1
|
Acien A, Calcagno N, Burke KM, Mondesire-Crump I, Holmes AA, Mruthik S, Goldy B, Syrotenko JE, Scheier Z, Iyer A, Clark A, Keegan M, Ushirogawa Y, Kato A, Yasuda T, Lahav A, Iwasaki S, Pascarella M, Johnson SA, Arroyo-Gallego T, Berry JD. A novel digital tool for detection and monitoring of amyotrophic lateral sclerosis motor impairment and progression via keystroke dynamics. Sci Rep 2024; 14:16851. [PMID: 39039102 PMCID: PMC11263713 DOI: 10.1038/s41598-024-67940-8] [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: 02/02/2024] [Accepted: 07/17/2024] [Indexed: 07/24/2024] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a debilitating neurodegenerative condition leading to progressive muscle weakness, atrophy, and ultimately death. Traditional ALS clinical evaluations often depend on subjective metrics, making accurate disease detection and monitoring disease trajectory challenging. To address these limitations, we developed the nQiALS toolkit, a machine learning-powered system that leverages smartphone typing dynamics to detect and track motor impairment in people with ALS. The study included 63 ALS patients and 30 age- and sex-matched healthy controls. We introduce the three core components of this toolkit: the nQiALS-Detection, which differentiated ALS from healthy typing patterns with an AUC of 0.89; the nQiALS-Progression, which separated slow and fast progression at specific thresholds with AUCs ranging between 0.65 and 0.8; and the nQiALS-Fine Motor, which identified subtle progression in fine motor dysfunction, suggesting earlier prediction than the state-of-the-art assessment. Together, these tools represent an innovative approach to ALS assessment, offering a complementary, objective metric to traditional clinical methods and which may reshape our understanding and monitoring of ALS progression.
Collapse
Affiliation(s)
- Alejandro Acien
- Area 2 AI Corporation, 245 Main St, Cambridge, MA, 02142, USA
- nQ Medical, Cambridge, MA, USA
| | - Narghes Calcagno
- Department of Neurology, Healey & AMG Center for ALS, Massachusetts General Hospital, Boston, MA, USA
- Neurology Residency Program, University of Milan, Milan, Italy
| | - Katherine M Burke
- Department of Neurology, Healey & AMG Center for ALS, Massachusetts General Hospital, Boston, MA, USA
| | | | - Ashley A Holmes
- nQ Medical, Cambridge, MA, USA
- ProKidney Corporation, Winston-Salem, NC, USA
| | | | | | | | - Zoe Scheier
- Department of Neurology, Healey & AMG Center for ALS, Massachusetts General Hospital, Boston, MA, USA
| | - Amrita Iyer
- Department of Neurology, Healey & AMG Center for ALS, Massachusetts General Hospital, Boston, MA, USA
| | - Alison Clark
- Department of Neurology, Healey & AMG Center for ALS, Massachusetts General Hospital, Boston, MA, USA
| | - Mackenzie Keegan
- Department of Neurology, Healey & AMG Center for ALS, Massachusetts General Hospital, Boston, MA, USA
| | | | - Atsushi Kato
- Mitsubishi Tanabe Pharma Corporation, Osaka, Osaka, Japan
| | - Taku Yasuda
- Mitsubishi Tanabe Pharma Corporation, Osaka, Osaka, Japan
| | - Amir Lahav
- Mitsubishi Tanabe Pharma America, Inc, Jersey City, NJ, USA
| | | | - Mark Pascarella
- Area 2 AI Corporation, 245 Main St, Cambridge, MA, 02142, USA
- nQ Medical, Cambridge, MA, USA
| | - Stephen A Johnson
- Department of Neurology, Healey & AMG Center for ALS, Massachusetts General Hospital, Boston, MA, USA
- Mayo ALS Clinic, Scottsdale, AZ, USA
| | - Teresa Arroyo-Gallego
- Area 2 AI Corporation, 245 Main St, Cambridge, MA, 02142, USA.
- nQ Medical, Cambridge, MA, USA.
| | - James D Berry
- Department of Neurology, Healey & AMG Center for ALS, Massachusetts General Hospital, Boston, MA, USA
| |
Collapse
|
2
|
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] [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.
Collapse
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
| |
Collapse
|
3
|
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.
Collapse
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
| |
Collapse
|
4
|
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] [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.
Collapse
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.
| |
Collapse
|
5
|
Lam KH, Twose J, Lissenberg-Witte B, Licitra G, Meijer K, Uitdehaag B, De Groot V, Killestein J. The Use of Smartphone Keystroke Dynamics to Passively Monitor Upper Limb and Cognitive Function in Multiple Sclerosis: Longitudinal Analysis. J Med Internet Res 2022; 24:e37614. [PMID: 36342763 PMCID: PMC9679948 DOI: 10.2196/37614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 07/31/2022] [Accepted: 08/22/2022] [Indexed: 11/09/2022] Open
Abstract
Background Typing on smartphones, which has become a near daily activity, requires both upper limb and cognitive function. Analysis of keyboard interactions during regular typing, that is, keystroke dynamics, could therefore potentially be utilized for passive and continuous monitoring of function in patients with multiple sclerosis. Objective To determine whether passively acquired smartphone keystroke dynamics correspond to multiple sclerosis outcomes, we investigated the association between keystroke dynamics and clinical outcomes (upper limb and cognitive function). This association was investigated longitudinally in order to study within-patient changes independently of between-patient differences. Methods During a 1-year follow-up, arm function and information processing speed were assessed every 3 months in 102 patients with multiple sclerosis with the Nine-Hole Peg Test and Symbol Digit Modalities Test, respectively. Keystroke-dynamics data were continuously obtained from regular typing on the participants’ own smartphones. Press-and-release latency of the alphanumeric keys constituted the fine motor score cluster, while latency of the punctuation and backspace keys constituted the cognition score cluster. The association over time between keystroke clusters and the corresponding clinical outcomes was assessed with linear mixed models with subjects as random intercepts. By centering around the mean and calculating deviation scores within subjects, between-subject and within-subject effects were distinguished. Results Mean (SD) scores for the fine motor score cluster and cognition score cluster were 0.43 (0.16) and 0.94 (0.41) seconds, respectively. The fine motor score cluster was significantly associated with the Nine-Hole Peg Test: between-subject β was 15.9 (95% CI 12.2-19.6) and within-subject β was 6.9 (95% CI 2.0-11.9). The cognition score cluster was significantly associated with the Symbol Digit Modalities Test between subjects (between-subject β –11.2, 95% CI –17.3 to –5.2) but not within subjects (within-subject β –0.4, 95% CI –5.6 to 4.9). Conclusions Smartphone keystroke dynamics were longitudinally associated with multiple sclerosis outcomes. Worse arm function corresponded with longer latency in typing both across and within patients. Worse processing speed corresponded with higher latency in using punctuation and backspace keys across subjects. Hence, keystroke dynamics are a potential digital biomarker for remote monitoring and predicting clinical outcomes in patients with multiple sclerosis. Trial Registration Netherlands Trial Register NTR7268; https://trialsearch.who.int/Trial2.aspx?TrialID=NTR7268
Collapse
Affiliation(s)
- Ka-Hoo Lam
- Department of Neurology, Amsterdam University Medical Centers (VU University Medical Center location), Amsterdam, Netherlands
| | | | - Birgit Lissenberg-Witte
- Department of Epidemiology and Data Science, Amsterdam University Medical Centers (VU University Medical Center location), Amsterdam, Netherlands
| | | | | | - Bernard Uitdehaag
- Department of Neurology, Amsterdam University Medical Centers (VU University Medical Center location), Amsterdam, Netherlands
| | - Vincent De Groot
- Department of Rehabilitation Medicine, Amsterdam University Medical Centers (VU University Medical Center location), Amsterdam, Netherlands
| | - Joep Killestein
- Department of Neurology, Amsterdam University Medical Centers (VU University Medical Center location), Amsterdam, Netherlands
| |
Collapse
|
6
|
Londral A. Assistive Technologies for Communication Empower Patients With ALS to Generate and Self-Report Health Data. Front Neurol 2022; 13:867567. [PMID: 35557618 PMCID: PMC9090469 DOI: 10.3389/fneur.2022.867567] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 03/25/2022] [Indexed: 11/25/2022] Open
Affiliation(s)
- Ana Londral
- Value for Health CoLAB, Lisbon, Portugal
- Comprehensive Health Research Center, Nova Medical School, Nova University of Lisbon, Lisbon, Portugal
- *Correspondence: Ana Londral
| |
Collapse
|
7
|
Gopal A, Hsu WY, Allen DD, Bove R. Remote Assessments of Hand Function in Neurological Disorders: Systematic Review. JMIR Rehabil Assist Technol 2022; 9:e33157. [PMID: 35262502 PMCID: PMC8943610 DOI: 10.2196/33157] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 01/17/2022] [Accepted: 01/26/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Loss of fine motor skills is observed in many neurological diseases, and remote monitoring assessments can aid in early diagnosis and intervention. Hand function can be regularly assessed to monitor loss of fine motor skills in people with central nervous system disorders; however, there are challenges to in-clinic assessments. Remotely assessing hand function could facilitate monitoring and supporting of early diagnosis and intervention when warranted. OBJECTIVE Remote assessments can facilitate the tracking of limitations, aiding in early diagnosis and intervention. This study aims to systematically review existing evidence regarding the remote assessment of hand function in populations with chronic neurological dysfunction. METHODS PubMed and MEDLINE, CINAHL, Web of Science, and Embase were searched for studies that reported remote assessment of hand function (ie, outside of traditional in-person clinical settings) in adults with chronic central nervous system disorders. We excluded studies that included participants with orthopedic upper limb dysfunction or used tools for intervention and treatment. We extracted data on the evaluated hand function domains, validity and reliability, feasibility, and stage of development. RESULTS In total, 74 studies met the inclusion criteria for Parkinson disease (n=57, 77% studies), stroke (n=9, 12%), multiple sclerosis (n=6, 8%), spinal cord injury (n=1, 1%), and amyotrophic lateral sclerosis (n=1, 1%). Three assessment modalities were identified: external device (eg, wrist-worn accelerometer), smartphone or tablet, and telerehabilitation. The feasibility and overall participant acceptability were high. The most common hand function domains assessed included finger tapping speed (fine motor control and rigidity), hand tremor (pharmacological and rehabilitation efficacy), and finger dexterity (manipulation of small objects required for daily tasks) and handwriting (coordination). Although validity and reliability data were heterogeneous across studies, statistically significant correlations with traditional in-clinic metrics were most commonly reported for telerehabilitation and smartphone or tablet apps. The most readily implementable assessments were smartphone or tablet-based. CONCLUSIONS The findings show that remote assessment of hand function is feasible in neurological disorders. Although varied, the assessments allow clinicians to objectively record performance in multiple hand function domains, improving the reliability of traditional in-clinic assessments. Remote assessments, particularly via telerehabilitation and smartphone- or tablet-based apps that align with in-clinic metrics, facilitate clinic to home transitions, have few barriers to implementation, and prompt remote identification and treatment of hand function impairments.
Collapse
Affiliation(s)
- Arpita Gopal
- Weill Institute of Neurosciences, University of California San Francisco, San Francisco, CA, United States
| | - Wan-Yu Hsu
- Weill Institute of Neurosciences, University of California San Francisco, San Francisco, CA, United States
| | - Diane D Allen
- Department of Physical Therapy and Rehabilitation Science, University of California San Francisco/San Francisco State University, San Francisco, CA, United States
| | - Riley Bove
- Weill Institute of Neurosciences, University of California San Francisco, San Francisco, CA, United States
| |
Collapse
|
8
|
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: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [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.
Collapse
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
| |
Collapse
|
9
|
Lam KH, Meijer KA, Loonstra FC, Coerver E, Twose J, Redeman E, Moraal B, Barkhof F, de Groot V, Uitdehaag B, Killestein J. Real-world keystroke dynamics are a potentially valid biomarker for clinical disability in multiple sclerosis. Mult Scler 2020; 27:1421-1431. [PMID: 33150823 PMCID: PMC8358561 DOI: 10.1177/1352458520968797] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Background: Clinical measures in multiple sclerosis (MS) face limitations that may be overcome by utilising smartphone keyboard interactions acquired continuously and remotely during regular typing. Objective: The aim of this study was to determine the reliability and validity of keystroke dynamics to assess clinical aspects of MS. Methods: In total, 102 MS patients and 24 controls were included in this observational study. Keyboard interactions were obtained with the Neurokeys keyboard app. Eight timing-related keystroke features were assessed for reliability with intraclass correlation coefficients (ICCs); construct validity by analysing group differences (in fatigue, gadolinium-enhancing lesions on magnetic resonance imaging (MRI), and patients vs controls); and concurrent validity by correlating with disability measures. Results: Reliability was moderate in two (ICC = 0.601 and 0.742) and good to excellent in the remaining six features (ICC = 0.760–0.965). Patients had significantly higher keystroke latencies than controls. Latency between key presses correlated the highest with Expanded Disability Status Scale (r = 0.407) and latency between key releases with Nine-Hole Peg Test and Symbol Digit Modalities Test (ρ = 0.503 and r = −0.553, respectively), ps < 0.001. Conclusion: Keystroke dynamics were reliable, distinguished patients and controls, and were associated with clinical disability measures. Consequently, keystroke dynamics are a promising valid surrogate marker for clinical disability in MS.
Collapse
Affiliation(s)
- K H Lam
- Department of Neurology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, MS Center Amsterdam, Amsterdam, The Netherlands
| | - K A Meijer
- Neurocast B.V., Amsterdam, The Netherlands
| | - F C Loonstra
- Department of Neurology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, MS Center Amsterdam, Amsterdam, The Netherlands
| | - Eme Coerver
- Department of Neurology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, MS Center Amsterdam, Amsterdam, The Netherlands
| | - J Twose
- Neurocast B.V., Amsterdam, The Netherlands
| | - E Redeman
- Neurocast B.V., Amsterdam, The Netherlands
| | - B Moraal
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, MS Center Amsterdam, Amsterdam, The Netherlands
| | - F Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, MS Center Amsterdam, Amsterdam, The Netherlands/Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, UK
| | - V de Groot
- Department of Rehabilitation Medicine, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Bmj Uitdehaag
- Department of Neurology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, MS Center Amsterdam, Amsterdam, The Netherlands
| | - J Killestein
- Department of Neurology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, MS Center Amsterdam, Amsterdam, The Netherlands
| |
Collapse
|
10
|
Chipika RH, Finegan E, Li Hi Shing S, Hardiman O, Bede P. Tracking a Fast-Moving Disease: Longitudinal Markers, Monitoring, and Clinical Trial Endpoints in ALS. Front Neurol 2019; 10:229. [PMID: 30941088 PMCID: PMC6433752 DOI: 10.3389/fneur.2019.00229] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2018] [Accepted: 02/22/2019] [Indexed: 12/13/2022] Open
Abstract
Amyotrophic lateral sclerosis (ALS) encompasses a heterogeneous group of phenotypes with different progression rates, varying degree of extra-motor involvement and divergent progression patterns. The natural history of ALS is increasingly evaluated by large, multi-time point longitudinal studies, many of which now incorporate presymptomatic and post-mortem assessments. These studies not only have the potential to characterize patterns of anatomical propagation, molecular mechanisms of disease spread, but also to identify pragmatic monitoring markers. Sensitive markers of progressive neurodegenerative change are indispensable for clinical trials and individualized patient care. Biofluid markers, neuroimaging indices, electrophysiological markers, rating scales, questionnaires, and other disease-specific instruments have divergent sensitivity profiles. The discussion of candidate monitoring markers in ALS has a dual academic and clinical relevance, and is particularly timely given the increasing number of pharmacological trials. The objective of this paper is to provide a comprehensive and critical review of longitudinal studies in ALS, focusing on the sensitivity profile of established and emerging monitoring markers.
Collapse
Affiliation(s)
| | - Eoin Finegan
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Stacey Li Hi Shing
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Orla Hardiman
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Peter Bede
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| |
Collapse
|
11
|
A Waist-Worn Inertial Measurement Unit for Long-Term Monitoring of Parkinson's Disease Patients. SENSORS 2017; 17:s17040827. [PMID: 28398265 PMCID: PMC5422188 DOI: 10.3390/s17040827] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2016] [Revised: 04/04/2017] [Accepted: 04/07/2017] [Indexed: 01/23/2023]
Abstract
Inertial measurement units (IMUs) are devices used, among other fields, in health applications, since they are light, small and effective. More concretely, IMUs have been demonstrated to be useful in the monitoring of motor symptoms of Parkinson’s disease (PD). In this sense, most of previous works have attempted to assess PD symptoms in controlled environments or short tests. This paper presents the design of an IMU, called 9 × 3, that aims to assess PD symptoms, enabling the possibility to perform a map of patients’ symptoms at their homes during long periods. The device is able to acquire and store raw inertial data for artificial intelligence algorithmic training purposes. Furthermore, the presented IMU enables the real-time execution of the developed and embedded learning models. Results show the great flexibility of the 9 × 3, storing inertial information and algorithm outputs, sending messages to external devices and being able to detect freezing of gait and bradykinetic gait. Results obtained in 12 patients exhibit a sensitivity and specificity over 80%. Additionally, the system enables working 23 days (at waking hours) with a 1200 mAh battery and a sampling rate of 50 Hz, opening up the possibility to be used for other applications like wellbeing and sports.
Collapse
|