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Németh AH, Antoniades CA, Dukart J, Minnerop M, Rentz C, Schuman BJ, van de Warrenburg B, Willemse I, Bertini E, Gupta AS, de Mello Monteiro CB, Almoajil H, Quinn L, Perlman SB, Horak F, Ilg W, Traschütz A, Vogel AP, Dawes H. Using Smartphone Sensors for Ataxia Trials: Consensus Guidance by the Ataxia Global Initiative Working Group on Digital-Motor Biomarkers. CEREBELLUM (LONDON, ENGLAND) 2024; 23:912-923. [PMID: 38015365 PMCID: PMC11102363 DOI: 10.1007/s12311-023-01608-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/25/2023] [Indexed: 11/29/2023]
Abstract
Smartphone sensors are used increasingly in the assessment of ataxias. To date, there is no specific consensus guidance regarding a priority set of smartphone sensor measurements, or standard assessment criteria that are appropriate for clinical trials. As part of the Ataxia Global Initiative Digital-Motor Biomarkers Working Group (AGI WG4), aimed at evaluating key ataxia clinical domains (gait/posture, upper limb, speech and oculomotor assessments), we provide consensus guidance for use of internal smartphone sensors to assess key domains. Guidance was developed by means of a literature review and a two stage Delphi study conducted by an Expert panel, which surveyed members of AGI WG4, representing clinical, research, industry and patient-led experts, and consensus meetings by the Expert panel to agree on standard criteria and map current literature to these criteria. Seven publications were identified that investigated ataxias using internal smartphone sensors. The Delphi 1 survey ascertained current practice, and systems in use or under development. Wide variations in smartphones sensor use for assessing ataxia were identified. The Delphi 2 survey identified seven measures that were strongly endorsed as priorities in assessing 3/4 domains, namely gait/posture, upper limb, and speech performance. The Expert panel recommended 15 standard criteria to be fulfilled in studies. Evaluation of current literature revealed that none of the studies met all criteria, with most being early-phase validation studies. Our guidance highlights the importance of consensus, identifies priority measures and standard criteria, and will encourage further research into the use of internal smartphone sensors to measure ataxia digital-motor biomarkers.
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Affiliation(s)
- Andrea H Németh
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Oxford Centre for Genomic Medicine, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Chrystalina A Antoniades
- Neurometrology Group, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Juergen Dukart
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Martina Minnerop
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Neurology, Center for Movement Disorders and Neuromodulation, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, (INM-1), Research Centre Jülich, Jülich, Germany
| | - Clara Rentz
- Institute of Neuroscience and Medicine, (INM-1), Research Centre Jülich, Jülich, Germany
| | | | - Bart van de Warrenburg
- Department of Neurology, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Center, 6525, Nijmegen, Netherlands
| | - Ilse Willemse
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
| | - Enrico Bertini
- Unit of Neuromuscular and Neurodegenerative Disorders, Dept Neurosciences, Bambino Gesu' Children's Research Hospital, IRCCS, Rome, Italy
| | - Anoopum S Gupta
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Carlos Bandeira de Mello Monteiro
- Faculty of Medicine, University of São Paulo, São Paulo, SP, Brazil
- School of Arts, Science and Humanities, University of São Paulo, São Paulo, SP, Brazil
| | - Hajar Almoajil
- Physical Therapy Department, College of Applied Medical Sciences, Imam Abdulrahman Bin Faisal University, Damman, Saudi Arabia
| | - Lori Quinn
- Department of Biobehavioral Sciences, Teachers College, Columbia University, New York, NY, USA
| | | | - Fay Horak
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA
- APDM Precision Motion, Clario, Portland, OR, USA
| | - Winfried Ilg
- Section Computational Sensomotorics, Hertie Institute for Clinical Brain Research, Tübingen, Germany
- Centre for Integrative Neuroscience (CIN), Tübingen, Germany
| | - Andreas Traschütz
- Research Division "Translational Genomics of Neurodegenerative Diseases", Hertie Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, Tübingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), University of Tübingen, Tübingen, Germany
| | - Adam P Vogel
- Centre for Neuroscience of Speech, The University of Melbourne, Melbourne, Australia
- Division of Translational Genomics of Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- Center for Neurology, University Hospital Tübingen, Tübingen, Germany
- Redenlab Inc, Melbourne, Australia
| | - Helen Dawes
- NIHR Exeter Biomedical Research Centre, Medical School, Faculty of Health and Life Sciences, College of Medicine and Health, St Lukes Campus, University of Exeter, Heavitree Road, Exeter, UK.
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Casey HL, Shah VV, Muzyka D, McNames J, El-Gohary M, Sowalsky K, Safarpour D, Carlson-Kuhta P, Schmahmann JD, Rosenthal LS, Perlman S, Rummey C, Horak FB, Gomez CM. Standing Balance Conditions and Digital Sway Measures for Clinical Trials of Friedreich's Ataxia. Mov Disord 2024; 39:996-1005. [PMID: 38469957 DOI: 10.1002/mds.29777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 02/05/2024] [Accepted: 02/23/2024] [Indexed: 03/13/2024] Open
Abstract
BACKGROUND Progressive loss of standing balance is a feature of Friedreich's ataxia (FRDA). OBJECTIVES This study aimed to identify standing balance conditions and digital postural sway measures that best discriminate between FRDA and healthy controls (HC). We assessed test-retest reliability and correlations between sway measures and clinical scores. METHODS Twenty-eight subjects with FRDA and 20 HC completed six standing conditions: feet apart, feet together, and feet tandem, both with eyes opened (EO) and eyes closed. Sway was measured using a wearable sensor on the lumbar spine for 30 seconds. Test completion rate, test-retest reliability with intraclass correlation coefficients, and areas under the receiver operating characteristic curves (AUCs) for each measure were compared to identify distinguishable FRDA sway characteristics from HC. Pearson correlations were used to evaluate the relationships between discriminative measures and clinical scores. RESULTS Three of the six standing conditions had completion rates over 70%. Of these three conditions, natural stance and feet together with EO showed the greatest completion rates. All six of the sway measures' mean values were significantly different between FRDA and HC. Four of these six measures discriminated between groups with >0.9 AUC in all three conditions. The Friedreich Ataxia Rating Scale Upright Stability and Total scores correlated with sway measures with P-values <0.05 and r-values (0.63-0.86) and (0.65-0.81), respectively. CONCLUSION Digital postural sway measures using wearable sensors are discriminative and reliable for assessing standing balance in individuals with FRDA. Natural stance and feet together stance with EO conditions suggest use in clinical trials for FRDA. © 2024 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Hannah L Casey
- Department of Neurology, The University of Chicago, Chicago, Illinois, USA
| | - Vrutangkumar V Shah
- Precision Motion, APDM Wearable Technologies - a Clario company, Portland, Oregon, USA
- Department of Neurology, Oregon Health and Science University, Portland, Oregon, USA
| | - Daniel Muzyka
- Precision Motion, APDM Wearable Technologies - a Clario company, Portland, Oregon, USA
| | - James McNames
- Precision Motion, APDM Wearable Technologies - a Clario company, Portland, Oregon, USA
- Department of Electrical and Computer Engineering, Portland State University, Portland, Oregon, USA
| | - Mahmoud El-Gohary
- Precision Motion, APDM Wearable Technologies - a Clario company, Portland, Oregon, USA
| | - Kristen Sowalsky
- Precision Motion, APDM Wearable Technologies - a Clario company, Portland, Oregon, USA
| | - Delaram Safarpour
- Department of Neurology, Oregon Health and Science University, Portland, Oregon, USA
| | | | - Jeremy D Schmahmann
- Ataxia Center, Laboratory for Neuroanatomy and Cerebellar Neurobiology, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Liana S Rosenthal
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Susan Perlman
- Department of Neurology, University of California, Los Angeles, California, USA
| | | | - Fay B Horak
- Precision Motion, APDM Wearable Technologies - a Clario company, Portland, Oregon, USA
- Department of Neurology, Oregon Health and Science University, Portland, Oregon, USA
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Shah VV, Muzyka D, Jagodinsky A, McNames J, Casey H, El-Gohary M, Sowalsky K, Safarpour D, Carlson-Kuhta P, Schmahmann JD, Rosenthal LS, Perlman S, Horak FB, Gomez CM. Digital Measures of Postural Sway Quantify Balance Deficits in Spinocerebellar Ataxia. Mov Disord 2024; 39:663-673. [PMID: 38357985 DOI: 10.1002/mds.29742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 12/21/2023] [Accepted: 01/23/2024] [Indexed: 02/16/2024] Open
Abstract
BACKGROUND Maintaining balance is crucial for independence and quality of life. Loss of balance is a hallmark of spinocerebellar ataxia (SCA). OBJECTIVE The aim of this study was to identify which standing balance conditions and digital measures of body sway were most discriminative, reliable, and valid for quantifying balance in SCA. METHODS Fifty-three people with SCA (13 SCA1, 13 SCA2, 14 SCA3, and 13 SCA6) and Scale for Assessment and Rating of Ataxia (SARA) scores 9.28 ± 4.36 and 31 healthy controls were recruited. Subjects stood in six test conditions (natural stance, feet together and tandem, each with eyes open [EO] and eyes closed [EC]) with an inertial sensor on their lower back for 30 seconds (×2). We compared test completion rate, test-retest reliability, and areas under the receiver operating characteristic curve (AUC) for seven digital sway measures. Pearson's correlations related sway with the SARA and the Patient-Reported Outcome Measure of Ataxia (PROM ataxia). RESULTS Most individuals with SCA (85%-100%) could stand for 30 seconds with natural stance EO or EC, and with feet together EO. The most discriminative digital sway measures (path length, range, area, and root mean square) from the two most reliable and discriminative conditions (natural stance EC and feet together EO) showed intraclass correlation coefficients from 0.70 to 0.91 and AUCs from 0.83 to 0.93. Correlations of sway with SARA were significant (maximum r = 0.65 and 0.73). Correlations with PROM ataxia were mild to moderate (maximum r = 0.56 and 0.34). CONCLUSION Inertial sensor measures of extent of postural sway in conditions of natural stance EC and feet together stance EO were discriminative, reliable, and valid for monitoring SCA. © 2024 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Vrutangkumar V Shah
- Precision Motion, APDM Wearable Technologies-A Clario Company, Portland, Oregon, USA
- Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - Daniel Muzyka
- Precision Motion, APDM Wearable Technologies-A Clario Company, Portland, Oregon, USA
| | - Adam Jagodinsky
- Precision Motion, APDM Wearable Technologies-A Clario Company, Portland, Oregon, USA
| | - James McNames
- Precision Motion, APDM Wearable Technologies-A Clario Company, Portland, Oregon, USA
- Department of Electrical and Computer Engineering, Portland State University, Portland, Oregon, USA
| | - Hannah Casey
- Department of Neurology, The University of Chicago, Chicago, Illinois, USA
| | - Mahmoud El-Gohary
- Precision Motion, APDM Wearable Technologies-A Clario Company, Portland, Oregon, USA
| | - Kristen Sowalsky
- Precision Motion, APDM Wearable Technologies-A Clario Company, Portland, Oregon, USA
| | - Delaram Safarpour
- Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | | | - Jeremy D Schmahmann
- Ataxia Center, Laboratory for Neuroanatomy and Cerebellar Neurobiology, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Liana S Rosenthal
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Susan Perlman
- Department of Neurology, University of California, Los Angeles, California, USA
| | - Fay B Horak
- Precision Motion, APDM Wearable Technologies-A Clario Company, Portland, Oregon, USA
- Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
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Willemse IHJ, Schootemeijer S, van den Bergh R, Dawes H, Nonnekes JH, van de Warrenburg BPC. Smartphone applications for Movement Disorders: Towards collaboration and re-use. Parkinsonism Relat Disord 2024; 120:105988. [PMID: 38184466 DOI: 10.1016/j.parkreldis.2023.105988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 12/20/2023] [Accepted: 12/31/2023] [Indexed: 01/08/2024]
Abstract
BACKGROUND Numerous smartphone and tablet applications (apps) are available to monitor movement disorders, but an overview of their purpose and stage of development is missing. OBJECTIVES To systematically review published literature and classify smartphone and tablet apps with objective measurement capabilities for the diagnosis, monitoring, assessment, or treatment of movement disorders. METHODS We systematically searched for publications covering smartphone or tablet apps to monitor movement disorders until November 22nd, 2023. We reviewed the target population, measured domains, purpose, and technology readiness level (TRL) of the proposed app and checked their availability in common app stores. RESULTS We identified 113 apps. Most apps were developed for Parkinson's disease specifically (n = 82; 73%) or for movement disorders in general (n = 17; 15%). Apps were either designed to momentarily assess symptoms (n = 65; 58%), support treatment (n = 22; 19%), aid in diagnosis (n = 16; 14%), or passively track symptoms (n = 11; 10%). Commonly assessed domains across movement disorders included fine motor skills (n = 34; 30%), gait (n = 36; 32%), and tremor (n = 32; 28%) for the motor domain and cognition (n = 16; 14%) for the non-motor domain. Twenty-six (23%) apps were proof-of-concepts (TRL 1-3), while most apps were tested in a controlled setting (TRL 4-6; n = 63; 56%). Twenty-four apps were tested in their target setting (TRL 7-9) of which 10 were accessible in common app stores or as Android Package. CONCLUSIONS The development of apps strongly gravitates towards Parkinson's disease and a selection of motor symptoms. Collaboration, re-use and further development of existing apps is encouraged to avoid reinventions of the wheel.
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Affiliation(s)
- Ilse H J Willemse
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands.
| | - Sabine Schootemeijer
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands
| | - Robin van den Bergh
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands
| | - Helen Dawes
- NIHR Exeter BRC, Medical School, Faculty of Health and Life Sciences, University of Exeter, UK
| | - Jorik H Nonnekes
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Rehabilitation, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands; Department of Rehabilitation, Sint Maartenskliniek, Nijmegen, the Netherlands
| | - Bart P C van de Warrenburg
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands
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Ferreira VR, Metting E, Schauble J, Seddighi H, Beumeler L, Gallo V. eHealth tools to assess the neurological function for research, in absence of the neurologist - a systematic review, part I (software). J Neurol 2024; 271:211-230. [PMID: 37847293 PMCID: PMC10770248 DOI: 10.1007/s00415-023-12012-6] [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: 06/28/2023] [Revised: 09/15/2023] [Accepted: 09/16/2023] [Indexed: 10/18/2023]
Abstract
BACKGROUND Neurological disorders remain a worldwide concern due to their increasing prevalence and mortality, combined with the lack of available treatment, in most cases. Exploring protective and risk factors associated with the development of neurological disorders will allow for improving prevention strategies. However, ascertaining neurological outcomes in population-based studies can be both complex and costly. The application of eHealth tools in research may contribute to lowering the costs and increase accessibility. The aim of this systematic review is to map existing eHealth tools assessing neurological signs and/or symptoms for epidemiological research. METHODS Four search engines (PubMed, Web of Science, Scopus & EBSCOHost) were used to retrieve articles on the development, validation, or implementation of eHealth tools to assess neurological signs and/or symptoms. The clinical and technical properties of the software tools were summarised. Due to high numbers, only software tools are presented here. FINDINGS A total of 42 tools were retrieved. These captured signs and/or symptoms belonging to four neurological domains: cognitive function, motor function, cranial nerves, and gait and coordination. An additional fifth category of composite tools was added. Most of the tools were available in English and were developed for smartphone device, with the remaining tools being available as web-based platforms. Less than half of the captured tools were fully validated, and only approximately half were still active at the time of data collection. INTERPRETATION The identified tools often presented limitations either due to language barriers or lack of proper validation. Maintenance and durability of most tools were low. The present mapping exercise offers a detailed guide for epidemiologists to identify the most appropriate eHealth tool for their research. FUNDING The current study was funded by a PhD position at the University of Groningen. No additional funding was acquired.
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Affiliation(s)
- Vasco Ribeiro Ferreira
- Department of Sustainable Health, University of Groningen, Campus Fryslân, Wirdumerdijk 34, 8911 CE, Leeuwarden, The Netherlands.
| | - Esther Metting
- Faculty of Economics and Business, University of Groningen, Groningen, The Netherlands
- University Medical College Groningen, Groningen, The Netherlands
| | - Joshua Schauble
- Department of Knowledge Infrastructure, University of Groningen, Campus Fryslân, Leeuwarden, The Netherlands
| | - Hamed Seddighi
- Department of Sustainable Health, University of Groningen, Campus Fryslân, Wirdumerdijk 34, 8911 CE, Leeuwarden, The Netherlands
- Department of Psychology, Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, The Netherlands
| | - Lise Beumeler
- Department of Sustainable Health, University of Groningen, Campus Fryslân, Wirdumerdijk 34, 8911 CE, Leeuwarden, The Netherlands
- Department of Intensive Care, Medical Centre Leeuwarden, Leeuwarden, The Netherlands
| | - Valentina Gallo
- Department of Sustainable Health, University of Groningen, Campus Fryslân, Wirdumerdijk 34, 8911 CE, Leeuwarden, The Netherlands
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Olsen S, Rashid U, Allerby C, Brown E, Leyser M, McDonnell G, Alder G, Barbado D, Shaikh N, Lord S, Niazi IK, Taylor D. Smartphone-based gait and balance accelerometry is sensitive to age and correlates with clinical and kinematic data. Gait Posture 2023; 100:57-64. [PMID: 36481647 DOI: 10.1016/j.gaitpost.2022.11.014] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Revised: 11/16/2022] [Accepted: 11/24/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND The Gait&Balance (G&B) App has produced valid and reliable measures of gait and balance in young healthy adults but has not been tested in older adults. RESEARCH QUESTION In healthy middle-to-older aged adults, are G&B App measurements sensitive to age, valid against clinical and kinematic measures, and reliable? METHOD Healthy participants (n = 34, 14 male, 42-94 years) completed the G&B App protocol three times within a single session. 3D kinematics were collected concurrently. Clinical balance measures were collected (Modified Clinical Test of Sensory Interaction in Balance (mCTSIB), Mini Balance Evaluation Systems Test (MBT), and Functional Gait Assessment (FGA)). Sensitivity to age was assessed with Pearson's correlations. Validity tests included Pearson's correlations and Bland-Altman limits of agreement. Reliability tests included intra-class correlation coefficients and standard error of the measure. RESULTS During quiet stance on a compliant surface, the G&B App was sensitive to age-related differences not detectable with the mCTSIB. During walking tasks, there was adequate convergent validity between the MBT and G&B App measures of step length, and between the FGA and G&B App measures of walking speed, step length, and periodicity. The G&B App had moderate-to-excellent validity against 3D kinematics for postural stability during quiet stance (r 0.98 [0.98, 0.99]), step time (r 0.97 [0.96, 0.98]), walking speed (r 0.79 [0.7, 0.86]), and step length (r 0.73 [0.61, 0.81]). Test-retest reliability was moderate-to-excellent for G&B App measures of postural stability, walking speed, periodicity, step length, and step time. G&B App measures of step length asymmetry, step length variability, step time asymmetry, and step time variability had poor validity and reliability. SIGNIFICANCE The G&B App was sensitive to age-related differences in balance not detectable with clinical measurement. It provides valid and reliable measures of postural stability, step length, step time, and periodicity, which are not currently available in standard practice.
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Affiliation(s)
- Sharon Olsen
- Rehabilitation Innovation Centre, Health & Rehabilitation Research Institute, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand.
| | - Usman Rashid
- Rehabilitation Innovation Centre, Health & Rehabilitation Research Institute, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand; Centre for Chiropractic Research, New Zealand College of Chiropractic, PO Box 113-044, Newmarket, Auckland 1149, New Zealand
| | - Celia Allerby
- Rehabilitation Innovation Centre, Health & Rehabilitation Research Institute, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand
| | - Eliza Brown
- Rehabilitation Innovation Centre, Health & Rehabilitation Research Institute, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand
| | - Michaela Leyser
- Rehabilitation Innovation Centre, Health & Rehabilitation Research Institute, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand
| | - Gabrielle McDonnell
- Rehabilitation Innovation Centre, Health & Rehabilitation Research Institute, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand
| | - Gemma Alder
- Rehabilitation Innovation Centre, Health & Rehabilitation Research Institute, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand
| | - David Barbado
- Department of Sport Science, Sports Research Centre, Miguel Hernandez University of Elche, Avda. de la Universidad s/n, Elche 03202, Spain; Institute for Health and Biomedical Research (ISABIAL Foundation), Avda. Pintor Baeza, 12 HGUA, Alicante 03550, Spain
| | - Nusratnaaz Shaikh
- Rehabilitation Innovation Centre, Health & Rehabilitation Research Institute, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand
| | - Sue Lord
- Rehabilitation Innovation Centre, Health & Rehabilitation Research Institute, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand
| | - Imran Khan Niazi
- Rehabilitation Innovation Centre, Health & Rehabilitation Research Institute, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand; Centre for Chiropractic Research, New Zealand College of Chiropractic, PO Box 113-044, Newmarket, Auckland 1149, New Zealand; Centre for Sensory-Motor Interaction (SMI), Department of Health Science and Technology, Aalborg University, 9220 Aalborg, Denmark
| | - Denise Taylor
- Rehabilitation Innovation Centre, Health & Rehabilitation Research Institute, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand
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Multimodal Mobility Assessment Predicts Fall Frequency and Severity in Cerebellar Ataxia. CEREBELLUM (LONDON, ENGLAND) 2023; 22:85-95. [PMID: 35122222 PMCID: PMC9883327 DOI: 10.1007/s12311-021-01365-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 12/29/2021] [Indexed: 02/01/2023]
Abstract
This cohort study aims to evaluate the predictive validity of multimodal clinical assessment and quantitative measures of in- and off-laboratory mobility for fall-risk estimation in patients with cerebellar ataxia (CA).Occurrence, severity, and consequences of falling were prospectively assessed for 6 months in 93 patients with hereditary (N = 36) and sporadic or secondary (N = 57) forms of CA and 63 healthy controls. Participants completed a multimodal clinical and functional fall risk assessment, in-laboratory gait examination, and a 2-week inertial sensor-based daily mobility monitoring. Multivariate logistic regression analyses were performed to evaluate the predictive capacity of all clinical and in- and off-laboratory mobility measures with respect to fall (1) status (non-faller vs. faller), (2) frequency (occasional vs. frequent falls), and (3) severity (benign vs. injurious fall) of patients. 64% of patients experienced one or recurrent falls and 65% of these severe fall-related injuries during prospective assessment. Mobility impairments in patients corresponded to a mild-to-moderate ataxic gait disorder. Patients' fall status and frequency could be reliably predicted (78% and 81% accuracy, respectively), primarily based on their retrospective fall status. Clinical scoring of ataxic symptoms and in- and off-laboratory gait and mobility measures improved classification and provided unique information for the prediction of fall severity (84% accuracy).These results encourage a stepwise approach for fall risk assessment in patients with CA: fall history-taking readily and reliably informs the clinician about patients' general fall risk. Clinical scoring and instrument-based mobility measures provide further in-depth information on the risk of recurrent and injurious falling.
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Meddar JM, Ponnapalli A, Azhar R, Turchioe MR, Duran AT, Creber RM. A Structured Review of Commercially Available Cardiac Rehabilitation mHealth Applications Using the Mobile Application Rating Scale. J Cardiopulm Rehabil Prev 2022; 42:141-147. [PMID: 35135963 PMCID: PMC11086945 DOI: 10.1097/hcr.0000000000000667] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
PURPOSE This study systematically evaluated the quality and functionalities of patient-facing, commercially available mobile health (mHealth) apps for cardiac rehabilitation (CR). METHODS We performed our search in two of the most widely used commercial mobile app stores: Apple iTunes Appstore and Google Play Store (Android apps). Six search terms were used to query relevant CR apps: "cardiac rehabilitation," "heart disease and remote therapy," "heart failure exercise," "heart therapy and cardiac recovery," "cardiac recovery," and "heart therapy." App quality was evaluated using the Mobile Application Rating Scale (MARS). App functionality was evaluated using the IQVIA functionality scale, and app content was evaluated against the American Heart Association guidelines for CR. Apps meeting our inclusion criteria were downloaded and evaluated by two to three reviewers, and interclass correlations between reviewers were calculated. RESULTS We reviewed 3121 apps and nine apps met our inclusion criteria. On average, the apps scored a 3.0 on the MARS (5-point Likert scale) for overall quality. The two top-ranking mHealth apps for CR for all three quality, functionality, and consistency with evidence-based guidelines were My Cardiac Coach and Love My Heart for Women, both of which scored ≥4.0 for behavior change. CONCLUSION Overall, the quality and functionality of free apps for mobile CR was high, with two apps performing the best across all three quality categories. High-quality CR apps are available that can expand access to CR for patients with cardiovascular disease.
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Affiliation(s)
- John M Meddar
- Department of Population Health Sciences, New York University Grossman School of Medicine, New York (Mr Meddar); Department of Population Health Sciences, Weill Cornell Medicine, New York, New York (Mr Ponnapalli, Ms Azhar, and Drs Turchioe and Creber); and Center for Behavioral Cardiovascular Health, Columbia Irving Medical Center, New York, New York (Dr Duran)
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da Costa Moraes AA, Duarte MB, Ferreira EV, da Silva Almeida GC, da Rocha Santos EG, Pinto GHL, de Oliveira PR, Amorim CF, Cabral ADS, de Athayde Costa e Silva A, Souza GS, Callegari B. Validity and Reliability of Smartphone App for Evaluating Postural Adjustments during Step Initiation. SENSORS (BASEL, SWITZERLAND) 2022; 22:2935. [PMID: 35458920 PMCID: PMC9030467 DOI: 10.3390/s22082935] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 02/17/2022] [Accepted: 03/03/2022] [Indexed: 06/14/2023]
Abstract
The evaluation of anticipatory postural adjustments (APAs) requires high-cost and complex handling systems, only available at research laboratories. New alternative methods are being developed in this field, on the other hand, to solve this issue and allow applicability in clinic, sport and hospital environments. The objective of this study was to validate an app for mobile devices to measure the APAs during gait initiation by comparing the signals obtained from cell phones using the Momentum app with measurements made by a kinematic system. The center-of-mass accelerations of a total of 20 healthy subjects were measured by the above app, which read the inertial sensors of the smartphones, and by kinematics, with a reflective marker positioned on their lumbar spine. The subjects took a step forward after hearing a command from an experimenter. The variables of the anticipatory phase, prior to the heel-off and the step phase, were measured. In the anticipatory phase, the linear correlation of all variables measured by the two measurement techniques was significant and indicated a high correlation between the devices (APAonset: r = 0.95, p < 0.0001; APAamp: r = 0.71, p = 0.003, and PEAKtime: r = 0.95, p < 0.0001). The linear correlation between the two measurement techniques for the step phase variables measured by ques was also significant (STEPinterval: r = 0.56, p = 0.008; STEPpeak1: r = 0.79, p < 0.0001; and STEPpeak2: r = 0.64, p < 0.0001). The Bland−Altman graphs indicated agreement between instruments with similar behavior as well as subjects within confidence limits and low dispersion. Thus, using the Momentum cell phone application is valid for the assessment of APAs during gait initiation compared to the gold standard instrument (kinematics), proving to be a useful, less complex, and less costly alternative for the assessment of healthy individuals.
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Affiliation(s)
- Anderson Antunes da Costa Moraes
- Human Motricity Studies Laboratory, Av. Generalíssimo Deodoro 01, Belém 66073-000, PA, Brazil; (A.A.d.C.M.); (M.B.D.); (E.V.F.); (G.C.d.S.A.)
| | - Manuela Brito Duarte
- Human Motricity Studies Laboratory, Av. Generalíssimo Deodoro 01, Belém 66073-000, PA, Brazil; (A.A.d.C.M.); (M.B.D.); (E.V.F.); (G.C.d.S.A.)
| | - Eduardo Veloso Ferreira
- Human Motricity Studies Laboratory, Av. Generalíssimo Deodoro 01, Belém 66073-000, PA, Brazil; (A.A.d.C.M.); (M.B.D.); (E.V.F.); (G.C.d.S.A.)
| | - Gizele Cristina da Silva Almeida
- Human Motricity Studies Laboratory, Av. Generalíssimo Deodoro 01, Belém 66073-000, PA, Brazil; (A.A.d.C.M.); (M.B.D.); (E.V.F.); (G.C.d.S.A.)
| | - Enzo Gabriel da Rocha Santos
- Institute of Exact and Natural Sciences, Federal University of Pará, R. Augusto Corrêa, 01, Belém 66093-020, PA, Brazil; (E.G.d.R.S.); (G.H.L.P.)
| | - Gustavo Henrique Lima Pinto
- Institute of Exact and Natural Sciences, Federal University of Pará, R. Augusto Corrêa, 01, Belém 66093-020, PA, Brazil; (E.G.d.R.S.); (G.H.L.P.)
| | - Paulo Rui de Oliveira
- Doctoral and Master’s Program in Physical Therapy, UNICID, 448/475 Cesário Galeno St., São Paulo 03071-000, SP, Brazil; (P.R.d.O.); (C.F.A.)
| | - César Ferreira Amorim
- Doctoral and Master’s Program in Physical Therapy, UNICID, 448/475 Cesário Galeno St., São Paulo 03071-000, SP, Brazil; (P.R.d.O.); (C.F.A.)
- Département des Sciences de la Santé, Programme de Physiothérapie de L’université McGill Offert en Extension à l’UQAC, Saguenay, QC G7H 2B1,Canada
- Physical Therapy and Neuroscience Departments, Wertheims’ Colleges of Nursing and Health Sciences and Medicine, Florida International University (FIU), Miami, FL 33199, USA
| | - André dos Santos Cabral
- Center for Biological and Health Sciences, Pará State University, Tv. Perebebuí, 2623—Marco, Belém 66087-662, PA, Brazil;
| | - Anselmo de Athayde Costa e Silva
- Postgraduate Program in Movement Science, Federal University of Pará, Av. Generalíssimo Deodoro 01, Belém 66073-000, PA, Brazil;
| | - Givago Silva Souza
- Institute of Biological Sciences, Federal University of Pará, R. Augusto Corrêa 01, Belém 66075-110, PA, Brazil;
- Tropical Medicine Nucleus, Federal University of Pará, Avenida Generalíssimo Deodoro 92, Belém 66055-240, PA, Brazil
| | - Bianca Callegari
- Human Motricity Studies Laboratory, Av. Generalíssimo Deodoro 01, Belém 66073-000, PA, Brazil; (A.A.d.C.M.); (M.B.D.); (E.V.F.); (G.C.d.S.A.)
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10
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Validity and Reliability of a Smartphone App for Gait and Balance Assessment. SENSORS 2021; 22:s22010124. [PMID: 35009667 PMCID: PMC8747233 DOI: 10.3390/s22010124] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 12/17/2021] [Accepted: 12/22/2021] [Indexed: 12/28/2022]
Abstract
Advances in technology provide an opportunity to enhance the accuracy of gait and balance assessment, improving the diagnosis and rehabilitation processes for people with acute or chronic health conditions. This study investigated the validity and reliability of a smartphone-based application to measure postural stability and spatiotemporal aspects of gait during four static balance and two gait tasks. Thirty healthy participants (aged 20–69 years) performed the following tasks: (1) standing on a firm surface with eyes opened, (2) standing on a firm surface with eyes closed, (3) standing on a compliant surface with eyes open, (4) standing on a compliant surface with eyes closed, (5) walking in a straight line, and (6) walking in a straight line while turning their head from side to side. During these tasks, the app quantified the participants’ postural stability and spatiotemporal gait parameters. The concurrent validity of the smartphone app with respect to a 3D motion capture system was evaluated using partial Pearson’s correlations (rp) and limits of the agreement (LoA%). The within-session test–retest reliability over three repeated measures was assessed with the intraclass correlation coefficient (ICC) and the standard error of measurement (SEM). One-way repeated measures analyses of variance (ANOVAs) were used to evaluate responsiveness to differences across tasks and repetitions. Periodicity index, step length, step time, and walking speed during the gait tasks and postural stability outcomes during the static tasks showed moderate-to-excellent validity (0.55 ≤ rp ≤ 0.98; 3% ≤ LoA% ≤ 12%) and reliability scores (0.52 ≤ ICC ≤ 0.92; 1% ≤ SEM% ≤ 6%) when the repetition effect was removed. Conversely, step variability and asymmetry parameters during both gait tasks generally showed poor validity and reliability except step length asymmetry, which showed moderate reliability (0.53 ≤ ICC ≤ 0.62) in both tasks when the repetition effect was removed. Postural stability and spatiotemporal gait parameters were found responsive (p < 0.05) to differences across tasks and test repetitions. Along with sound clinical judgement, the app can potentially be used in clinical practice to detect gait and balance impairments and track the effectiveness of rehabilitation programs. Further evaluation and refinement of the app in people with significant gait and balance deficits is needed.
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11
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Zhou H, Nguyen H, Enriquez A, Morsy L, Curtis M, Piser T, Kenney C, Stephen CD, Gupta AS, Schmahmann JD, Vaziri A. Assessment of gait and balance impairment in people with spinocerebellar ataxia using wearable sensors. Neurol Sci 2021; 43:2589-2599. [PMID: 34664180 DOI: 10.1007/s10072-021-05657-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 10/05/2021] [Indexed: 11/30/2022]
Abstract
OBJECTIVE To explore the use of wearable sensors for objective measurement of motor impairment in spinocerebellar ataxia (SCA) patients during clinical assessments of gait and balance. METHODS In total, 14 patients with genetically confirmed SCA (mean age 61.6 ± 8.6 years) and 4 healthy controls (mean age 49.0 ± 16.4 years) were recruited through the Massachusetts General Hospital (MGH) Ataxia Center. Participants donned seven inertial sensors while performing two independent trials of gait and balance assessments from the Scale for the Assessment and Rating of Ataxia (SARA) and Brief Ataxia Rating Scale (BARS2). Univariate analysis was used to identify sensor-derived metrics from wearable sensors that discriminate motor function between the SCA and control groups. Multivariate linear regression models were used to estimate the subjective in-person SARA/BARS2 ratings. Spearman correlation coefficients were used to evaluate the performance of the model. RESULTS Stride length variability, stride duration, cadence, stance phase, pelvis sway, and turn duration were different between SCA and controls (p < 0.05). Similarly, sway and sway velocity of the ankle, hip, and center of mass differentiated SCA and controls (p < 0.05). Using these features, linear regression models showed moderate-to-strong correlation with clinical scores from the in-person rater during SARA assessments of gait (r = 0.73, p = 0.003) and stance (r = 0.90, p < 0.001) and the BARS2 gait assessment (r = 0.74, p = 0.003). CONCLUSION This study demonstrates that sensor-derived metrics can potentially be used to estimate the level of motor impairment in patient with SCA quickly and objectively. Thus, digital biomarkers from wearable sensors have the potential to be an integral tool for SCA clinical trials and care.
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Affiliation(s)
- He Zhou
- BioSensics LLC, Newton, MA, USA
| | | | | | | | | | | | | | - Christopher D Stephen
- Ataxia Center, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Anoopum S Gupta
- Ataxia Center, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Jeremy D Schmahmann
- Ataxia Center, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
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12
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Vyšata O, Ťupa O, Procházka A, Doležal R, Cejnar P, Bhorkar AM, Dostál O, Vališ M. Classification of Ataxic Gait. SENSORS 2021; 21:s21165576. [PMID: 34451018 PMCID: PMC8402252 DOI: 10.3390/s21165576] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 08/08/2021] [Accepted: 08/12/2021] [Indexed: 12/17/2022]
Abstract
Gait disorders accompany a number of neurological and musculoskeletal disorders that significantly reduce the quality of life. Motion sensors enable high-quality modelling of gait stereotypes. However, they produce large volumes of data, the evaluation of which is a challenge. In this publication, we compare different data reduction methods and classification of reduced data for use in clinical practice. The best accuracy achieved between a group of healthy individuals and patients with ataxic gait extracted from the records of 43 participants (23 ataxic, 20 healthy), forming 418 segments of straight gait pattern, is 98% by random forest classifier preprocessed by t-distributed stochastic neighbour embedding.
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Affiliation(s)
- Oldřich Vyšata
- Department of Neurology, Faculty of Medicine in Hradec Králové, Charles University, 500 03 Hradec Králové, Czech Republic; (A.M.B.); (O.D.); (M.V.)
- Correspondence:
| | - Ondřej Ťupa
- Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, 166 28 Praha 6, Czech Republic; (O.Ť.); (A.P.); (P.C.)
| | - Aleš Procházka
- Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, 166 28 Praha 6, Czech Republic; (O.Ť.); (A.P.); (P.C.)
- Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, 160 00 Prague 6, Czech Republic
| | - Rafael Doležal
- Department of Chemistry, Faculty of Science, University of Hradec Králové, 500 03 Hradec Králové, Czech Republic;
| | - Pavel Cejnar
- Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, 166 28 Praha 6, Czech Republic; (O.Ť.); (A.P.); (P.C.)
| | - Aprajita Milind Bhorkar
- Department of Neurology, Faculty of Medicine in Hradec Králové, Charles University, 500 03 Hradec Králové, Czech Republic; (A.M.B.); (O.D.); (M.V.)
| | - Ondřej Dostál
- Department of Neurology, Faculty of Medicine in Hradec Králové, Charles University, 500 03 Hradec Králové, Czech Republic; (A.M.B.); (O.D.); (M.V.)
| | - Martin Vališ
- Department of Neurology, Faculty of Medicine in Hradec Králové, Charles University, 500 03 Hradec Králové, Czech Republic; (A.M.B.); (O.D.); (M.V.)
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13
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The Contribution of Machine Learning in the Validation of Commercial Wearable Sensors for Gait Monitoring in Patients: A Systematic Review. SENSORS 2021; 21:s21144808. [PMID: 34300546 PMCID: PMC8309920 DOI: 10.3390/s21144808] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 07/05/2021] [Accepted: 07/08/2021] [Indexed: 12/28/2022]
Abstract
Gait, balance, and coordination are important in the development of chronic disease, but the ability to accurately assess these in the daily lives of patients may be limited by traditional biased assessment tools. Wearable sensors offer the possibility of minimizing the main limitations of traditional assessment tools by generating quantitative data on a regular basis, which can greatly improve the home monitoring of patients. However, these commercial sensors must be validated in this context with rigorous validation methods. This scoping review summarizes the state-of-the-art between 2010 and 2020 in terms of the use of commercial wearable devices for gait monitoring in patients. For this specific period, 10 databases were searched and 564 records were retrieved from the associated search. This scoping review included 70 studies investigating one or more wearable sensors used to automatically track patient gait in the field. The majority of studies (95%) utilized accelerometers either by itself (N = 17 of 70) or embedded into a device (N = 57 of 70) and/or gyroscopes (51%) to automatically monitor gait via wearable sensors. All of the studies (N = 70) used one or more validation methods in which “ground truth” data were reported. Regarding the validation of wearable sensors, studies using machine learning have become more numerous since 2010, at 17% of included studies. This scoping review highlights the current state of the ability of commercial sensors to enhance traditional methods of gait assessment by passively monitoring gait in daily life, over long periods of time, and with minimal user interaction. Considering our review of the last 10 years in this field, machine learning approaches are algorithms to be considered for the future. These are in fact data-based approaches which, as long as the data collected are numerous, annotated, and representative, allow for the training of an effective model. In this context, commercial wearable sensors allowing for increased data collection and good patient adherence through efforts of miniaturization, energy consumption, and comfort will contribute to its future success.
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14
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Grobe-Einsler M, Taheri Amin A, Faber J, Schaprian T, Jacobi H, Schmitz-Hübsch T, Diallo A, Tezenas du Montcel S, Klockgether T. Development of SARA home , a New Video-Based Tool for the Assessment of Ataxia at Home. Mov Disord 2021; 36:1242-1246. [PMID: 33433030 DOI: 10.1002/mds.28478] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 11/20/2020] [Accepted: 12/03/2020] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Clinical scales such as the Scale for the Assessment and Rating of Ataxia (SARA) cannot be used to study ataxia at home or to assess daily fluctuations. The objective of the current study was to develop a video-based instrument, SARAhome , for measuring ataxia severity easily and independently at home. METHODS Based on feasibility of self-application, we selected 5 SARA items (gait, stance, speech, nose-finger test, fast alternating hand movements) for SARAhome (range, 0-28). We compared SARAhome items with total SARA scores in 526 patients with spinocerebellar ataxia types 1, 2, 3, and 6 from the EUROSCA natural history study. To prospectively validate the SARAhome , we directly compared the self-applied SARAhome and the conventional SARA in 50 ataxia patients. To demonstrate feasibility of independent home recordings in a pilot study, 12 ataxia patients were instructed to obtain a video each morning and evening over a period of 14 days. All videos were rated offline by a trained rater. RESULTS SARAhome extracted from the EUROSCA baseline data was highly correlated with conventional SARA (r = 0.9854, P < 0.0001). In the prospective validation study, the SARAhome was highly correlated with the conventional SARA (r = 0.9254, P < 0.0001). Five of 12 participants of the pilot study obtained a complete set of 28 evaluable videos. Seven participants obtained 13-27 videos. The intraindividual differences between the lowest and highest SARAhome scores ranged from 1 to 5.5. CONCLUSION The SARAhome and the conventional SARA are highly correlated. Application at home is feasible. There was a considerable degree of intraindividual variability of the SARAhome scores. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Marcus Grobe-Einsler
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurology, University Hospital Bonn, Bonn, Germany
| | - Arian Taheri Amin
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurology, University Hospital Bonn, Bonn, Germany
| | - Jennifer Faber
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurology, University Hospital Bonn, Bonn, Germany
| | - Tamara Schaprian
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Heike Jacobi
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurology, University Hospital of Heidelberg, Heidelberg, Germany
| | - Tanja Schmitz-Hübsch
- Experimental and Clinical Research Center, A Cooperation of Charité - Universitätsmedizin Berlin and Max-Delbrueck Center for Molecular Medicine, Berlin, Germany
| | - Alhassane Diallo
- INSERM U1137-IAME, Department of Biostatistical Modeling, Clinical Investigation, Pharmacometrics in Infectious Diseases, University Paris Diderot, Paris, France.,Department of Epidemiology, Biostatistics and Clinical Research, APHP Bichât-Claude-Bernard Hospital, Paris, France
| | - Sophie Tezenas du Montcel
- Sorbonne Université, Institut, Pierre Louis d'Epidémiologie et de Santé Publique, Assistance Publique-Hôpitaux de Paris, Institut National de la Santé et de la Recherche Médicale, University Hospital Pitié-Salpêtrière, Paris, France
| | - Thomas Klockgether
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurology, University Hospital Bonn, Bonn, Germany
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