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Straczkiewicz M, Karas M, Johnson SA, Burke KM, Scheier Z, Royse TB, Calcagno N, Clark A, Iyer A, Berry JD, Onnela JP. Upper limb movements as digital biomarkers in people with ALS. EBioMedicine 2024; 101:105036. [PMID: 38432083 PMCID: PMC10914560 DOI: 10.1016/j.ebiom.2024.105036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 02/06/2024] [Accepted: 02/12/2024] [Indexed: 03/05/2024] Open
Abstract
BACKGROUND Objective evaluation of people with amyotrophic lateral sclerosis (PALS) in free-living settings is challenging. The introduction of portable digital devices, such as wearables and smartphones, may improve quantifying disease progression and hasten therapeutic development. However, there is a need for tools to characterize upper limb movements in neurologic disease and disability. METHODS Twenty PALS wore a wearable accelerometer, ActiGraph Insight Watch, on their wrist for six months. They also used Beiwe, a smartphone application that collected self-entry ALS Functional Rating Scale-Revised (ALSFRS-RSE) survey responses every 1-4 weeks. We developed several measures that quantify count and duration of upper limb movements: flexion, extension, supination, and pronation. New measures were compared against ALSFRS-RSE total score (Q1-12), and individual responses to specific questions related to handwriting (Q4), cutting food (Q5), dressing and performing hygiene (Q6), and turning in bed and adjusting bed clothes (Q7). Additional analysis considered adjusting for total activity counts (TAC). FINDINGS At baseline, PALS with higher Q1-12 performed more upper limb movements, and these movements were faster compared to individuals with more advanced disease. Most upper limb movement metrics had statistically significant change over time, indicating declining function either by decreasing count metrics or by increasing duration metric. All count and duration metrics were significantly associated with Q1-12, flexion and extension counts were significantly associated with Q6 and Q7, supination and pronation counts were also associated with Q4. All duration metrics were associated with Q6 and Q7. All duration metrics retained their statistical significance after adjusting for TAC. INTERPRETATION Wearable accelerometer data can be used to generate digital biomarkers on upper limb movements and facilitate patient monitoring in free-living environments. The presented method offers interpretable monitoring of patients' functioning and versatile tracking of disease progression in the limb of interest. FUNDING Mitsubishi-Tanabe Pharma Holdings America, Inc.
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Affiliation(s)
- Marcin Straczkiewicz
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Marta Karas
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | - Katherine M Burke
- Neurological Clinical Research Institute and Sean M. Healey & AMG Center for ALS, Massachusetts General Hospital, Boston, MA, USA
| | - Zoe Scheier
- Neurological Clinical Research Institute and Sean M. Healey & AMG Center for ALS, Massachusetts General Hospital, Boston, MA, USA
| | - Tim B Royse
- Neurological Clinical Research Institute and Sean M. Healey & AMG Center for ALS, Massachusetts General Hospital, Boston, MA, USA
| | - Narghes Calcagno
- Neurological Clinical Research Institute and Sean M. Healey & AMG Center for ALS, Massachusetts General Hospital, Boston, MA, USA; Neurology Residency Program, University of Milan, Milan, Italy
| | - Alison Clark
- Neurological Clinical Research Institute and Sean M. Healey & AMG Center for ALS, Massachusetts General Hospital, Boston, MA, USA
| | - Amrita Iyer
- Neurological Clinical Research Institute and Sean M. Healey & AMG Center for ALS, Massachusetts General Hospital, Boston, MA, USA
| | - James D Berry
- Neurological Clinical Research Institute and Sean M. Healey & AMG Center for ALS, Massachusetts General Hospital, Boston, MA, USA
| | - Jukka-Pekka Onnela
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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Lisi E, Abellan JJ. Statistical analysis of actigraphy data with generalised additive models. Pharm Stat 2023. [PMID: 37973064 DOI: 10.1002/pst.2350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 09/23/2023] [Accepted: 10/29/2023] [Indexed: 11/19/2023]
Abstract
There is a growing interest in the use of physical activity data in clinical studies, particularly in diseases that limit mobility in patients. High-frequency data collected with digital sensors are typically summarised into actigraphy features aggregated at epoch level (e.g., by minute). The statistical analysis of such volume of data is not straightforward. The general trend is to derive metrics, capturing specific aspects of physical activity, that condense (say) a week worth of data into a single numerical value. Here we propose to analyse the entire time-series data using Generalised Additive Models (GAMs). GAMs are semi-parametric models that allow inclusion of both parametric and non-parametric terms in the linear predictor. The latter are smooth terms (e.g., splines) and, in the context of actigraphy minute-by-minute data analysis, they can be used to assess daily patterns of physical activity. This in turn can be used to better understand changes over time in longitudinal studies as well as to compare treatment groups. We illustrate the application of GAMs in two clinical studies where actigraphy data was collected: a non-drug, single-arm study in patients with amyotrophic lateral sclerosis, and a physical-activity sub-study included in a phase 2b clinical trial in patients with chronic obstructive pulmonary disease.
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Hamy V, Llop C, Yee CW, Garcia-Gancedo L, Maxwell A, Chen WH, Tomlinson R, Bobbili P, Bendelac J, Landry J, DerSarkissian M, Yenikomshian M, Mody EA, Duh MS, Williams R. Patient-centric assessment of rheumatoid arthritis using a smartwatch and bespoke mobile app in a clinical setting. Sci Rep 2023; 13:18311. [PMID: 37880288 PMCID: PMC10600111 DOI: 10.1038/s41598-023-45387-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 10/19/2023] [Indexed: 10/27/2023] Open
Abstract
Rheumatoid arthritis (RA) is a fluctuating progressive disease requiring frequent symptom assessment for appropriate management. Continuous tracking using digital technologies may provide greater insights of a patient's experience. This prospective study assessed the feasibility, reliability, and clinical utility of using novel digital technologies to remotely monitor participants with RA. Participants with moderate to severe RA and non-RA controls were monitored continuously for 14 days using an iPhone with an integrated bespoke application and an Apple Watch. Participants completed patient-reported outcome measures and objective guided tests designed to assess disease-related impact on physical function. The study was completed by 28 participants with RA, 28 matched controls, and 2 unmatched controls. Completion rates for all assessments were > 97% and were reproducible over time. Several guided tests distinguished between RA and control cohorts (e.g., mean lie-to-stand time [seconds]: RA: 4.77, control: 3.25; P < 0.001). Participants with RA reporting greater stiffness, pain, and fatigue had worse guided test performances (e.g., wrist movement [P < 0.001] and sit-to-stand transition time [P = 0.009]) compared with those reporting lower stiffness, pain, and fatigue. This study demonstrates that digital technologies can be used in a well-controlled, remote clinical setting to assess the daily impact of RA.
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Affiliation(s)
- Valentin Hamy
- Value Evidence and Outcomes, GSK, Brentford, TW8 9GS, UK.
| | | | | | | | - Aoife Maxwell
- Value Evidence and Outcomes, GSK, Brentford, TW8 9GS, UK
| | | | | | | | | | | | | | | | - Elinor A Mody
- Rheumatology Department, Reliant Medical Group, Auburn, USA
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Jenciūtė G, Kasputytė G, Bunevičienė I, Korobeinikova E, Vaitiekus D, Inčiūra A, Jaruševičius L, Bunevičius R, Krikštolaitis R, Krilavičius T, Juozaitytė E, Bunevičius A. Digital Phenotyping for Monitoring and Disease Trajectory Prediction of Patients With Cancer: Protocol for a Prospective Observational Cohort Study. JMIR Res Protoc 2023; 12:e49096. [PMID: 37815850 PMCID: PMC10599285 DOI: 10.2196/49096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 07/24/2023] [Accepted: 07/31/2023] [Indexed: 10/11/2023] Open
Abstract
BACKGROUND Timely recognition of cancer progression and treatment complications is important for treatment guidance. Digital phenotyping is a promising method for precise and remote monitoring of patients in their natural environments by using passively generated data from sensors of personal wearable devices. Further studies are needed to better understand the potential clinical benefits of digital phenotyping approaches to optimize care of patients with cancer. OBJECTIVE We aim to evaluate whether passively generated data from smartphone sensors are feasible for remote monitoring of patients with cancer to predict their disease trajectories and patient-centered health outcomes. METHODS We will recruit 200 patients undergoing treatment for cancer. Patients will be followed up for 6 months. Passively generated data by sensors of personal smartphone devices (eg, accelerometer, gyroscope, GPS) will be continuously collected using the developed LAIMA smartphone app during follow-up. We will evaluate (1) mobility data by using an accelerometer (mean time of active period, mean time of exertional physical activity, distance covered per day, duration of inactive period), GPS (places of interest visited daily, hospital visits), and gyroscope sensors and (2) sociability indices (frequency of duration of phone calls, frequency and length of text messages, and internet browsing time). Every 2 weeks, patients will be asked to complete questionnaires pertaining to quality of life (European Organization for Research and Treatment of Cancer Core Quality of Life Questionnaire [EORTC QLQ-C30]), depression symptoms (Patient Health Questionnaire-9 [PHQ-9]), and anxiety symptoms (General Anxiety Disorder-7 [GAD-7]) that will be deployed via the LAIMA app. Clinic visits will take place at 1-3 months and 3-6 months of the study. Patients will be evaluated for disease progression, cancer and treatment complications, and functional status (Eastern Cooperative Oncology Group) by the study oncologist and will complete the questionnaire for evaluating quality of life (EORTC QLQ-C30), depression symptoms (PHQ-9), and anxiety symptoms (GAD-7). We will examine the associations among digital, clinical, and patient-reported health outcomes to develop prediction models with clinically meaningful outcomes. RESULTS As of July 2023, we have reached the planned recruitment target, and patients are undergoing follow-up. Data collection is expected to be completed by September 2023. The final results should be available within 6 months after study completion. CONCLUSIONS This study will provide in-depth insight into temporally and spatially precise trajectories of patients with cancer that will provide a novel digital health approach and will inform the design of future interventional clinical trials in oncology. Our findings will allow a better understanding of the potential clinical value of passively generated smartphone sensor data (digital phenotyping) for continuous and real-time monitoring of patients with cancer for treatment side effects, cancer complications, functional status, and patient-reported outcomes as well as prediction of disease progression or trajectories. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) PRR1-10.2196/49096.
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Affiliation(s)
- Gabrielė Jenciūtė
- Faculty of Informatics, Vytautas Magnus University, Kaunas, Lithuania
| | | | - Inesa Bunevičienė
- Faculty of Political Science and Diplomacy, Vytautas Magnus University, Kaunas, Lithuania
| | - Erika Korobeinikova
- Oncology Institute, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Domas Vaitiekus
- Oncology Institute, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Arturas Inčiūra
- Oncology Institute, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | | | | | | | - Tomas Krilavičius
- Faculty of Informatics, Vytautas Magnus University, Kaunas, Lithuania
| | - Elona Juozaitytė
- Oncology Institute, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Adomas Bunevičius
- Department of Neurology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States
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Kudritzki V, Howard IM. Telehealth-based exercise in amyotrophic lateral sclerosis. Front Neurol 2023; 14:1238916. [PMID: 37564731 PMCID: PMC10410446 DOI: 10.3389/fneur.2023.1238916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 07/04/2023] [Indexed: 08/12/2023] Open
Abstract
The Veterans Health Administration (VHA) has served as a leader in the implementation of telerehabilitation technologies and continues to expand utilization of non-traditional patient encounters to better serve a geographically and demographically diverse population. Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disease impacting Veterans at a higher rate than the civilian population and associated with high levels of disability and limited access to subspecialized care. There is growing evidence supporting exercise-based interventions as an independent or adjunctive treatment to maintain or restore function for this patient population; many of these interventions can be delivered remotely by telehealth. The recent advancements in disease-modifying therapies for neuromuscular disorders will likely increase the importance of rehabilitation interventions to maximize functional outcomes. Here, we review the evidence for specific exercise interventions in ALS and the evidence for telehealth-based exercise in neuromuscular disorders. We then use this existing literature to propose a framework for telehealth delivery of these treatments, including feasible exercise interventions and remote outcome measures, recommended peripheral devices, and an example of a current remote group exercise program offered through VHA.
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Affiliation(s)
- Virginia Kudritzki
- Rehabilitation Care Services, VA Puget Sound Healthcare System, Seattle, WA, United States
| | - Ileana M. Howard
- Rehabilitation Care Services, VA Puget Sound Healthcare System, Seattle, WA, United States
- Department of Rehabilitation Medicine, University of Washington, Seattle, WA, United States
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6
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Johnson SA, Karas M, Burke KM, Straczkiewicz M, Scheier ZA, Clark AP, Iwasaki S, Lahav A, Iyer AS, Onnela JP, Berry JD. Wearable device and smartphone data quantify ALS progression and may provide novel outcome measures. NPJ Digit Med 2023; 6:34. [PMID: 36879025 DOI: 10.1038/s41746-023-00778-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 02/14/2023] [Indexed: 03/08/2023] Open
Abstract
Amyotrophic lateral sclerosis (ALS) therapeutic development has largely relied on staff-administered functional rating scales to determine treatment efficacy. We sought to determine if mobile applications (apps) and wearable devices can be used to quantify ALS disease progression through active (surveys) and passive (sensors) data collection. Forty ambulatory adults with ALS were followed for 6-months. The Beiwe app was used to administer the self-entry ALS functional rating scale-revised (ALSFRS-RSE) and the Rasch Overall ALS Disability Scale (ROADS) surveys every 2-4 weeks. Each participant used a wrist-worn activity monitor (ActiGraph Insight Watch) or an ankle-worn activity monitor (Modus StepWatch) continuously. Wearable device wear and app survey compliance were adequate. ALSFRS-R highly correlated with ALSFRS-RSE. Several wearable data daily physical activity measures demonstrated statistically significant change over time and associations with ALSFRS-RSE and ROADS. Active and passive digital data collection hold promise for novel ALS trial outcome measure development.
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7
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Holdom CJ, van Unnik JWJ, van Eijk RPA, van den Berg LH, Henderson RD, Ngo ST, Steyn FJ. Use of hip- versus wrist-based actigraphy for assessing functional decline and disease progression in patients with motor neuron disease. J Neurol 2023; 270:2597-2605. [PMID: 36740646 PMCID: PMC10129939 DOI: 10.1007/s00415-023-11584-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 01/19/2023] [Accepted: 01/20/2023] [Indexed: 02/07/2023]
Abstract
BACKGROUND Actigraphy has been proposed as a measure for tracking functional decline and disease progression in patients with Motor Neuron Disease (MND). There is, however, little evidence to show that wrist-based actigraphy measures correlate with functional decline, and no consensus on how best to implement actigraphy. We report on the use of wrist actigraphy to show decreased activity in patients compared to controls, and compared the utility of wrist- and hip-based actigraphy for assessing functional decline in patients with MND. METHODS In this multi-cohort, multi-centre, natural history study, wrist- and hip-based actigraphy were assessed in 139 patients with MND (wrist, n = 97; hip, n = 42) and 56 non-neurological control participants (wrist, n = 56). For patients with MND, longitudinal measures were contrasted with clinical outcomes commonly used to define functional decline. RESULTS Patients with MND have reduced wrist-based actigraphy scores when compared to controls (median differences: prop. active = - 0.053 [- 0.075, - 0.026], variation axis 1 = - 0.073 [- 0.112, - 0.021]). When comparing wrist- and hip-based measures, hip-based accelerometery had stronger correlations with disease progression (prop. active: τ = 0.20 vs 0.12; variation axis 1: τ = 0.33 vs 0.23), whereas baseline wrist-based accelerometery was better related with future decline in fine-motor function (τ = 0.14-0.23 vs 0.06-0.16). CONCLUSIONS Actigraphy outcomes measured from the wrist are more variable than from the hip and present differing sensitivity to specific functional outcomes. Outcomes and analysis should be carefully constructed to maximise benefit, should wrist-worn devices be used for at-home monitoring of disease progression in patients with MND.
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Affiliation(s)
- Cory J Holdom
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
| | - Jordi W J van Unnik
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Ruben P A van Eijk
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.,Biostatistics and Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Leonard H van den Berg
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Robert D Henderson
- Centre for Clinical Research, The University of Queensland, Brisbane, Australia.,Department of Neurology, Royal Brisbane and Women's Hospital, Herston, Australia.,Wesley Medical Research, The Wesley Hospital, Brisbane, Australia
| | - Shyuan T Ngo
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia.,Department of Neurology, Royal Brisbane and Women's Hospital, Herston, Australia.,Wesley Medical Research, The Wesley Hospital, Brisbane, Australia
| | - Frederik J Steyn
- Department of Neurology, Royal Brisbane and Women's Hospital, Herston, Australia. .,Wesley Medical Research, The Wesley Hospital, Brisbane, Australia. .,School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Australia.
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Beswick E, Fawcett T, Hassan Z, Forbes D, Dakin R, Newton J, Abrahams S, Carson A, Chandran S, Perry D, Pal S. A systematic review of digital technology to evaluate motor function and disease progression in motor neuron disease. J Neurol 2022; 269:6254-6268. [PMID: 35945397 PMCID: PMC9363141 DOI: 10.1007/s00415-022-11312-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 07/22/2022] [Accepted: 07/25/2022] [Indexed: 11/17/2022]
Abstract
Amyotrophic lateral sclerosis (ALS) is the most common subtype of motor neuron disease (MND). The current gold-standard measure of progression is the ALS Functional Rating Scale-Revised (ALS-FRS(R)), a clinician-administered questionnaire providing a composite score on physical functioning. Technology offers a potential alternative for assessing motor progression in both a clinical and research capacity that is more sensitive to detecting smaller changes in function. We reviewed studies evaluating the utility and suitability of these devices to evaluate motor function and disease progression in people with MND (pwMND). We systematically searched Google Scholar, PubMed and EMBASE applying no language or date restrictions. We extracted information on devices used and additional assessments undertaken. Twenty studies, involving 1275 (median 28 and ranging 6-584) pwMND, were included. Sensor type included accelerometers (n = 9), activity monitors (n = 4), smartphone apps (n = 4), gait (n = 3), kinetic sensors (n = 3), electrical impedance myography (n = 1) and dynamometers (n = 2). Seventeen (85%) of studies used the ALS-FRS(R) to evaluate concurrent validity. Participant feedback on device utility was generally positive, where evaluated in 25% of studies. All studies showed initial feasibility, warranting larger longitudinal studies to compare device sensitivity and validity beyond ALS-FRS(R). Risk of bias in the included studies was high, with a large amount of information to determine study quality unclear. Measurement of motor pathology and progression using technology is an emerging, and promising, area of MND research. Further well-powered longitudinal validation studies are needed.
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Affiliation(s)
- Emily Beswick
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland, UK.,Anne Rowling Regenerative Neurology Clinic, The University of Edinburgh, 49 Little France Crescent, Edinburgh, EH16 4SB, Scotland, UK.,Euan MacDonald Centre for MND Research, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Thomas Fawcett
- The School of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Zack Hassan
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland, UK.,Anne Rowling Regenerative Neurology Clinic, The University of Edinburgh, 49 Little France Crescent, Edinburgh, EH16 4SB, Scotland, UK.,Euan MacDonald Centre for MND Research, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Deborah Forbes
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland, UK.,Anne Rowling Regenerative Neurology Clinic, The University of Edinburgh, 49 Little France Crescent, Edinburgh, EH16 4SB, Scotland, UK.,Euan MacDonald Centre for MND Research, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Rachel Dakin
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland, UK.,Anne Rowling Regenerative Neurology Clinic, The University of Edinburgh, 49 Little France Crescent, Edinburgh, EH16 4SB, Scotland, UK.,Euan MacDonald Centre for MND Research, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Judith Newton
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland, UK.,Anne Rowling Regenerative Neurology Clinic, The University of Edinburgh, 49 Little France Crescent, Edinburgh, EH16 4SB, Scotland, UK.,Euan MacDonald Centre for MND Research, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Sharon Abrahams
- Euan MacDonald Centre for MND Research, The University of Edinburgh, Edinburgh, Scotland, UK.,Human Cognitive Neurosciences, Psychology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Alan Carson
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland, UK
| | - Siddharthan Chandran
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland, UK.,Anne Rowling Regenerative Neurology Clinic, The University of Edinburgh, 49 Little France Crescent, Edinburgh, EH16 4SB, Scotland, UK.,Euan MacDonald Centre for MND Research, The University of Edinburgh, Edinburgh, Scotland, UK.,UK Dementia Research Institute, The University of Edinburgh, Edinburgh, Scotland, UK
| | - David Perry
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland, UK.,Anne Rowling Regenerative Neurology Clinic, The University of Edinburgh, 49 Little France Crescent, Edinburgh, EH16 4SB, Scotland, UK
| | - Suvankar Pal
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland, UK. .,Anne Rowling Regenerative Neurology Clinic, The University of Edinburgh, 49 Little France Crescent, Edinburgh, EH16 4SB, Scotland, UK. .,Euan MacDonald Centre for MND Research, The University of Edinburgh, Edinburgh, Scotland, UK.
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Khamaysa M, Pradat P. Status of ALS Treatment, Insights into Therapeutic Challenges and Dilemmas. J Pers Med 2022; 12:1601. [PMID: 36294741 PMCID: PMC9605458 DOI: 10.3390/jpm12101601] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/14/2022] [Accepted: 09/23/2022] [Indexed: 12/18/2022] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is an extremely heterogeneous disease of motor neurons that eventually leads to death. Despite impressive advances in understanding the genetic, molecular, and pathological mechanisms of the disease, the only drug approved to date by both the FDA and EMA is riluzole, with a modest effect on survival. In this opinion view paper, we will discuss how to address some challenges for drug development in ALS at the conceptual, technological, and methodological levels. In addition, socioeconomic and ethical issues related to the legitimate need of patients to benefit quickly from new treatments will also be addressed. In conclusion, this brief review takes a more optimistic view, given the recent approval of two new drugs in some countries and the development of targeted gene therapies.
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Meyer T, Spittel S, Grehl T, Weyen U, Steinbach R, Kettemann D, Petri S, Weydt P, Günther R, Baum P, Schlapakow E, Koch JC, Boentert M, Wolf J, Grosskreutz J, Rödiger A, Ilse B, Metelmann M, Norden J, Koc RY, Körtvélyessy P, Riitano A, Walter B, Hildebrandt B, Schaudinn F, Münch C, Maier A. Remote digital assessment of amyotrophic lateral sclerosis functional rating scale - a multicenter observational study. Amyotroph Lateral Scler Frontotemporal Degener 2022; 24:175-184. [PMID: 35912984 DOI: 10.1080/21678421.2022.2104649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Abstract
Objective: Remote self-assessment of the revised amyotrophic lateral sclerosis functional rating scale (ALSFRS-R) using digital data capture was investigated for its feasibility as an add-on to ALSFRS-R assessments during multidisciplinary clinic visits. Methods: From August 2017 to December 2021, at 12 ALS centers in Germany, an observational study on remote assessment of the ALSFRS-R was performed. In addition to the assessment of ALSFRS-R during clinic visits, patients were offered a digital self-assessment of the ALSFRS-R - either on a computer or on a mobile application ("ALS-App"). Results: An estimated multicenter cohort of 4,670 ALS patients received care at participating ALS centers. Of these patients, 971 remotely submitted the ALSFRS-R, representing 21% of the multicenter cohort. Of those who opted for remote assessment, 53.7% (n = 521) completed a minimum of 4 ALSFRS-R per year with a mean number of 10.9 assessments per year. Different assessment frequencies were found for patients using a computer (7.9 per year, n = 857) and mobile app (14.6 per year, n = 234). Patients doing remote assessments were more likely to be male and less functionally impaired but many patients with severe disability managed to complete it themselves or with a caregiver (35% of remote ALSFRS-R cohort in King's Stage 4). Conclusions: In a dedicated ALS center setting remote digital self-assessment of ALSFRS-R can provide substantial data which is complementary and potentially an alternative to clinic assessments and could be used for research purposes and person-level patient management. Addressing barriers relating to patient uptake and adherence are key to its success.
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Affiliation(s)
- Thomas Meyer
- Department of Neurology, Center for ALS and other Motor Neuron Disorders, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.,Ambulanzpartner Soziotechnologie APST GmbH, Berlin, Germany
| | - Susanne Spittel
- Department of Neurology, Center for ALS and other Motor Neuron Disorders, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.,Ambulanzpartner Soziotechnologie APST GmbH, Berlin, Germany
| | - Torsten Grehl
- Department of Neurology, Center for ALS and other Motor Neuron Disorders, Alfried Krupp Krankenhaus, Essen, Germany
| | - Ute Weyen
- Department of Neurology, Center for ALS and other Motor Neuron Disorders, Berufsgenossenschaftliches Universitätsklinikum Bergmannsheil, Bochum, Germany
| | - Robert Steinbach
- Hans Berger Department of Neurology, Jena University Hospital, Jena, Germany
| | - Dagmar Kettemann
- Department of Neurology, Center for ALS and other Motor Neuron Disorders, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Susanne Petri
- Department of Neurology, Hannover Medical School, Hannover, Germany
| | - Patrick Weydt
- Department for Neurodegenerative Disorders and Gerontopsychiatry, Bonn University, Bonn, Germany
| | - René Günther
- Department of Neurology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.,DZNE, German Center for Neurodegenerative Diseases, Research Site Dresden, Dresden, Germany
| | - Petra Baum
- Department of Neurology, Universitätsklinikum Leipzig, Leipzig, Germany
| | - Elena Schlapakow
- Department of Neurology, Universitätsklinikum Halle, Halle (Saale), Germany
| | - Jan Christoph Koch
- Department of Neurology, Universitätsmedizin Göttingen, Göttingen, Germany
| | - Matthias Boentert
- Department of Sleep Medicine and Neuromuscular Disorders, Universitätsklinikum Münster, Münster, Germany
| | - Joachim Wolf
- Department of Neurology, Diako Mannheim, Mannheim, Germany
| | - Julian Grosskreutz
- Precision Neurology, Universitätsklinikum Schleswig-Holstein, Lübeck, Germany
| | - Annekathrin Rödiger
- Hans Berger Department of Neurology, Jena University Hospital, Jena, Germany
| | - Benjamin Ilse
- Hans Berger Department of Neurology, Jena University Hospital, Jena, Germany
| | - Moritz Metelmann
- Department of Neurology, Universitätsklinikum Leipzig, Leipzig, Germany
| | - Jenny Norden
- Department of Neurology, Center for ALS and other Motor Neuron Disorders, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Ruhan Yasemin Koc
- Department of Neurology, Center for ALS and other Motor Neuron Disorders, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Péter Körtvélyessy
- Department of Neurology, Center for ALS and other Motor Neuron Disorders, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Alessio Riitano
- Department of Neurology, Center for ALS and other Motor Neuron Disorders, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Bertram Walter
- Department of Neurology, Center for ALS and other Motor Neuron Disorders, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | | | | | - Christoph Münch
- Department of Neurology, Center for ALS and other Motor Neuron Disorders, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.,Ambulanzpartner Soziotechnologie APST GmbH, Berlin, Germany
| | - André Maier
- Department of Neurology, Center for ALS and other Motor Neuron Disorders, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
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11
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Hecker P, Steckhan N, Eyben F, Schuller BW, Arnrich B. Voice Analysis for Neurological Disorder Recognition–A Systematic Review and Perspective on Emerging Trends. Front Digit Health 2022; 4:842301. [PMID: 35899034 PMCID: PMC9309252 DOI: 10.3389/fdgth.2022.842301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 05/25/2022] [Indexed: 11/25/2022] Open
Abstract
Quantifying neurological disorders from voice is a rapidly growing field of research and holds promise for unobtrusive and large-scale disorder monitoring. The data recording setup and data analysis pipelines are both crucial aspects to effectively obtain relevant information from participants. Therefore, we performed a systematic review to provide a high-level overview of practices across various neurological disorders and highlight emerging trends. PRISMA-based literature searches were conducted through PubMed, Web of Science, and IEEE Xplore to identify publications in which original (i.e., newly recorded) datasets were collected. Disorders of interest were psychiatric as well as neurodegenerative disorders, such as bipolar disorder, depression, and stress, as well as amyotrophic lateral sclerosis amyotrophic lateral sclerosis, Alzheimer's, and Parkinson's disease, and speech impairments (aphasia, dysarthria, and dysphonia). Of the 43 retrieved studies, Parkinson's disease is represented most prominently with 19 discovered datasets. Free speech and read speech tasks are most commonly used across disorders. Besides popular feature extraction toolkits, many studies utilise custom-built feature sets. Correlations of acoustic features with psychiatric and neurodegenerative disorders are presented. In terms of analysis, statistical analysis for significance of individual features is commonly used, as well as predictive modeling approaches, especially with support vector machines and a small number of artificial neural networks. An emerging trend and recommendation for future studies is to collect data in everyday life to facilitate longitudinal data collection and to capture the behavior of participants more naturally. Another emerging trend is to record additional modalities to voice, which can potentially increase analytical performance.
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Affiliation(s)
- Pascal Hecker
- Digital Health – Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
- audEERING GmbH, Gilching, Germany
- *Correspondence: Pascal Hecker ; orcid.org/0000-0001-6604-1671
| | - Nico Steckhan
- Digital Health – Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
| | | | - Björn W. Schuller
- audEERING GmbH, Gilching, Germany
- EIHW – Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany
- GLAM – Group on Language, Audio, & Music, Imperial College London, London, United Kingdom
| | - Bert Arnrich
- Digital Health – Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
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12
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Abstract
Thoughtful clinical trial design is critical for efficient therapeutic development, particularly in the field of amyotrophic lateral sclerosis (ALS), where trials often aim to detect modest treatment effects among a population with heterogeneous disease progression. Appropriate outcome measure selection is necessary for trials to provide decisive and informative results. Investigators must consider the outcome measure's reliability, responsiveness to detect change when change has actually occurred, clinical relevance, and psychometric performance. ALS clinical trials can also be performed more efficiently by utilizing statistical enrichment techniques. Innovations in ALS prediction models allow for selection of participants with less heterogeneity in disease progression rates without requiring a lead-in period, or participants can be stratified according to predicted progression. Statistical enrichment can reduce the needed sample size and improve study power, but investigators must find a balance between optimizing statistical efficiency and retaining generalizability of study findings to the broader ALS population. Additional progress is still needed for biomarker development and validation to confirm target engagement in ALS treatment trials. Selection of an appropriate biofluid biomarker depends on the treatment mechanism of interest, and biomarker studies should be incorporated into early phase trials. Inclusion of patients with ALS as advisors and advocates can strengthen clinical trial design and study retention, but more engagement efforts are needed to improve diversity and equity in ALS research studies. Another challenge for ALS therapeutic development is identifying ways to respect patient autonomy and improve access to experimental treatment, something that is strongly desired by many patients with ALS and ALS advocacy organizations. Expanded access programs that run concurrently to well-designed and adequately powered randomized controlled trials may provide an opportunity to broaden access to promising therapeutics without compromising scientific integrity or rushing regulatory approval of therapies without adequate proof of efficacy.
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Affiliation(s)
- Christina N Fournier
- Department of Neurology, Emory University, Atlanta, GA, USA.
- Department of Veterans Affairs, Atlanta, GA, USA.
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13
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Das R, Paul S, Mourya GK, Kumar N, Hussain M. Recent Trends and Practices Toward Assessment and Rehabilitation of Neurodegenerative Disorders: Insights From Human Gait. Front Neurosci 2022; 16:859298. [PMID: 35495059 PMCID: PMC9051393 DOI: 10.3389/fnins.2022.859298] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 03/01/2022] [Indexed: 12/06/2022] Open
Abstract
The study of human movement and biomechanics forms an integral part of various clinical assessments and provides valuable information toward diagnosing neurodegenerative disorders where the motor symptoms predominate. Conventional gait and postural balance analysis techniques like force platforms, motion cameras, etc., are complex, expensive equipment requiring specialist operators, thereby posing a significant challenge toward translation to the clinics. The current manuscript presents an overview and relevant literature summarizing the umbrella of factors associated with neurodegenerative disorder management: from the pathogenesis and motor symptoms of commonly occurring disorders to current alternate practices toward its quantification and mitigation. This article reviews recent advances in technologies and methodologies for managing important neurodegenerative gait and balance disorders, emphasizing assessment and rehabilitation/assistance. The review predominantly focuses on the application of inertial sensors toward various facets of gait analysis, including event detection, spatiotemporal gait parameter measurement, estimation of joint kinematics, and postural balance analysis. In addition, the use of other sensing principles such as foot-force interaction measurement, electromyography techniques, electrogoniometers, force-myography, ultrasonic, piezoelectric, and microphone sensors has also been explored. The review also examined the commercially available wearable gait analysis systems. Additionally, a summary of recent progress in therapeutic approaches, viz., wearables, virtual reality (VR), and phytochemical compounds, has also been presented, explicitly targeting the neuro-motor and functional impairments associated with these disorders. Efforts toward therapeutic and functional rehabilitation through VR, wearables, and different phytochemical compounds are presented using recent examples of research across the commonly occurring neurodegenerative conditions [viz., Parkinson's disease (PD), Alzheimer's disease (AD), multiple sclerosis, Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS)]. Studies exploring the potential role of Phyto compounds in mitigating commonly associated neurodegenerative pathologies such as mitochondrial dysfunction, α-synuclein accumulation, imbalance of free radicals, etc., are also discussed in breadth. Parameters such as joint angles, plantar pressure, and muscle force can be measured using portable and wearable sensors like accelerometers, gyroscopes, footswitches, force sensors, etc. Kinetic foot insoles and inertial measurement tools are widely explored for studying kinematic and kinetic parameters associated with gait. With advanced correlation algorithms and extensive RCTs, such measurement techniques can be an effective clinical and home-based monitoring and rehabilitation tool for neuro-impaired gait. As evident from the present literature, although the vast majority of works reported are not clinically and extensively validated to derive a firm conclusion about the effectiveness of such techniques, wearable sensors present a promising impact toward dealing with neurodegenerative motor disorders.
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Affiliation(s)
- Ratan Das
- Department of Biomedical Engineering, North-Eastern Hill University, Shillong, India
| | - Sudip Paul
- Department of Biomedical Engineering, North-Eastern Hill University, Shillong, India
| | - Gajendra Kumar Mourya
- Department of Biomedical Engineering, North-Eastern Hill University, Shillong, India
| | - Neelesh Kumar
- Biomedical Applications Unit, Central Scientific Instruments Organisation, Chandigarh, India
| | - Masaraf Hussain
- Department of Neurology, North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences, Shillong, India
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14
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Ding Y, Botchway BOA, Zhang Y, Jin T, Liu X. The combination of autologous mesenchymal stem cell-derived exosomes and neurotrophic factors as an intervention for amyotrophic lateral sclerosis. Ann Anat 2022; 242:151921. [PMID: 35278658 DOI: 10.1016/j.aanat.2022.151921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 02/19/2022] [Accepted: 02/24/2022] [Indexed: 10/18/2022]
Abstract
Amyotrophic lateral sclerosis is a chronic progressive degeneration of motor neurons and has a high mortality. Riluzole and edaravone are the only approved medications currently being used for amyotrophic lateral sclerosis in clinical settings. However, they can lead to serious complications, such as injuries to the liver and kidney. To date, there is no effective treatment for amyotrophic lateral sclerosis. In this regard, investigations concerning the employment of exosomes, mesenchymal stem cells, and neurotrophic factors to ameliorate amyotrophic lateral sclerosis are attracting considerable attention in the scientific community. Herein, we systematically analyze the relationship relevant to autologous mesenchymal stem cell derived-exosomes, neurotrophic factors and amyotrophic lateral sclerosis. Mesenchymal stem cells modulate immune response, mitigate oxidative stress, promote neuronal regeneration, and differentiate into neuronal and glial cells. Furthermore, exosomes from mesenchymal stem cells exert beneficial effects on their mother cells by preventing abnormal differentiation of mesenchymal stem cells. Similarly, neurotrophic factors regulate inflammatory response, stimulate the neuron repair, and the recovery of neuronal functioning. Therefore, autologous mesenchymal stem cells-derived exosomes combined with neurotrophic factors could potentially be an effective interventional medium for amyotrophic lateral sclerosis.
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Affiliation(s)
- Yingying Ding
- Department of Histology and Embryology, Medical College, Shaoxing University, Zhejiang, China; School of Basic Medical Sciences, Hangzhou Normal University, Zhejiang, China
| | - Benson O A Botchway
- Institute of Neuroscience, Zhejiang University School of Medicine, Hangzhou, China
| | - Yong Zhang
- Department of Histology and Embryology, Medical College, Shaoxing University, Zhejiang, China
| | - Tian Jin
- Department of Histology and Embryology, Medical College, Shaoxing University, Zhejiang, China
| | - Xuehong Liu
- Department of Histology and Embryology, Medical College, Shaoxing University, Zhejiang, China.
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15
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Abstract
Long-term neurological conditions (LTNCs) cause physical and psychological symptoms that have a significant impact on activities of daily living and quality of life. Multidisciplinary teams are effective at providing treatment for people with LTNCs; however, access to such services by people with disabilities can be difficult and as a result, good quality care is not universal. One potential solution is telehealth. This review describes the potential of telehealth to support people with LTNCs, the challenges of designing and implementing these systems, and the key recommendations for those involved in telehealth to facilitate connected services that can benefit patients, carers and healthcare professionals. These recommendations include understanding the problems posed by LTNCs and the needs of the end-user through a person-centred approach. We discuss how to work collaboratively and use shared learning, and consider how to effectively evaluate the intervention at every stage of the development process.
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Affiliation(s)
- Liam Knox
- Department of Neuroscience, University of Sheffield, Sheffield, UK
| | - Christopher McDermott
- Department of Neuroscience, University of Sheffield, Sheffield, UK.,Department of Neuroscience, Sheffield Teaching Hospitals, Sheffield, UK
| | - Esther Hobson
- Department of Neuroscience, University of Sheffield, Sheffield, UK.,Department of Neuroscience, Sheffield Teaching Hospitals, Sheffield, UK
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16
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Sakamaki T, Furusawa Y, Hayashi A, Otsuka M, Fernandez J. Remote Patient Monitoring for Neuropsychiatric Disorders: A Scoping Review of Current Trends and Future Perspectives from Recent Publications and Upcoming Clinical Trials. Telemed J E Health 2022; 28:1235-1250. [PMID: 35073206 PMCID: PMC9508442 DOI: 10.1089/tmj.2021.0489] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
Introduction: Telemedicine and remote patient monitoring are rapidly growing fields. This scoping review provides an update on remote patient monitoring for neuropsychiatric disorders from recent publications and upcoming clinical trials. Methods: Publications (PubMed and ICHUSHI; published January 2010 to February 2021) and trials (ClinicalTrials.gov and Japanese registries; active or recruiting by March 2021) that assessed wearable devices for remote management and/or monitoring of patients with neuropsychiatric disorders were searched. The review focuses on disorders with ≥3 publications. Results: We identified 44 publications and 51 active or recruiting trials, mostly from 2019 or 2020. Research on digital devices was most common for Parkinson's disease (11 publications and 19 trials), primarily for monitoring motor symptoms and/or preventing falls. Other disorders (3–5 publications each) included epilepsy (electroencephalogram [EEG] and seizure prediction), sleep disorder (sleep outcomes and behavioral therapies), multiple sclerosis (physical activity and symptoms), depression (physical activity, symptoms, and behavioral therapies), and amyotrophic lateral sclerosis (symptoms). Very few studies focused on newly emerging technologies (e.g., in-ear EEG and portable oximeters), and few studies integrated remote symptom monitoring with telemedicine. Discussion: Currently, development of digital devices for daily symptom monitoring is focused on Parkinson's disease. For the diseases reviewed, studies mostly focused on physical activity rather than psychiatric or nonmotor symptoms. Although the validity and usefulness of many devices are established, models for implementing remote patient monitoring in telehealth settings have not been established. Conclusions: Verification of the clinical effectiveness of digital devices combined with telemedicine is needed to further advance remote patient care for neuropsychiatric disorders.
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Affiliation(s)
- Tetsuo Sakamaki
- Medical Informatics and Decision Sciences, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Yoshihiko Furusawa
- Japan Medical Office, Takeda Pharmaceutical Company Limited, Tokyo, Japan
| | - Ayako Hayashi
- Japan Medical Office, Takeda Pharmaceutical Company Limited, Tokyo, Japan
| | - Masaru Otsuka
- Enterprise Digital Lead, Takeda Pharmaceutical Company Limited, Tokyo, Japan
| | - Jovelle Fernandez
- Japan Medical Office, Takeda Pharmaceutical Company Limited, Tokyo, Japan
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17
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Pugliese R, Sala R, Regondi S, Beltrami B, Lunetta C. Emerging technologies for management of patients with amyotrophic lateral sclerosis: from telehealth to assistive robotics and neural interfaces. J Neurol 2022; 269:2910-2921. [PMID: 35059816 PMCID: PMC8776511 DOI: 10.1007/s00415-022-10971-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 01/11/2022] [Accepted: 01/12/2022] [Indexed: 12/17/2022]
Abstract
Amyotrophic lateral sclerosis (ALS), also known as motor neuron disease, is characterized by the degeneration of both upper and lower motor neurons, which leads to muscle weakness and subsequently paralysis. It begins subtly with focal weakness but spreads relentlessly to involve most muscles, thus proving to be effectively incurable. Typically, death due to respiratory paralysis occurs in 3–5 years. To date, it has been shown that the management of ALS patients is best achieved with a multidisciplinary approach, and with the help of emerging technologies ranging from multidisciplinary teleconsults (for monitoring the dysphagia, respiratory function, and nutritional status) to brain-computer interfaces and eye tracking for alternative augmentative communication, until robotics, it may increase effectiveness. The COVID-19 pandemic created a spasmodic need to accelerate the development and implementation of such technologies in clinical practice, to improve the daily lives of both ALS patients and caregivers. However, despite the remarkable strides that have been made in the field, there are still issues to be addressed. This review will be discussed on the eureka moment of emerging technologies for ALS, used as a blueprint not only for neurodegenerative diseases, examining the current technologies already in place or being evaluated, highlighting the pros and cons for future clinical applications.
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Affiliation(s)
| | - Riccardo Sala
- NeMO Lab, ASST Niguarda Cà Granda Hospital, Milan, Italy
| | - Stefano Regondi
- NeMO Lab, ASST Niguarda Cà Granda Hospital, Milan, Italy
- NEuroMuscolar Omnicentre, Milan, Italy
| | | | - Christian Lunetta
- NeMO Lab, ASST Niguarda Cà Granda Hospital, Milan, Italy.
- NEuroMuscolar Omnicentre, Milan, Italy.
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18
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Jeong H, Jeong YW, Park Y, Kim K, Park J, Kang DR. Applications of deep learning methods in digital biomarker research using noninvasive sensing data. Digit Health 2022; 8:20552076221136642. [DOI: 10.1177/20552076221136642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 10/04/2022] [Indexed: 11/06/2022] Open
Abstract
Introduction: Noninvasive digital biomarkers are critical elements in digital healthcare in terms of not only the ease of measurement but also their use of raw data. In recent years, deep learning methods have been put to use to analyze these diverse heterogeneous data; these methods include representation learning for feature extraction and supervised learning for the prediction of these biomarkers. Methods: We introduce clinical cases of digital biomarkers and various deep-learning methods applied according to each data type. In addition, deep learning methods for the integrated analysis of multidimensional heterogeneous data are introduced, and the utility of these data as an integrated digital biomarker is presented. The current status of digital biomarker research is examined by surveying research cases applied to various types of data as well as modeling methods. Results: We present a future research direction for using data from heterogeneous sources together by introducing deep learning methods for dimensionality reduction and mode integration from multimodal digital biomarker studies covering related domains. The integration of multimodality has led to advances in research through the improvement of performance and complementarity between modes. Discussion: The integrative digital biomarker will be more useful for research on diseases that require data from multiple sources to be treated together. Since delicate signals from patients are not missed and the interaction effects between signals are also considered, it will be helpful for immediate detection and more accurate prediction of symptoms.
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Affiliation(s)
- Hoyeon Jeong
- Department of Biostatistics, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea
| | - Yong W Jeong
- Department of Biostatistics, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea
| | - Yeonjae Park
- Department of Biostatistics, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea
| | - Kise Kim
- School of Health and Environmental Science, Korea University, Seoul, Republic of Korea
| | | | - Dae R Kang
- Department of Biostatistics, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea
- Department of Precision Medicine, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea
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19
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Palumbo A, Ielpo N, Calabrese B, Corchiola D, Garropoli R, Gramigna V, Perri G. SIMpLE: A Mobile Cloud-Based System for Health Monitoring of People with ALS. Sensors (Basel) 2021; 21:s21217239. [PMID: 34770548 PMCID: PMC8587347 DOI: 10.3390/s21217239] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 10/18/2021] [Accepted: 10/26/2021] [Indexed: 01/05/2023]
Abstract
Adopting telemonitoring services during the pandemic for people affected by chronic disease is fundamental to ensure access to health care services avoiding the risk of COVID-19 infection. Among chronic diseases, Amyotrophic Lateral Sclerosis (ALS), also known as Lou Gehrig’s disease, is a progressive neurodegenerative disease of adulthood, caused by the loss of spinal, bulbar and cortical motor neurons, which leads to paralysis of the voluntary muscles and, also, involves respiratory ones. Therefore, remote monitoring and teleconsulting are essential services for ALS patients with limited mobility, as the disease progresses, and for those living far from ALS centres and hospitals. In addition, the COVID 19 pandemic has increased the need to remotely provide the best care to patients, avoiding infection during ALS centre visits. The paper illustrates an innovative, secure medical monitoring and teleconsultation mobile cloud-based system for disabled people, such as those with ALS (Amyotrophic Lateral Sclerosis). The design aims to remotely monitor biosignals, such as ECG (electrocardiographic) and EMG (electromyographic) signals of ALS patients in order to prevent complications related to the pathology.
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Affiliation(s)
- Arrigo Palumbo
- Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy; (A.P.); (N.I.)
| | - Nicola Ielpo
- Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy; (A.P.); (N.I.)
| | - Barbara Calabrese
- Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy; (A.P.); (N.I.)
- Correspondence:
| | | | - Remo Garropoli
- Garropoli Computer Science Consulting, 87100 Cosenza, Italy;
| | - Vera Gramigna
- Neuroscience Research Center, Magna Graecia University, 88100 Catanzaro, Italy;
| | - Giovanni Perri
- Radiological Center Perri-Bilotti, 87100 Cosenza, Italy;
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20
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van Eijk RPA, Beelen A, Kruitwagen ET, Murray D, Radakovic R, Hobson E, Knox L, Helleman J, Burke T, Rubio Pérez MÁ, Reviers E, Genge A, Steyn FJ, Ngo S, Eaglesham J, Roes KCB, van den Berg LH, Hardiman O, McDermott CJ. A Road Map for Remote Digital Health Technology for Motor Neuron Disease. J Med Internet Res 2021; 23:e28766. [PMID: 34550089 PMCID: PMC8495582 DOI: 10.2196/28766] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 05/23/2021] [Accepted: 05/24/2021] [Indexed: 12/05/2022] Open
Abstract
Despite recent and potent technological advances, the real-world implementation of remote digital health technology in the care and monitoring of patients with motor neuron disease has not yet been realized. Digital health technology may increase the accessibility to and personalization of care, whereas remote biosensors could optimize the collection of vital clinical parameters, irrespective of patients’ ability to visit the clinic. To facilitate the wide-scale adoption of digital health care technology and to align current initiatives, we outline a road map that will identify clinically relevant digital parameters; mediate the development of benefit-to-burden criteria for innovative technology; and direct the validation, harmonization, and adoption of digital health care technology in real-world settings. We define two key end products of the road map: (1) a set of reliable digital parameters to capture data collected under free-living conditions that reflect patient-centric measures and facilitate clinical decision making and (2) an integrated, open-source system that provides personalized feedback to patients, health care providers, clinical researchers, and caregivers and is linked to a flexible and adaptable platform that integrates patient data in real time. Given the ever-changing care needs of patients and the relentless progression rate of motor neuron disease, the adoption of digital health care technology will significantly benefit the delivery of care and accelerate the development of effective treatments.
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Affiliation(s)
- Ruben P A van Eijk
- UMC Utrecht Brain Centre, University Medical Centre Utrecht, Utrecht, Netherlands.,Biostatistics & Research Support, Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, Netherlands
| | - Anita Beelen
- Department of Rehabilitation, University Medical Centre Utrecht, Utrecht, Netherlands.,Center of Excellence for Rehabilitation Medicine, University Medical Centre Utrecht and De Hoogstraat Rehabilitation, Utrecht, Netherlands
| | - Esther T Kruitwagen
- Department of Rehabilitation, University Medical Centre Utrecht, Utrecht, Netherlands.,Center of Excellence for Rehabilitation Medicine, University Medical Centre Utrecht and De Hoogstraat Rehabilitation, Utrecht, Netherlands
| | - Deirdre Murray
- Academic Unit of Neurology, Trinity College Dublin, Dublin, Ireland.,Department of Physiotherapy, Beaumont Hospital, Dublin, Ireland
| | - Ratko Radakovic
- Faculty of Medicine and Health Sciences, University of East Anglia, Norwich, United Kingdom.,Euan MacDonald Centre for Motor Neuron Disease Research, University of Edinburgh, Edinburgh, United Kingdom.,Norfolk and Norwich University Hospital, Norwich, United Kingdom.,Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, United Kingdom.,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
| | - Esther Hobson
- Department of Neuroscience, Sheffield Institute for Translational Neuroscien, University of Sheffield, Sheffield, United Kingdom
| | - Liam Knox
- Department of Neuroscience, Sheffield Institute for Translational Neuroscien, University of Sheffield, Sheffield, United Kingdom
| | - Jochem Helleman
- Department of Rehabilitation, University Medical Centre Utrecht, Utrecht, Netherlands.,Center of Excellence for Rehabilitation Medicine, University Medical Centre Utrecht and De Hoogstraat Rehabilitation, Utrecht, Netherlands
| | - Tom Burke
- Academic Unit of Neurology, Trinity College Dublin, Dublin, Ireland.,Department of Psychology, Beaumont Hospital, Dublin, Ireland
| | | | - Evy Reviers
- European Organization for Professionals and Patients with ALS (EUpALS), Leuven, Belgium
| | - Angela Genge
- Department of Neurology, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Frederik J Steyn
- School of Biomedical Sciences, Faculty of Medicine, University of Queensland, Brisbane, Australia.,The Royal Brisbane and Women's Hospital, Herston, Australia.,Wesley Medical Research, the Wesley Hospital, Auchenflower, Australia
| | - Shyuan Ngo
- The Royal Brisbane and Women's Hospital, Herston, Australia.,Wesley Medical Research, the Wesley Hospital, Auchenflower, Australia.,Centre for Clinical Research, University of Queensland, Brisbane, Australia.,Australian Institute for Bioengineering and Nanotechnology, University of Queensland, Brisbane, Australia
| | - John Eaglesham
- Advanced Digital Innovation (UK) Ltd, Salts Mill, United Kingdom
| | - Kit C B Roes
- Department of Health Evidence, Section Biostatistics, Radboud Medical Centre Nijmegen, Nijmegen, Netherlands
| | | | - Orla Hardiman
- Department of Neurology, National Neuroscience Centre, Beaumont Hospital, Dublin, Ireland.,FutureNeuro SFI Research Centre, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Christopher J McDermott
- Department of Neuroscience, Sheffield Institute for Translational Neuroscien, University of Sheffield, Sheffield, United Kingdom
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21
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Youn BY, Ko Y, Moon S, Lee J, Ko SG, Kim JY. Digital Biomarkers for Neuromuscular Disorders: A Systematic Scoping Review. Diagnostics (Basel) 2021; 11:diagnostics11071275. [PMID: 34359358 PMCID: PMC8307187 DOI: 10.3390/diagnostics11071275] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 06/30/2021] [Accepted: 07/13/2021] [Indexed: 11/16/2022] Open
Abstract
Biomarkers play a vital role in clinical care. They enable early diagnosis and treatment by identifying a patient's condition and disease course and act as an outcome measure that accurately evaluates the efficacy of a new treatment or drug. Due to the rapid development of digital technologies, digital biomarkers are expected to grow tremendously. In the era of change, this scoping review was conducted to see which digital biomarkers are progressing in neuromuscular disorders, a diverse and broad-range disease group among the neurological diseases, to discover available evidence for their feasibility and reliability. Thus, a total of 10 studies were examined: 9 observational studies and 1 animal study. Of the observational studies, studies were conducted with amyotrophic lateral sclerosis (ALS), Duchenne muscular dystrophy (DMD), and spinal muscular atrophy (SMA) patients. Non-peer reviewed poster presentations were not considered, as the articles may lead to erroneous results. The only animal study included in the present review investigated the mice model of ALS for detecting rest disturbances using a non-invasive digital biomarker.
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Affiliation(s)
- Bo-Young Youn
- Department of Global Public Health and Korean Medicine Management, Graduate School, Kyung Hee University, Seoul 02447, Korea; (B.-Y.Y.); (S.M.)
| | - Youme Ko
- Department of Preventive Medicine, Kyung Hee University, Seoul 02447, Korea; (Y.K.); (S.-G.K.)
| | - Seunghwan Moon
- Department of Global Public Health and Korean Medicine Management, Graduate School, Kyung Hee University, Seoul 02447, Korea; (B.-Y.Y.); (S.M.)
| | - Jinhee Lee
- Department of Korean Medicine, Graduate School, Kyung Hee University, Seoul 02447, Korea;
| | - Seung-Gyu Ko
- Department of Preventive Medicine, Kyung Hee University, Seoul 02447, Korea; (Y.K.); (S.-G.K.)
| | - Jee-Young Kim
- Department of Neurology, Cheongna Best Rehabilitation Hospital, Incheon 22883, Korea
- Correspondence:
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22
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Tena A, Claria F, Solsona F, Meister E, Povedano M. Detection of Bulbar Involvement in Patients With Amyotrophic Lateral Sclerosis by Machine Learning Voice Analysis: Diagnostic Decision Support Development Study. JMIR Med Inform 2021; 9:e21331. [PMID: 33688838 PMCID: PMC7991994 DOI: 10.2196/21331] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 10/26/2020] [Accepted: 01/17/2021] [Indexed: 11/13/2022] Open
Abstract
Background Bulbar involvement is a term used in amyotrophic lateral sclerosis (ALS) that refers to motor neuron impairment in the corticobulbar area of the brainstem, which produces a dysfunction of speech and swallowing. One of the earliest symptoms of bulbar involvement is voice deterioration characterized by grossly defective articulation; extremely slow, laborious speech; marked hypernasality; and severe harshness. Bulbar involvement requires well-timed and carefully coordinated interventions. Therefore, early detection is crucial to improving the quality of life and lengthening the life expectancy of patients with ALS who present with this dysfunction. Recent research efforts have focused on voice analysis to capture bulbar involvement. Objective The main objective of this paper was (1) to design a methodology for diagnosing bulbar involvement efficiently through the acoustic parameters of uttered vowels in Spanish, and (2) to demonstrate that the performance of the automated diagnosis of bulbar involvement is superior to human diagnosis. Methods The study focused on the extraction of features from the phonatory subsystem—jitter, shimmer, harmonics-to-noise ratio, and pitch—from the utterance of the five Spanish vowels. Then, we used various supervised classification algorithms, preceded by principal component analysis of the features obtained. Results To date, support vector machines have performed better (accuracy 95.8%) than the models analyzed in the related work. We also show how the model can improve human diagnosis, which can often misdiagnose bulbar involvement. Conclusions The results obtained are very encouraging and demonstrate the efficiency and applicability of the automated model presented in this paper. It may be an appropriate tool to help in the diagnosis of ALS by multidisciplinary clinical teams, in particular to improve the diagnosis of bulbar involvement.
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Affiliation(s)
- Alberto Tena
- Information and Communication Technologies Group, International Centre for Numerical Methods in Engineering, Barcelona, Spain
| | - Francec Claria
- Department of Computer Science, Universitat de Lleida, Lleida, Spain
| | - Francesc Solsona
- Department of Computer Science, Universitat de Lleida, Lleida, Spain
| | - Einar Meister
- Institute of Cybernetics, Tallinn University of Technology, Tallinn, Estonia
| | - Monica Povedano
- Motoneuron Functional Unit, Hospital Universitari de Bellvitge, Barcelona, Spain
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23
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Pinto S, Quintarelli S, Silani V. New technologies and Amyotrophic Lateral Sclerosis - Which step forward rushed by the COVID-19 pandemic? J Neurol Sci 2020; 418:117081. [PMID: 32882437 PMCID: PMC7403097 DOI: 10.1016/j.jns.2020.117081] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 07/09/2020] [Accepted: 08/01/2020] [Indexed: 12/11/2022]
Abstract
Amyotrophic Lateral Sclerosis (ALS) is a fast-progressive neurodegenerative disease leading to progressive physical immobility with usually normal or mild cognitive and/or behavioural involvement. Many patients are relatively young, instructed, sensitive to new technologies, and professionally active when developing the first symptoms. Older patients usually require more time, encouragement, reinforcement and a closer support but, nevertheless, selecting user-friendly devices, provided earlier in the course of the disease, and engaging motivated carers may overcome many technological barriers. ALS may be considered a model for neurodegenerative diseases to further develop and test new technologies. From multidisciplinary teleconsults to telemonitoring of the respiratory function, telemedicine has the potentiality to embrace other fields, including nutrition, physical mobility, and the interaction with the environment. Brain-computer interfaces and eye tracking expanded the field of augmentative and alternative communication in ALS but their potentialities go beyond communication, to cognition and robotics. Virtual reality and different forms of artificial intelligence present further interesting possibilities that deserve to be investigated. COVID-19 pandemic is an unprecedented opportunity to speed up the development and implementation of new technologies in clinical practice, improving the daily living of both ALS patients and carers. The present work reviews the current technologies for ALS patients already in place or being under evaluation with published publications, prompted by the COVID-19 pandemic.
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Affiliation(s)
- Susana Pinto
- Translational and Clinical Physiology Unit, Instituto de Medicina Molecular, Lisbon, Portugal.
| | - Stefano Quintarelli
- AgID - Italian digital agency and Clusit - Italian Computer Security Association, Italy
| | - Vincenzo Silani
- Department of Neurology-Stroke Unit and Laboratory of Neuroscience, Istituto Auxologico Italiano IRCCS - Department of Pathophysiology and Transplantation, “Dino Ferrari” Center and Center for Neurotechnology and Brain Therapeutics, Università degli Studi di Milano, Milan, Italy
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24
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Kelly M, Lavrov A, Garcia-Gancedo L, Parr J, Hart R, Chiwera T, Shaw CE, Al-Chalabi A, Marsden R, Turner MR, Talbot K. The use of biotelemetry to explore disease progression markers in amyotrophic lateral sclerosis. Amyotroph Lateral Scler Frontotemporal Degener 2020; 21:563-573. [PMID: 32573278 DOI: 10.1080/21678421.2020.1773501] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
OBJECTIVE To explore novel, real-world biotelemetry disease progression markers in patients with amyotrophic lateral sclerosis (ALS) and to compare with clinical gold-standard measures. Methods: This was an exploratory, non-controlled, non-drug 2-phase study comprising a variable length Pilot Phase (n = 5) and a 48-week Core study Phase (n = 25; NCT02447952). Patients with mild or moderate ALS wore biotelemetry sensors for ∼3 days/month at home, measuring physical activity, heart rate variability (HRV), and speech over 48 weeks. These measures were assessed longitudinally in relation to ALS Functional Rating Scale-Revised (ALSFRS-R) score and forced vital capacity (FVC); assessed by telephone [monthly] and clinic visits [every 12 weeks]). Results: Pilot Phase data supported progression into the Core Phase, where a decline in physical activity from baseline followed ALS progression as measured by ALSFRS-R and FVC. Four endpoints showed moderate or strong between-patient correlations with ALSFRS-R total and gross motor domain scores (defined as a correlation coefficient of ≥0.5 or >0.7, respectively): average daytime active; percentage of daytime active; total daytime activity score; total 24-hour activity score. Moderate correlations were observed between speech endpoints and ALSFRS-R bulbar domain scores; HRV data quality was insufficient for reliable assessment. The sensor was generally well tolerated; 6/25 patients reported mostly mild or moderate intensity skin and subcutaneous tissue disorder adverse events. Conclusions: Biotelemetry measures of physical activity in this Pilot Study tracked ALS progression over time, highlighting their potential as endpoints for future clinical trials. A larger, formally powered study is required to further support activity endpoints as novel disease progression markers.
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Affiliation(s)
- Madeline Kelly
- Clinical Translational Medicine, Future Pipeline Discovery, GSK R&D, Hertfordshire, UK
| | - Arseniy Lavrov
- Clinical Translational Medicine, Future Pipeline Discovery, GSK R&D, Middlesex, UK
| | | | - Jim Parr
- McLaren Applied Technologies, Surrey, UK
| | | | - Theresa Chiwera
- United Kingdom Dementia Research Institute, Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Christopher E Shaw
- United Kingdom Dementia Research Institute, Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Ammar Al-Chalabi
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King's College London, London, UK
- Department of Neurology, King's College Hospital, London, UK
| | - Rachael Marsden
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Martin R Turner
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Kevin Talbot
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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25
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Bombaci A, Abbadessa G, Trojsi F, Leocani L, Bonavita S, Lavorgna L; Digital Technologies, Web and Social Media Study Group of the Italian Society of Neurology. Telemedicine for management of patients with amyotrophic lateral sclerosis through COVID-19 tail. Neurol Sci 2021; 42:9-13. [PMID: 33025327 DOI: 10.1007/s10072-020-04783-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 09/26/2020] [Indexed: 12/12/2022]
Abstract
Over the last months, due to coronavirus disease (COVID-19) pandemic, containment measures have led to important social restriction. Healthcare systems have faced a complete rearrangement of resources and spaces, with the creation of wards devoted to COVID-19 patients. In this context, patients affected by chronic neurological diseases, such as amyotrophic lateral sclerosis (ALS), are at risk to be lost at follow-up, leading to a higher risk of morbidity and mortality. Telemedicine may allow meet the needs of these patients. In this commentary, we briefly discuss the digital tools to remotely monitor and manage ALS patients. Focusing on detecting disease progression and preventing life-threatening conditions, we propose a toolset able to improve ALS management during this unprecedented situation.
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26
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Andrews JA, Berry JD, Baloh RH, Carberry N, Cudkowicz ME, Dedi B, Glass J, Maragakis NJ, Miller TM, Paganoni S, Rothstein JD, Shefner JM, Simmons Z, Weiss MD, Bedlack RS. Amyotrophic lateral sclerosis care and research in the United States during the COVID-19 pandemic: Challenges and opportunities. Muscle Nerve 2020; 62:182-186. [PMID: 32445195 PMCID: PMC7283687 DOI: 10.1002/mus.26989] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 05/21/2020] [Indexed: 12/30/2022]
Abstract
Coronavirus disease 2019 has created unprecedented challenges for amyotrophic lateral sclerosis (ALS) clinical care and research in the United States. Traditional evaluations for making an ALS diagnosis, measuring progression, and planning interventions rely on in‐person visits that may now be unsafe or impossible. Evidence‐ and experience‐based treatment options, such as multidisciplinary team care, feeding tubes, wheelchairs, home health, and hospice, have become more difficult to obtain and in some places are unavailable. In addition, the pandemic has impacted ALS clinical trials by impairing the ability to obtain measurements for trial eligibility, to monitor safety and efficacy outcomes, and to dispense study drug, as these also often rely on in‐person visits. We review opportunities for overcoming some of these challenges through telemedicine and novel measurements. These can reoptimize ALS care and research in the current setting and during future events that may limit travel and face‐to‐face interactions.
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Affiliation(s)
- Jinsy A Andrews
- The Neurological Institute, Columbia University, New York, New York
| | - James D Berry
- Healey Center for ALS, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Robert H Baloh
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, California
| | - Nathan Carberry
- The Neurological Institute, Columbia University, New York, New York
| | - Merit E Cudkowicz
- Healey Center for ALS, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Brixhilda Dedi
- The Neurological Institute, Columbia University, New York, New York
| | - Jonathan Glass
- Department of Neurology, Emory University School of Medicine, Atlanta, Georgia
| | - Nicholas J Maragakis
- Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Timothy M Miller
- Department of Neurology, Washington University School of Medicine, St Louis, Missouri
| | - Sabrina Paganoni
- Healey Center for ALS, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Jeffrey D Rothstein
- Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, Maryland
| | | | - Zachary Simmons
- Neurology, Penn State Health Milton S Hershey Medical Center, Hershey, Pennsylvania
| | - Michael D Weiss
- Department of Neurology, University of Washington, Seattle, Washington
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