha
- Department of Mechanical Engineering, TTK Center for Rehabilitation Research and Device Development (R2D2), IIT Madras, Chennai, India
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17
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Omberg L, Chaibub Neto E, Perumal TM, Pratap A, Tediarjo A, Adams J, Bloem BR, Bot BM, Elson M, Goldman SM, Kellen MR, Kieburtz K, Klein A, Little MA, Schneider R, Suver C, Tarolli C, Tanner CM, Trister AD, Wilbanks J, Dorsey ER, Mangravite LM. Remote smartphone monitoring of Parkinson's disease and individual response to therapy. Nat Biotechnol 2022; 40:480-487. [PMID: 34373643 DOI: 10.1038/s41587-021-00974-9] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Accepted: 06/04/2021] [Indexed: 02/07/2023]
Abstract
Remote health assessments that gather real-world data (RWD) outside clinic settings require a clear understanding of appropriate methods for data collection, quality assessment, analysis and interpretation. Here we examine the performance and limitations of smartphones in collecting RWD in the remote mPower observational study of Parkinson's disease (PD). Within the first 6 months of study commencement, 960 participants had enrolled and performed at least five self-administered active PD symptom assessments (speeded tapping, gait/balance, phonation or memory). Task performance, especially speeded tapping, was predictive of self-reported PD status (area under the receiver operating characteristic curve (AUC) = 0.8) and correlated with in-clinic evaluation of disease severity (r = 0.71; P < 1.8 × 10-6) when compared with motor Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Although remote assessment requires careful consideration for accurate interpretation of RWD, our results support the use of smartphones and wearables in objective and personalized disease assessments.
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Affiliation(s)
| | | | | | - Abhishek Pratap
- Sage Bionetworks, Seattle, WA, USA.,Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | | | - Jamie Adams
- Center for Health and Technology, University of Rochester Medical Center, Rochester, NY, USA.,Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Bastiaan R Bloem
- Radboud University Medical Center; Donders Institute for Brain, Cognition and Behaviour; Department of Neurology, Nijmegen, the Netherlands
| | | | - Molly Elson
- Center for Health and Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Samuel M Goldman
- Department of Neurology, University of California-San Francisco and Parkinson's Disease Research, Education and Clinical Center, San Francisco Veterans Affairs Health Care System, San Francisco, CA, USA
| | | | - Karl Kieburtz
- Center for Health and Technology, University of Rochester Medical Center, Rochester, NY, USA.,Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | | | - Max A Little
- School of Computer Science, University of Birmingham, Birmingham, UK.,Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ruth Schneider
- Center for Health and Technology, University of Rochester Medical Center, Rochester, NY, USA.,Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | | | - Christopher Tarolli
- Center for Health and Technology, University of Rochester Medical Center, Rochester, NY, USA.,Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Caroline M Tanner
- Department of Neurology, University of California-San Francisco and Parkinson's Disease Research, Education and Clinical Center, San Francisco Veterans Affairs Health Care System, San Francisco, CA, USA
| | | | | | - E Ray Dorsey
- Center for Health and Technology, University of Rochester Medical Center, Rochester, NY, USA.,Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
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18
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Shema-Shiratzky S, Beer Y, Mor A, Elbaz A. Smartphone-based inertial sensors technology - Validation of a new application to measure spatiotemporal gait metrics. Gait Posture 2022; 93:102-106. [PMID: 35121485 DOI: 10.1016/j.gaitpost.2022.01.024] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 01/23/2022] [Accepted: 01/27/2022] [Indexed: 02/02/2023]
Abstract
BACKGROUND Smartphones are increasingly recognized as the future technology for clinical gait assessment. RESEARCH QUESTION To determine the concurrent validity of gait parameters obtained using the smartphone technology and application in a group of patients with musculoskeletal pathologies. METHODS Patients with knee, lower back, hip, or ankle pain were included in the study (n = 72). Spatiotemporal outcomes were derived from the walkway and the smartphone simultaneously. Pearson's correlations and limits of agreement (LoA) determined the association between the two methods. RESULTS Cadence and gait cycle time showed excellent correlation and agreement between the smartphone and the walkway (cadence: r = 0.997, LoA=1.4%, gait cycle time: r = 0.996, LoA = 1.6%). Gait speed, double-limb support and left and right step length demonstrated strong correlations and moderate agreement between methods (gait speed: r = 0.914, LoA=15.4%, left step length: r = 0.842, LoA = 17.0%, right step length: r = 0.800, LoA=16.4%). The left and right measures of single-limb support and stance percent showed a consistent 4% bias across instruments, yielding moderate correlation and very good agreement between the smartphone and the walkway (r = 0.532, LoA = 9% and r = 0.460, LoA=9.8% for left and right single-limb support; r = 0.463, LoA = 5.1% and r = 0.533, LoA = 4.4% for left and right stance). SIGNIFICANCE The examined application appears to be a valid tool for gait analysis, providing clinically significant metrics for the assessment of patients with musculoskeletal pathologies. However, additional studies should examine the technology amongst patients with severe gait abnormalities.
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Affiliation(s)
| | - Yiftah Beer
- Department of Orthopaedic Surgery, Assaf Harofeh Medical Center, Zerifin, Israel.
| | - Amit Mor
- AposTherapy Research Group, Herzliya, Israel.
| | - Avi Elbaz
- AposTherapy Research Group, Herzliya, Israel.
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19
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Salchow-Hömmen C, Skrobot M, Jochner MCE, Schauer T, Kühn AA, Wenger N. Review-Emerging Portable Technologies for Gait Analysis in Neurological Disorders. Front Hum Neurosci 2022; 16:768575. [PMID: 35185496 PMCID: PMC8850274 DOI: 10.3389/fnhum.2022.768575] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 01/07/2022] [Indexed: 01/29/2023] Open
Abstract
The understanding of locomotion in neurological disorders requires technologies for quantitative gait analysis. Numerous modalities are available today to objectively capture spatiotemporal gait and postural control features. Nevertheless, many obstacles prevent the application of these technologies to their full potential in neurological research and especially clinical practice. These include the required expert knowledge, time for data collection, and missing standards for data analysis and reporting. Here, we provide a technological review of wearable and vision-based portable motion analysis tools that emerged in the last decade with recent applications in neurological disorders such as Parkinson's disease and Multiple Sclerosis. The goal is to enable the reader to understand the available technologies with their individual strengths and limitations in order to make an informed decision for own investigations and clinical applications. We foresee that ongoing developments toward user-friendly automated devices will allow for closed-loop applications, long-term monitoring, and telemedical consulting in real-life environments.
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Affiliation(s)
- Christina Salchow-Hömmen
- Department of Neurology With Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Matej Skrobot
- Department of Neurology With Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Magdalena C E Jochner
- Department of Neurology With Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Thomas Schauer
- Control Systems Group, Technische Universität Berlin, Berlin, Germany
| | - Andrea A Kühn
- Department of Neurology With Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Berlin School of Mind and Brain, Charité-Universitätsmedizin Berlin, Berlin, Germany
- NeuroCure Clinical Research Centre, Charité-Universitätsmedizin Berlin, Berlin, Germany
- German Center for Neurodegenerative Diseases, DZNE, Berlin, Germany
| | - Nikolaus Wenger
- Department of Neurology With Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
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20
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Tetreault L, Garwood P, Gharooni AA, Touzet AY, Nanna-Lohkamp L, Martin A, Wilson J, Harrop JS, Guest J, Kwon BK, Milligan J, Arizala AM, Riew KD, Fehlings MG, Kotter MRN, Kalsi-Ryan S, Davies BM. Improving Assessment of Disease Severity and Strategies for Monitoring Progression in Degenerative Cervical Myelopathy [AO Spine RECODE-DCM Research Priority Number 4]. Global Spine J 2022; 12:64S-77S. [PMID: 34971524 PMCID: PMC8859700 DOI: 10.1177/21925682211063854] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
STUDY DESIGN Narrative Review. OBJECTIVE To (i) discuss why assessment and monitoring of disease progression is critical in Degenerative cervical myelopathy (DCM); (ii) outline the important features of an ideal assessment tool and (iii) discuss current and novel strategies for detecting subtle deterioration in DCM. METHODS Literature review. RESULTS Degenerative cervical myelopathy is an overarching term used to describe progressive injury to the cervical spinal cord by age-related changes of the spinal axis. Based on a study by Smith et al (2020), the prevalence of DCM is approximately 2.3% and is expected to rise as the global population ages. Given the global impact of this disease, it is essential to address important knowledge gaps and prioritize areas for future investigation. As part of the AO Spine RECODE-DCM (Research Objectives and Common Data Elements for Degenerative Cervical Myelopathy) project, a priority setting partnership was initiated to increase research efficiency by identifying the top ten research priorities for DCM. One of the top ten priorities for future DCM research was: What assessment tools can be used to evaluate functional impairment, disability and quality of life in people with DCM? What instruments, tools or methods can be used or developed to monitor people with DCM for disease progression or improvement either before or after surgical treatment? CONCLUSIONS With the increasing prevalence of DCM, effective surveillance of this population will require both the implementation of a monitoring framework as well as the development of new assessment tools.
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Affiliation(s)
- Lindsay Tetreault
- Department of Neurology, Langone Health, Graduate Medical Education, New York University, New York, NY, USA
| | - Philip Garwood
- Graduate Medical Education, Internal Medicine, University of Toronto, Toronto, ON, Canada
| | - Aref-Ali Gharooni
- Neurosurgery Unit, Department of Clinical Neuroscience, University of Cambridge, Cambridge, UK
| | | | - Laura Nanna-Lohkamp
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Allan Martin
- Department of Neurosurgery, University of California Davis, Sacramento, CA, USA
| | - Jefferson Wilson
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - James S. Harrop
- Department of Neurological Surgery, Thomas Jefferson University, Philadelphia, PA, USA
| | - James Guest
- Department of Neurosurgery and The Miami Project to Cure Paralysis, The Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Brian K. Kwon
- Vancouver Spine Surgery Institute, Department of Orthopedics, The University of British Columbia, Vancouver, BC, Canada
| | - James Milligan
- McMaster University Department of Family Medicine, Hamilton, ON, Canada
| | - Alberto Martinez Arizala
- The Miami Project to Cure Paralysis, The Miller School of Medicine University of Miami, Miami, FL, USA
| | - K. Daniel Riew
- Department of Orthopaedics, The Och Spine Hospital at New York-Presbyterian, Columbia University Medical Center, New York, NY, USA
| | - Michael G. Fehlings
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | | | - Sukhvinder Kalsi-Ryan
- KITE Research Institute, University Health Network, Toronto, ON, Canada
- Department of Medicine, Division of Physical Medicine and Rehabilitation, University of Toronto, Toronto, ON, Canada
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21
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Page A, Yung N, Auinger P, Venuto C, Glidden A, Macklin E, Omberg L, Schwarzschild MA, Dorsey ER. A Smartphone Application as an Exploratory Endpoint in a Phase 3 Parkinson's Disease Clinical Trial: A Pilot Study. Digit Biomark 2022; 6:1-8. [PMID: 35224425 PMCID: PMC8832247 DOI: 10.1159/000521232] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 11/30/2021] [Indexed: 02/16/2024] Open
Abstract
BACKGROUND Smartphones can generate objective measures of Parkinson's disease (PD) and supplement traditional in-person rating scales. However, smartphone use in clinical trials has been limited. OBJECTIVE This study aimed to determine the feasibility of introducing a smartphone research application into a PD clinical trial and to evaluate the resulting measures. METHODS A smartphone application was introduced part-way into a phase 3 randomized clinical trial of inosine. The application included finger tapping, gait, and cognition tests, and participants were asked to complete an assessment battery at home and in clinic alongside the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS). RESULTS Of 236 eligible participants in the parent study, 88 (37%) consented to participate, and 59 (27 randomized to inosine and 32 to placebo) completed a baseline smartphone assessment. These 59 participants collectively completed 1,292 batteries of assessments. The proportion of participants who completed at least one smartphone assessment was 61% at 3, 54% at 6, and 35% at 12 months. Finger tapping speed correlated weakly with the part III motor portion (r = -0.16, left hand; r = -0.04, right hand) and total (r = -0.14) MDS-UPDRS. Gait speed correlated better with the same measures (r = -0.25, part III motor; r = -0.34, total). Over 6 months, finger tapping speed, gait speed, and memory scores did not differ between those randomized to active drug or placebo. CONCLUSIONS Introducing a smartphone application midway into a phase 3 clinical trial was challenging. Measures of bradykinesia and gait speed correlated modestly with traditional outcomes and were consistent with the study's overall findings, which found no benefit of the active drug.
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Affiliation(s)
- Alex Page
- Center for Health + Technology, University of Rochester Medical Center, Rochester, New York, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, New York, USA
| | - Norman Yung
- Center for Health + Technology, University of Rochester Medical Center, Rochester, New York, USA
| | - Peggy Auinger
- Center for Health + Technology, University of Rochester Medical Center, Rochester, New York, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, New York, USA
| | - Charles Venuto
- Center for Health + Technology, University of Rochester Medical Center, Rochester, New York, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, New York, USA
| | - Alistair Glidden
- Center for Health + Technology, University of Rochester Medical Center, Rochester, New York, USA
| | - Eric Macklin
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | | | | | - E. Ray Dorsey
- Center for Health + Technology, University of Rochester Medical Center, Rochester, New York, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, New York, USA
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22
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WHISPER: Wireless Home Identification and Sensing Platform for Energy Reduction. JOURNAL OF SENSOR AND ACTUATOR NETWORKS 2021. [DOI: 10.3390/jsan10040071] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Many regions of the world benefit from heating, ventilating, and air-conditioning (HVAC) systems to provide productive, comfortable, and healthy indoor environments, which are enabled by automatic building controls. Due to climate change, population growth, and industrialization, HVAC use is globally on the rise. Unfortunately, these systems often operate in a continuous fashion without regard to actual human presence, leading to unnecessary energy consumption. As a result, the heating, ventilation, and cooling of unoccupied building spaces makes a substantial contribution to the harmful environmental impacts associated with carbon-based electric power generation, which is important to remedy. For our modern electric power system, transitioning to low-carbon renewable energy is facilitated by integration with distributed energy resources. Automatic engagement between the grid and consumers will be necessary to enable a clean yet stable electric grid, when integrating these variable and uncertain renewable energy sources. We present the WHISPER (Wireless Home Identification and Sensing Platform for Energy Reduction) system to address the energy and power demand triggered by human presence in homes. The presented system includes a maintenance-free and privacy-preserving human occupancy detection system wherein a local wireless network of battery-free environmental, acoustic energy, and image sensors are deployed to monitor homes, record empirical data for a range of monitored modalities, and transmit it to a base station. Several machine learning algorithms are implemented at the base station to infer human presence based on the received data, harnessing a hierarchical sensor fusion algorithm. Results from the prototype system demonstrate an accuracy in human presence detection in excess of 95%; ongoing commercialization efforts suggest approximately 99% accuracy. Using machine learning, WHISPER enables various applications based on its binary occupancy prediction, allowing situation-specific controls targeted at both personalized smart home and electric grid modernization opportunities.
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de Carvalho Lana R, Ribeiro de Paula A, Souza Silva AF, Vieira Costa PH, Polese JC. Validity of mHealth devices for counting steps in individuals with Parkinson's disease. J Bodyw Mov Ther 2021; 28:496-501. [PMID: 34776185 DOI: 10.1016/j.jbmt.2021.06.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 06/02/2021] [Accepted: 06/09/2021] [Indexed: 11/17/2022]
Abstract
BACKGROUND Step quantification is a good way to characterize the mobility and functional status of individuals with some functional disorder. Therefore, a validation study may lead to the feasibility of devices to stimulate an increase in the number of steps and physical activity level of individuals with Parkinson's Disease (PD). AIM To investigate the validity of mHealth devices to estimate the number of steps in individuals with PD and compare the estimate with a standard criterion measure. METHOD An observational study in a university laboratory with 34 individuals with idiopathic PD. The number of steps was measured using mHealth devices (Google Fit, Health, STEPZ, Pacer, and Fitbit INC®), and compared against a criterionstandard measure during the Two-Minute Walk Test using habitual speed. RESULTS Our sample was 82% men with a Hoehn and Yahr mean of 2.3 ± 1.3 and mean walking speed of 1.2 ± 0.2 m/s. Positive and statistically significant associations were found between Google Fit (r = 0.92; p < 0.01), STEPZ (r = 0.91; p < 0.01), Pacer (r = 0.77; p < 0.01), Health (r = 0.54; p < 0.01), and Fitbit Inc® (r = 0.82; p < 0.01) with the criterion-standard measure. CONCLUSIONS GoogleFit, STEPZ, Fitbit Inc.®, Pacer, and Health are valid instruments to measure the number of steps over a given period of time with moderate to high correlation with the criterion-standard in individuals with PD. This result shows that technology such as smartphone applications and activity monitor can be used to assess the number of steps in individuals with PD, and allows the possibility of using this technology for assessment and intervention purposes.
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Affiliation(s)
- Raquel de Carvalho Lana
- Post Graduate Program of Health Sciences, Department of Physical Therapy, Faculdade Ciências Médicas de Minas Gerais, Belo Horizonte, Brazil
| | - André Ribeiro de Paula
- Post Graduate Program of Health Sciences, Department of Physical Therapy, Faculdade Ciências Médicas de Minas Gerais, Belo Horizonte, Brazil
| | - Ana Flávia Souza Silva
- Post Graduate Program of Health Sciences, Department of Physical Therapy, Faculdade Ciências Médicas de Minas Gerais, Belo Horizonte, Brazil
| | - Pollyana Helena Vieira Costa
- Post Graduate Program of Health Sciences, Department of Physical Therapy, Faculdade Ciências Médicas de Minas Gerais, Belo Horizonte, Brazil
| | - Janaine Cunha Polese
- Post Graduate Program of Health Sciences, Department of Physical Therapy, Faculdade Ciências Médicas de Minas Gerais, Belo Horizonte, Brazil.
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24
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Abou L, Peters J, Wong E, Akers R, Dossou MS, Sosnoff JJ, Rice LA. Gait and Balance Assessments using Smartphone Applications in Parkinson's Disease: A Systematic Review. J Med Syst 2021; 45:87. [PMID: 34392429 PMCID: PMC8364438 DOI: 10.1007/s10916-021-01760-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 08/04/2021] [Indexed: 01/21/2023]
Abstract
Gait dysfunctions and balance impairments are key fall risk factors and associated with reduced quality of life in individuals with Parkinson's Disease (PD). Smartphone-based assessments show potential to increase remote monitoring of the disease. This review aimed to summarize the validity, reliability, and discriminative abilities of smartphone applications to assess gait, balance, and falls in PD. Two independent reviewers screened articles systematically identified through PubMed, Web of Science, Scopus, CINAHL, and SportDiscuss. Studies that used smartphone-based gait, balance, or fall applications in PD were retrieved. The validity, reliability, and discriminative abilities of the smartphone applications were summarized and qualitatively discussed. Methodological quality appraisal of the studies was performed using the quality assessment tool for observational cohort and cross-sectional studies. Thirty-one articles were included in this review. The studies present mostly with low risk of bias. In total, 52% of the studies reported validity, 22% reported reliability, and 55% reported discriminative abilities of smartphone applications to evaluate gait, balance, and falls in PD. Those studies reported strong validity, good to excellent reliability, and good discriminative properties of smartphone applications. Only 19% of the studies formally evaluated the usability of their smartphone applications. The current evidence supports the use of smartphone to assess gait and balance, and detect freezing of gait in PD. More studies are needed to explore the use of smartphone to predict falls in this population. Further studies are also warranted to evaluate the usability of smartphone applications to improve remote monitoring in this population.Registration: PROSPERO CRD 42020198510.
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Affiliation(s)
- Libak Abou
- Department of Kinesiology and Community Health, College of Applied Health Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Joseph Peters
- Department of Kinesiology and Community Health, College of Applied Health Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Ellyce Wong
- Department of Kinesiology and Community Health, College of Applied Health Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Rebecca Akers
- Department of Rehabilitation Science, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Mauricette Sènan Dossou
- Centre National Hospitalier et Universitaire de Pneumo-Phtisiologie, Cotonou, Littoral, Benin
| | - Jacob J Sosnoff
- Department of Physical Therapy and Rehabilitation Science, School of Health Professions, Medical Center, University of Kansas, Kansas City, KS, USA
| | - Laura A Rice
- Department of Kinesiology and Community Health, College of Applied Health Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
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25
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Speed but Not Smoothness of Gait Reacts to Rehabilitation in Multiple Sclerosis. Mult Scler Int 2021; 2021:5589562. [PMID: 34123427 PMCID: PMC8192191 DOI: 10.1155/2021/5589562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 04/22/2021] [Accepted: 05/06/2021] [Indexed: 11/25/2022] Open
Abstract
Introduction Improved gait is one of the leading therapy goals in multiple sclerosis. A plethora of clinical timed trials and state-of-the-art technology-based approaches are available to assess gait performance. Objectives To examine what aspects of gait react to inpatient rehabilitation in MS and which parameters should be best assessed. Design In this longitudinal study, we examined the performance of 76 patients with MS to shed further light on factors influencing gait, associations between tests, and the reaction to inpatient rehabilitation during an average time span of 16 d. Setting. Private specialist clinic for inpatient neurorehabilitation. Main Outcome Measures. Clinical walk tests (timed 25-foot walk test at normal pace, maximum pace over 10 m or 6 min) and IMU-based measures of movement smoothness. Results All gait parameters were strongly intercorrelated (all p < 0.05), and a model multiple linear regression for the 6MWT revealed short distance velocity (10 m) and movement smoothness as predictors in a strong model (R2adjusted 0.75, p < 0.01). A second model with natural pace on short distance and movement smoothness was almost equally strong (R2adjusted 0.71, p < 0.01). Patients improved their walking speed (p < 0.01), but not smoothness (p = 0.08–0.12), over the course of rehabilitation. Conclusions Since we were not able to observe improvements in smoothness of gait, we conclude that rehabilitation programs should be adapted to the patient's physiological capacities in order to allow for such improvements in smoothness of gait. Externally valid gait capacity (6MWT) could be predicted by a single walk for 10 s at natural pace.
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26
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Bat-Erdene BO, Saver JL. Automatic Acute Stroke Symptom Detection and Emergency Medical Systems Alerting by Mobile Health Technologies: A Review. J Stroke Cerebrovasc Dis 2021; 30:105826. [PMID: 33932749 DOI: 10.1016/j.jstrokecerebrovasdis.2021.105826] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Revised: 03/28/2021] [Accepted: 04/07/2021] [Indexed: 11/24/2022] Open
Abstract
OBJECTIVES To survey recent advances in acute stroke symptom automatic detection and Emergency Medical Systems (EMS) alerting by mobile health technologies. MATERIALS AND METHODS Narrative review RESULTS: Delayed activation of EMS for stroke symptoms by patients and witnesses deprives patients of rapid access to brain-saving therapies and occurs due to public unawareness of stroke features, cognitive and motor deficits produced by the stroke itself, and sleep onset. A promising emerging approach to overcoming the inherent biologic constraints of patient capacity to self-detect and respond to stroke symptoms is continuous monitoring by mobile health technologies with wireless sensors and artificial intelligence recognition systems. This review surveys 11 sensing technologies - accelerometers, gyroscopes, magnetometers, pressure sensors, touch screen and keyboard input detectors, artificial vision, and artificial hearing; and 10 consumer device form factors in which they are increasingly implemented: smartphones, smart speakers, smart watches and fitness bands, smart speakers/voice assistants, home health robots, smart clothing, smart beds, closed circuit television, smart rings, and desktop/laptop/tablet computers. CONCLUSIONS The increase in computing power, wearable sensors, and mobile connectivity have ushered in an array of mobile health technologies that can transform stroke detection and EMS activation. By continuously monitoring a diverse range of biometric parameters, commercially available devices provide the technologic capability to detect cardinal language, motor, gait, and sensory signs of stroke onset. Intensified translational research to convert the promise of these technologies to validated, accurate real-world deployments are an important next priority for stroke investigation.
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Affiliation(s)
- Bat-Orgil Bat-Erdene
- Comprehensive Stroke Center and Department of Neurology, David Geffen School of Medicine at UCLA, Sukhbaatar District, Khoroo-1, 42-55, 11000 Ulaanbaatar, Mongolia.
| | - Jeffrey L Saver
- Comprehensive Stroke Center and Department of Neurology, David Geffen School of Medicine at UCLA, Sukhbaatar District, Khoroo-1, 42-55, 11000 Ulaanbaatar, Mongolia
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27
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Babrak LM, Smakaj E, Agac T, Asprion PM, Grimberg F, der Werf DV, van Ginkel EW, Tosoni DD, Clay I, Degen M, Brodbeck D, Natali EN, Schkommodau E, Miho E. RWD-Cockpit: Application for Quality Assessment of Real-World Data (Preprint). JMIR Form Res 2021; 6:e29920. [PMID: 35266872 PMCID: PMC9627468 DOI: 10.2196/29920] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 10/31/2021] [Accepted: 02/19/2022] [Indexed: 11/22/2022] Open
Abstract
Background Digital technologies are transforming the health care system. A large part of information is generated as real-world data (RWD). Data from electronic health records and digital biomarkers have the potential to reveal associations between the benefits and adverse events of medicines, establish new patient-stratification principles, expose unknown disease correlations, and inform on preventive measures. The impact for health care payers and providers, the biopharmaceutical industry, and governments is massive in terms of health outcomes, quality of care, and cost. However, a framework to assess the preliminary quality of RWD is missing, thus hindering the conduct of population-based observational studies to support regulatory decision-making and real-world evidence. Objective To address the need to qualify RWD, we aimed to build a web application as a tool to translate characterization of some quality parameters of RWD into a metric and propose a standard framework for evaluating the quality of the RWD. Methods The RWD-Cockpit systematically scores data sets based on proposed quality metrics and customizable variables chosen by the user. Sleep RWD generated de novo and publicly available data sets were used to validate the usability and applicability of the web application. The RWD quality score is based on the evaluation of 7 variables: manageability specifies access and publication status; complexity defines univariate, multivariate, and longitudinal data; sample size indicates the size of the sample or samples; privacy and liability stipulates privacy rules; accessibility specifies how the data set can be accessed and to what granularity; periodicity specifies how often the data set is updated; and standardization specifies whether the data set adheres to any specific technical or metadata standard. These variables are associated with several descriptors that define specific characteristics of the data set. Results To address the need to qualify RWD, we built the RWD-Cockpit web application, which proposes a framework and applies a common standard for a preliminary evaluation of RWD quality across data sets—molecular, phenotypical, and social—and proposes a standard that can be further personalized by the community retaining an internal standard. Applied to 2 different case studies—de novo–generated sleep data and publicly available data sets—the RWD-Cockpit could identify and provide researchers with variables that might increase quality. Conclusions The results from the application of the framework of RWD metrics implemented in the RWD-Cockpit application suggests that multiple data sets can be preliminarily evaluated in terms of quality using the proposed metrics. The output scores—quality identifiers—provide a first quality assessment for the use of RWD. Although extensive challenges remain to be addressed to set RWD quality standards, our proposal can serve as an initial blueprint for community efforts in the characterization of RWD quality for regulated settings.
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Affiliation(s)
- Lmar Marie Babrak
- University of Applied Sciences and Arts Northwestern Switzerland, School of Life Sciences, Muttenz, Switzerland
| | - Erand Smakaj
- University of Applied Sciences and Arts Northwestern Switzerland, School of Life Sciences, Muttenz, Switzerland
| | - Teyfik Agac
- University of Applied Sciences and Arts Northwestern Switzerland, School of Life Sciences, Muttenz, Switzerland
| | - Petra Maria Asprion
- Fachhochschule Nordwestschweiz University of Applied Sciences and Arts Northwestern Switzerland, School of Business, Olten, Switzerland
| | - Frank Grimberg
- Fachhochschule Nordwestschweiz University of Applied Sciences and Arts Northwestern Switzerland, School of Business, Olten, Switzerland
| | | | | | - Deniz David Tosoni
- University of Applied Sciences and Arts Northwestern Switzerland, School of Life Sciences, Muttenz, Switzerland
| | - Ieuan Clay
- Evidation Health Inc, San Mateo, CA, United States
| | - Markus Degen
- University of Applied Sciences and Arts Northwestern Switzerland, School of Life Sciences, Muttenz, Switzerland
| | - Dominique Brodbeck
- University of Applied Sciences and Arts Northwestern Switzerland, School of Life Sciences, Muttenz, Switzerland
| | - Eriberto Noel Natali
- University of Applied Sciences and Arts Northwestern Switzerland, School of Life Sciences, Muttenz, Switzerland
| | - Erik Schkommodau
- University of Applied Sciences and Arts Northwestern Switzerland, School of Life Sciences, Muttenz, Switzerland
| | - Enkelejda Miho
- University of Applied Sciences and Arts Northwestern Switzerland, School of Life Sciences, Muttenz, Switzerland
- aiNET GmbH, Basel, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
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28
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Gait variability is affected more by peripheral artery disease than by vascular occlusion. PLoS One 2021; 16:e0241727. [PMID: 33788839 PMCID: PMC8011739 DOI: 10.1371/journal.pone.0241727] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 03/15/2021] [Indexed: 11/19/2022] Open
Abstract
Background Patients with peripheral artery disease with intermittent claudication (PAD-IC) have altered gait variability from the first step they take, well before the onset of claudication pain. The mechanisms underlying these gait alterations are poorly understood. Aims To determine the effect of reduced blood flow on gait variability by comparing healthy older controls and patients with PAD-IC. We also determined the diagnostic value of gait variability parameters to identify the presence of PAD. Methods A cross-sectional cohort design was used. Thirty healthy older controls and thirty patients with PAD-IC walked on a treadmill at their self-selected speed in pain free walking (normal walking for healthy older controls; prior to claudication onset for PAD) and reduced blood flow (post vascular occlusion with thigh tourniquet for healthy older controls; pain for PAD) conditions. Gait variability was assessed using the largest Lyapunov exponent, approximate entropy, standard deviation, and coefficient of variation of ankle, knee, and hip joints range of motion. Receiver operating characteristics curve analyses of the pain free walking condition were performed to determine the optimal cut-off values for separating individuals with PAD-IC from those without PAD-IC. Results and discussion Patients with PAD-IC have increased amount of variability for knee and hip ranges of motion compared with the healthy older control group. Regarding the main effect of condition, reduced blood flow demonstrated increased amount of variability compared with pain free walking. Significant interactions between group and condition at the ankle show increased values for temporal structure of variability, but a similar amount of variability in the reduced blood flow condition. This demonstrates subtle interactions in the movement patterns remain distinct between PAD-IC versus healthy older controls during the reduced blood flow condition. A combination of gait variability parameters correctly identifies PAD-IC disease 70% of the time or more. Conclusions Gait variability is affected both by PAD and by the mechanical induction of reduced blood flow. Gait variability parameters have potential diagnostic ability, as some measures had 90.0% probability of correctly identifying patients with PAD-IC.
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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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30
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Altilio R, Rossetti A, Fang Q, Gu X, Panella M. A comparison of machine learning classifiers for smartphone-based gait analysis. Med Biol Eng Comput 2021; 59:535-546. [PMID: 33548017 PMCID: PMC7925506 DOI: 10.1007/s11517-020-02295-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 12/14/2020] [Indexed: 11/25/2022]
Abstract
This paper proposes a reliable monitoring scheme that can assist medical specialists in watching over the patient's condition. Although several technologies are traditionally used to acquire motion data of patients, the high costs as well as the large spaces they require make them difficult to be applied in a home context for rehabilitation. A reliable patient monitoring technique, which can automatically record and classify patient movements, is mandatory for a telemedicine protocol. In this paper, a comparison of several state-of-the-art machine learning classifiers is proposed, where stride data are collected by using a smartphone. The main goal is to identify a robust methodology able to assure a suited classification of gait movements, in order to allow the monitoring of patients in time as well as to discriminate among a pathological and physiological gait. Additionally, the advantages of smartphones of being compact, cost-effective and relatively easy to operate make these devices particularly suited for home-based rehabilitation programs. Graphical Abstract. This paper proposes a reliable monitoring scheme that can assist medical specialists in watching over the patient's condition. Although several technologies are traditionally used to acquire motion data of patients, the high costs as well as the large spaces they require make them difficult to be applied in a home context for rehabilitation. A reliable patient monitoring technique, which can automatically record and classify patient movements, is mandatory for a telemedicine protocol. In this paper, a comparison of several state-of-the-art machine learning classifiers is proposed, where stride data are collected and processed by using a smartphone(see figure). The main goal is to identify a robust methodology able to assure a suited classification of gait movements, in order to allow the monitoring of patients in time as well as to discriminate among a pathological and physiological gait. Additionally, the advantages of smartphones of being compact, cost-effective and relatively easy to operate make these devices particularly suited for home-based rehabilitation programs.
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Affiliation(s)
- Rosa Altilio
- Department of Information Engineering, Electronics and Telecommunications (DIET), University of Rome “La Sapienza”, Via Eudossiana 18, 00184 Rome, Italy
| | - Andrea Rossetti
- Department of Information Engineering, Electronics and Telecommunications (DIET), University of Rome “La Sapienza”, Via Eudossiana 18, 00184 Rome, Italy
| | - Qiang Fang
- Department of Biomedical Engineering, College of Engineering, Shantou University, Shantou, 515063 China
| | - Xudong Gu
- Second Hospital of Jiaxing, Jiaxing, 314000 China
| | - Massimo Panella
- Department of Information Engineering, Electronics and Telecommunications (DIET), University of Rome “La Sapienza”, Via Eudossiana 18, 00184 Rome, Italy
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31
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Su D, Liu Z, Jiang X, Zhang F, Yu W, Ma H, Wang C, Wang Z, Wang X, Hu W, Manor B, Feng T, Zhou J. Simple Smartphone-Based Assessment of Gait Characteristics in Parkinson Disease: Validation Study. JMIR Mhealth Uhealth 2021; 9:e25451. [PMID: 33605894 PMCID: PMC7935653 DOI: 10.2196/25451] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 12/08/2020] [Accepted: 01/20/2021] [Indexed: 01/14/2023] Open
Abstract
Background Parkinson disease (PD) is a common movement disorder. Patients with PD have multiple gait impairments that result in an increased risk of falls and diminished quality of life. Therefore, gait measurement is important for the management of PD. Objective We previously developed a smartphone-based dual-task gait assessment that was validated in healthy adults. The aim of this study was to test the validity of this gait assessment in people with PD, and to examine the association between app-derived gait metrics and the clinical and functional characteristics of PD. Methods Fifty-two participants with clinically diagnosed PD completed assessments of walking, Movement Disorder Society Unified Parkinson Disease Rating Scale III (UPDRS III), Montreal Cognitive Assessment (MoCA), Hamilton Anxiety (HAM-A), and Hamilton Depression (HAM-D) rating scale tests. Participants followed multimedia instructions provided by the app to complete two 20-meter trials each of walking normally (single task) and walking while performing a serial subtraction dual task (dual task). Gait data were simultaneously collected with the app and gold-standard wearable motion sensors. Stride times and stride time variability were derived from the acceleration and angular velocity signal acquired from the internal motion sensor of the phone and from the wearable sensor system. Results High correlations were observed between the stride time and stride time variability derived from the app and from the gold-standard system (r=0.98-0.99, P<.001), revealing excellent validity of the app-based gait assessment in PD. Compared with those from the single-task condition, the stride time (F1,103=14.1, P<.001) and stride time variability (F1,103=6.8, P=.008) in the dual-task condition were significantly greater. Participants who walked with greater stride time variability exhibited a greater UPDRS III total score (single task: β=.39, P<.001; dual task: β=.37, P=.01), HAM-A (single-task: β=.49, P=.007; dual-task: β=.48, P=.009), and HAM-D (single task: β=.44, P=.01; dual task: β=.49, P=.009). Moreover, those with greater dual-task stride time variability (β=.48, P=.001) or dual-task cost of stride time variability (β=.44, P=.004) exhibited lower MoCA scores. Conclusions A smartphone-based gait assessment can be used to provide meaningful metrics of single- and dual-task gait that are associated with disease severity and functional outcomes in individuals with PD.
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Affiliation(s)
- Dongning Su
- Department of Neurology, Beijing Tiantan Hospital, Beijing, China
| | - Zhu Liu
- Department of Neurology, Beijing Tiantan Hospital, Beijing, China
| | - Xin Jiang
- The Second Clinical Medical College, Jinan University, Guangzhou, China.,Department of Geriatrics, Shenzhen People's Hospital, Shenzhen, Guangdong, China.,The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Fangzhao Zhang
- Department of Computer Science, The University of British Columbia, Vancouver, BC, Canada
| | - Wanting Yu
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Roslindale, MA, United States
| | - Huizi Ma
- Department of Neurology, Beijing Tiantan Hospital, Beijing, China
| | - Chunxue Wang
- Department of Neurology, Beijing Tiantan Hospital, Beijing, China
| | - Zhan Wang
- Department of Neurology, Beijing Tiantan Hospital, Beijing, China
| | - Xuemei Wang
- Department of Neurology, Beijing Tiantan Hospital, Beijing, China
| | - Wanli Hu
- Department of Hematology and Oncology, Jingxi Campus, Capital Medical University, Beijing ChaoYang Hospital, Beijing, China
| | - Brad Manor
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Roslindale, MA, United States.,Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Tao Feng
- Department of Neurology, Beijing Tiantan Hospital, Beijing, China
| | - Junhong Zhou
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Roslindale, MA, United States.,Beth Israel Deaconess Medical Center, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States
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Smith E, Storch EA, Vahia I, Wong STC, Lavretsky H, Cummings JL, Eyre HA. Affective Computing for Late-Life Mood and Cognitive Disorders. Front Psychiatry 2021; 12:782183. [PMID: 35002802 PMCID: PMC8732874 DOI: 10.3389/fpsyt.2021.782183] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 11/29/2021] [Indexed: 12/20/2022] Open
Abstract
Affective computing (also referred to as artificial emotion intelligence or emotion AI) is the study and development of systems and devices that can recognize, interpret, process, and simulate emotion or other affective phenomena. With the rapid growth in the aging population around the world, affective computing has immense potential to benefit the treatment and care of late-life mood and cognitive disorders. For late-life depression, affective computing ranging from vocal biomarkers to facial expressions to social media behavioral analysis can be used to address inadequacies of current screening and diagnostic approaches, mitigate loneliness and isolation, provide more personalized treatment approaches, and detect risk of suicide. Similarly, for Alzheimer's disease, eye movement analysis, vocal biomarkers, and driving and behavior can provide objective biomarkers for early identification and monitoring, allow more comprehensive understanding of daily life and disease fluctuations, and facilitate an understanding of behavioral and psychological symptoms such as agitation. To optimize the utility of affective computing while mitigating potential risks and ensure responsible development, ethical development of affective computing applications for late-life mood and cognitive disorders is needed.
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Affiliation(s)
- Erin Smith
- The PRODEO Institute, San Francisco, CA, United States.,Organisation for Economic Co-operation and Development (OECD), Paris, France.,Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA, United States.,Global Brain Health Institute, University of California, San Francisco, San Francisco, CA, United States.,Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Eric A Storch
- Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
| | - Ipsit Vahia
- Division of Geriatric Psychiatry, McLean Hospital, Boston, MA, United States.,Division of Geriatric Psychiatry, Harvard Medical School, Boston, MA, United States
| | - Stephen T C Wong
- Systems Medicine and Biomedical Engineering Houston Methodist, Houston, TX, United States
| | - Helen Lavretsky
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States
| | - Jeffrey L Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada, Las Vegas (UNLV), Las Vegas, NV, United States
| | - Harris A Eyre
- The PRODEO Institute, San Francisco, CA, United States.,Organisation for Economic Co-operation and Development (OECD), Paris, France.,Global Brain Health Institute, University of California, San Francisco, San Francisco, CA, United States.,Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland.,Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States.,IMPACT, The Institute for Mental and Physical Health and Clinical Translation, Deakin University, Geelong, VIC, Australia
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Lellis-Santos C, Abdulkader F. Smartphone-assisted experimentation as a didactic strategy to maintain practical lessons in remote education: alternatives for physiology education during the COVID-19 pandemic. ADVANCES IN PHYSIOLOGY EDUCATION 2020; 44:579-586. [PMID: 32955344 PMCID: PMC7516380 DOI: 10.1152/advan.00066.2020] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Online and distance education may be dismissed by educators who argue that these methods are not equivalent to traditional face-to-face education due to the lack of laboratory classes. However, smartphone-assisted experimentation is an innovative and powerful didactic tool that helps educators in the teaching process of physiology, particularly in situations with a lack of financial support for purchasing laboratory equipment, or lack of support for homework and assignments, distance learning courses, and emergency remote education, such as during the COVID-19 pandemic. Therefore, we present the concept of the mobile learning laboratory (MobLeLab), which is a collection of smartphone applications that allow scientific data collection, such as physiological variables, for educational purposes. The three types of MobLeLabs (simulators, built-in, and plug-in) are presented, as well as ideas on how to use smartphone sensors to collect physiological data. Additionally, we elaborate on the principles of the protocols for physiology education with MobLeLabs and discuss their importance to fostering scientific method reasoning by students.
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Affiliation(s)
- Camilo Lellis-Santos
- Department of Biological Sciences, Institute of Environmental, Chemical and Pharmaceutical Sciences, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Fernando Abdulkader
- Department of Physiology and Biophysics, Instituto de Ciencias Biomedicas, Universidade de Sao Paulo, São Paulo, Brazil
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Pfau T, Reilly P. How low can we go? Influence of sample rate on equine pelvic displacement calculated from inertial sensor data. Equine Vet J 2020; 53:1075-1081. [PMID: 33113248 DOI: 10.1111/evj.13371] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 09/02/2020] [Accepted: 10/22/2020] [Indexed: 11/29/2022]
Abstract
BACKGROUND Low-cost sensor devices are often limited in terms of sample rate. Based on signal periodicity, the Nyquist theorem allows determining the minimum theoretical sample rate required to adequately capture cyclical events, such as pelvic movement in trotting horses. OBJECTIVES To quantify the magnitude of errors arising with reduced sample rates when capturing biological signals using the example of pelvic time-displacement series and derived minima and maxima used to quantify movement asymmetry in lame horses. STUDY DESIGN Data comparison. METHODS Root mean square (RMS) errors between the 'reference' time-displacement series, captured with a validated inertial sensor at 100 Hz sample rate, and down-sampled time-series (8 Hz to 50 Hz) are calculated. Accuracy and precision are determined for maxima and minima derived from the time-displacement series. RESULTS Average RMS errors are <2 mm at 50 Hz sample rate, <4 mm at 40 Hz, <7 mm between 25 and 35 Hz, and increase to up to 20 mm at 20 Hz and below. Accuracy for maxima and minima is generally below 1mm. Precision is 1 mm at 50 Hz sample rate, 3 mm at 40Hz and ≥9 mm at 20 Hz and below. MAIN LIMITATIONS Only sample rate, no other sensor parameters were investigated. CONCLUSIONS Sample rate related errors for inertial sensor derived time-displacement series of pelvic movement are <2mm at 50 Hz, a rate that many low-cost loggers, smartphones or wireless sensors can sustain hence rendering these devices valid options for quantifying parameters relevant for lameness examinations in horses.
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Affiliation(s)
- Thilo Pfau
- Department of Clinical Science and Services, The Royal Veterinary College, North Mymms, Hatfield, Hertfordshire, UK
| | - Patrick Reilly
- Department of Clinical Studies New Bolton Center, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Wu HK, Chen HR, Chen WY, Lu CF, Tsai MW, Yu CH. A novel instrumented walker for individualized visual cue setting for gait training in patients with Parkinson's disease. Assist Technol 2020; 32:203-213. [PMID: 30592441 DOI: 10.1080/10400435.2018.1525442] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022] Open
Abstract
Patients with Parkinson's disease suffer from gait disturbances, such as a shuffling and festinating gait, which reduces their quality of life. To circumvent this problem, external visual cues may be applied in gait training to maintain the integrity of motor function. However, conventional training methods, such as transverse lines stuck on the ground, are difficult to adjust and adapt to personalized gait ability. This study proposes a convenient instrumented wheel walker that provides gap adjustable visual cues and selectable projection modes onto the ground with and without motion relative to the user. Ten subjects with Parkinson's disease were recruited, and the efficacy of the proposed device for their gait training was assessed. We demonstrated the applicability of our device to address personalized demands in gait guidance. With a personalized setting for patients with Parkinson's disease, a significantly lengthened stride length may be achieved.
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Affiliation(s)
- Hsiao-Kuan Wu
- Department of Physical Therapy and Assistive Technology, National Yang-Ming University , Taipei, Taiwan, ROC
| | - Huang-Ren Chen
- Department of Computer Science and Information Engineering, National Central University , Taoyuan, Taiwan, ROC
| | - Wei-Ying Chen
- Department of Physical Therapy and Assistive Technology, National Yang-Ming University , Taipei, Taiwan, ROC
| | - Chia-Feng Lu
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University , Taipei, Taiwan, ROC
| | - Mei-Wun Tsai
- Department of Physical Therapy and Assistive Technology, National Yang-Ming University , Taipei, Taiwan, ROC
| | - Chung-Huang Yu
- Department of Physical Therapy and Assistive Technology, National Yang-Ming University , Taipei, Taiwan, ROC
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Vaz PG, Reis AL, Cardoso J. Supination/pronation movement quantification using stereoscopic vision based system towards Parkinson’s Disease assessment – A pilot study. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.101976] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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de Oliveira Gondim ITG, de Souza CDCB, Rodrigues MAB, Azevedo IM, de Sales Coriolano MDGW, Lins OG. Portable accelerometers for the evaluation of spatio-temporal gait parameters in people with Parkinson's disease: an integrative review. Arch Gerontol Geriatr 2020; 90:104097. [PMID: 32531644 DOI: 10.1016/j.archger.2020.104097] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 04/28/2020] [Accepted: 05/04/2020] [Indexed: 11/30/2022]
Abstract
PURPOSE The progression of Parkinson's disease causes an increase in motor dysfunctions, which makes it necessary to evaluate and monitor these changes. This integrative review aimed to gather studies - without any language restrictions - on the use, advantages and disadvantages of portable accelerometers for the evaluation of spatio-temporal gait parameters in people with Parkinson's disease published between 2014 and 2019. METHODS Articles were selected from the PubMed, Web of Science and Science Direct databases by combining descriptors from the Health Sciences Descriptors (DeCS) and Medical Subject Headings (MeSH) - "accelerometry", "accelerometer", "ActiGraph", "gait", "gait analysis", "gait rehabilitation", "walking inertial sensors", "Parkinson disease", "Parkinson" and "Parkinson's disease" - using OR and AND. The adapted Critical Appraisal Skill Program was used to analyze the methodological quality. RESULTS All the studies used portable wearable and wireless triaxial accelerometers. Among all types of accelerometers discussed, commercial wearable devices not based on smartphones and prototypes of wearable devices based and not based on smartphones can be pointed out. There was no standardization for the protocols of use, but the sensors were more often attached to the lower back (L3/L4/L5 vertebrae). The advantages included lower cost, possibility of use in outdoor environments and less complexity of data reading for non-specialized users. However, they still seem to show reduced precision and accuracy. CONCLUSIONS Due to the still insufficient number of articles published on the subject, we consider the need for further research, which should detail protocols of evaluation, advantages and disadvantages in stages of disease.
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Affiliation(s)
- Ihana Thaís Guerra de Oliveira Gondim
- Programa de Pós-Graduação em Neuropsiquiatria e Ciências do Comportamento, Universidade Federal de Pernambuco, Avenida da Engenharia, S/N, Prédio dos Programas de Pós-Graduação do Centro de Ciências da Saúde, Cidade Universitária, Recife, PE, CEP 50740-600, Brazil.
| | - Caroline de Cássia Batista de Souza
- Programa de Pós-Graduação em Engenharia Biomédica, Universidade Federal de Pernambuco, Av. da Arquitetura, s/nº - Cidade Universitária, Prédio da Coordenação da Área II, Recife, PE, CEP: 50.740-550, Brazil
| | - Marco Aurélio Benedetti Rodrigues
- Programa de Pós-Graduação em Engenharia Biomédica, Universidade Federal de Pernambuco, Av. da Arquitetura, s/nº - Cidade Universitária, Prédio da Coordenação da Área II, Recife, PE, CEP: 50.740-550, Brazil
| | - Izaura Muniz Azevedo
- Programa de Pós-Graduação em Gerontologia, Universidade Federal de Pernambuco, Av. Prof. Moraes Rego S/N, Cidade Universitária, Recife, PE, CEP: 50.739.970, Brazil
| | | | - Otávio Gomes Lins
- Programa de Pós-Graduação em Neuropsiquiatria e Ciências do Comportamento, Universidade Federal de Pernambuco, Avenida da Engenharia, S/N, Prédio dos Programas de Pós-Graduação do Centro de Ciências da Saúde, Cidade Universitária, Recife, PE, CEP 50740-600, Brazil
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IoT Wearable Sensors and Devices in Elderly Care: A Literature Review. SENSORS 2020; 20:s20102826. [PMID: 32429331 PMCID: PMC7288187 DOI: 10.3390/s20102826] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 05/10/2020] [Accepted: 05/13/2020] [Indexed: 12/23/2022]
Abstract
The increasing ageing global population is causing an upsurge in ailments related to old age, primarily dementia and Alzheimer’s disease, frailty, Parkinson’s, and cardiovascular disease, but also a general need for general eldercare as well as active and healthy ageing. In turn, there is a need for constant monitoring and assistance, intervention, and support, causing a considerable financial and human burden on individuals and their caregivers. Interconnected sensing technology, such as IoT wearables and devices, present a promising solution for objective, reliable, and remote monitoring, assessment, and support through ambient assisted living. This paper presents a review of such solutions including both earlier review studies and individual case studies, rapidly evolving in the last decade. In doing so, it examines and categorizes them according to common aspects of interest such as health focus, from specific ailments to general eldercare; IoT technologies, from wearables to smart home sensors; aims, from assessment to fall detection and indoor positioning to intervention; and experimental evaluation participants duration and outcome measures, from acceptability to accuracy. Statistics drawn from this categorization aim to outline the current state-of-the-art, as well as trends and effective practices for the future of effective, accessible, and acceptable eldercare with technology.
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Abrami A, Heisig S, Ramos V, Thomas KC, Ho BK, Caggiano V. Using an unbiased symbolic movement representation to characterize Parkinson's disease states. Sci Rep 2020; 10:7377. [PMID: 32355166 PMCID: PMC7193555 DOI: 10.1038/s41598-020-64181-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Accepted: 04/09/2020] [Indexed: 11/16/2022] Open
Abstract
Unconstrained human movement can be broken down into a series of stereotyped motifs or 'syllables' in an unsupervised fashion. Sequences of these syllables can be represented by symbols and characterized by a statistical grammar which varies with external situational context and internal neurological state. By first constructing a Markov chain from the transitions between these syllables then calculating the stationary distribution of this chain, we estimate the overall severity of Parkinson's symptoms by capturing the increasingly disorganized transitions between syllables as motor impairment increases. Comparing stationary distributions of movement syllables has several advantages over traditional neurologist administered in-clinic assessments. This technique can be used on unconstrained at-home behavior as well as scripted in-clinic exercises, it avoids differences across human evaluators, and can be used continuously without requiring scripted tasks be performed. We demonstrate the effectiveness of this technique using movement data captured with commercially available wrist worn sensors in 35 participants with Parkinson's disease in-clinic and 25 participants monitored at home.
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Affiliation(s)
- Avner Abrami
- IBM Research - Healthcare and Life Sciences - 1101 Kitchawan Rd, Yorktown Heights, NY, 10598, USA
| | - Stephen Heisig
- IBM Research - Healthcare and Life Sciences - 1101 Kitchawan Rd, Yorktown Heights, NY, 10598, USA
| | - Vesper Ramos
- Digital Medicine and the Pfizer Innovation Research Lab, Pfizer, 610 Main Street, Cambridge, MA, 02139, USA
| | - Kevin C Thomas
- Laboratory for Human Neurobiology, Spivack Center for Clinical and Translational Neuroscience, 650 Albany Street, X-140, Boston, MA, 02118, USA
| | - Bryan K Ho
- Department of Neurology Tufts Medical Center 800 Washington Street, Box 314, Boston, MA, 02111-1800, USA
| | - Vittorio Caggiano
- IBM Research - Healthcare and Life Sciences - 1101 Kitchawan Rd, Yorktown Heights, NY, 10598, USA.
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Abujrida H, Agu E, Pahlavan K. Machine learning-based motor assessment of Parkinson's disease using postural sway, gait and lifestyle features on crowdsourced smartphone data. Biomed Phys Eng Express 2020; 6:035005. [PMID: 33438650 DOI: 10.1088/2057-1976/ab39a8] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVES Remote assessment of gait in patients' homes has become a valuable tool for monitoring the progression of Parkinson's disease (PD). However, these measurements are often not as accurate or reliable as clinical evaluations because it is challenging to objectively distinguish the unique gait characteristics of PD. We explore the inference of patients' stage of PD from their gait using machine learning analyses of data gathered from their smartphone sensors. Specifically, we investigate supervised machine learning (ML) models to classify the severity of the motor part of the UPDRS (MDS-UPDRS 2.10-2.13). Our goals are to facilitate remote monitoring of PD patients and to answer the following questions: (1) What is the patient PD stage based on their gait? (2) Which features are best for understanding and classifying PD gait severities? (3) Which ML classifier types best discriminate PD patients from healthy controls (HC)? and (4) Which ML classifier types can discriminate the severity of PD gait anomalies? METHODOLOGY Our work uses smartphone sensor data gathered from 9520 patients in the mPower study, of whom 3101 participants uploaded gait recordings and 344 subjects and 471 controls uploaded at least 3 walking activities. We selected 152 PD patients who performed at least 3 recordings before and 3 recordings after taking medications and 304 HC who performed at least 3 walking recordings. From the accelerometer and gyroscope sensor data, we extracted statistical, time, wavelet and frequency domain features, and other lifestyle features were derived directly from participants' survey data. We conducted supervised classification experiments using 10-fold cross-validation and measured the model precision, accuracy, and area under the curve (AUC). RESULTS The best classification model, best feature, highest classification accuracy, and AUC were (1) random forest and entropy rate, 93% and 0.97, respectively, for walking balance (MDS-UPDRS-2.12); (2) bagged trees and MinMaxDiff, 95% and 0.92, respectively, for shaking/tremor (MDS-UPDRS-2.10); (3) bagged trees and entropy rate, 98% and 0.98, respectively, for freeze of gait; and (4) random forest and MinMaxDiff, 95% and 0.99, respectively, for distinguishing PD patients from HC. CONCLUSION Machine learning classification was challenging due to the use of data that were subjectively labeled based on patients' answers to the MDS-UPDRS survey questions. However, with use of a significantly larger number of subjects than in prior work and clinically validated gait features, we were able to demonstrate that automatic patient classification based on smartphone sensor data can be used to objectively infer the severity of PD and the extent of specific gait anomalies.
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Affiliation(s)
- Hamza Abujrida
- Worcester Polytechnic Institute, 100 Institute Rd, Worcester, MA 01609, United States of America
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Montero-Odasso M, Almeida QJ, Bherer L, Burhan AM, Camicioli R, Doyon J, Fraser S, Muir-Hunter S, Li KZH, Liu-Ambrose T, McIlroy W, Middleton L, Morais JA, Sakurai R, Speechley M, Vasudev A, Beauchet O, Hausdorff JM, Rosano C, Studenski S, Verghese J. Consensus on Shared Measures of Mobility and Cognition: From the Canadian Consortium on Neurodegeneration in Aging (CCNA). J Gerontol A Biol Sci Med Sci 2020; 74:897-909. [PMID: 30101279 PMCID: PMC6521916 DOI: 10.1093/gerona/gly148] [Citation(s) in RCA: 103] [Impact Index Per Article: 25.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Indexed: 02/02/2023] Open
Abstract
Background A new paradigm is emerging in which mobility and cognitive impairments, previously studied, diagnosed, and managed separately in older adults, are in fact regulated by shared brain resources. Deterioration in these shared brain mechanisms by normal aging and neurodegeneration increases the risk of developing dementia, falls, and fractures. This new paradigm requires an integrated approach to measuring both domains. We aim to identify a complementary battery of existing tests of mobility and cognition in community-dwelling older adults that enable assessment of motor-cognitive interactions. Methods Experts on mobility and cognition in aging participated in a semistructured consensus based on the Delphi process. After performing a scoping review to select candidate tests, multiple rounds of consultations provided structured feedback on tests that captured shared characteristics of mobility and cognition. These tests needed to be sensitive to changes in both mobility and cognition, applicable across research studies and clinics, sensitive to interventions, feasible to perform in older adults, been previously validated, and have minimal ceiling/floor effects. Results From 17 tests appraised, 10 tests fulfilled prespecified criteria and were selected as part of the “Core-battery” of tests. The expert panel also recommended a “Minimum-battery” of tests that included gait speed, dual-task gait speed, the Montreal Cognitive Assessment and Trail Making Test A&B. Conclusions A standardized assessment battery that captures shared characteristics of mobility and cognition seen in aging and neurodegeneration may increase comparability across research studies, detection of subtle or common reversible factors, and accelerate research progress in dementia, falls, and aging-related disabilities.
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Affiliation(s)
- Manuel Montero-Odasso
- Department of Medicine, Division of Geriatric Medicine, University of Western Ontario, London, Canada
- Department of Epidemiology and Biostatistics, Schulich School of Medicine & Dentistry, University of Western Ontario, London, Canada
- Gait and Brain Lab, Parkwood Institute, Lawson Health Research Institute, London, Ontario, Canada
- Address correspondence to: Manuel Montero-Odasso MD, PhD, AGSF, FRCPC, FGSA, Gait and Brain Lab, Parkwood Institute, University of Western Ontario and Lawson Health Research Institute, 550 Wellington Road, London, Ontario N6C 0A7, Canada. E-mail:
| | - Quincy J Almeida
- Department of Kinesiology and Physical Education, Sun Life Financial Movement Disorders Research Centre, Wilfrid Laurier University, Waterloo, Ontario, Canada
| | - Louis Bherer
- Department of Psychology and PERFORM Centre, Concordia University, Montréal, Quebec, Canada
- Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Quebec, Canada
- Department of Medicine, University of Montreal, Quebec, Canada
- Montreal Heart Institute, Research Centre, Quebec, Canada
| | - Amer M Burhan
- Department of Psychiatry, Geriatric Psychiatry, Schulich School of Medicine, University of Western Ontario, London, Canada
- Lawson Health Research Institute, London, Ontario, Canada
| | - Richard Camicioli
- Department of Medicine, Geriatric and Cognitive Neurology, University of Alberta, Edmonton, Canada
| | - Julien Doyon
- Functional Neuroimaging Unit, University of Montreal, Quebec, Canada
| | - Sarah Fraser
- Interdisciplinary School of Health Sciences, Faculty of Health Sciences, University of Ottawa, Ontario, Canada
| | - Susan Muir-Hunter
- Department of Medicine, Division of Geriatric Medicine, University of Western Ontario, London, Canada
- Faculty of Health Sciences, School of Physical Therapy, University of Western Ontario, London, Canada
| | - Karen Z H Li
- Department of Psychology and PERFORM Centre, Concordia University, Montréal, Quebec, Canada
| | - Teresa Liu-Ambrose
- Department of Physical Therapy, Centre for Hip Health and Mobility, University of British Columbia, Canada
- Djavad Mowafaghian Centre for Brain Health, Vancouver Coastal Research Institute, University of British Columbia, Canada
| | - William McIlroy
- Division of Neurology and Department of Medicine, University of Toronto, Ontario, Canada
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Ontario, Canada
- Department of Kinesiology, University of Waterloo, Ontario, Canada
| | - Laura Middleton
- Department of Kinesiology, University of Waterloo, Ontario, Canada
| | - José A Morais
- Department of Medicine, Division of Geriatrics and Centre of Excellence in Aging and Chronic Disease, McGill University, Montréal, Quebec, Canada
| | - Ryota Sakurai
- Gait and Brain Lab, Parkwood Institute, Lawson Health Research Institute, London, Ontario, Canada
| | - Mark Speechley
- Department of Epidemiology and Biostatistics, Schulich School of Medicine & Dentistry, University of Western Ontario, London, Canada
| | - Akshya Vasudev
- Department of Psychiatry, Geriatric Psychiatry, Schulich School of Medicine, University of Western Ontario, London, Canada
- Department of Medicine, Division of Clinical Pharmacology, University of Western Ontario, London, Canada
| | - Olivier Beauchet
- Department of Medicine, Division of Geriatric Medicine, McGill University, Montréal, Quebec, Canada
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Quebec, Canada
- RUIS McGill Centre of Excellence on Aging and Chronic Disease – CEViMaC, Montréal, Quebec, Canada
| | - Jeffrey M Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Israel
- Department of Physical Therapy, Sackler Faculty of Medicine, and Sagol School of Neuroscience, Tel Aviv University, Israel
- Rush Alzheimer’s Disease Center and Department of Orthopedic Surgery, Rush University Medical Center, Chicago, Illinois
| | - Caterina Rosano
- Department of Epidemiology, University of Pittsburgh, Pennsylvania
| | - Stephanie Studenski
- Division of Geriatric Medicine, School of Medicine, University of Pittsburgh, Pennsylvania
| | - Joe Verghese
- Department of Medicine, Albert Einstein College of Medicine, Bronx, New York
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Howell DR, Lugade V, Taksir M, Meehan WP. Determining the utility of a smartphone-based gait evaluation for possible use in concussion management. PHYSICIAN SPORTSMED 2020; 48:75-80. [PMID: 31198074 DOI: 10.1080/00913847.2019.1632155] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Objectives: Our was objectives were to (1) assess the validity of a smartphone-based application to obtain spatiotemporal gait variables relative to an established movement monitoring system used previously to evaluate post-concussion gait, and (2) determine the test-retest reliability of gait variables obtained with a smartphone.Methods: Twenty healthy participants (n = 14 females, mean age = 22.2, SD = 2.1 years) were assessed at two time points, approximately two weeks apart. Two measurement systems (inertial sensor system, smartphone application) acquired and analyzed single-task and dual-task spatio-temporal gait variables simultaneously. Our primary outcome measures were average walking speed (m/s), cadence (steps/min), and stride length (m) measured by the inertial sensor system and smartphone application.Results: Correlations between the systems were high to very high (Pearson r = 0.77-0.98) at both time points, with the exception of dual-task stride length at time 2 (Pearson r = 0.55). Bland-Altman analysis for average gait speed and cadence indicated the average disagreement between systems was close to zero, suggesting little evidence for systematic bias between acquisition systems. Test-retest consistency measures using the smartphone revealed high to very high reliability for all measurements (ICC = 0.81-0.95).Conclusions: Our results indicate that sensors within a smartphone are capable of measuring spatio-temporal gait variables similar to a validated three-sensor inertial sensor system in single-task and dual-task conditions, and that data are reliable across a two-week time interval. A smartphone-based application might allow clinicians to objectively evaluate gait in the management of concussion with high ease-of-use and a relatively low financial burden.
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Affiliation(s)
- David R Howell
- Sports Medicine Center, Children's Hospital, Aurora, CO, USA.,Department of Orthopedics, University of Colorado School of Medicine, Aurora, CO, USA.,The Micheli Center for Sports Injury Prevention, Waltham, MA, USA
| | - Vipul Lugade
- Control One LLC, Albuquerque, NM, USA.,Department of Physical Therapy, Chiang Mai University, Chiang Mai, Thailand
| | - Mikhail Taksir
- The Micheli Center for Sports Injury Prevention, Waltham, MA, USA
| | - William P Meehan
- The Micheli Center for Sports Injury Prevention, Waltham, MA, USA.,Division of Sports Medicine, Department of Orthopaedics, Boston Children's Hospital, Boston, MA, USA.,Departments of Pediatrics and Orthopaedic Surgery, Harvard Medical School, Boston, MA, USA
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Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app10010234] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
More than 8.6 million people suffer from neurological disorders that affect their gait and balance. Physical therapists provide interventions to improve patient’s functional outcomes, yet balance and gait are often evaluated in a subjective and observational manner. The use of quantitative methods allows for assessment and tracking of patient progress during and after rehabilitation or for early diagnosis of movement disorders. This paper surveys the state-of-the-art in wearable sensor technology in gait, balance, and range of motion research. It serves as a point of reference for future research, describing current solutions and challenges in the field. A two-level taxonomy of rehabilitation assessment is introduced with evaluation metrics and common algorithms utilized in wearable sensor systems.
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Burt J, Ravid EN, Bradford S, Fisher NJ, Zeng Y, Chomiak T, Brown L, McKeown MJ, Hu B, Camicioli R. The Effects of Music-Contingent Gait Training on Cognition and Mood in Parkinson Disease: A Feasibility Study. Neurorehabil Neural Repair 2019; 34:82-92. [PMID: 31878824 DOI: 10.1177/1545968319893303] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background. In Parkinson disease (PD), gait impairments often coexist with nonmotor symptoms such as anxiety and depression. Biofeedback training may improve gait function in PD, but its effect on nonmotor symptoms remains unclear. This study explored the cognitive and global effects of Ambulosono, a cognitive gait training method utilizing step size to contingently control the real-time play of motivational music. Objective. This study examined the feasibility of music-contingent gait training and its effects on neuropsychological test performance and mood in persons with PD. Methods. A total of 30 participants with mild to moderate PD were semirandomized via sequential alternating assignment into an experimental training group or control music group. The training group received 12 weeks of music-contingent training, whereby music play was dependent on the user achieving a set stride length, adjusted online based on individual performance. The control group received hybrid training beginning with 6 weeks of noncontingent music walking, whereby music played continuously regardless of step size, followed by 6 weeks of music-contingent training. Global cognition, memory, executive function, attention, and working memory assessments were completed by blinded assessors at baseline, 6 weeks, and 12 weeks. Motor function, mood, and anxiety were assessed. Results. Average training adherence was 97%, with no falls occurring during training sessions. Improvements on cognitive measures were not clinically significant; however, significant decreases in depression and anxiety were observed in both groups over time (P < .05). Conclusions. Music-contingent gait training is feasible and safe in individuals with PD. Further investigation into potential therapeutic applications of this technology is recommended.
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Affiliation(s)
| | | | | | | | - Yiye Zeng
- University of Alberta, Edmonton, AB, Canada
| | | | | | | | - Bin Hu
- University of Calgary, Calgary, AB, Canada
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Psychiatric Patients on Neuroleptics: Evaluation of Parkinsonism and Quantified Assessment of Gait. Clin Neuropharmacol 2019; 43:1-6. [DOI: 10.1097/wnf.0000000000000371] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Evaluation of Suitability of Current Industrial Standards in Designing Control Applications for Internet of Things Healthcare Sensor Networks. JOURNAL OF SENSOR AND ACTUATOR NETWORKS 2019. [DOI: 10.3390/jsan8040054] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Internet of Things (IoT) holds great promises for industrial, commercial, and consumer applications. While wireless techniques have matured with time and have gained the users’ confidence in relaying data containing qualitative as well as quantitative information, cynicism still exists on trusting them for applications involving control. The wireless protocols and techniques used for industrial control have proved their robustness. In this work we have attempted to test some aspects of feasibility on the use of wireless control involving such protocols for IoT healthcare sensor networks (IoT-HSNs). We conceptualized and simulated a 24-channel IoT-HSN model that includes biosensors as well as bioactuators. Currently, no protocol supporting control in such networks has been standardized. We tried to fit in the widely used WirelessHART (Highway Addressable Remote Transducer) industrial protocol for sensing as well as control in the model to test if it would work for a healthcare sensor network. We probed the performance of the model with respect to network parameters such as channels, bandwidth, Quality of Service (QoS) requirements, payload, transmission delays, and allowable errors. For the parameters considered, the results obtained from the model were encouraging, suggesting that WirelessHART fits the IoT-HSN control requirements according to this initial probe. The findings could provide useful insights for researchers working in the field of control in IoT-HSNs and for designers and manufacturers of IoT-HSN equipment.
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Gaprielian P, Scott SH, Lowrey C, Reid S, Pari G, Levy R. Integrated robotics platform with haptic control differentiates subjects with Parkinson's disease from controls and quantifies the motor effects of levodopa. J Neuroeng Rehabil 2019; 16:124. [PMID: 31655612 PMCID: PMC6815040 DOI: 10.1186/s12984-019-0598-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Accepted: 09/20/2019] [Indexed: 02/01/2023] Open
Abstract
Background The use of integrated robotic technology to quantify the spectrum of motor symptoms of Parkinson’s Disease (PD) has the potential to facilitate objective assessment that is independent of clinical ratings. The purpose of this study is to use the KINARM exoskeleton robot to (1) differentiate subjects with PD from controls and (2) quantify the motor effects of dopamine replacement therapies (DRTs). Methods Twenty-six subjects (Hoehn and Yahr mean 2.2; disease duration 0.5 to 15 years) were evaluated OFF (after > 12 h of their last dose) and ON their DRTs with the Unified Parkinson’s Disease Rating Scale (UPDRS) and the KINARM exoskeleton robot. Bilateral upper extremity bradykinesia, rigidity, and postural stability were quantified using a repetitive movement task to hit moving targets, a passive stretch task, and a torque unloading task, respectively. Performance was compared against healthy age-matched controls. Results Mean hand speed was 41% slower and 25% fewer targets were hit in subjects with PD OFF medication than in controls. Receiver operating characteristic (ROC) area for hand speed was 0.94. The torque required to stop elbow movement during the passive stretch task was 34% lower in PD subjects versus controls and resulted in an ROC area of 0.91. The torque unloading task showed a maximum displacement that was 29% shorter than controls and had an ROC area of 0.71. Laterality indices for speed and end total torque were correlated to the most affected side. Hand speed laterality index had an ROC area of 0.80 against healthy controls. DRT administration resulted in a significant reduction in a cumulative score of parameter Z-scores (a measure of global performance compared to healthy controls) in subjects with clinically effective levodopa doses. The cumulative score was also correlated to UPDRS scores for the effect of DRT. Conclusions Robotic assessment is able to objectively quantify parkinsonian symptoms of bradykinesia, rigidity and postural stability similar to the UPDRS. This integrated testing platform has the potential to aid clinicians in the management of PD and help assess the effects of novel therapies.
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Affiliation(s)
- Pauline Gaprielian
- Centre for Neuroscience Studies, Queen's University, Kingston, Ontario, K7L 3N6, Canada
| | - Stephen H Scott
- Centre for Neuroscience Studies, Queen's University, Kingston, Ontario, K7L 3N6, Canada.,Department of Biomedical and Molecular Sciences, Queen's University, Kingston, Ontario, K7L 3N6, Canada.,Department of Medicine, Queen's University, Kingston General Hospital, Kingston, Ontario, Canada
| | - Catherine Lowrey
- Centre for Neuroscience Studies, Queen's University, Kingston, Ontario, K7L 3N6, Canada
| | - Stuart Reid
- Centre for Neuroscience Studies, Queen's University, Kingston, Ontario, K7L 3N6, Canada.,Department of Biomedical and Molecular Sciences, Queen's University, Kingston, Ontario, K7L 3N6, Canada
| | - Giovanna Pari
- Centre for Neuroscience Studies, Queen's University, Kingston, Ontario, K7L 3N6, Canada.,Department of Medicine, Queen's University, Kingston General Hospital, Kingston, Ontario, Canada
| | - Ron Levy
- Centre for Neuroscience Studies, Queen's University, Kingston, Ontario, K7L 3N6, Canada. .,Department of Surgery, Queen's University, Kingston General Hospital, Kingston, Ontario, Canada.
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Kuo CH, White-Dzuro GA, Ko AL. Approaches to closed-loop deep brain stimulation for movement disorders. Neurosurg Focus 2019; 45:E2. [PMID: 30064321 DOI: 10.3171/2018.5.focus18173] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Deep brain stimulation (DBS) is a safe and effective therapy for movement disorders, such as Parkinson's disease (PD), essential tremor (ET), and dystonia. There is considerable interest in developing "closed-loop" DBS devices capable of modulating stimulation in response to sensor feedback. In this paper, the authors review related literature and present selected approaches to signal sources and approaches to feedback being considered for deployment in closed-loop systems. METHODS A literature search using the keywords "closed-loop DBS" and "adaptive DBS" was performed in the PubMed database. The search was conducted for all articles published up until March 2018. An in-depth review was not performed for publications not written in the English language, nonhuman studies, or topics other than Parkinson's disease or essential tremor, specifically epilepsy and psychiatric conditions. RESULTS The search returned 256 articles. A total of 71 articles were primary studies in humans, of which 50 focused on treatment of movement disorders. These articles were reviewed with the aim of providing an overview of the features of closed-loop systems, with particular attention paid to signal sources and biomarkers, general approaches to feedback control, and clinical data when available. CONCLUSIONS Closed-loop DBS seeks to employ biomarkers, derived from sensors such as electromyography, electrocorticography, and local field potentials, to provide real-time, patient-responsive therapy for movement disorders. Most studies appear to focus on the treatment of Parkinson's disease. Several approaches hold promise, but additional studies are required to determine which approaches are feasible, efficacious, and efficient.
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Affiliation(s)
- Chao-Hung Kuo
- 1Neurological Surgery, University of Washington, Seattle, Washington.,3School of Medicine, National Yang-Ming University, Taipei, Taiwan; and
| | | | - Andrew L Ko
- 1Neurological Surgery, University of Washington, Seattle, Washington.,4NSF Engineering Research Center for Sensorimotor Neural Engineering, Seattle, Washington
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Zhan A, Mohan S, Tarolli C, Schneider RB, Adams JL, Sharma S, Elson MJ, Spear KL, Glidden AM, Little MA, Terzis A, Dorsey ER, Saria S. Using Smartphones and Machine Learning to Quantify Parkinson Disease Severity: The Mobile Parkinson Disease Score. JAMA Neurol 2019; 75:876-880. [PMID: 29582075 DOI: 10.1001/jamaneurol.2018.0809] [Citation(s) in RCA: 217] [Impact Index Per Article: 43.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Importance Current Parkinson disease (PD) measures are subjective, rater-dependent, and assessed in clinic. Smartphones can measure PD features, yet no smartphone-derived rating score exists to assess motor symptom severity in real-world settings. Objectives To develop an objective measure of PD severity and test construct validity by evaluating the ability of the measure to capture intraday symptom fluctuations, correlate with current standard PD outcome measures, and respond to dopaminergic therapy. Design, Setting, and Participants This observational study assessed individuals with PD who remotely completed 5 tasks (voice, finger tapping, gait, balance, and reaction time) on the smartphone application. We used a novel machine-learning-based approach to generate a mobile Parkinson disease score (mPDS) that objectively weighs features derived from each smartphone activity (eg, stride length from the gait activity) and is scaled from 0 to 100 (where higher scores indicate greater severity). Individuals with and without PD additionally completed standard in-person assessments of PD with smartphone assessments during a period of 6 months. Main Outcomes and Measures Ability of the mPDS to detect intraday symptom fluctuations, the correlation between the mPDS and standard measures, and the ability of the mPDS to respond to dopaminergic medication. Results The mPDS was derived from 6148 smartphone activity assessments from 129 individuals (mean [SD] age, 58.7 [8.6] years; 56 [43.4%] women). Gait features contributed most to the total mPDS (33.4%). In addition, 23 individuals with PD (mean [SD] age, 64.6 [11.5] years; 11 [48%] women) and 17 without PD (mean [SD] age 54.2 [16.5] years; 12 [71%] women) completed in-clinic assessments. The mPDS detected symptom fluctuations with a mean (SD) intraday change of 13.9 (10.3) points on a scale of 0 to 100. The measure correlated well with the Movement Disorder Society Unified Parkinson Disease's Rating Scale total (r = 0.81; P < .001) and part III only (r = 0.88; P < .001), the Timed Up and Go assessment (r = 0.72; P = .002), and the Hoehn and Yahr stage (r = 0.91; P < .001). The mPDS improved by a mean (SD) of 16.3 (5.6) points in response to dopaminergic therapy. Conclusions and Relevance Using a novel machine-learning approach, we created and demonstrated construct validity of an objective PD severity score derived from smartphone assessments. This score complements standard PD measures by providing frequent, objective, real-world assessments that could enhance clinical care and evaluation of novel therapeutics.
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Affiliation(s)
- Andong Zhan
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland
| | - Srihari Mohan
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland
| | - Christopher Tarolli
- Department of Neurology, University of Rochester Medical Center, Rochester, New York.,Center for Health + Technology, University of Rochester Medical Center, Rochester, New York
| | - Ruth B Schneider
- Department of Neurology, University of Rochester Medical Center, Rochester, New York
| | - Jamie L Adams
- Department of Neurology, University of Rochester Medical Center, Rochester, New York.,Center for Health + Technology, University of Rochester Medical Center, Rochester, New York
| | - Saloni Sharma
- Center for Health + Technology, University of Rochester Medical Center, Rochester, New York
| | - Molly J Elson
- Center for Health + Technology, University of Rochester Medical Center, Rochester, New York
| | - Kelsey L Spear
- Center for Health + Technology, University of Rochester Medical Center, Rochester, New York
| | - Alistair M Glidden
- Center for Health + Technology, University of Rochester Medical Center, Rochester, New York
| | - Max A Little
- Department of Mathematics, Aston University, Birmingham, England
| | - Andreas Terzis
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland
| | - E Ray Dorsey
- Department of Neurology, University of Rochester Medical Center, Rochester, New York.,Center for Health + Technology, University of Rochester Medical Center, Rochester, New York
| | - Suchi Saria
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland.,Armstrong Institute for Patient Safety and Quality, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland.,Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
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Next Steps in Wearable Technology and Community Ambulation in Multiple Sclerosis. Curr Neurol Neurosci Rep 2019; 19:80. [DOI: 10.1007/s11910-019-0997-9] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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