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Sapienza S, Tsurkalenko O, Giraitis M, Mejia AC, Zelimkhanov G, Schwaninger I, Klucken J. Assessing the clinical utility of inertial sensors for home monitoring in Parkinson's disease: a comprehensive review. NPJ Parkinsons Dis 2024; 10:161. [PMID: 39164257 PMCID: PMC11335938 DOI: 10.1038/s41531-024-00755-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 07/24/2024] [Indexed: 08/22/2024] Open
Abstract
This review screened 296 articles on wearable sensors for home monitoring of people with Parkinson's Disease within the PubMed Database, from January 2017 to May 2023. A three-level maturity framework was applied for classifying the aims of 59 studies included: demonstrating technical efficacy, diagnostic sensitivity, or clinical utility. As secondary analysis, user experience (usability and patient adherence) was evaluated. The evidences provided by the studies were categorized and stratified according to the level of maturity. Our results indicate that approximately 75% of articles investigated diagnostic sensitivity, i.e. correlation of sensor-data with clinical parameters. Evidence of clinical utility, defined as improvement on health outcomes or clinical decisions after the use of the wearables, was found only in nine papers. A third of the articles included reported evidence of user experience. Future research should focus more on clinical utility, to facilitate the translation of research results within the management of Parkinson's Disease.
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Affiliation(s)
- Stefano Sapienza
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Luxembourg Institute of Health (LIH), Strassen, Luxembourg
| | - Olena Tsurkalenko
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Luxembourg Institute of Health (LIH), Strassen, Luxembourg
- Centre Hospitalier de Luxembourg (CHL), Rollengergronn-belair-nord, Luxembourg
| | - Marijus Giraitis
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Luxembourg Institute of Health (LIH), Strassen, Luxembourg
- Centre Hospitalier de Luxembourg (CHL), Rollengergronn-belair-nord, Luxembourg
| | - Alan Castro Mejia
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Luxembourg Institute of Health (LIH), Strassen, Luxembourg
| | - Gelani Zelimkhanov
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Luxembourg Institute of Health (LIH), Strassen, Luxembourg
- Centre Hospitalier de Luxembourg (CHL), Rollengergronn-belair-nord, Luxembourg
| | - Isabel Schwaninger
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Luxembourg Institute of Health (LIH), Strassen, Luxembourg
| | - Jochen Klucken
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg.
- Luxembourg Institute of Health (LIH), Strassen, Luxembourg.
- Centre Hospitalier de Luxembourg (CHL), Rollengergronn-belair-nord, Luxembourg.
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Lavorgna L, Maida E, Reinhard C, Cras P, Reetz K, Molnar MJ, Nonnekes J, Medijainen K, Summa S, Diserens K, Petrarca M, Albanese A, Leocani L, Delussi M, Vinciguerra C, Pagliano E, Kubica J, Lallemant P, Wenning G, Sival D, Groleger Srsen K, Bertini ES, Lopane G, Boesch S, Bonavita S, Crosiers D, Muresanu D, Timmann D, Federico A. The Growing Role of Telerehabilitation and Teleassessment in the Management of Movement Disorders in Rare Neurological Diseases: A Scoping Review. Telemed J E Health 2024. [PMID: 38946606 DOI: 10.1089/tmj.2023.0702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/02/2024] Open
Abstract
Background: People with rare neurological diseases (RNDs) often experience symptoms related to movement disorders, requiring a multidisciplinary approach, including rehabilitation. Telemedicine applied to rehabilitation and symptom monitoring may be suitable to ensure treatment consistency and personalized intervention. The objective of this scoping review aimed to emphasize the potential role of telerehabilitation and teleassessment in managing movement disorders within RNDs. By providing a systematic overview of the available literature, we sought to highlight potential interventions, outcomes, and critical issues. Methods: A literature search was conducted on PubMed, Google Scholar, IEEE, and Scopus up to March 2024. Two inclusion criteria were followed: (1) papers focusing on telerehabilitation and teleassessment and (2) papers dealing with movement disorders in RNDs. Results: Eighteen papers fulfilled the inclusion criteria. The main interventions were home-based software and training programs, exergames, wearable sensors, smartphone applications, virtual reality and digital music players for telerehabilitation; wearable sensors, mobile applications, and patient home video for teleassessment. Key findings revealed positive outcomes in gait, balance, limb disability, and in remote monitoring. Limitations include small sample sizes, short intervention durations, and the lack of standardized protocols. Conclusion: This review highlighted the potential of telerehabilitation and teleassessment in addressing movement disorders within RNDs. Data indicate that these modalities may play a major role in supporting conventional programs. Addressing limitations through multicenter studies, longer-term follow-ups, and standardized protocols is essential. These measures are essential for improving remote rehabilitation and assessment, contributing to an improved quality of life for people with RNDs.
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Affiliation(s)
- Luigi Lavorgna
- Department of Advanced Medical and Surgical Sciences Napoli, University of Campania Luigi Vanvitelli, Napoli, Italy
| | - Elisabetta Maida
- Department of Advanced Medical and Surgical Sciences Napoli, University of Campania Luigi Vanvitelli, Napoli, Italy
| | - Carola Reinhard
- Centre for Rare Diseases and Institute of Medical Genetics and Applied Genomics, University Hospitals Tubingen, Tubingen, Germany
| | - Patrick Cras
- Department of Neurology, University Hospital Antwerp, Edegem, Belgium
- Translational Neurosciences, Faculty of Medicine and Health Sciences, University Hospital Antwerp, Edegem, Belgium
| | - Kathrin Reetz
- Department of Neurology, RWTH Aachen University, Aachen, Germany
- Forschungszentrum Julich GmbH, JARA Institute Molecular Neuroscience and Neuroimaging, Julich, Germany
| | - Maria Judit Molnar
- Institute of Genomic Medicine and Rare Disorders, Semmelweis University, Budapest, Hungary
| | - Jorik Nonnekes
- Department of Rehabilitation, Sint Maartenskliniek, Nijmegen, The Netherlands
| | | | - Susanna Summa
- Department of Neurorehabilitation and Robotics, Movement Analysis and Robotics Laboratory (MARLab), Bambino Gesu Pediatric Hospital, Roma, Italy
| | | | - Maurizio Petrarca
- Department of Neurorehabilitation and Robotics, Movement Analysis and Robotics Laboratory (MARLab), Bambino Gesu Pediatric Hospital, Roma, Italy
| | | | - Letizia Leocani
- Institute of Experimental Neurology and Neurological Department, San Raffaele Hospital, Milano, Italy
| | - Marianna Delussi
- Department of translational biomedicine and neuroscience "DiBraiN", University of Bari Aldo Moro, Bari, Italy
| | | | | | - Jadwiga Kubica
- Institute of Physiotherapy, Faculty of Health Science, Jagiellonian University Medical College, Krakow, Poland
| | - Pauline Lallemant
- Paris Brain Institute (ICM Institut du Cerveau), INSERM, CNRS, Assistance Publique-Hôpitaux de Paris (APHP), University Hospital Pitié-Salpêtrière, Sorbonne Université, Paris, France
- Pediatric Physical Medicine and Rehabilitation Department, Sorbonne Université, Paris, France
| | - Gregor Wenning
- Department of Neurology and Neurosurgery, Medical University Innsbruck, Innsbruck, Austria
| | - Deborah Sival
- Department of Pediatrics, University of Groningen, Beatrix Children's Hospital, Groningen, The Netherlands
| | - Katja Groleger Srsen
- Rehabilitation Institute of Republic Slovenia, University of Ljubljana, Ljubljana, Slovenia
- Medical faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Enrico Silvio Bertini
- Unit of Neuromuscular and Neurodegenerative Disorders, IRCCS, Ospedale Pediatrico Bambino Gesù, Rome, Italy
| | - Giovanna Lopane
- IRCCS Istituto Delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Sylvia Boesch
- Department of Neurology and Neurosurgery, Center for rare movement disorders, Innsbruck Medical University, Innsbruck, Austria
| | - Simona Bonavita
- Department of Advanced Medical and Surgical Sciences Napoli, University of Campania Luigi Vanvitelli, Napoli, Italy
| | - David Crosiers
- Department of Neurology, University Hospital Antwerp, Edegem, Belgium
- Translational Neurosciences, Faculty of Medicine and Health Sciences, University Hospital Antwerp, Edegem, Belgium
| | - Dafin Muresanu
- Department of Neuroscience, Iuliu Hagieganu University of Medicine and Pharmacy Faculty of Medicine, Cluj Napoca, Romania
- RoNeuro Institute for Neurological Research and Diagnostic, Cluj-Napoca, Romania
| | - Dagmar Timmann
- Department of Neurology, Center for Translational Neuro, and Behavioral Sciences (C-TNBS), University Hospital Essen, Essen, Germnany
| | - Antonio Federico
- Dept. Medicine, Surgery and Neurosciences, Siena University Hospital, Siena, Italy
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Uddin M, Ganapathy K, Syed-Abdul S. Digital Technology Enablers of Tele-Neurorehabilitation in Pre- and Post-COVID-19 Pandemic Era - A Scoping Review. Int J Telerehabil 2024; 16:e6611. [PMID: 39022438 PMCID: PMC11250154 DOI: 10.5195/ijt.2024.6611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2024] Open
Abstract
Neurorehabilitation (NR), a major component of neurosciences, is the process of restoring a patient's damaged/disorganized neurological function, through training, therapy, and education, while focusing on patient's independence and well-being. Since the advent of the COVID-19 pandemic, various applications of telecare and telehealth services surged drastically and became an integral part of current clinical practices. Tele-Neurorehabilitation (TNR) is one of such applications. When rehabilitation services were disrupted globally due to lockdown and travel restrictions, the importance of TNR was recognized, especially in developed, low, and middle-income countries. With exponential deployment of telehealth interventions in neurosciences, TNR has become a distinct stand-alone sub-specialty of neurosciences and telehealth. Digital technologies, such as wearables, robotics, and Virtual Reality (VR) have enabled TNR to improve the quality of patients' lives. Providing NR remotely using digital technologies and customized digital devices is now a reality, and likely to be the new norm soon. This article provides an overview of the needs, utilization, and deployment of TNR, and focuses on digital technology enablers of TNR in pre- and post-COVID-19 pandemic era.
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Affiliation(s)
- Mohy Uddin
- Research Quality Management Section, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Krishnan Ganapathy
- Distinguished Visiting Professor IIT Kanpur & Director Apollo Telemedicine Networking Foundation, India
| | - Shabbir Syed-Abdul
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
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Lebleu J, Daniels K, Pauwels A, Dekimpe L, Mapinduzi J, Poilvache H, Bonnechère B. Incorporating Wearable Technology for Enhanced Rehabilitation Monitoring after Hip and Knee Replacement. SENSORS (BASEL, SWITZERLAND) 2024; 24:1163. [PMID: 38400321 PMCID: PMC10892564 DOI: 10.3390/s24041163] [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: 12/22/2023] [Revised: 01/20/2024] [Accepted: 02/08/2024] [Indexed: 02/25/2024]
Abstract
Osteoarthritis (OA) poses a growing challenge for the aging population, especially in the hip and knee joints, contributing significantly to disability and societal costs. Exploring the integration of wearable technology, this study addresses the limitations of traditional rehabilitation assessments in capturing real-world experiences and dynamic variations. Specifically, it focuses on continuously monitoring physical activity in hip and knee OA patients using automated unsupervised evaluations within the rehabilitation process. We analyzed data from 1144 patients who used a mobile health application after surgery; the activity data were collected using the Garmin Vivofit 4. Several parameters, such as the total number of steps per day, the peak 6-minute consecutive cadence (P6MC) and peak 1-minute cadence (P1M), were computed and analyzed on a daily basis. The results indicated that cadence-based measurements can effectively, and earlier, differ among patients with hip and knee conditions, as well as in the recovery process. Comparisons based on recovery status and type of surgery reveal distinctive trajectories, emphasizing the effectiveness of P6MC and P1M in detecting variations earlier than total steps per day. Furthermore, cadence-based measurements showed a lower inter-day variability (40%) compared to the total number of steps per day (80%). Automated assessments, including P1M and P6MC, offer nuanced insights into the patients' dynamic activity profiles.
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Affiliation(s)
- Julien Lebleu
- moveUp, 1000 Brussels, Belgium; (J.L.); (A.P.); (L.D.)
| | - Kim Daniels
- Department of PXL—Healthcare, PXL University of Applied Sciences and Arts, 3500 Hasselt, Belgium;
- REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium;
| | | | - Lucie Dekimpe
- moveUp, 1000 Brussels, Belgium; (J.L.); (A.P.); (L.D.)
| | - Jean Mapinduzi
- REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium;
- Filière de Kinésithérapie et Réadaptation, Département des Sciences Clinique, Institut National de la Santé Publique, 6807 Bujumbura, Burundi
| | - Hervé Poilvache
- Orthopedic Surgery Department, CHIREC, 1420 Braine-l’Alleud, Belgium
| | - Bruno Bonnechère
- Department of PXL—Healthcare, PXL University of Applied Sciences and Arts, 3500 Hasselt, Belgium;
- REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium;
- Technology-Supported and Data-Driven Rehabilitation, Data Sciences Institute, Hasselt University, 3590 Diepenbeek, Belgium
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Nunes AS, Pawlik M, Mishra RK, Waddell E, Coffey M, Tarolli CG, Schneider RB, Dorsey ER, Vaziri A, Adams JL. Digital assessment of speech in Huntington disease. Front Neurol 2024; 15:1310548. [PMID: 38322583 PMCID: PMC10844459 DOI: 10.3389/fneur.2024.1310548] [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: 10/09/2023] [Accepted: 01/08/2024] [Indexed: 02/08/2024] Open
Abstract
Background Speech changes are an early symptom of Huntington disease (HD) and may occur prior to other motor and cognitive symptoms. Assessment of HD commonly uses clinician-rated outcome measures, which can be limited by observer variability and episodic administration. Speech symptoms are well suited for evaluation by digital measures which can enable sensitive, frequent, passive, and remote administration. Methods We collected audio recordings using an external microphone of 36 (18 HD, 7 prodromal HD, and 11 control) participants completing passage reading, counting forward, and counting backwards speech tasks. Motor and cognitive assessments were also administered. Features including pausing, pitch, and accuracy were automatically extracted from recordings using the BioDigit Speech software and compared between the three groups. Speech features were also analyzed by the Unified Huntington Disease Rating Scale (UHDRS) dysarthria score. Random forest machine learning models were implemented to predict clinical status and clinical scores from speech features. Results Significant differences in pausing, intelligibility, and accuracy features were observed between HD, prodromal HD, and control groups for the passage reading task (e.g., p < 0.001 with Cohen'd = -2 between HD and control groups for pause ratio). A few parameters were significantly different between the HD and control groups for the counting forward and backwards speech tasks. A random forest classifier predicted clinical status from speech tasks with a balanced accuracy of 73% and an AUC of 0.92. Random forest regressors predicted clinical outcomes from speech features with mean absolute error ranging from 2.43-9.64 for UHDRS total functional capacity, motor and dysarthria scores, and explained variance ranging from 14 to 65%. Montreal Cognitive Assessment scores were predicted with mean absolute error of 2.3 and explained variance of 30%. Conclusion Speech data have the potential to be a valuable digital measure of HD progression, and can also enable remote, frequent disease assessment in prodromal HD and HD. Clinical status and disease severity were predicted from extracted speech features using random forest machine learning models. Speech measurements could be leveraged as sensitive marker of clinical onset and disease progression in future clinical trials.
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Affiliation(s)
| | - Meghan Pawlik
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, United States
| | | | - Emma Waddell
- Warren Alpert Medical School of Brown University, Providence, RI, United States
| | - Madeleine Coffey
- Donald and Barbara Zucker School of Medicine, Uniondale, NY, United States
| | - Christopher G. Tarolli
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, United States
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, United States
| | - Ruth B. Schneider
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, United States
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, United States
| | - E. Ray Dorsey
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, United States
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, United States
| | | | - Jamie L. Adams
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, United States
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, United States
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Dinesh K, White N, Baker L, Sowden JE, Behrens-Spraggins S, Wood E, Charles J, Herrmann DN, Sharma G, Eichinger K. Disease-specific wearable sensor algorithms for profiling activity, gait, and balance in individuals with Charcot-Marie-Tooth disease type 1A. J Peripher Nerv Syst 2023; 28:368-381. [PMID: 37209301 DOI: 10.1111/jns.12562] [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: 03/20/2023] [Revised: 05/05/2023] [Accepted: 05/15/2023] [Indexed: 05/22/2023]
Abstract
BACKGROUND/AIMS Charcot-Marie-Tooth Disease type 1A (CMT1A), the most common inherited peripheral neuropathy, is characterized by progressive sensory loss and weakness, which results in impaired mobility. Increased understanding of the genetics and pathophysiology of CMT1A has led to development of potential therapeutic agents, necessitating clinical trial readiness. Wearable sensors may provide useful outcome measures for future trials. METHODS Individuals with CMT1A and unaffected controls were recruited for this 12-month study. Participants wore sensors for in-clinic assessments and at-home, from which activity, gait, and balance metrics were derived. Mann-Whitney U tests were used to analyze group differences for activity, gait, and balance parameters. Test-retest reliability of gait and balance parameters and correlations of these parameters with clinical outcome assessments (COAs) were examined. RESULTS Thirty individuals, 15 CMT1A, and 15 controls, participated. Gait and balance metrics demonstrated moderate to excellent reliability. CMT1A participants had longer step durations (p < .001), shorter step lengths (p = .03), slower gait speeds (p < .001), and greater postural sway (p < .001) than healthy controls. Moderate correlations were found between CMT-Functional Outcome Measure and step length (r = -0.59; p = .02), and gait speed (r = 0.64; p = .01); 11 out of 15 CMT1A participants demonstrated significant increases in stride duration between the first and last quarter of the 6-min walk test, suggesting fatigue. INTERPRETATION In this initial study, gait and balance metrics derived from wearable sensors were reliable and associated with COAs in individuals with CMT1A. Larger longitudinal studies are needed to confirm our findings and evaluate sensitivity and utility of these disease-specific algorithms for clinical trial use.
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Affiliation(s)
- K Dinesh
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, New York, USA
| | - N White
- Department of Neurology, University of Rochester Medical Center, Rochester, New York, USA
| | - L Baker
- Department of Neurology, University of Rochester Medical Center, Rochester, New York, USA
| | - J E Sowden
- Department of Neurology, University of Rochester Medical Center, Rochester, New York, USA
| | - S Behrens-Spraggins
- Department of Neurology, University of Rochester Medical Center, Rochester, New York, USA
| | - E Wood
- Department of Neurology, University of Rochester Medical Center, Rochester, New York, USA
| | - J Charles
- Department of Neurology, University of Rochester Medical Center, Rochester, New York, USA
| | - D N Herrmann
- Department of Neurology, University of Rochester Medical Center, Rochester, New York, USA
| | - G Sharma
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, New York, USA
| | - K Eichinger
- Department of Neurology, University of Rochester Medical Center, Rochester, New York, USA
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Poleur M, Markati T, Servais L. The use of digital outcome measures in clinical trials in rare neurological diseases: a systematic literature review. Orphanet J Rare Dis 2023; 18:224. [PMID: 37533072 PMCID: PMC10398976 DOI: 10.1186/s13023-023-02813-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 07/07/2023] [Indexed: 08/04/2023] Open
Abstract
Developing drugs for rare diseases is challenging, and the precision and objectivity of outcome measures is critical to this process. In recent years, a number of technologies have increasingly been used for remote monitoring of patient health. We report a systematic literature review that aims to summarize the current state of progress with regard to the use of digital outcome measures for real-life motor function assessment of patients with rare neurological diseases. Our search of published literature identified 3826 records, of which 139 were included across 27 different diseases. This review shows that use of digital outcome measures for motor function outside a clinical setting is feasible and employed in a broad range of diseases, although we found few outcome measures that have been robustly validated and adopted as endpoints in clinical trials. Future research should focus on validation of devices, variables, and algorithms to allow for regulatory qualification and widespread adoption.
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Affiliation(s)
- Margaux Poleur
- Department of Neurology, Liege University Hospital Center, Liège, Belgium.
- Neuromuscular Reference Center, Division of Paediatrics University, Hospital University of Liège, Liège, Belgium.
- Centre de Référence des Maladies Neuromusculaires, Centre Hospitalier Régional de la Citadelle, Boulevard du 12eme de Ligne 1, 4000, Liège, Belgium.
| | - Theodora Markati
- MDUK Oxford Neuromuscular Centre and NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Laurent Servais
- MDUK Oxford Neuromuscular Centre and NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Neuromuscular Reference Center, Division of Paediatrics University, Hospital University of Liège, Liège, Belgium
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Rabuffetti M, De Giovannini E, Carpinella I, Lencioni T, Fornia L, Ferrarin M. Association of 7-Day Profiles of Motor Activity in Marital Dyads with One Component Affected by Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2023; 23:1087. [PMID: 36772127 PMCID: PMC9921738 DOI: 10.3390/s23031087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/12/2023] [Accepted: 01/16/2023] [Indexed: 06/18/2023]
Abstract
(1) Background: A noticeable association between the motor activity (MA) profiles of persons living together has been found in previous studies. Social actigraphy methods have shown that this association, in marital dyads composed of healthy individuals, is greater than that of a single person compared to itself. This study aims at verifying the association of MA profiles in dyads where one component is affected by Parkinson's disease (PD). (2) Methods: Using a wearable sensor-based social actigraphy approach, we continuously monitored, for 7 days, the activities of 27 marital dyads including one component with PD. (3) Results: The association of motor activity profiles within a marital dyad (cross-correlation coefficient 0.344) is comparable to the association of any participant with themselves (0.325). However, when considering the disease severity quantified by the UPDRS III score, it turns out that the less severe the symptoms, the more associated are the MA profiles. (4) Conclusions: Our findings suggest that PD treatment could be improved by leveraging the MA of the healthy spouse, thus promoting lifestyles also beneficial for the component affected by PD. The actigraphy approach provided valuable information on habitual functions and motor fluctuations, and could be useful in investigating the response to treatment.
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Affiliation(s)
| | - Ennio De Giovannini
- Centro Medico Riabilita Cooperativa Sociale Mano Amica Onlus, 36015 Schio, Italy
| | | | | | - Luca Fornia
- IRCCS Fondazione Don Carlo Gnocchi, 20148 Milano, Italy
- Department of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, 20133 Milano, Italy
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Bonnechère B, Timmermans A, Michiels S. Current Technology Developments Can Improve the Quality of Research and Level of Evidence for Rehabilitation Interventions: A Narrative Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23020875. [PMID: 36679672 PMCID: PMC9866361 DOI: 10.3390/s23020875] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/19/2022] [Accepted: 01/05/2023] [Indexed: 06/01/2023]
Abstract
The current important limitations to the implementation of Evidence-Based Practice (EBP) in the rehabilitation field are related to the validation process of interventions. Indeed, most of the strict guidelines that have been developed for the validation of new drugs (i.e., double or triple blinded, strict control of the doses and intensity) cannot-or can only partially-be applied in rehabilitation. Well-powered, high-quality randomized controlled trials are more difficult to organize in rehabilitation (e.g., longer duration of the intervention in rehabilitation, more difficult to standardize the intervention compared to drug validation studies, limited funding since not sponsored by big pharma companies), which reduces the possibility of conducting systematic reviews and meta-analyses, as currently high levels of evidence are sparse. The current limitations of EBP in rehabilitation are presented in this narrative review, and innovative solutions are suggested, such as technology-supported rehabilitation systems, continuous assessment, pragmatic trials, rehabilitation treatment specification systems, and advanced statistical methods, to tackle the current limitations. The development and implementation of new technologies can increase the quality of research and the level of evidence supporting rehabilitation, provided some adaptations are made to our research methodology.
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Affiliation(s)
- Bruno Bonnechère
- REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium
- Technology-Supported and Data-Driven Rehabilitation, Data Science Institute, Hasselt University, 3590 Diepenbeek, Belgium
| | - Annick Timmermans
- REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium
| | - Sarah Michiels
- REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium
- Department of Otorhinolaryngology, Antwerp University Hospital, 2650 Edegem, Belgium
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Bonnechère B. Integrating Rehabilomics into the Multi-Omics Approach in the Management of Multiple Sclerosis: The Way for Precision Medicine? Genes (Basel) 2022; 14:63. [PMID: 36672802 PMCID: PMC9858788 DOI: 10.3390/genes14010063] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/05/2022] [Accepted: 12/22/2022] [Indexed: 12/28/2022] Open
Abstract
Over recent years, significant improvements have been made in the understanding of (epi)genetics and neuropathophysiological mechanisms driving the different forms of multiple sclerosis (MS). For example, the role and importance of the bidirectional communications between the brain and the gut-also referred to as the gut-brain axis-in the pathogenesis of MS is receiving increasing interest in recent years and is probably one of the most promising areas of research for the management of people with MS. However, despite these important advances, it must be noted that these data are not-yet-used in rehabilitation. Neurorehabilitation is a cornerstone of MS patient management, and there are many techniques available to clinicians and patients, including technology-supported rehabilitation. In this paper, we will discuss how new findings on the gut microbiome could help us to better understand how rehabilitation can improve motor and cognitive functions. We will also see how the data gathered during the rehabilitation can help to get a better diagnosis of the patients. Finally, we will discuss how these new techniques can better guide rehabilitation to lead to precision rehabilitation and ultimately increase the quality of patient care.
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Affiliation(s)
- Bruno Bonnechère
- REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium;
- Technology-Supported and Data-Driven Rehabilitation, Data Science Institute, Hasselt University, 3590 Diepenbeek, Belgium
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11
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Lee KH, Lee JY, Kim B. Information and communication technology for physical activity in persons living with dementia: A systematic review with implications for evidence-based practice. Worldviews Evid Based Nurs 2022; 19:275-281. [PMID: 35635249 DOI: 10.1111/wvn.12591] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 01/30/2022] [Accepted: 04/03/2022] [Indexed: 11/30/2022]
Abstract
BACKGROUND Persons living with dementia often encounter many difficulties in their community due to functional limitations. Information and Communication Technology (ICT) could be useful to monitor changes in their physical function. However, there is a lack of systematic reviews about using ICT for physical activity. AIM This review aimed to synthesize the literature regarding the use of ICT to monitor the physical activity of persons living with dementia. METHODS A systematic search was conducted in five electronic databases using search terms derived from the Patient, Intervention, Comparison, Outcome (PICO) framework. We included articles published in English from 2011 to 2021. Quality of the included studies was evaluated by two independent authors using the Mixed Methods Appraisal Tool (MMAT). RESULTS Thirty-three quantitative studies were included for review. Included studies showed fairly good quality in the MMAT evaluation. Wearable devices were mainly employed (88%). The ICTs were used to objectively measure physical activity, activity status, gait, and circadian rhythm. ICTs have been utilized for four purposes: (1) comparing physical activity within the dementia subgroups or with the normal group, (2) exploring the relationship with other variables, 3) examining the experimental study's outcomes, and (4) checking the sensors' feasibility. The results demonstrated that ICT devices were feasible to use in persons living with dementia in the community, helpful for monitoring the physical activity of persons living with dementia, and useful for improving physical activity when properly incorporated in care planning. LINKING EVIDENCE TO ACTION ICTs can help gather objective data regarding the type, intensity, and level of physical activity in persons living with dementia without time constraints. Also, ICTs use in persons living with dementia in the community was acceptable. We suggest future studies to activate and use ICTs in dementia research.
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Affiliation(s)
- Kyung Hee Lee
- Yonsei University College of Nursing and Mo-Im Kim Nursing Research Institute, Seoul, South Korea
| | - Ji Yeon Lee
- Yonsei University College of Nursing and Mo-Im Kim Nursing Research Institute, Seoul, South Korea
| | - Bora Kim
- Yonsei University College of Nursing and the Brain Korea 21 FOUR Project, Seoul, South Korea
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12
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Abstract
Thanks to the proliferation of the Internet of Things (IoT), pervasive healthcare is gaining popularity day by day as it offers health support to patients irrespective of their location. In emergency medical situations, medical aid can be sent quickly. Though not yet standardized, this research direction, healthcare Internet of Things (H-IoT), attracts the attention of the research community, both academia and industry. In this article, we conduct a comprehensive survey of pervasive computing H-IoT. We would like to visit the wide range of applications. We provide a broad vision of key components, their roles, and connections in the big picture. We classify the vast amount of publications into different categories such as sensors, communication, artificial intelligence, infrastructure, and security. Intensively covering 118 research works, we survey (1) applications, (2) key components, their roles and connections, and (3) the challenges. Our survey also discusses the potential solutions to overcome the challenges in this research field.
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13
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Tabrizi SJ, Estevez-Fraga C, van Roon-Mom WMC, Flower MD, Scahill RI, Wild EJ, Muñoz-Sanjuan I, Sampaio C, Rosser AE, Leavitt BR. Potential disease-modifying therapies for Huntington's disease: lessons learned and future opportunities. Lancet Neurol 2022; 21:645-658. [PMID: 35716694 PMCID: PMC7613206 DOI: 10.1016/s1474-4422(22)00121-1] [Citation(s) in RCA: 101] [Impact Index Per Article: 50.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 02/18/2022] [Accepted: 03/04/2022] [Indexed: 01/03/2023]
Abstract
Huntington's disease is the most frequent autosomal dominant neurodegenerative disorder; however, no disease-modifying interventions are available for patients with this disease. The molecular pathogenesis of Huntington's disease is complex, with toxicity that arises from full-length expanded huntingtin and N-terminal fragments of huntingtin, which are both prone to misfolding due to proteolysis; aberrant intron-1 splicing of the HTT gene; and somatic expansion of the CAG repeat in the HTT gene. Potential interventions for Huntington's disease include therapies targeting huntingtin DNA and RNA, clearance of huntingtin protein, DNA repair pathways, and other treatment strategies targeting inflammation and cell replacement. The early termination of trials of the antisense oligonucleotide tominersen suggest that it is time to reflect on lessons learned, where the field stands now, and the challenges and opportunities for the future.
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Affiliation(s)
- Sarah J Tabrizi
- Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, UK.
| | - Carlos Estevez-Fraga
- Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | | | - Michael D Flower
- Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Rachael I Scahill
- Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Edward J Wild
- Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | | | - Cristina Sampaio
- CHDI Management, CHDI Foundation Los Angeles, CA, USA; Laboratory of Clinical Pharmacology, Faculdade de Medicina de Lisboa, Lisbon, Portugal
| | - Anne E Rosser
- BRAIN unit, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Blair R Leavitt
- Centre for Huntington's disease, University of British Columbia, Vancouver, BC, Canada
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14
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Lipsmeier F, Simillion C, Bamdadian A, Tortelli R, Byrne LM, Zhang YP, Wolf D, Smith AV, Czech C, Gossens C, Weydt P, Schobel SA, Rodrigues FB, Wild EJ, Lindemann M. A Remote Digital Monitoring Platform to Assess Cognitive and Motor Symptoms in Huntington Disease: Cross-sectional Validation Study. J Med Internet Res 2022; 24:e32997. [PMID: 35763342 PMCID: PMC9277525 DOI: 10.2196/32997] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 02/17/2022] [Accepted: 05/04/2022] [Indexed: 11/13/2022] Open
Abstract
Background Remote monitoring of Huntington disease (HD) signs and symptoms using digital technologies may enhance early clinical diagnosis and tracking of disease progression, guide treatment decisions, and monitor response to disease-modifying agents. Several recent studies in neurodegenerative diseases have demonstrated the feasibility of digital symptom monitoring. Objective The aim of this study was to evaluate a novel smartwatch- and smartphone-based digital monitoring platform to remotely monitor signs and symptoms of HD. Methods This analysis aimed to determine the feasibility and reliability of the Roche HD Digital Monitoring Platform over a 4-week period and cross-sectional validity over a 2-week interval. Key criteria assessed were feasibility, evaluated by adherence and quality control failure rates; test-retest reliability; known-groups validity; and convergent validity of sensor-based measures with existing clinical measures. Data from 3 studies were used: the predrug screening phase of an open-label extension study evaluating tominersen (NCT03342053) and 2 untreated cohorts—the HD Natural History Study (NCT03664804) and the Digital-HD study. Across these studies, controls (n=20) and individuals with premanifest (n=20) or manifest (n=179) HD completed 6 motor and 2 cognitive tests at home and in the clinic. Results Participants in the open-label extension study, the HD Natural History Study, and the Digital-HD study completed 89.95% (1164/1294), 72.01% (2025/2812), and 68.98% (1454/2108) of the active tests, respectively. All sensor-based features showed good to excellent test-retest reliability (intraclass correlation coefficient 0.89-0.98) and generally low quality control failure rates. Good overall convergent validity of sensor-derived features to Unified HD Rating Scale outcomes and good overall known-groups validity among controls, premanifest, and manifest participants were observed. Among participants with manifest HD, the digital cognitive tests demonstrated the strongest correlations with analogous in-clinic tests (Pearson correlation coefficient 0.79-0.90). Conclusions These results show the potential of the HD Digital Monitoring Platform to provide reliable, valid, continuous remote monitoring of HD symptoms, facilitating the evaluation of novel treatments and enhanced clinical monitoring and care for individuals with HD.
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Affiliation(s)
- Florian Lipsmeier
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Cedric Simillion
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Atieh Bamdadian
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Rosanna Tortelli
- Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Lauren M Byrne
- Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Yan-Ping Zhang
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Detlef Wolf
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Anne V Smith
- Ionis Pharmaceuticals Inc, Carlsbad, CA, United States
| | - Christian Czech
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland.,Rare Disease Research Unit, Pfizer, Nice, France
| | - Christian Gossens
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Patrick Weydt
- Department of Neurology, University of Ulm Medical Center, Ulm, Germany.,Department of Neurodegenerative Disease and Gerontopsychiatry/Neurology, University of Bonn Medical Center, Bonn, Germany
| | | | - Filipe B Rodrigues
- Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Edward J Wild
- Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Michael Lindemann
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
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15
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Rodríguez-Martín D, Cabestany J, Pérez-López C, Pie M, Calvet J, Samà A, Capra C, Català A, Rodríguez-Molinero A. A New Paradigm in Parkinson's Disease Evaluation With Wearable Medical Devices: A Review of STAT-ON TM. Front Neurol 2022; 13:912343. [PMID: 35720090 PMCID: PMC9202426 DOI: 10.3389/fneur.2022.912343] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 04/22/2022] [Indexed: 11/13/2022] Open
Abstract
In the past decade, the use of wearable medical devices has been a great breakthrough in clinical practice, trials, and research. In the Parkinson's disease field, clinical evaluation is time limited, and healthcare professionals need to rely on retrospective data collected through patients' self-filled diaries and administered questionnaires. As this often leads to inaccurate evaluations, a more objective system for symptom monitoring in a patient's daily life is claimed. In this regard, the use of wearable medical devices is crucial. This study aims at presenting a review on STAT-ONTM, a wearable medical device Class IIa, which provides objective information on the distribution and severity of PD motor symptoms in home environments. The sensor analyzes inertial signals, with a set of validated machine learning algorithms running in real time. The device was developed for 12 years, and this review aims at gathering all the results achieved within this time frame. First, a compendium of the complete journey of STAT-ONTM since 2009 is presented, encompassing different studies and developments in funded European and Spanish national projects. Subsequently, the methodology of database construction and machine learning algorithms design and development is described. Finally, clinical validation and external studies of STAT-ONTM are presented.
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Affiliation(s)
| | - Joan Cabestany
- Technical Research Centre for Dependency Care and Autonomous Living, Universitat Politecnica de Catalunya, Barcelona, Spain
| | - Carlos Pérez-López
- Department of Investigation, Consorci Sanitari Alt Penedès - Garraf, Vilanova i la Geltrú, Spain
| | - Marti Pie
- Sense4Care S.L., Cornellà de Llobregat, Spain
| | - Joan Calvet
- Sense4Care S.L., Cornellà de Llobregat, Spain
| | - Albert Samà
- Sense4Care S.L., Cornellà de Llobregat, Spain
| | | | - Andreu Català
- Technical Research Centre for Dependency Care and Autonomous Living, Universitat Politecnica de Catalunya, Barcelona, Spain
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16
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Desai R, Blacutt M, Youdan G, Fritz NE, Muratori LM, Hausdorff JM, Busse M, Quinn L. Postural control and gait measures derived from wearable inertial measurement unit devices in Huntington's disease: Recommendations for clinical outcomes. Clin Biomech (Bristol, Avon) 2022; 96:105658. [PMID: 35588586 DOI: 10.1016/j.clinbiomech.2022.105658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 04/13/2022] [Accepted: 04/22/2022] [Indexed: 02/07/2023]
Abstract
BACKGROUND Postural control impairments begin early in Huntington's disease yet measures most sensitive to progression have not been identified. The aims of this study were to: 1) evaluate postural control and gait in people with and without Huntington's disease using wearable sensors; and 2) identify measures related to diagnosis and clinical severity. METHODS 43 individuals with Huntington's disease and 15 age-matched peers performed standing with feet together and feet apart, sitting, and walking with wearable inertial sensors. One-way analysis of variance determined differences in measures of postural control and gait between early and mid-disease stage, and non-Huntington's disease peers. A random forest analysis identified feature importance for Huntington's disease diagnosis. Stepwise and ordinal regressions were used to determine predictors of clinical chorea and tandem walking scores respectively. FINDINGS There was a significant main effect for all postural control and gait measures comparing early stage, mid stage and non-Huntington's disease peers, except for gait cycle duration and step duration. Total sway, root mean square and mean velocity during sitting, as well as gait speed had the greatest importance in classifying disease status. Stepwise regression showed that root mean square during standing with feet apart significantly predicted clinical measure of chorea, and ordinal regression model showed that root mean square and total sway standing feet together significantly predicted clinical measure of tandem walking. INTERPRETATIONS Root mean square measures obtained in sitting and standing using wearable sensors have the potential to serve as biomarkers of postural control impairments in Huntington's disease.
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Affiliation(s)
- Radhika Desai
- Department of Biobehavioral Sciences, Teachers College, Columbia University, New York, NY, USA.
| | - Miguel Blacutt
- Department of Biobehavioral Sciences, Teachers College, Columbia University, New York, NY, USA.
| | - Gregory Youdan
- Department of Biobehavioral Sciences, Teachers College, Columbia University, New York, NY, USA.
| | - Nora E Fritz
- Wayne State University, Departments of Health Care Sciences and Neurology, Detroit, MI, USA.
| | - Lisa M Muratori
- Department Physical Therapy, Stony Brook University, New York, USA.
| | - Jeffrey M Hausdorff
- Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Department of Physical Therapy, Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel; Rush Alzheimer's Disease Center and Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA.
| | - Monica Busse
- Centre for Trials Research, Cardiff University, Cardiff, UK.
| | - Lori Quinn
- Department of Biobehavioral Sciences, Teachers College, Columbia University, New York, NY, USA; Centre for Trials Research, Cardiff University, Cardiff, UK.
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17
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A Deep Learning Approach for Gait Event Detection from a Single Shank-Worn IMU: Validation in Healthy and Neurological Cohorts. SENSORS 2022; 22:s22103859. [PMID: 35632266 PMCID: PMC9143761 DOI: 10.3390/s22103859] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 05/12/2022] [Accepted: 05/17/2022] [Indexed: 12/17/2022]
Abstract
Many algorithms use 3D accelerometer and/or gyroscope data from inertial measurement unit (IMU) sensors to detect gait events (i.e., initial and final foot contact). However, these algorithms often require knowledge about sensor orientation and use empirically derived thresholds. As alignment cannot always be controlled for in ambulatory assessments, methods are needed that require little knowledge on sensor location and orientation, e.g., a convolutional neural network-based deep learning model. Therefore, 157 participants from healthy and neurologically diseased cohorts walked 5 m distances at slow, preferred, and fast walking speed, while data were collected from IMUs on the left and right ankle and shank. Gait events were detected and stride parameters were extracted using a deep learning model and an optoelectronic motion capture (OMC) system for reference. The deep learning model consisted of convolutional layers using dilated convolutions, followed by two independent fully connected layers to predict whether a time step corresponded to the event of initial contact (IC) or final contact (FC), respectively. Results showed a high detection rate for both initial and final contacts across sensor locations (recall ≥92%, precision ≥97%). Time agreement was excellent as witnessed from the median time error (0.005 s) and corresponding inter-quartile range (0.020 s). The extracted stride-specific parameters were in good agreement with parameters derived from the OMC system (maximum mean difference 0.003 s and corresponding maximum limits of agreement (−0.049 s, 0.051 s) for a 95% confidence level). Thus, the deep learning approach was considered a valid approach for detecting gait events and extracting stride-specific parameters with little knowledge on exact IMU location and orientation in conditions with and without walking pathologies due to neurological diseases.
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18
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Breasail MÓ, Biswas B, Smith MD, Mazhar MKA, Tenison E, Cullen A, Lithander FE, Roudaut A, Henderson EJ. Wearable GPS and Accelerometer Technologies for Monitoring Mobility and Physical Activity in Neurodegenerative Disorders: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2021; 21:8261. [PMID: 34960353 PMCID: PMC8705556 DOI: 10.3390/s21248261] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 11/26/2021] [Accepted: 12/02/2021] [Indexed: 12/21/2022]
Abstract
Neurodegenerative disorders (NDDs) constitute an increasing global burden and can significantly impair an individual's mobility, physical activity (PA), and independence. Remote monitoring has been difficult without relying on diaries/questionnaires which are more challenging for people with dementia to complete. Wearable global positioning system (GPS) sensors and accelerometers present a cost-effective and noninvasive way to passively monitor mobility and PA. In addition, changes in sensor-derived outcomes (such as walking behaviour, sedentary, and active activity) may serve as potential biomarkers of disease onset, progression, and response to treatment. We performed a systematic search across four databases to identify papers published within the past 5 years, in which wearable GPS or accelerometers were used to monitor mobility or PA in patients with common NDDs (Parkinson's disease, Alzheimer's disease, motor neuron diseases/amyotrophic lateral sclerosis, vascular parkinsonism, and vascular dementia). Disease and technology-specific vocabulary were searched singly, and then in combination, identifying 4985 papers. Following deduplication, we screened 3115 papers and retained 28 studies following a full text review. One study used wearable GPS and accelerometers, while 27 studies used solely accelerometers in NDDs. GPS-derived measures had been validated against current gold standard measures in one Parkinson's cohort, suggesting that the technology may be applicable to other NDDs. In contrast, accelerometers are widely utilised in NDDs and have been operationalised in well-designed clinical trials.
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Affiliation(s)
- Mícheál Ó. Breasail
- Population Health Sciences, Bristol Medical School, University of Bristol, 1-5 Whiteladies Road, Bristol BS8 1NU, UK; (M.Ó.B.); (M.D.S.); (M.K.A.M.); (E.T.); (A.C.); (F.E.L.); (E.J.H.)
| | - Bijetri Biswas
- Department of Electronic and Electrical Engineering, Computer Science and Mathematics, University of Bristol, Bristol BS8 1TH, UK
| | - Matthew D. Smith
- Population Health Sciences, Bristol Medical School, University of Bristol, 1-5 Whiteladies Road, Bristol BS8 1NU, UK; (M.Ó.B.); (M.D.S.); (M.K.A.M.); (E.T.); (A.C.); (F.E.L.); (E.J.H.)
- Older Peoples Unit, Royal United Hospital NHS Foundation Trust, Bath BN1 3NG, UK
| | - Md Khadimul A. Mazhar
- Population Health Sciences, Bristol Medical School, University of Bristol, 1-5 Whiteladies Road, Bristol BS8 1NU, UK; (M.Ó.B.); (M.D.S.); (M.K.A.M.); (E.T.); (A.C.); (F.E.L.); (E.J.H.)
| | - Emma Tenison
- Population Health Sciences, Bristol Medical School, University of Bristol, 1-5 Whiteladies Road, Bristol BS8 1NU, UK; (M.Ó.B.); (M.D.S.); (M.K.A.M.); (E.T.); (A.C.); (F.E.L.); (E.J.H.)
| | - Anisha Cullen
- Population Health Sciences, Bristol Medical School, University of Bristol, 1-5 Whiteladies Road, Bristol BS8 1NU, UK; (M.Ó.B.); (M.D.S.); (M.K.A.M.); (E.T.); (A.C.); (F.E.L.); (E.J.H.)
| | - Fiona E. Lithander
- Population Health Sciences, Bristol Medical School, University of Bristol, 1-5 Whiteladies Road, Bristol BS8 1NU, UK; (M.Ó.B.); (M.D.S.); (M.K.A.M.); (E.T.); (A.C.); (F.E.L.); (E.J.H.)
| | - Anne Roudaut
- Department of Computer Science, University of Bristol, Bristol BS8 1TH, UK;
| | - Emily J. Henderson
- Population Health Sciences, Bristol Medical School, University of Bristol, 1-5 Whiteladies Road, Bristol BS8 1NU, UK; (M.Ó.B.); (M.D.S.); (M.K.A.M.); (E.T.); (A.C.); (F.E.L.); (E.J.H.)
- Older Peoples Unit, Royal United Hospital NHS Foundation Trust, Bath BN1 3NG, UK
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19
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Adams JL, Dinesh K, Snyder CW, Xiong M, Tarolli CG, Sharma S, Dorsey ER, Sharma G. A real-world study of wearable sensors in Parkinson's disease. NPJ Parkinsons Dis 2021; 7:106. [PMID: 34845224 PMCID: PMC8629990 DOI: 10.1038/s41531-021-00248-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 10/27/2021] [Indexed: 12/17/2022] Open
Abstract
Most wearable sensor studies in Parkinson's disease have been conducted in the clinic and thus may not be a true representation of everyday symptoms and symptom variation. Our goal was to measure activity, gait, and tremor using wearable sensors inside and outside the clinic. In this observational study, we assessed motor features using wearable sensors developed by MC10, Inc. Participants wore five sensors, one on each limb and on the trunk, during an in-person clinic visit and for two days thereafter. Using the accelerometer data from the sensors, activity states (lying, sitting, standing, walking) were determined and steps per day were also computed by aggregating over 2 s walking intervals. For non-walking periods, tremor durations were identified that had a characteristic frequency between 3 and 10 Hz. We analyzed data from 17 individuals with Parkinson's disease and 17 age-matched controls over an average 45.4 h of sensor wear. Individuals with Parkinson's walked significantly less (median [inter-quartile range]: 4980 [2835-7163] steps/day) than controls (7367 [5106-8928] steps/day; P = 0.04). Tremor was present for 1.6 [0.4-5.9] hours (median [range]) per day in most-affected hands (MDS-UPDRS 3.17a or 3.17b = 1-4) of individuals with Parkinson's, which was significantly higher than the 0.5 [0.3-2.3] hours per day in less-affected hands (MDS-UPDRS 3.17a or 3.17b = 0). These results, which require replication in larger cohorts, advance our understanding of the manifestations of Parkinson's in real-world settings.
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Affiliation(s)
- Jamie L Adams
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA.
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
| | - Karthik Dinesh
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA
| | | | - Mulin Xiong
- Michigan State University College of Human Medicine, East Lansing, MI, USA
| | - Christopher G Tarolli
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Saloni Sharma
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - E Ray Dorsey
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Gaurav Sharma
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA
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20
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Tarolli CG, Lizarraga KJ. Approach to the Patient with Gait Disturbance. Semin Neurol 2021; 41:717-730. [PMID: 34826874 DOI: 10.1055/s-0041-1726355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
The assessment of patients presenting with disorders of gait can be a daunting task for neurologists given the broad potential localization and differential diagnosis. However, gait disorders are extremely common in outpatient neurology, and all neurologists should be comfortable with the assessment, triage, and management of patients presenting with difficulty walking. Here, we aim to present a manageable framework for neurologists to approach the assessment of patients presenting with gait dysfunction. We suggest a chief complaint-based phenomenological characterization of gait, using components of the neurological history and examination to guide testing and treatment. We present the framework to mirror the outpatient visit with the patient, highlighting (1) important features of the gait history, including the most common gait-related chief complaints and common secondary (medical) causes of gait dysfunction; (2) gait physiology and a systematic approach to the gait examination allowing appropriate characterization of gait phenomenology; (3) an algorithmic approach to ancillary testing for patients with gait dysfunction based on historical and examination features; and (4) definitive and supportive therapies for the management of patients presenting with common neurological disorders of gait.
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Affiliation(s)
- Christopher G Tarolli
- Motor Physiology and Neuromodulation Program, Division of Movement Disorders, Center for Health + Technology (CHeT), Department of Neurology, University of Rochester Medical Center, Rochester, New York
| | - Karlo J Lizarraga
- Motor Physiology and Neuromodulation Program, Division of Movement Disorders, Center for Health + Technology (CHeT), Department of Neurology, University of Rochester Medical Center, Rochester, New York.,Departamento Academico de Neurociencias, Facultad de Medicina, Universidad Nacional de San Agustin de Arequipa, Arequipa, Peru
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21
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Godkin FE, Turner E, Demnati Y, Vert A, Roberts A, Swartz RH, McLaughlin PM, Weber KS, Thai V, Beyer KB, Cornish B, Abrahao A, Black SE, Masellis M, Zinman L, Beaton D, Binns MA, Chau V, Kwan D, Lim A, Munoz DP, Strother SC, Sunderland KM, Tan B, McIlroy WE, Van Ooteghem K. Feasibility of a continuous, multi-sensor remote health monitoring approach in persons living with neurodegenerative disease. J Neurol 2021; 269:2673-2686. [PMID: 34705114 PMCID: PMC8548705 DOI: 10.1007/s00415-021-10831-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 09/27/2021] [Accepted: 09/28/2021] [Indexed: 01/02/2023]
Abstract
BACKGROUND Remote health monitoring with wearable sensor technology may positively impact patient self-management and clinical care. In individuals with complex health conditions, multi-sensor wear may yield meaningful information about health-related behaviors. Despite available technology, feasibility of device-wearing in daily life has received little attention in persons with physical or cognitive limitations. This mixed methods study assessed the feasibility of continuous, multi-sensor wear in persons with cerebrovascular (CVD) or neurodegenerative disease (NDD). METHODS Thirty-nine participants with CVD, Alzheimer's disease/amnestic mild cognitive impairment, frontotemporal dementia, Parkinson's disease, or amyotrophic lateral sclerosis (median age 68 (45-83) years, 36% female) wore five devices (bilateral ankles and wrists, chest) continuously for a 7-day period. Adherence to device wearing was quantified by examining volume and pattern of device removal (non-wear). A thematic analysis of semi-structured de-brief interviews with participants and study partners was used to examine user acceptance. RESULTS Adherence to multi-sensor wear, defined as a minimum of three devices worn concurrently, was high (median 98.2% of the study period). Non-wear rates were low across all sensor locations (median 17-22 min/day), with significant differences between some locations (p = 0.006). Multi-sensor non-wear was higher for daytime versus nighttime wear (p < 0.001) and there was a small but significant increase in non-wear over the collection period (p = 0.04). Feedback from de-brief interviews suggested that multi-sensor wear was generally well accepted by both participants and study partners. CONCLUSION A continuous, multi-sensor remote health monitoring approach is feasible in a cohort of persons with CVD or NDD.
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Affiliation(s)
- F Elizabeth Godkin
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Erin Turner
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Youness Demnati
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Adam Vert
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Angela Roberts
- School of Communication Sciences and Disorders, Elborn College, Western University, London, ON, Canada.,Roxelyn and Richard Pepper Department of Communication Sciences and Disorders, Northwestern University, Evanston, IL, USA
| | - Richard H Swartz
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | | | - Kyle S Weber
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Vanessa Thai
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Kit B Beyer
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Benjamin Cornish
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Agessandro Abrahao
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Sandra E Black
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Mario Masellis
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Lorne Zinman
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Derek Beaton
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - Malcolm A Binns
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Vivian Chau
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - Donna Kwan
- Centre for Neuroscience Studies, Queen's University, Kingston, ON, Canada
| | - Andrew Lim
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Douglas P Munoz
- Centre for Neuroscience Studies, Queen's University, Kingston, ON, Canada
| | - Stephen C Strother
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Kelly M Sunderland
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - Brian Tan
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - William E McIlroy
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Karen Van Ooteghem
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada.
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22
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Keren K, Busse M, Fritz NE, Muratori LM, Gazit E, Hillel I, Scheinowitz M, Gurevich T, Inbar N, Omer N, Hausdorff JM, Quinn L. Quantification of Daily-Living Gait Quantity and Quality Using a Wrist-Worn Accelerometer in Huntington's Disease. Front Neurol 2021; 12:719442. [PMID: 34777196 PMCID: PMC8579964 DOI: 10.3389/fneur.2021.719442] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 09/06/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Huntington's disease (HD) leads to altered gait patterns and reduced daily-living physical activity. Accurate measurement of daily-living walking that takes into account involuntary movements (e.g. chorea) is needed. Objective: To evaluate daily-living gait quantity and quality in HD, taking into account irregular movements. Methods: Forty-two individuals with HD and fourteen age-matched non-HD peers completed clinic-based assessments and a standardized laboratory-based circuit of functional activities, wearing inertial measurement units on the wrists, legs, and trunk. These activities were used to train and test an algorithm for the automated detection of walking. Subsequently, 29 HD participants and 22 age-matched non-HD peers wore a tri-axial accelerometer on their non-dominant wrist for 7 days. Measures included gait quantity (e.g., steps per day), gait quality (e.g., regularity) metrics, and percentage of walking bouts with irregular movements. Results: Measures of daily-living gait quantity including step counts, walking time and bouts per day were similar in HD participants and non-HD peers (p > 0.05). HD participants with higher clinician-rated upper body chorea had a greater percentage of walking bouts with irregular movements compared to those with lower chorea (p = 0.060) and non-HD peers (p < 0.001). Even after accounting for irregular movements, within-bout walking consistency was lower in HD participants compared to non-HD peers (p < 0.001), while across-bout variability of these measures was higher (p < 0.001). Many of the daily-living measures were associated with disease-specific measures of motor function. Conclusions: Results suggest that a wrist-worn accelerometer can be used to evaluate the quantity and quality of daily-living gait in people with HD, while accounting for the influence of irregular (choreic-like) movements, and that gait features related to within- and across-bout consistency markedly differ in individuals with HD and non-HD peers.
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Affiliation(s)
- Karin Keren
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Monica Busse
- Centre for Trials Research, Cardiff University, Cardiff, United Kingdom
| | - Nora E. Fritz
- Departments of Health Care Sciences and Neurology, Wayne State University, Detroit, MI, United States
| | - Lisa M. Muratori
- Department of Physical Therapy, School of Health Technology and Management, Stony Brook University, Stony Brook, NY, United States
- George Huntington's Institute, Muenster, Germany
| | - Eran Gazit
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Inbar Hillel
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Micky Scheinowitz
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
- School of Public Health, Tel Aviv University, Tel Aviv, Israel
| | - Tanya Gurevich
- Movement Disorders Unit, Tel Aviv Medical Center, Tel Aviv, Israel
- Sackler School of Medicine and Sagol, School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Noit Inbar
- Movement Disorders Unit, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Nurit Omer
- Movement Disorders Unit, Tel Aviv Medical Center, Tel Aviv, Israel
- Sackler School of Medicine and Sagol, School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Jeffrey M. Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sackler School of Medicine and Sagol, School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Rush Alzheimer's Disease Center and Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, United States
| | - Lori Quinn
- Centre for Trials Research, Cardiff University, Cardiff, United Kingdom
- Department of Biobehavioral Sciences, Teachers College, Columbia University, New York, NY, United States
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23
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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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24
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Robotic-Based Well-Being Monitoring and Coaching System for the Elderly in Their Daily Activities. SENSORS 2021; 21:s21206865. [PMID: 34696078 PMCID: PMC8540718 DOI: 10.3390/s21206865] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 10/06/2021] [Accepted: 10/12/2021] [Indexed: 11/25/2022]
Abstract
The increasingly ageing population and the tendency to live alone have led science and engineering researchers to search for health care solutions. In the COVID 19 pandemic, the elderly have been seriously affected in addition to suffering from isolation and its associated and psychological consequences. This paper provides an overview of the RobWell (Robotic-based Well-Being Monitoring and Coaching System for the Elderly in their Daily Activities) system. It is a system focused on the field of artificial intelligence for mood prediction and coaching. This paper presents a general overview of the initially proposed system as well as the preliminary results related to the home automation subsystem, autonomous robot navigation and mood estimation through machine learning prior to the final system integration, which will be discussed in future works. The main goal is to improve their mental well-being during their daily household activities. The system is composed of ambient intelligence with intelligent sensors, actuators and a robotic platform that interacts with the user. A test smart home system was set up in which the sensors, actuators and robotic platform were integrated and tested. For artificial intelligence applied to mood prediction, we used machine learning to classify several physiological signals into different moods. In robotics, it was concluded that the ROS autonomous navigation stack and its autodocking algorithm were not reliable enough for this task, while the robot’s autonomy was sufficient. Semantic navigation, artificial intelligence and computer vision alternatives are being sought.
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25
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Keogh A, Argent R, Anderson A, Caulfield B, Johnston W. Assessing the usability of wearable devices to measure gait and physical activity in chronic conditions: a systematic review. J Neuroeng Rehabil 2021; 18:138. [PMID: 34526053 PMCID: PMC8444467 DOI: 10.1186/s12984-021-00931-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 09/01/2021] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND The World Health Organisation's global strategy for digital health emphasises the importance of patient involvement. Understanding the usability and acceptability of wearable devices is a core component of this. However, usability assessments to date have focused predominantly on healthy adults. There is a need to understand the patient perspective of wearable devices in participants with chronic health conditions. METHODS A systematic review was conducted to identify any study design that included a usability assessment of wearable devices to measure mobility, through gait and physical activity, within five cohorts with chronic conditions (Parkinson's disease [PD], multiple sclerosis [MS], congestive heart failure, [CHF], chronic obstructive pulmonary disorder [COPD], and proximal femoral fracture [PFF]). RESULTS Thirty-seven studies were identified. Substantial heterogeneity in the quality of reporting, the methods used to assess usability, the devices used, and the aims of the studies precluded any meaningful comparisons. Questionnaires were used in the majority of studies (70.3%; n = 26) with a reliance on intervention specific measures (n = 16; 61.5%). For those who used interviews (n = 17; 45.9%), no topic guides were provided, while methods of analysis were not reported in over a third of studies (n = 6; 35.3%). CONCLUSION Usability of wearable devices is a poorly measured and reported variable in chronic health conditions. Although the heterogeneity in how these devices are implemented implies acceptance, the patient voice should not be assumed. In the absence of being able to make specific usability conclusions, the results of this review instead recommends that future research needs to: (1) Conduct usability assessments as standard, irrespective of the cohort under investigation or the type of study undertaken. (2) Adhere to basic reporting standards (e.g. COREQ) including the basic details of the study. Full copies of any questionnaires and interview guides should be supplied through supplemental files. (3) Utilise mixed methods research to gather a more comprehensive understanding of usability than either qualitative or quantitative research alone will provide. (4) Use previously validated questionnaires alongside any intervention specific measures.
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Affiliation(s)
- Alison Keogh
- UCD School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland.
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland.
| | - Rob Argent
- UCD School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
| | | | - Brian Caulfield
- UCD School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
| | - William Johnston
- UCD School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
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26
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Talman LS, Hiller AL. Approach to Posture and Gait in Huntington's Disease. Front Bioeng Biotechnol 2021; 9:668699. [PMID: 34386484 PMCID: PMC8353382 DOI: 10.3389/fbioe.2021.668699] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 06/28/2021] [Indexed: 11/30/2022] Open
Abstract
Disturbances of gait occur in all stages of Huntington’s disease (HD) including the premanifest and prodromal stages. Individuals with HD demonstrate the slower speed of gait, shorter stride length, and increased variability of gait parameters as compared to controls; cognitive disturbances in HD often compound these differences. Abnormalities of gait and recurrent falls lead to decreased quality of life for individuals with HD throughout the disease. This scoping review aims to outline the cross-disciplinary approach to gait evaluation in HD and will highlight the utility of objective measures in defining gait abnormalities in this patient population.
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Affiliation(s)
- Lauren S Talman
- Department of Neurology, Oregon Health & Science University, Portland, OR, United States
| | - Amie L Hiller
- Department of Neurology, Oregon Health & Science University, Portland, OR, United States.,Portland VA Healthcare System, Portland, OR, United States
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27
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Gibson JS, Claassen DO. State-of-the-art pharmacological approaches to reduce chorea in Huntington's disease. Expert Opin Pharmacother 2021; 22:1015-1024. [PMID: 33550875 PMCID: PMC8222076 DOI: 10.1080/14656566.2021.1876666] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 01/12/2021] [Indexed: 02/07/2023]
Abstract
Introduction Chorea is a common motor manifestation of Huntington's disease (HD). Two vesicular monoamine transporter type 2 (VMAT-2) inhibitors have been approved by the FDA for treatment of HD chorea, and a third is currently being assessed in a phase 3 trial. Antipsychotic therapies are used off-label for treatment of chorea and can treat comorbid psychiatric symptoms. There is considerable clinical equipoise regarding the safe and effective treatment of chorea and comorbid symptoms in HD.Areas covered: The authors review existing medications used to treat HD chorea in the United States of America (USA). Implications for common comorbid symptoms (e.g. psychiatric, metabolic) are also discussed. Available therapies vary widely in cost, dosing frequency, and off -target effects, both beneficial or negative.Expert opinion: Treatment considerations for chorea should account for patient comorbidities. The authors recommend prospective, observational clinical effectiveness studies which can evaluate the long-term comparative effectiveness and safety of VMAT-2 inhibitors and antipsychotics in HD. Data regarding safety of dual therapy is another critical need. This is especially timely given the changing landscape of HD therapies which may increase cost burden and possibly extend both asymptomatic and symptomatic years for HD patients.
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Affiliation(s)
| | - Daniel O. Claassen
- Department of Neurology, Division of Behavioral and Cognitive Neurology, Vanderbilt University Medical Center
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28
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Tortelli R, Rodrigues FB, Wild EJ. The use of wearable/portable digital sensors in Huntington's disease: A systematic review. Parkinsonism Relat Disord 2021; 83:93-104. [PMID: 33493786 PMCID: PMC7957324 DOI: 10.1016/j.parkreldis.2021.01.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 10/13/2020] [Accepted: 01/08/2021] [Indexed: 01/26/2023]
Abstract
In chronic neurological conditions, wearable/portable devices have potential as innovative tools to detect subtle early disease manifestations and disease fluctuations for the purpose of clinical diagnosis, care and therapeutic development. Huntington's disease (HD) has a unique combination of motor and non-motor features which, combined with recent and anticipated therapeutic progress, gives great potential for such devices to prove useful. The present work aims to provide a comprehensive account of the use of wearable/portable devices in HD and of what they have contributed so far. We conducted a systematic review searching MEDLINE, Embase, and IEEE Xplore. Thirty references were identified. Our results revealed large variability in the types of sensors used, study design, and the measured outcomes. Digital technologies show considerable promise for therapeutic research and clinical management of HD. However, more studies with standardized devices and harmonized protocols are needed to optimize the potential applicability of wearable/portable devices in HD. Wearable/portable sensors have been proposed to detect and quantify manifestations of many neurodegenerative diseases. No systematic review so far has examined their use in Huntington's disease (HD). This work draws a broad picture of the digital wearable-based landscape in HD. The utility of wearables in clinical practice and therapeutic research still needs to be proved. Collaborative efforts are needed to further investigate their clinical use in HD.
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Affiliation(s)
- Rosanna Tortelli
- UCL Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Filipe B Rodrigues
- UCL Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Edward J Wild
- UCL Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, UK.
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29
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Kim J. Networks and near-field communication: up-close but far away. Digit Health 2021. [DOI: 10.1016/b978-0-12-818914-6.00019-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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30
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Shah VV, McNames J, Mancini M, Carlson-Kuhta P, Spain RI, Nutt JG, El-Gohary M, Curtze C, Horak FB. Laboratory versus daily life gait characteristics in patients with multiple sclerosis, Parkinson's disease, and matched controls. J Neuroeng Rehabil 2020; 17:159. [PMID: 33261625 PMCID: PMC7708140 DOI: 10.1186/s12984-020-00781-4] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 10/25/2020] [Indexed: 01/30/2023] Open
Abstract
BACKGROUND AND PURPOSE Recent findings suggest that a gait assessment at a discrete moment in a clinic or laboratory setting may not reflect functional, everyday mobility. As a step towards better understanding gait during daily life in neurological populations, we compared gait measures that best discriminated people with multiple sclerosis (MS) and people with Parkinson's Disease (PD) from their respective, age-matched, healthy control subjects (MS-Ctl, PD-Ctl) in laboratory tests versus a week of daily life monitoring. METHODS We recruited 15 people with MS (age mean ± SD: 49 ± 10 years), 16 MS-Ctl (45 ± 11 years), 16 people with idiopathic PD (71 ± 5 years), and 15 PD-Ctl (69 ± 7 years). Subjects wore 3 inertial sensors (one each foot and lower back) in the laboratory followed by 7 days during daily life. Mann-Whitney U test and area under the curve (AUC) compared differences between PD and PD-Ctl, and between MS and MS-Ctl in the laboratory and in daily life. RESULTS Participants wore sensors for 60-68 h in daily life. Measures that best discriminated gait characteristics in people with MS and PD from their respective control groups were different between the laboratory gait test and a week of daily life. Specifically, the toe-off angle best discriminated MS versus MS-Ctl in the laboratory (AUC [95% CI] = 0.80 [0.63-0.96]) whereas gait speed in daily life (AUC = 0.84 [0.69-1.00]). In contrast, the lumbar coronal range of motion best discriminated PD versus PD-Ctl in the laboratory (AUC = 0.78 [0.59-0.96]) whereas foot-strike angle in daily life (AUC = 0.84 [0.70-0.98]). AUCs were larger in daily life compared to the laboratory. CONCLUSIONS Larger AUC for daily life gait measures compared to the laboratory gait measures suggest that daily life monitoring may be more sensitive to impairments from neurological disease, but each neurological disease may require different gait outcome measures.
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Affiliation(s)
- Vrutangkumar V Shah
- Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA.
| | - James McNames
- Department of Electrical and Computer Engineering, Portland State University, Portland, OR, USA
- APDM Wearable Technologies, Portland, OR, USA
| | - Martina Mancini
- Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA
| | - Patricia Carlson-Kuhta
- Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA
| | - Rebecca I Spain
- Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA
- Veterans Affairs Portland Health Care System, Portland, OR, USA
| | - John G Nutt
- Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA
| | | | - Carolin Curtze
- Department of Biomechanics, University of Nebraska At Omaha, Omaha, NE, USA
| | - Fay B Horak
- Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA
- APDM Wearable Technologies, Portland, OR, USA
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31
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Celik Y, Stuart S, Woo WL, Godfrey A. Gait analysis in neurological populations: Progression in the use of wearables. Med Eng Phys 2020; 87:9-29. [PMID: 33461679 DOI: 10.1016/j.medengphy.2020.11.005] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 11/02/2020] [Accepted: 11/11/2020] [Indexed: 12/19/2022]
Abstract
Gait assessment is an essential tool for clinical applications not only to diagnose different neurological conditions but also to monitor disease progression as it contributes to the understanding of underlying deficits. There are established methods and models for data collection and interpretation of gait assessment within different pathologies. This narrative review aims to depict the evolution of gait assessment from observation and rating scales to wearable sensors and laboratory technologies and provide limitations and possible future directions in the field of gait assessment. In this context, we first present an extensive review of current clinical outcomes and gait models. Then, we demonstrate commercially available wearable technologies with their technical capabilities along with their use in gait assessment studies for various neurological conditions. In the next sections, a descriptive knowledge for existing inertial and EMG based algorithms and a sign based guide that shows the outcomes of previous neurological gait assessment studies are presented. Finally, we state a discussion for the use of wearables in gait assessment and speculate the possible research directions by revealing the limitations and knowledge gaps in the literature.
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Affiliation(s)
- Y Celik
- Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - S Stuart
- Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - W L Woo
- Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - A Godfrey
- Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK.
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Shah VV, McNames J, Harker G, Mancini M, Carlson-Kuhta P, Nutt JG, El-Gohary M, Curtze C, Horak FB. Effect of Bout Length on Gait Measures in People with and without Parkinson's Disease during Daily Life. SENSORS (BASEL, SWITZERLAND) 2020; 20:E5769. [PMID: 33053703 PMCID: PMC7601493 DOI: 10.3390/s20205769] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 09/30/2020] [Accepted: 10/09/2020] [Indexed: 01/06/2023]
Abstract
Although the use of wearable technology to characterize gait disorders in daily life is increasing, there is no consensus on which specific gait bout length should be used to characterize gait. Clinical trialists using daily life gait quality as study outcomes need to understand how gait bout length affects the sensitivity and specificity of measures to discriminate pathological gait as well as the reliability of gait measures across gait bout lengths. We investigated whether Parkinson's disease (PD) affects how gait characteristics change as bout length changes, and how gait bout length affects the reliability and discriminative ability of gait measures to identify gait impairments in people with PD compared to neurotypical Old Adults (OA). We recruited 29 people with PD and 20 neurotypical OA of similar age for this study. Subjects wore 3 inertial sensors, one on each foot and one over the lumbar spine all day, for 7 days. To investigate which gait bout lengths should be included to extract gait measures, we determined the range of gait bout lengths available across all subjects. To investigate if the effect of bout length on each gait measure is similar or not between subjects with PD and OA, we used a growth curve analysis. For reliability and discriminative ability of each gait measure as a function of gait bout length, we used the intraclass correlation coefficient (ICC) and area under the curve (AUC), respectively. Ninety percent of subjects walked with a bout length of less than 53 strides during the week, and the majority (>50%) of gait bouts consisted of less than 12 strides. Although bout length affected all gait measures, the effects depended on the specific measure and sometimes differed for PD versus OA. Specifically, people with PD did not increase/decrease cadence and swing duration with bout length in the same way as OA. ICC and AUC characteristics tended to be larger for shorter than longer gait bouts. Our findings suggest that PD interferes with the scaling of cadence and swing duration with gait bout length. Whereas control subjects gradually increased cadence and decreased swing duration as bout length increased, participants with PD started with higher than normal cadence and shorter than normal stride duration for the smallest bouts, and cadence and stride duration changed little as bout length increased, so differences between PD and OA disappeared for the longer bout lengths. Gait measures extracted from shorter bouts are more common, more reliable, and more discriminative, suggesting that shorter gait bouts should be used to extract potential digital biomarkers for people with PD.
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Affiliation(s)
- Vrutangkumar V. Shah
- Department of Neurology, Oregon Health & Science University, Portland, OR 97239, USA; (G.H.); (M.M.); (P.C.-K.); (J.G.N.); (F.B.H.)
| | - James McNames
- Department of Electrical and Computer Engineering, Portland State University, Portland, OR 97207, USA;
| | - Graham Harker
- Department of Neurology, Oregon Health & Science University, Portland, OR 97239, USA; (G.H.); (M.M.); (P.C.-K.); (J.G.N.); (F.B.H.)
| | - Martina Mancini
- Department of Neurology, Oregon Health & Science University, Portland, OR 97239, USA; (G.H.); (M.M.); (P.C.-K.); (J.G.N.); (F.B.H.)
| | - Patricia Carlson-Kuhta
- Department of Neurology, Oregon Health & Science University, Portland, OR 97239, USA; (G.H.); (M.M.); (P.C.-K.); (J.G.N.); (F.B.H.)
| | - John G. Nutt
- Department of Neurology, Oregon Health & Science University, Portland, OR 97239, USA; (G.H.); (M.M.); (P.C.-K.); (J.G.N.); (F.B.H.)
| | | | - Carolin Curtze
- Department of Biomechanics, University of Nebraska at Omaha, Omaha, NE 68182, USA;
| | - Fay B. Horak
- Department of Neurology, Oregon Health & Science University, Portland, OR 97239, USA; (G.H.); (M.M.); (P.C.-K.); (J.G.N.); (F.B.H.)
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Manta C, Patrick-Lake B, Goldsack JC. Digital Measures That Matter to Patients: A Framework to Guide the Selection and Development of Digital Measures of Health. Digit Biomark 2020; 4:69-77. [PMID: 33083687 DOI: 10.1159/000509725] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Accepted: 06/25/2020] [Indexed: 11/19/2022] Open
Abstract
Background With the rise of connected sensor technologies, there are seemingly endless possibilities for new ways to measure health. These technologies offer researchers and clinicians opportunities to go beyond brief snapshots of data captured by traditional in-clinic assessments, to redefine health and disease. Given the myriad opportunities for measurement, how do research or clinical teams know what they should be measuring? Patient engagement, early and often, is paramount to thoughtfully selecting what is most important. Regulators encourage stakeholders to have a patient focus but actionable steps for continuous engagement are not well defined. Without patient-focused measurement, stakeholders risk entrenching digital versions of poor traditional assessments and proliferating low-value tools that are ineffective, burdensome, and reduce both quality and efficiency in clinical care and research. Summary This article synthesizes and defines a sequential framework of core principles for selecting and developing measurements in research and clinical care that are meaningful for patients. We propose next steps to drive forward the science of high-quality patient engagement in support of measures of health that matter in the era of digital medicine. Key Messages All measures of health should be meaningful, regardless of the product's regulatory classification, type of measure, or context of use. To evaluate meaningfulness of signals derived from digital sensors, the following four-level framework is useful: Meaningful Aspect of Health, Concept of Interest, Outcome to be measured, and Endpoint (exclusive to research). Incorporating patient input is a dynamic process that requires more than a single, transactional touch point but rather should be conducted continuously throughout the measurement selection process. We recommend that developers, clinicians, and researchers reevaluate processes for more continuous patient engagement in the development, deployment, and interpretation of digital measures of health.
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Affiliation(s)
- Christine Manta
- Digital Medicine Society, Boston, Massachusetts, USA.,Elektra Labs, Boston, Massachusetts, USA
| | - Bray Patrick-Lake
- Digital Medicine Society, Boston, Massachusetts, USA.,Evidation Health, Inc., San Mateo, California, USA
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Shah VV, McNames J, Mancini M, Carlson-Kuhta P, Spain RI, Nutt JG, El-Gohary M, Curtze C, Horak FB. Quantity and quality of gait and turning in people with multiple sclerosis, Parkinson's disease and matched controls during daily living. J Neurol 2020; 267:1188-1196. [PMID: 31927614 PMCID: PMC7294824 DOI: 10.1007/s00415-020-09696-5] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 12/31/2019] [Accepted: 01/03/2020] [Indexed: 12/12/2022]
Abstract
Clinical trials need to specify which specific gait characteristics to monitor as mobility measures for each neurological disorder. As a first step, this study aimed to investigate a set of measures from daily-life monitoring that best discriminate mobility between people with multiple sclerosis (MS) and age-matched healthy control subjects (MS-Ctl) and between people with Parkinson's disease (PD) and age-matched healthy control subjects (PD-Ctl). Further, we investigated how these discriminative measures relate to the disease severity of MS or PD. We recruited 13 people with MS, 21 MS-Ctl, 29 people with idiopathic PD, and 20 PD-Ctl. Subjects wore 3 inertial sensors on their feet and the lumbar back for a week. The Area Under Curves (AUC) from the receiver operator characteristic (ROC) plot was calculated for each measure to determine the objective measures that best separated the MS and PD groups from their respective control cohorts. Adherence wearing the sensors was similar among groups for 58-66 h of recording (p = 0.14). Quantity of mobility (activity measures, such as a median number of strides per gait bout, AUC = 0.93) best discriminated mobility impairments in MS from MS-Ctl. In contrast, quality of mobility (such as turn angle, AUC = 0.90) best discriminated mobility impairments in PD from PD-Ctl. Mobility measures with AUC > 0.80 were correlated with MS and PD clinical scores of disease severity. Thus, measures characterizing mobility impairments differ for MS versus PD during daily life suggesting that mobility measures for clinical trials and clinical practice need to be specific to each neurological disorder.
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Affiliation(s)
- Vrutangkumar V Shah
- Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA.
| | - James McNames
- Department of Electrical and Computer Engineering, Portland State University, Portland, OR, USA
- APDM, Inc., Portland, OR, USA
| | - Martina Mancini
- Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA
| | - Patricia Carlson-Kuhta
- Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA
| | - Rebecca I Spain
- Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA
- Veterans Affairs Portland Health Care System, Portland, OR, USA
| | - John G Nutt
- Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA
| | | | - Carolin Curtze
- Department of Biomechanics, University of Nebraska at Omaha, Omaha, NE, USA
| | - Fay B Horak
- Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA
- APDM, Inc., Portland, OR, USA
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Dinesh K, Snyder CW, Xiong M, Tarolli CG, Sharma S, Dorsey ER, Sharma G, Adams JL. A Longitudinal Wearable Sensor Study in Huntington’s Disease. J Huntingtons Dis 2020; 9:69-81. [DOI: 10.3233/jhd-190375] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Karthik Dinesh
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA
| | - Christopher W. Snyder
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Mulin Xiong
- Michigan State University College of Human Medicine, East Lansing, MI, USA
| | - Christopher G. Tarolli
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Saloni Sharma
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - E. Ray Dorsey
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Gaurav Sharma
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA
| | - Jamie L. Adams
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
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Habibzadeh H, Dinesh K, Shishvan OR, Boggio-Dandry A, Sharma G, Soyata T. A Survey of Healthcare Internet-of-Things (HIoT): A Clinical Perspective. IEEE INTERNET OF THINGS JOURNAL 2020; 7:53-71. [PMID: 33748312 PMCID: PMC7970885 DOI: 10.1109/jiot.2019.2946359] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
In combination with current sociological trends, the maturing development of IoT devices is projected to revolutionize healthcare. A network of body-worn sensors, each with a unique ID, can collect health data that is orders-of-magnitude richer than what is available today from sporadic observations in clinical/hospital environments. When databased, analyzed, and compared against information from other individuals using data analytics, HIoT data enables the personalization and modernization of care with radical improvements in outcomes and reductions in cost. In this paper, we survey existing and emerging technologies that can enable this vision for the future of healthcare, particularly in the clinical practice of healthcare. Three main technology areas underlie the development of this field: (a) sensing, where there is an increased drive for miniaturization and power efficiency; (b) communications, where the enabling factors are ubiquitous connectivity, standardized protocols, and the wide availability of cloud infrastructure, and (c) data analytics and inference, where the availability of large amounts of data and computational resources is revolutionizing algorithms for individualizing inference and actions in health management. Throughout the paper, we use a case study to concretely illustrate the impact of these trends. We conclude our paper with a discussion of the emerging directions, open issues, and challenges.
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Affiliation(s)
- Hadi Habibzadeh
- Department of Electrical and Computer Engineering, SUNY Albany, Albany NY, 12203
| | - Karthik Dinesh
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY 14627
| | - Omid Rajabi Shishvan
- Department of Electrical and Computer Engineering, SUNY Albany, Albany NY, 12203
| | - Andrew Boggio-Dandry
- Department of Electrical and Computer Engineering, SUNY Albany, Albany NY, 12203
| | - Gaurav Sharma
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY 14627
| | - Tolga Soyata
- Department of Electrical and Computer Engineering, SUNY Albany, Albany NY, 12203
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37
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Dorsey ER, Omberg L, Waddell E, Adams JL, Adams R, Ali MR, Amodeo K, Arky A, Augustine EF, Dinesh K, Hoque ME, Glidden AM, Jensen-Roberts S, Kabelac Z, Katabi D, Kieburtz K, Kinel DR, Little MA, Lizarraga KJ, Myers T, Riggare S, Rosero SZ, Saria S, Schifitto G, Schneider RB, Sharma G, Shoulson I, Stevenson EA, Tarolli CG, Luo J, McDermott MP. Deep Phenotyping of Parkinson's Disease. JOURNAL OF PARKINSON'S DISEASE 2020; 10:855-873. [PMID: 32444562 PMCID: PMC7458535 DOI: 10.3233/jpd-202006] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/01/2020] [Indexed: 12/13/2022]
Abstract
Phenotype is the set of observable traits of an organism or condition. While advances in genetics, imaging, and molecular biology have improved our understanding of the underlying biology of Parkinson's disease (PD), clinical phenotyping of PD still relies primarily on history and physical examination. These subjective, episodic, categorical assessments are valuable for diagnosis and care but have left gaps in our understanding of the PD phenotype. Sensors can provide objective, continuous, real-world data about the PD clinical phenotype, increase our knowledge of its pathology, enhance evaluation of therapies, and ultimately, improve patient care. In this paper, we explore the concept of deep phenotyping-the comprehensive assessment of a condition using multiple clinical, biological, genetic, imaging, and sensor-based tools-for PD. We discuss the rationale for, outline current approaches to, identify benefits and limitations of, and consider future directions for deep clinical phenotyping.
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Affiliation(s)
- E. Ray Dorsey
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | | | - Emma Waddell
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Jamie L. Adams
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Roy Adams
- Machine Learning, AI and Healthcare Lab, Johns Hopkins University, Baltimore, MD, USA
| | | | - Katherine Amodeo
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Abigail Arky
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Erika F. Augustine
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Karthik Dinesh
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA
| | | | - Alistair M. Glidden
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Stella Jensen-Roberts
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Zachary Kabelac
- Department of Computer Science and Artificial Intelligence, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Dina Katabi
- Department of Computer Science and Artificial Intelligence, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Karl Kieburtz
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Daniel R. Kinel
- Center for Health + 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, UK
- Massachusetts Institute of Technology, MA, USA
| | - Karlo J. Lizarraga
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Taylor Myers
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Sara Riggare
- Department of Women’s and Children’s Health, Uppsala University, Uppsala, Sweden
| | | | - Suchi Saria
- Machine Learning, AI and Healthcare Lab, Johns Hopkins University, Baltimore, MD, USA
- Department of Computer Science, Statistics, and Health Policy, Johns Hopkins University, MD, USA
| | - Giovanni Schifitto
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Ruth B. Schneider
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Gaurav Sharma
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA
| | - Ira Shoulson
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
- Grey Matter Technologies, Sarasota, FL, USA
| | - E. Anna Stevenson
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Christopher G. Tarolli
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Jiebo Luo
- Department of Computer Science, University of Rochester, Rochester, NY, USA
| | - Michael P. McDermott
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA
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Müller MLTM, Marusic U, van Emde Boas M, Weiss D, Bohnen NI. Treatment options for postural instability and gait difficulties in Parkinson's disease. Expert Rev Neurother 2019; 19:1229-1251. [PMID: 31418599 DOI: 10.1080/14737175.2019.1656067] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Introduction: Gait and balance disorders in Parkinson's disease (PD) represent a major therapeutic challenge as frequent falls and freezing of gait impair quality of life and predict mortality. Limited dopaminergic therapy responses implicate non-dopaminergic mechanisms calling for alternative therapies.Areas covered: The authors provide a review that encompasses pathophysiological changes involved in axial motor impairments in PD, pharmacological approaches, exercise, and physical therapy, improving physical activity levels, invasive and non-invasive neurostimulation, cueing interventions and wearable technology, and cognitive interventions.Expert opinion: There are many promising therapies available that, to a variable degree, affect gait and balance disorders in PD. However, not one therapy is the 'silver bullet' that provides full relief and ultimately meaningfully improves the patient's quality of life. Sedentariness, apathy, and emergence of frailty in advancing PD, especially in the setting of medical comorbidities, are perhaps the biggest threats to experience sustained benefits with any of the available therapeutic options and therefore need to be aggressively treated as early as possible. Multimodal or combination therapies may provide complementary benefits to manage axial motor features in PD, but selection of treatment modalities should be tailored to the individual patient's needs.
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Affiliation(s)
- Martijn L T M Müller
- Functional Neuroimaging, Cognitive and Mobility Laboratory, Department of Radiology, University of Michigan, Ann Arbor, MI, USA.,Morris K. Udall Center of Excellence for Parkinson's Disease Research, University of Michigan, Ann Arbor, MI, USA
| | - Uros Marusic
- Institute for Kinesiology Research, Science and Research Centre of Koper, Koper, Slovenia.,Department of Health Sciences, Alma Mater Europaea - ECM, Maribor, Slovenia
| | - Miriam van Emde Boas
- Functional Neuroimaging, Cognitive and Mobility Laboratory, Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - Daniel Weiss
- Centre for Neurology, Department for Neurodegenerative Diseases and Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Nicolaas I Bohnen
- Functional Neuroimaging, Cognitive and Mobility Laboratory, Department of Radiology, University of Michigan, Ann Arbor, MI, USA.,Morris K. Udall Center of Excellence for Parkinson's Disease Research, University of Michigan, Ann Arbor, MI, USA.,Geriatric Research Education and Clinical Center, Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, MI, USA.,Department of Neurology, University of Michigan, Ann Arbor, USA
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Shoulson I, Eberly S, Oakes D, Kayson E, Young AB. Phenotype-genotype discrepancies in the prospective Huntington at-risk observational study. Ann Clin Transl Neurol 2019; 6:1046-1052. [PMID: 31211168 PMCID: PMC6562068 DOI: 10.1002/acn3.781] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Revised: 02/28/2019] [Accepted: 03/20/2019] [Indexed: 11/30/2022] Open
Abstract
OBJECTIVE To examine phenotype-genotype discrepancies (PGDs) wherein genotype-concealed and prospective judgments of the motor onset of Huntington disease (HD) occurred among at-risk adults who had nonexpanded (<37) cytosine-adenine-guanine (CAG) trinucleotide DNA repeats. METHODS We examined the prospective clinical assessments of investigators who were kept unaware of individual CAG lengths in the Prospective Huntington At-Risk Observational Study (PHAROS) who enrolled and followed undiagnosed adults at risk for HD who chose not to learn their gene status. Subjects (n = 1001) at 43 Huntington Study Group research sites in the US and Canada were evaluated prospectively and systematically between 1999 and 2009. At each site, an investigator was designated to perform comprehensive clinic assessments and another investigator to rate only the motor examination. Phenoconversion from a "premanifest" status to a confidently "manifest" status was based on investigator judgment (diagnostic confidence level) of the extrapyramidal motor features of HD. RESULTS There were 20 PGDs that over time had less severe motor scores than the 101 phenoconversions with CAG ≥37, but more severe motor scores than nonconversions. Following conversion, subjects with CAG ≥37 expansions worsened more motorically and cognitively than PGD subjects in the < 37 group. PGDs were concentrated among three sites and a few investigators, especially raters who only assessed the motor examination. INTERPRETATION The ability to detect the clinical onset of HD in a timely and reliable fashion remains the key for developing experimental treatments aimed at postponing the clinical onset of HD. Comprehensive clinical evaluation is a more accurate and reliable basis for determining HD clinical onset than sole reliance on judging the extrapyramidal features of HD.
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Affiliation(s)
- Ira Shoulson
- University of Rochester Medical CenterRochesterNew York
| | | | - David Oakes
- University of Rochester Medical CenterRochesterNew York
| | - Elise Kayson
- University of Rochester Medical CenterRochesterNew York
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Herzog–Krzywoszanska R, Krzywoszanski L. Sleep Disorders in Huntington's Disease. Front Psychiatry 2019; 10:221. [PMID: 31031659 PMCID: PMC6474183 DOI: 10.3389/fpsyt.2019.00221] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2018] [Accepted: 03/26/2019] [Indexed: 12/13/2022] Open
Abstract
Huntington's chorea (Huntington's disease, HD) is a genetic disorder caused by autosomal dominant mutation, leading to progressive neurodegenerative changes in the central nervous system. Involuntary movements such as chorea occur typically in HD patients, accompanied by progressive cognitive and psychiatric disturbances. Other common symptoms of HD are circadian and sleep abnormalities, which are observed from the earliest stages of the disease or even before the occurrence of clinical symptoms. The most common sleep problems reported by HD patients include insomnia, difficulties in falling asleep, frequent nocturnal awakenings, and excessive daytime sleepiness. Also, specific changes in sleep architecture have been identified in HD. In this paper, we review studies on sleep and circadian rhythm disorders in HD. We outline findings concerning sleep patterns and disturbances of circadian rhythms in HD patients, as well as the role of psychiatric disorders and motor disorders in HD patients' sleep problems. We also discuss problems related to the different methods of diagnosing sleep disorders in HD. Furthermore, the adverse effects of medication used for the treatment of core HD symptoms as one of the sources of sleep disturbances in HD are emphasized. In conclusion, the diversity and complexity of the determinants of sleep and circadian rhythm disorders in HD are highlighted. Finally, the relevance of effective treatment to improve patients' functioning and quality of life as well as the potential relief of their cognitive and emotional symptoms is addressed.
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Affiliation(s)
| | - Lukasz Krzywoszanski
- Neurocognitive Psychology Unit, Chair of Psychology, Faculty of Pedagogy, Pedagogical University of Krakow, Krakow, Poland
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Sen-Gupta E, Wright DE, Caccese JW, Wright Jr. JA, Jortberg E, Bhatkar V, Ceruolo M, Ghaffari R, Clason DL, Maynard JP, Combs AH. A Pivotal Study to Validate the Performance of a Novel Wearable Sensor and System for Biometric Monitoring in Clinical and Remote Environments. Digit Biomark 2019; 3:1-13. [PMID: 32095764 PMCID: PMC7015390 DOI: 10.1159/000493642] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Accepted: 09/11/2018] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Increasingly, drug and device clinical trials are tracking activity levels and other quality of life indices as endpoints for therapeutic efficacy. Trials have traditionally required intermittent subject visits to the clinic that are artificial, activity-intensive, and infrequent, making trend and event detection between visits difficult. Thus, there is an unmet need for wearable sensors that produce clinical quality and medical grade physiological data from subjects in the home. The current study was designed to validate the BioStamp nPoint® system (MC10 Inc., Lexington, MA, USA), a new technology designed to meet this need. OBJECTIVE To evaluate the accuracy, performance, and ease of use of an end-to-end system called the BioStamp nPoint. The system consists of an investigator portal for design of trials and data review, conformal, low-profile, wearable biosensors that adhere to the skin, a companion technology for wireless data transfer to a proprietary cloud, and algorithms for analyzing physiological, biometric, and contextual data for clinical research. METHODS A prospective, nonrandomized clinical trial was conducted on 30 healthy adult volunteers over the course of two continuous days and nights. Supervised and unsupervised study activities enabled performance validation in clinical and remote (simulated "at home") environments. System outputs for heart rate (HR), heart rate variability (HRV) (including root mean square of successive differences [RMSSD] and low frequency/high frequency ratio), activity classification during prescribed activities (lying, sitting, standing, walking, stationary biking, and sleep), step count during walking, posture characterization, and sleep metrics including onset/wake times, sleep duration, and respiration rate (RR) during sleep were evaluated. Outputs were compared to FDA-cleared comparator devices for HR, HRV, and RR and to ground truth investigator observations for activity and posture classifications, step count, and sleep events. RESULTS Thirty participants (77% male, 23% female; mean age 35.9 ± 10.1 years; mean BMI 28.1 ± 3.6) were enrolled in the study. The BioStamp nPoint system accurately measured HR and HRV (correlations: HR = 0.957, HRV RMSSD = 0.965, HRV ratio = 0.861) when compared to ActiheartTM. The system accurately monitored RR (mean absolute error [MAE] = 1.3 breaths/min) during sleep when compared to a Capnostream35TM end-tidal CO2 monitor. When compared with investigator observations, the system correctly classified activities and posture (agreement = 98.7 and 92.9%, respectively), step count (MAE = 14.7, < 3% of actual steps during a 6-min walk), and sleep events (MAE: sleep onset = 6.8 min, wake = 11.5 min, sleep duration = 13.7 min) with high accuracy. Participants indicated "good" to "excellent" usability (average System Usability Scale score of 81.3) and preferred the BioStamp nPoint system over both the Actiheart (86%) and Capnostream (97%) devices. CONCLUSIONS The present study validated the BioStamp nPoint system's performance and ease of use compared to FDA-cleared comparator devices in both the clinic and remote (home) environments.
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Cohen S, Waks Z, Elm JJ, Gordon MF, Grachev ID, Navon-Perry L, Fine S, Grossman I, Papapetropoulos S, Savola JM. Characterizing patient compliance over six months in remote digital trials of Parkinson's and Huntington disease. BMC Med Inform Decis Mak 2018; 18:138. [PMID: 30572891 PMCID: PMC6302308 DOI: 10.1186/s12911-018-0714-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2018] [Accepted: 11/23/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A growing number of clinical trials use various sensors and smartphone applications to collect data outside of the clinic or hospital, raising the question to what extent patients comply with the unique requirements of remote study protocols. Compliance is particularly important in conditions where patients are motorically and cognitively impaired. Here, we sought to understand patient compliance in digital trials of two such pathologies, Parkinson's disease (PD) and Huntington disease (HD). METHODS Patient compliance was assessed in two remote, six-month clinical trials of PD (n = 51, Clinician Input Study funded by the Michael J. Fox Foundation for Parkinson's Research) and HD (n = 17, sponsored by Teva Pharmaceuticals). We monitored four compliance metrics specific to remote studies: smartphone app-based medication reporting, app-based symptoms reporting, the duration of smartwatch data streaming except while charging, and the performance of structured motor tasks at home. RESULTS While compliance over time differed between the PD and HD studies, both studies maintained high compliance levels for their entire six month duration. None (- 1%) to a 30% reduction in compliance rate was registered for HD patients, and a reduction of 34 to 53% was registered for the PD study. Both studies exhibited marked changes in compliance rates during the initial days of enrollment. Interestingly, daily smartwatch data streaming patterns were similar, peaking around noon, dropping sharply in the late evening hours around 8 pm, and having a mean of 8.6 daily streaming hours for the PD study and 10.5 h for the HD study. Individual patients tended to have either high or low compliance across all compliance metrics as measured by pairwise correlation. Encouragingly, predefined schedules and app-based reminders fulfilled their intended effect on the timing of medication intake reporting and performance of structured motor tasks at home. CONCLUSIONS Our findings suggest that maintaining compliance over long durations is feasible, promote the use of predefined app-based reminders, and highlight the importance of patient selection as highly compliant patients typically have a higher adherence rate across the different aspects of the protocol. Overall, these data can serve as a reference point for the design of upcoming remote digital studies. TRIAL REGISTRATION Trials described in this study include a sub-study of the Open PRIDE-HD Huntington's disease study (TV7820-CNS-20016), which was registered on July 7th, 2015, sponsored by Teva Pharmaceuticals Ltd., and registered on Clinicaltrials.gov as NCT02494778 and EudraCT as 2015-000904-24 .
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Affiliation(s)
- Shani Cohen
- Advanced Analytics Department, Intel, 94 Em Hamoshavot Road, Petah Tikva, Israel
| | - Zeev Waks
- Advanced Analytics Department, Intel, 94 Em Hamoshavot Road, Petah Tikva, Israel.
| | - Jordan J Elm
- Department of Public Health Sciences, Medical University of South Carolina, 135 Cannon St., Suite 303, PO Box 250835, Charleston, SC, 29425, USA
| | - Mark Forrest Gordon
- Teva Branded Pharmaceutical Products R&D, Inc, 41 Moores Rd., Frazer, Petah Tikva, PA, 19355, USA
| | - Igor D Grachev
- Guide Pharmaceutical Consulting, LLC, Millstone Township, NJ, 08535, USA
| | - Leehee Navon-Perry
- Teva Pharmaceutical Industries Ltd, 12 Hatrufa St, 4250483, Netanya, Israel
| | - Shai Fine
- Data Science Institute, Interdisciplinary Center, 1 Kanfei Nesharim St, 4610101, Herzliya, Israel
| | - Iris Grossman
- CAMP4 Therapeutics, One Kendall Square, Bldg 1400 West, 3rd Floor, Cambridge, MA, 02139, USA
| | | | - Juha-Matti Savola
- Teva Pharmaceuticals International GmbH, Elisabethenstrasse 15, 4051, Basel, Switzerland
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Snyder CW, Dorsey ER, Atreja A. The Best Digital Biomarkers Papers of 2017. Digit Biomark 2018; 2:64-73. [PMID: 32095757 PMCID: PMC7015358 DOI: 10.1159/000489224] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Accepted: 04/13/2018] [Indexed: 12/24/2022] Open
Abstract
The use and evaluation of digital biomarkers, objective and quantifiable measures of biology, and health collected through digital devices is growing rapidly. To highlight some of the most promising work in the field, we have compiled a list of the top digital biomarkers papers from the past year. Eligible papers reported on original research that evaluated a digital sensor (e.g., smartphone, wearable sensor, implantable device) in humans and was published in a peer-reviewed journal in 2017. Nominations were solicited from the editorial board of Digital Biomarkers and supplemented by papers the editorial team identified from Web of Science, Google Scholar, and PubMed. The editorial board then selected up to ten papers to be recognized among 28 nominations. Here, we present all of the nominated papers and profile the eight that received the most votes. The top eight papers evaluated 1,290 individuals with digital pills, smartwatches, wearable devices, and electronic inhalers in disease states ranging from dementia to diabetes and from Parkinson disease to pain.
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Affiliation(s)
- Christopher W. Snyder
- Center for Health + Technology, University of Rochester Medical Center, Rochester, New York, USA
| | - E. Ray Dorsey
- Center for Health + Technology, University of Rochester Medical Center, Rochester, New York, USA
| | - Ashish Atreja
- AppLab, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Yamagami M, Peters KM, Milovanovic I, Kuang I, Yang Z, Lu N, Steele KM. Assessment of Dry Epidermal Electrodes for Long-Term Electromyography Measurements. SENSORS 2018; 18:s18041269. [PMID: 29677129 PMCID: PMC5948629 DOI: 10.3390/s18041269] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Revised: 04/19/2018] [Accepted: 04/19/2018] [Indexed: 11/16/2022]
Abstract
Commercially available electrodes can only provide quality surface electromyography (sEMG) measurements for a limited duration due to user discomfort and signal degradation, but in many applications, collecting sEMG data for a full day or longer is desirable to enhance clinical care. Few studies for long-term sEMG have assessed signal quality of electrodes using clinically relevant tests. The goal of this research was to evaluate flexible, gold-based epidermal sensor system (ESS) electrodes for long-term sEMG recordings. We collected sEMG and impedance data from eight subjects from ESS and standard clinical electrodes on upper extremity muscles during maximum voluntary isometric contraction tests, dynamic range of motion tests, the Jebsen Taylor Hand Function Test, and the Box & Block Test. Four additional subjects were recruited to test the stability of ESS signals over four days. Signals from the ESS and traditional electrodes were strongly correlated across tasks. Measures of signal quality, such as signal-to-noise ratio and signal-to-motion ratio, were also similar for both electrodes. Over the four-day trial, no significant decrease in signal quality was observed in the ESS electrodes, suggesting that thin, flexible electrodes may provide a robust tool that does not inhibit movement or irritate the skin for long-term measurements of muscle activity in rehabilitation and other applications.
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Affiliation(s)
- Momona Yamagami
- Department of Electrical Engineering, University of Washington, Seattle, WA 98195, USA.
| | - Keshia M Peters
- Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA.
| | - Ivana Milovanovic
- Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA.
| | - Irene Kuang
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Zeyu Yang
- Chengdu Rotex Technology Company Ltd., Chengdu 610041, China.
| | - Nanshu Lu
- Department of Biomedical Engineering, Aerospace Engineering and Engineering Mechanics, University of Texas, Austin, TX 78712, USA.
| | - Katherine M Steele
- Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA.
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Abstract
Neurological disorders are the leading cause of global disability. However, for most people around the world, current neurological care is poor. In low-income countries, most individuals lack access to proper neurological care, and in high-income countries, distance and disability limit access. With the global proliferation of smartphones, teleneurology - the use of technology to provide neurological care and education remotely - has the potential to improve and increase access to care for billions of people. Telestroke has already fulfilled this promise, but teleneurology applications for chronic conditions are still in their infancy. Similarly, few studies have explored the capabilities of mobile technologies such as smartphones and wearable sensors, which can guide care by providing objective, frequent, real-world assessments of patients. In low-income settings, teleneurology can increase the capacity of local care systems through professional development, diagnostic support and consultative services. In high-income settings, teleneurology is likely to promote the expansion and migration of neurological care away from institutions, incorporate systems of asynchronous communication (such as e-mail), integrate clinicians with diverse skill sets and reach new populations. Inertia, outdated policies and social barriers - especially the digital divide - will slow this progress at considerable cost. However, a future increasingly will be possible in which neurological care can be accessed by anyone, anywhere. Here, we examine the emerging evidence regarding the benefits of teleneurology for chronic conditions, its role and risks in low-income countries and the promise of mobile technologies to measure disease status and deliver care. We conclude by discussing the future trends, barriers and timing for the adoption of teleneurology.
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Kovalchick C, Sirkar R, Regele OB, Kourtis LC, Schiller M, Wolpert H, Alden RG, Jones GB, Wright JM. Can composite digital monitoring biomarkers come of age? A framework for utilization. J Clin Transl Sci 2017; 1:373-380. [PMID: 29707260 PMCID: PMC5916505 DOI: 10.1017/cts.2018.4] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Revised: 01/16/2018] [Accepted: 01/19/2018] [Indexed: 12/20/2022] Open
Abstract
INTRODUCTION The application of digital monitoring biomarkers in health, wellness and disease management is reviewed. Harnessing the near limitless capacity of these approaches in the managed healthcare continuum will benefit from a systems-based architecture which presents data quality, quantity, and ease of capture within a decision-making dashboard. METHODS A framework was developed which stratifies key components and advances the concept of contextualized biomarkers. The framework codifies how direct, indirect, composite, and contextualized composite data can drive innovation for the application of digital biomarkers in healthcare. RESULTS The de novo framework implies consideration of physiological, behavioral, and environmental factors in the context of biomarker capture and analysis. Application in disease and wellness is highlighted, and incorporation in clinical feedback loops and closed-loop systems is illustrated. CONCLUSIONS The study of contextualized biomarkers has the potential to offer rich and insightful data for clinical decision making. Moreover, advancement of the field will benefit from innovation at the intersection of medicine, engineering, and science. Technological developments in this dynamic field will thus fuel its logical evolution guided by inputs from patients, physicians, healthcare providers, end-payors, actuarists, medical device manufacturers, and drug companies.
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Affiliation(s)
| | - Rhea Sirkar
- Eli Lilly Innovation Center, Cambridge, MA, USA
| | | | | | | | | | | | - Graham B. Jones
- Clinical & Translational Science Institute, Tufts University Medical Center, Boston, MA, USA
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