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Paolillo EW, Casaletto KB, Clark AL, Taylor JC, Heuer HW, Wise AB, Dhanam S, Sanderson-Cimino M, Saloner R, Kramer JH, Kornak J, Kremers W, Forsberg L, Appleby B, Bayram E, Bozoki A, Brushaber D, Darby RR, Day GS, Dickerson BC, Domoto-Reilly K, Elahi F, Fields JA, Ghoshal N, Graff-Radford N, G H Hall M, Honig LS, Huey ED, Lapid MI, Litvan I, Mackenzie IR, Masdeu JC, Mendez MF, Mester C, Miyagawa T, Naasan G, Pascual B, Pressman P, Ramos EM, Rankin KP, Rexach J, Rojas JC, VandeVrede L, Wong B, Wszolek ZK, Boeve BF, Rosen HJ, Boxer AL, Staffaroni AM. Examining Associations Between Smartphone Use and Clinical Severity in Frontotemporal Dementia: Proof-of-Concept Study. JMIR Aging 2024; 7:e52831. [PMID: 38922667 DOI: 10.2196/52831] [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: 09/16/2023] [Revised: 02/09/2024] [Accepted: 03/07/2024] [Indexed: 06/27/2024] Open
Abstract
BACKGROUND Frontotemporal lobar degeneration (FTLD) is a leading cause of dementia in individuals aged <65 years. Several challenges to conducting in-person evaluations in FTLD illustrate an urgent need to develop remote, accessible, and low-burden assessment techniques. Studies of unobtrusive monitoring of at-home computer use in older adults with mild cognitive impairment show that declining function is reflected in reduced computer use; however, associations with smartphone use are unknown. OBJECTIVE This study aims to characterize daily trajectories in smartphone battery use, a proxy for smartphone use, and examine relationships with clinical indicators of severity in FTLD. METHODS Participants were 231 adults (mean age 52.5, SD 14.9 years; n=94, 40.7% men; n=223, 96.5% non-Hispanic White) enrolled in the Advancing Research and Treatment of Frontotemporal Lobar Degeneration (ARTFL study) and Longitudinal Evaluation of Familial Frontotemporal Dementia Subjects (LEFFTDS study) Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) Mobile App study, including 49 (21.2%) with mild neurobehavioral changes and no functional impairment (ie, prodromal FTLD), 43 (18.6%) with neurobehavioral changes and functional impairment (ie, symptomatic FTLD), and 139 (60.2%) clinically normal adults, of whom 55 (39.6%) harbored heterozygous pathogenic or likely pathogenic variants in an autosomal dominant FTLD gene. Participants completed the Clinical Dementia Rating plus National Alzheimer's Coordinating Center Frontotemporal Lobar Degeneration Behavior and Language Domains (CDR+NACC FTLD) scale, a neuropsychological battery; the Neuropsychiatric Inventory; and brain magnetic resonance imaging. The ALLFTD Mobile App was installed on participants' smartphones for remote, passive, and continuous monitoring of smartphone use. Battery percentage was collected every 15 minutes over an average of 28 (SD 4.2; range 14-30) days. To determine whether temporal patterns of battery percentage varied as a function of disease severity, linear mixed effects models examined linear, quadratic, and cubic effects of the time of day and their interactions with each measure of disease severity on battery percentage. Models covaried for age, sex, smartphone type, and estimated smartphone age. RESULTS The CDR+NACC FTLD global score interacted with time on battery percentage such that participants with prodromal or symptomatic FTLD demonstrated less change in battery percentage throughout the day (a proxy for less smartphone use) than clinically normal participants (P<.001 in both cases). Additional models showed that worse performance in all cognitive domains assessed (ie, executive functioning, memory, language, and visuospatial skills), more neuropsychiatric symptoms, and smaller brain volumes also associated with less battery use throughout the day (P<.001 in all cases). CONCLUSIONS These findings support a proof of concept that passively collected data about smartphone use behaviors associate with clinical impairment in FTLD. This work underscores the need for future studies to develop and validate passive digital markers sensitive to longitudinal clinical decline across neurodegenerative diseases, with potential to enhance real-world monitoring of neurobehavioral change.
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Affiliation(s)
- Emily W Paolillo
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Kaitlin B Casaletto
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Annie L Clark
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Jack C Taylor
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Hilary W Heuer
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Amy B Wise
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Sreya Dhanam
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Mark Sanderson-Cimino
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Rowan Saloner
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Joel H Kramer
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - John Kornak
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, United States
| | - Walter Kremers
- Department of Quantitative Health Sciences, Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, MN, United States
| | - Leah Forsberg
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Brian Appleby
- Department of Neurology, Case Western Reserve University, Cleveland, OH, United States
| | - Ece Bayram
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, United States
| | - Andrea Bozoki
- Department of Neurology, University of North Carolina, Chapel Hill, NC, United States
| | - Danielle Brushaber
- Department of Quantitative Health Sciences, Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, MN, United States
| | - R Ryan Darby
- Department of Neurology, Vanderbilt University, Nashville, TN, United States
| | - Gregory S Day
- Department of Neurology, Mayo Clinic, Jacksonville, FL, United States
| | - Bradford C Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | | | - Fanny Elahi
- Department of Neurology, The Deane Center for Wellness and Cognitive Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- James J. Peters Veterans Affairs Medical Center, New York, NY, United States
| | - Julie A Fields
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Nupur Ghoshal
- Department of Neurology, Knight Alzheimer's Disease Research Center, Washington University, St. Louis, MO, United States
| | | | - Matthew G H Hall
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Lawrence S Honig
- Department of Neurology, Columbia University, New York, NY, United States
| | - Edward D Huey
- Department of Psychiatry and Human Behavior, Brown University, Providence, RI, United States
| | - Maria I Lapid
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Irene Litvan
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, United States
| | - Ian R Mackenzie
- Department of Pathology, University of British Columbia, Vancouver, BC, Canada
| | - Joseph C Masdeu
- Stanley H. Appel Department of Neurology, Nantz National Alzheimer Center, Houston Methodist Research Institute, Weill Cornell Medicine, Houston, TX, United States
| | - Mario F Mendez
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Carly Mester
- Department of Quantitative Health Sciences, Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, MN, United States
| | - Toji Miyagawa
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Georges Naasan
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Belen Pascual
- Stanley H. Appel Department of Neurology, Nantz National Alzheimer Center, Houston Methodist Research Institute, Weill Cornell Medicine, Houston, TX, United States
| | - Peter Pressman
- Department of Neurology, University of Colorado, Aurora, CO, United States
| | - Eliana Marisa Ramos
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Katherine P Rankin
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Jessica Rexach
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Julio C Rojas
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Lawren VandeVrede
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Bonnie Wong
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | | | - Bradley F Boeve
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Howard J Rosen
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Adam L Boxer
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Adam M Staffaroni
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
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Torri F, Vadi G, Meli A, Loprieno S, Schirinzi E, Lopriore P, Ricci G, Siciliano G, Mancuso M. The use of digital tools in rare neurological diseases towards a new care model: a narrative review. Neurol Sci 2024:10.1007/s10072-024-07631-4. [PMID: 38856822 DOI: 10.1007/s10072-024-07631-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 05/31/2024] [Indexed: 06/11/2024]
Abstract
Rare neurological diseases as a whole share peculiar features as motor and/or cognitive impairment, an elevated disability burden, a frequently chronic course and, in present times, scarcity of therapeutic options. The rarity of those conditions hampers both the identification of significant prognostic outcome measures, and the development of novel therapeutic approaches and clinical trials. Collection of objective clinical data through digital devices can support diagnosis, care, and therapeutic research. We provide an overview on recent developments in the field of digital tools applied to rare neurological diseases, both in the care setting and as providers of outcome measures in clinical trials in a representative subgroup of conditions, including ataxias, hereditary spastic paraplegias, motoneuron diseases and myopathies.
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Affiliation(s)
- Francesca Torri
- Department of Clinical and Experimental Medicine, Neurology Unit, University of Pisa, Pisa, Italy
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Gabriele Vadi
- Department of Clinical and Experimental Medicine, Neurology Unit, University of Pisa, Pisa, Italy
| | - Adriana Meli
- Department of Clinical and Experimental Medicine, Neurology Unit, University of Pisa, Pisa, Italy
| | - Sara Loprieno
- Department of Clinical and Experimental Medicine, Neurology Unit, University of Pisa, Pisa, Italy
| | - Erika Schirinzi
- Department of Clinical and Experimental Medicine, Neurology Unit, University of Pisa, Pisa, Italy
| | - Piervito Lopriore
- Department of Clinical and Experimental Medicine, Neurology Unit, University of Pisa, Pisa, Italy
| | - Giulia Ricci
- Department of Clinical and Experimental Medicine, Neurology Unit, University of Pisa, Pisa, Italy
| | - Gabriele Siciliano
- Department of Clinical and Experimental Medicine, Neurology Unit, University of Pisa, Pisa, Italy
| | - Michelangelo Mancuso
- Department of Clinical and Experimental Medicine, Neurology Unit, University of Pisa, Pisa, Italy.
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Zawada SJ, Ganjizadeh A, Hagen CE, Demaerschalk BM, Erickson BJ. Feasibility of Observing Cerebrovascular Disease Phenotypes with Smartphone Monitoring: Study Design Considerations for Real-World Studies. SENSORS (BASEL, SWITZERLAND) 2024; 24:3595. [PMID: 38894385 PMCID: PMC11175199 DOI: 10.3390/s24113595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2024] [Revised: 05/28/2024] [Accepted: 05/31/2024] [Indexed: 06/21/2024]
Abstract
Accelerated by the adoption of remote monitoring during the COVID-19 pandemic, interest in using digitally captured behavioral data to predict patient outcomes has grown; however, it is unclear how feasible digital phenotyping studies may be in patients with recent ischemic stroke or transient ischemic attack. In this perspective, we present participant feedback and relevant smartphone data metrics suggesting that digital phenotyping of post-stroke depression is feasible. Additionally, we proffer thoughtful considerations for designing feasible real-world study protocols tracking cerebrovascular dysfunction with smartphone sensors.
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Affiliation(s)
- Stephanie J. Zawada
- Mayo Clinic College of Medicine and Science, 5777 E. Mayo Boulevard, Scottsdale, AZ 85054, USA
| | - Ali Ganjizadeh
- Mayo Clinic AI Laboratory, 200 1st Street SW, Rochester, MN 55902, USA; (A.G.); (B.J.E.)
| | - Clint E. Hagen
- Mayo Clinic Division of Biomedical Statistics and Informatics, 200 1st Street SW, Rochester, MN 55902, USA;
| | - Bart M. Demaerschalk
- Mayo Clinic Center for Digital Health, 5777 E. Mayo Boulevard, Scottsdale, AZ 85054, USA;
| | - Bradley J. Erickson
- Mayo Clinic AI Laboratory, 200 1st Street SW, Rochester, MN 55902, USA; (A.G.); (B.J.E.)
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Karas M, Olsen J, Straczkiewicz M, Johnson SA, Burke KM, Iwasaki S, Lahav A, Scheier ZA, Clark AP, Iyer AS, Huang E, Berry JD, Onnela J. Tracking amyotrophic lateral sclerosis disease progression using passively collected smartphone sensor data. Ann Clin Transl Neurol 2024; 11:1380-1392. [PMID: 38816946 PMCID: PMC11187949 DOI: 10.1002/acn3.52050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 02/03/2024] [Accepted: 03/05/2024] [Indexed: 06/01/2024] Open
Abstract
BACKGROUND Passively collected smartphone sensor data provide an opportunity to study physical activity and mobility unobtrusively over long periods of time and may enable disease monitoring in people with amyotrophic lateral sclerosis (PALS). METHODS We enrolled 63 PALS who used Beiwe mobile application that collected their smartphone accelerometer and GPS data and administered the self-entry ALS Functional Rating Scale-Revised (ALSFRS-RSE) survey. We identified individual steps from accelerometer data and used the Activity Index to summarize activity at the minute level. Walking, Activity Index, and GPS outcomes were then aggregated into day-level measures. We used linear mixed effect models (LMMs) to estimate baseline and monthly change for ALSFRS-RSE scores (total score, subscores Q1-3, Q4-6, Q7-9, Q10-12) and smartphone sensor data measures, as well as the associations between them. FINDINGS The analytic sample (N = 45) was 64.4% male with a mean age of 60.1 years. The mean observation period was 292.3 days. The ALSFRS-RSE total score baseline mean was 35.8 and had a monthly rate of decline of -0.48 (p-value <0.001). We observed statistically significant change over time and association with ALSFRS-RSE total score for four smartphone sensor data-derived measures: walking cadence from top 1 min and log-transformed step count, step count from top 1 min, and Activity Index from top 1 min. INTERPRETATION Smartphone sensors can unobtrusively track physical changes in PALS, potentially aiding disease monitoring and future research.
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Affiliation(s)
- Marta Karas
- Department of Biostatistics, Harvard T.H. Chan School of Public HealthHarvard University677 Huntington Ave.BostonMassachusetts02115USA
| | - Julia Olsen
- Department of Biostatistics, Harvard T.H. Chan School of Public HealthHarvard University677 Huntington Ave.BostonMassachusetts02115USA
| | - Marcin Straczkiewicz
- Department of Biostatistics, Harvard T.H. Chan School of Public HealthHarvard University677 Huntington Ave.BostonMassachusetts02115USA
| | - Stephen A. Johnson
- Department of NeurologyMayo Clinic13400 E. Shea Blvd.ScottsdaleArizona85259USA
| | - Katherine M. Burke
- Neurological Clinical Research Institute and Sean M. Healey & AMG Center for ALS, Massachusetts General Hospital15 Parkman St #835BostonMassachusetts02114USA
| | - Satoshi Iwasaki
- Mitsubishi Tanabe Pharma Holdings America, Inc.525 Washington Blvd.Jersey CityNew Jersey07310USA
| | - Amir Lahav
- Mitsubishi Tanabe Pharma Holdings America, Inc.525 Washington Blvd.Jersey CityNew Jersey07310USA
| | - Zoe A. Scheier
- Neurological Clinical Research Institute and Sean M. Healey & AMG Center for ALS, Massachusetts General Hospital15 Parkman St #835BostonMassachusetts02114USA
| | - Alison P. Clark
- Neurological Clinical Research Institute and Sean M. Healey & AMG Center for ALS, Massachusetts General Hospital15 Parkman St #835BostonMassachusetts02114USA
| | - Amrita S. Iyer
- Neurological Clinical Research Institute and Sean M. Healey & AMG Center for ALS, Massachusetts General Hospital15 Parkman St #835BostonMassachusetts02114USA
| | - Emily Huang
- Department of Statistical SciencesWake Forest UniversityWinston‐SalemNorth Carolina27106USA
| | - James D. Berry
- Neurological Clinical Research Institute and Sean M. Healey & AMG Center for ALS, Massachusetts General Hospital15 Parkman St #835BostonMassachusetts02114USA
| | - Jukka‐Pekka Onnela
- Department of Biostatistics, Harvard T.H. Chan School of Public HealthHarvard University677 Huntington Ave.BostonMassachusetts02115USA
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Beswick E, Christides A, Symonds A, Johnson M, Fawcett T, Newton J, Lyle D, Weaver C, Chandran S, Pal S. Exploratory study to evaluate the acceptability of a wearable accelerometer to assess motor progression in motor neuron disease. J Neurol 2024:10.1007/s00415-024-12449-3. [PMID: 38805054 DOI: 10.1007/s00415-024-12449-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 05/10/2024] [Accepted: 05/14/2024] [Indexed: 05/29/2024]
Abstract
Motor neuron disease (MND) is a rapidly progressive condition traditionally assessed using a questionnaire to evaluate physical function, the revised amyotrophic lateral sclerosis functional rating scale (ALSFRS-R). Its use can be associated with poor sensitivity in detecting subtle changes over time and there is an urgent need for more sensitive and specific outcome measures. The ActiGraph GT9X is a wearable device containing multiple sensors that can be used to provide metrics that represent physical activity. The primary aim of this study was to investigate the initial suitability and acceptability of limb-worn wearable devices to group of people with MND in Scotland. A secondary aim was to explore the preliminary associations between the accelerometer sensor data within the ActiGraph GT9X and established measures of physical function. 10 participants with MND completed a 12-week schedule of assessments including fortnightly study visits, both in-person and over videoconferencing software. Participants wore the device on their right wrist and right ankle for a series of movements, during a 6-min walking test and for a period of 24-h wear, including overnight. Participants also completed an ALSFRS-R and questionnaires on their experience with the devices. 80% of the participants found wearing these devices to be a positive experience and no one reported interference with daily living or added burden. However, 30% of the participants experienced technical issues with their devices. Data from the wearable devices correlated with established measures of physical function.
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Affiliation(s)
- Emily Beswick
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland
- Anne Rowling Regenerative Neurology Clinic, The University of Edinburgh, 49 Little France Crescent, Edinburgh, EH16 4SB, UK
- Euan MacDonald Centre for MND Research, The University of Edinburgh, Edinburgh, Scotland
| | - Alexander Christides
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland
- Anne Rowling Regenerative Neurology Clinic, The University of Edinburgh, 49 Little France Crescent, Edinburgh, EH16 4SB, UK
- Euan MacDonald Centre for MND Research, The University of Edinburgh, Edinburgh, Scotland
| | - Alexander Symonds
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland
- Anne Rowling Regenerative Neurology Clinic, The University of Edinburgh, 49 Little France Crescent, Edinburgh, EH16 4SB, UK
- Euan MacDonald Centre for MND Research, The University of Edinburgh, Edinburgh, Scotland
| | - Micheaela Johnson
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland
- Anne Rowling Regenerative Neurology Clinic, The University of Edinburgh, 49 Little France Crescent, Edinburgh, EH16 4SB, UK
- Euan MacDonald Centre for MND Research, The University of Edinburgh, Edinburgh, Scotland
| | - Thomas Fawcett
- The School of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh, Scotland
| | - Judith Newton
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland
- Anne Rowling Regenerative Neurology Clinic, The University of Edinburgh, 49 Little France Crescent, Edinburgh, EH16 4SB, UK
- Euan MacDonald Centre for MND Research, The University of Edinburgh, Edinburgh, Scotland
| | - Dawn Lyle
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland
- Anne Rowling Regenerative Neurology Clinic, The University of Edinburgh, 49 Little France Crescent, Edinburgh, EH16 4SB, UK
- Euan MacDonald Centre for MND Research, The University of Edinburgh, Edinburgh, Scotland
| | - Christine Weaver
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland
- Anne Rowling Regenerative Neurology Clinic, The University of Edinburgh, 49 Little France Crescent, Edinburgh, EH16 4SB, UK
- Euan MacDonald Centre for MND Research, The University of Edinburgh, Edinburgh, Scotland
| | - Siddharthan Chandran
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland
- Anne Rowling Regenerative Neurology Clinic, The University of Edinburgh, 49 Little France Crescent, Edinburgh, EH16 4SB, UK
- Euan MacDonald Centre for MND Research, The University of Edinburgh, Edinburgh, Scotland
- UK Dementia Research Institute, The University of Edinburgh, Edinburgh, Scotland
| | - Suvankar Pal
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland.
- Anne Rowling Regenerative Neurology Clinic, The University of Edinburgh, 49 Little France Crescent, Edinburgh, EH16 4SB, UK.
- Euan MacDonald Centre for MND Research, The University of Edinburgh, Edinburgh, Scotland.
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Riva N, Domi T, Pozzi L, Lunetta C, Schito P, Spinelli EG, Cabras S, Matteoni E, Consonni M, Bella ED, Agosta F, Filippi M, Calvo A, Quattrini A. Update on recent advances in amyotrophic lateral sclerosis. J Neurol 2024:10.1007/s00415-024-12435-9. [PMID: 38802624 DOI: 10.1007/s00415-024-12435-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 05/07/2024] [Accepted: 05/09/2024] [Indexed: 05/29/2024]
Abstract
In the last few years, our understanding of disease molecular mechanisms underpinning ALS has advanced greatly, allowing the first steps in translating into clinical practice novel research findings, including gene therapy approaches. Similarly, the recent advent of assistive technologies has greatly improved the possibility of a more personalized approach to supportive and symptomatic care, in the context of an increasingly complex multidisciplinary line of actions, which remains the cornerstone of ALS management. Against this rapidly growing background, here we provide an comprehensive update on the most recent studies that have contributed towards our understanding of ALS pathogenesis, the latest results from clinical trials as well as the future directions for improving the clinical management of ALS patients.
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Affiliation(s)
- Nilo Riva
- 3Rd Neurology Unit and Motor Neuron Disease Centre, Fondazione IRCCS "Carlo Besta" Neurological Insitute, Milan, Italy.
| | - Teuta Domi
- Experimental Neuropathology Unit, Division of Neuroscience, Institute of Experimental Neurology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Laura Pozzi
- Experimental Neuropathology Unit, Division of Neuroscience, Institute of Experimental Neurology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Christian Lunetta
- Istituti Clinici Scientifici Maugeri IRCCS, Neurorehabilitation Unit of Milan Institute, 20138, Milan, Italy
| | - Paride Schito
- Experimental Neuropathology Unit, Division of Neuroscience, Institute of Experimental Neurology, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Department of Neurology, Division of Neuroscience, Institute of Experimental Neurology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Edoardo Gioele Spinelli
- Department of Neurology, Division of Neuroscience, Institute of Experimental Neurology, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neuroimaging Research Unit, Department of Neurology, Division of Neuroscience, Institute of Experimental Neurology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Sara Cabras
- ALS Centre, 'Rita Levi Montalcini' Department of Neuroscience, University of Turin; SC Neurologia 1U, AOU città della Salute e della Scienza di Torino, Turin, Italy
| | - Enrico Matteoni
- ALS Centre, 'Rita Levi Montalcini' Department of Neuroscience, University of Turin; SC Neurologia 1U, AOU città della Salute e della Scienza di Torino, Turin, Italy
| | - Monica Consonni
- 3Rd Neurology Unit and Motor Neuron Disease Centre, Fondazione IRCCS "Carlo Besta" Neurological Insitute, Milan, Italy
| | - Eleonora Dalla Bella
- 3Rd Neurology Unit and Motor Neuron Disease Centre, Fondazione IRCCS "Carlo Besta" Neurological Insitute, Milan, Italy
| | - Federica Agosta
- Department of Neurology, Division of Neuroscience, Institute of Experimental Neurology, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neuroimaging Research Unit, Department of Neurology, Division of Neuroscience, Institute of Experimental Neurology, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute Huniversity, Milan, Italy
| | - Massimo Filippi
- Department of Neurology, Division of Neuroscience, Institute of Experimental Neurology, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neuroimaging Research Unit, Department of Neurology, Division of Neuroscience, Institute of Experimental Neurology, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute Huniversity, Milan, Italy
| | - Andrea Calvo
- ALS Centre, 'Rita Levi Montalcini' Department of Neuroscience, University of Turin; SC Neurologia 1U, AOU città della Salute e della Scienza di Torino, Turin, Italy
| | - Angelo Quattrini
- Experimental Neuropathology Unit, Division of Neuroscience, Institute of Experimental Neurology, IRCCS San Raffaele Scientific Institute, Milan, Italy
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7
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Matabuena M, Sartini J. Multilevel functional data analysis modeling of human glucose response to meal intake. ARXIV 2024:arXiv:2405.14690v1. [PMID: 38827463 PMCID: PMC11142320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Glucose meal response information collected via Continuous Glucose Monitoring (CGM) is relevant to the assessment of individual metabolic status and the support of personalized diet prescriptions. However, the complexity of the data produced by CGM monitors pushes the limits of existing analytic methods. CGM data often exhibits substantial within-person variability and has a natural multilevel structure. This research is motivated by the analysis of CGM data from individuals without diabetes in the AEGIS study. The dataset includes detailed information on meal timing and nutrition for each individual over different days. The primary focus of this study is to examine CGM glucose responses following patients' meals and explore the time-dependent associations with dietary and patient characteristics. Motivated by this problem, we propose a new analytical framework based on multilevel functional models, including a new functional mixed R-square coefficient. The use of these models illustrates 3 key points: (i) The importance of analyzing glucose responses across the entire functional domain when making diet recommendations; (ii) The differential metabolic responses between normoglycemic and prediabetic patients, particularly with regards to lipid intake; (iii) The importance of including random, person-level effects when modelling this scientific problem.
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Affiliation(s)
- Marcos Matabuena
- Universidad de Santiago de Compostela and Department of Biostatistics, Harvard University, Boston, MA 02115, USA
| | - Joe Sartini
- Department of Biostatistics, Johns Hopkins University, Francisco Gude, Universidad de Santiago de Compostela
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8
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van Unnik JWJ, Meyjes M, Janse van Mantgem MR, van den Berg LH, van Eijk RPA. Remote monitoring of amyotrophic lateral sclerosis using wearable sensors detects differences in disease progression and survival: a prospective cohort study. EBioMedicine 2024; 103:105104. [PMID: 38582030 PMCID: PMC11004066 DOI: 10.1016/j.ebiom.2024.105104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 03/20/2024] [Accepted: 03/20/2024] [Indexed: 04/08/2024] Open
Abstract
BACKGROUND There is an urgent need for objective and sensitive measures to quantify clinical disease progression and gauge the response to treatment in clinical trials for amyotrophic lateral sclerosis (ALS). Here, we evaluate the ability of an accelerometer-derived outcome to detect differential clinical disease progression and assess its longitudinal associations with overall survival in patients with ALS. METHODS Patients with ALS wore an accelerometer on the hip for 3-7 days, every 2-3 months during a multi-year observation period. An accelerometer-derived outcome, the Vertical Movement Index (VMI), was calculated, together with predicted disease progression rates, and jointly analysed with overall survival. The clinical utility of VMI was evaluated using comparisons to patient-reported functionality, while the impact of various monitoring schemes on empirical power was explored through simulations. FINDINGS In total, 97 patients (70.1% male) wore the accelerometer for 1995 days, for a total of 27,701 h. The VMI was highly discriminatory for predicted disease progression rates, revealing faster rates of decline in patients with a worse predicted prognosis compared to those with a better predicted prognosis (p < 0.0001). The VMI was strongly associated with the hazard for death (HR 0.20, 95% CI: 0.09-0.44, p < 0.0001), where a decrease of 0.19-0.41 unit was associated with reduced ambulatory status. Recommendations for future studies using accelerometery are provided. INTERPRETATION The results serve as motivation to incorporate accelerometer-derived outcomes in clinical trials, which is essential for further validation of these markers to meaningful endpoints. FUNDING Stichting ALS Nederland (TRICALS-Reactive-II).
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Affiliation(s)
- Jordi W J van Unnik
- Department of Neurology, UMC Utrecht Brain Centre, University Medical Centre Utrecht, Utrecht, the Netherlands
| | - Myrte Meyjes
- Department of Neurology, UMC Utrecht Brain Centre, University Medical Centre Utrecht, Utrecht, the Netherlands
| | - Mark R Janse van Mantgem
- Department of Neurology, UMC Utrecht Brain Centre, University Medical Centre Utrecht, Utrecht, the Netherlands
| | - Leonard H van den Berg
- Department of Neurology, UMC Utrecht Brain Centre, University Medical Centre Utrecht, Utrecht, the Netherlands
| | - Ruben P A van Eijk
- Department of Neurology, UMC Utrecht Brain Centre, University Medical Centre Utrecht, Utrecht, the Netherlands; Biostatistics & Research Support, Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, the Netherlands.
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9
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Erb MK, Calcagno N, Brown R, Burke KM, Scheier ZA, Iyer AS, Clark A, Higgins MP, Keegan M, Gupta AS, Johnson SA, Chew S, Berry JD. Longitudinal comparison of the self-administered ALSFRS-RSE and ALSFRS-R as functional outcome measures in ALS. Amyotroph Lateral Scler Frontotemporal Degener 2024:1-11. [PMID: 38501453 DOI: 10.1080/21678421.2024.2322549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 02/19/2024] [Indexed: 03/20/2024]
Abstract
Objective: Test the feasibility, adherence rates and optimal frequency of digital, remote assessments using the ALSFRS-RSE via a customized smartphone-based app. Methods: This fully remote, longitudinal study was conducted over a 24-week period, with virtual visits every 3 months and weekly digital assessments. 19 ALS participants completed digital assessments via smartphone, including a digital version of the ALSFRS-RSE and mood survey. Interclass correlation coefficients (ICC) and Bland-Altman plots were used to assess agreement between staff-administered and self-reported ALSFRS-R pairs. Longitudinal change was evaluated using ANCOVA models and linear mixed models, including impact of mood and time of day. Impact of frequency of administration of the ALSFRS-RSE on precision of the estimate slope was tested using a mixed effects model. Results: In our ALS cohort, digital assessments were well-accepted and adherence was robust, with completion rates of 86%. There was excellent agreement between the digital self-entry and staff-administered scores computing multiple ICCs (ICC range = 0.925-0.961), with scores on the ALSFRS-RSE slightly higher (1.304 points). Digital assessments were associated with increased precision of the slope, resulting in higher standardized response mean estimates for higher frequencies, though benefit appeared to diminish at biweekly and weekly frequency. Effects of participant mood and time of day on total ALSFRS-RSE score were evaluated but were minimal and not statistically significant. Conclusion: Remote collection of digital patient-reported outcomes of functional status such as the ALSFRS-RSE yield more accurate estimates of change over time and provide a broader understanding of the lived experience of people with ALS.
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Affiliation(s)
| | - Narghes Calcagno
- Department of Neurology, Massachusetts General Hospital, Sean M. Healey & AMG Center for ALS, Boston, MA, USA, and
- Neurology Residency Program, University of Milan, Milan, Italy
| | | | - Katherine M Burke
- Department of Neurology, Massachusetts General Hospital, Sean M. Healey & AMG Center for ALS, Boston, MA, USA, and
| | - Zoe A Scheier
- Department of Neurology, Massachusetts General Hospital, Sean M. Healey & AMG Center for ALS, Boston, MA, USA, and
| | - Amrita S Iyer
- Department of Neurology, Massachusetts General Hospital, Sean M. Healey & AMG Center for ALS, Boston, MA, USA, and
| | - Alison Clark
- Department of Neurology, Massachusetts General Hospital, Sean M. Healey & AMG Center for ALS, Boston, MA, USA, and
| | - Max P Higgins
- Department of Neurology, Massachusetts General Hospital, Sean M. Healey & AMG Center for ALS, Boston, MA, USA, and
| | - Mackenzie Keegan
- Department of Neurology, Massachusetts General Hospital, Sean M. Healey & AMG Center for ALS, Boston, MA, USA, and
| | - Anoopum S Gupta
- Department of Neurology, Massachusetts General Hospital, Sean M. Healey & AMG Center for ALS, Boston, MA, USA, and
| | - Stephen A Johnson
- Department of Neurology, Massachusetts General Hospital, Sean M. Healey & AMG Center for ALS, Boston, MA, USA, and
| | - Sheena Chew
- Biogen, Inc, Cambridge, MA, USA
- Department of Neurology, Massachusetts General Hospital, Sean M. Healey & AMG Center for ALS, Boston, MA, USA, and
| | - James D Berry
- Department of Neurology, Massachusetts General Hospital, Sean M. Healey & AMG Center for ALS, Boston, MA, USA, and
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Tzeplaeff L, Jürs AV, Wohnrade C, Demleitner AF. Unraveling the Heterogeneity of ALS-A Call to Redefine Patient Stratification for Better Outcomes in Clinical Trials. Cells 2024; 13:452. [PMID: 38474416 PMCID: PMC10930688 DOI: 10.3390/cells13050452] [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: 01/31/2024] [Revised: 02/26/2024] [Accepted: 02/28/2024] [Indexed: 03/14/2024] Open
Abstract
Despite tremendous efforts in basic research and a growing number of clinical trials aiming to find effective treatments, amyotrophic lateral sclerosis (ALS) remains an incurable disease. One possible reason for the lack of effective causative treatment options is that ALS may not be a single disease entity but rather may represent a clinical syndrome, with diverse genetic and molecular causes, histopathological alterations, and subsequent clinical presentations contributing to its complexity and variability among individuals. Defining a way to subcluster ALS patients is becoming a central endeavor in the field. Identifying specific clusters and applying them in clinical trials could enable the development of more effective treatments. This review aims to summarize the available data on heterogeneity in ALS with regard to various aspects, e.g., clinical, genetic, and molecular.
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Affiliation(s)
- Laura Tzeplaeff
- Department of Neurology, Rechts der Isar Hospital, Technical University of Munich, 81675 München, Germany
| | - Alexandra V. Jürs
- Translational Neurodegeneration Section “Albrecht Kossel”, Department of Neurology, University Medical Center Rostock, 18057 Rostock, Germany
| | - Camilla Wohnrade
- Department of Neurology, Hannover Medical School, 30625 Hannover, Germany;
| | - Antonia F. Demleitner
- Department of Neurology, Rechts der Isar Hospital, Technical University of Munich, 81675 München, Germany
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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] [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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Biskupiak Z, Ha VV, Rohaj A, Bulaj G. Digital Therapeutics for Improving Effectiveness of Pharmaceutical Drugs and Biological Products: Preclinical and Clinical Studies Supporting Development of Drug + Digital Combination Therapies for Chronic Diseases. J Clin Med 2024; 13:403. [PMID: 38256537 PMCID: PMC10816409 DOI: 10.3390/jcm13020403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/08/2024] [Accepted: 01/09/2024] [Indexed: 01/24/2024] Open
Abstract
Limitations of pharmaceutical drugs and biologics for chronic diseases (e.g., medication non-adherence, adverse effects, toxicity, or inadequate efficacy) can be mitigated by mobile medical apps, known as digital therapeutics (DTx). Authorization of adjunct DTx by the US Food and Drug Administration and draft guidelines on "prescription drug use-related software" illustrate opportunities to create drug + digital combination therapies, ultimately leading towards drug-device combination products (DTx has a status of medical devices). Digital interventions (mobile, web-based, virtual reality, and video game applications) demonstrate clinically meaningful benefits for people living with Alzheimer's disease, dementia, rheumatoid arthritis, cancer, chronic pain, epilepsy, depression, and anxiety. In the respective animal disease models, preclinical studies on environmental enrichment and other non-pharmacological modalities (physical activity, social interactions, learning, and music) as surrogates for DTx "active ingredients" also show improved outcomes. In this narrative review, we discuss how drug + digital combination therapies can impact translational research, drug discovery and development, generic drug repurposing, and gene therapies. Market-driven incentives to create drug-device combination products are illustrated by Humira® (adalimumab) facing a "patent-cliff" competition with cheaper and more effective biosimilars seamlessly integrated with DTx. In conclusion, pharma and biotech companies, patients, and healthcare professionals will benefit from accelerating integration of digital interventions with pharmacotherapies.
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Affiliation(s)
- Zack Biskupiak
- Department of Medicinal Chemistry, College of Pharmacy, University of Utah, Salt Lake City, UT 84112, USA
| | - Victor Vinh Ha
- Department of Medicinal Chemistry, College of Pharmacy, University of Utah, Salt Lake City, UT 84112, USA
| | - Aarushi Rohaj
- Department of Medicinal Chemistry, College of Pharmacy, University of Utah, Salt Lake City, UT 84112, USA
- The Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT 84113, USA
| | - Grzegorz Bulaj
- Department of Medicinal Chemistry, College of Pharmacy, University of Utah, Salt Lake City, UT 84112, USA
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Theme 10 - Disease Stratification and Phenotyping of Patients. Amyotroph Lateral Scler Frontotemporal Degener 2023; 24:230-244. [PMID: 37966327 DOI: 10.1080/21678421.2023.2260202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
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