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Johnson S, Kantartjis M, Severson J, Dorsey R, Adams JL, Kangarloo T, Kostrzebski MA, Best A, Merickel M, Amato D, Severson B, Jezewski S, Polyak S, Keil A, Cosman J, Anderson D. Wearable Sensor-Based Assessments for Remotely Screening Early-Stage Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2024; 24:5637. [PMID: 39275547 PMCID: PMC11397844 DOI: 10.3390/s24175637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2024] [Revised: 08/26/2024] [Accepted: 08/27/2024] [Indexed: 09/16/2024]
Abstract
Prevalence estimates of Parkinson's disease (PD)-the fastest-growing neurodegenerative disease-are generally underestimated due to issues surrounding diagnostic accuracy, symptomatic undiagnosed cases, suboptimal prodromal monitoring, and limited screening access. Remotely monitored wearable devices and sensors provide precise, objective, and frequent measures of motor and non-motor symptoms. Here, we used consumer-grade wearable device and sensor data from the WATCH-PD study to develop a PD screening tool aimed at eliminating the gap between patient symptoms and diagnosis. Early-stage PD patients (n = 82) and age-matched comparison participants (n = 50) completed a multidomain assessment battery during a one-year longitudinal multicenter study. Using disease- and behavior-relevant feature engineering and multivariate machine learning modeling of early-stage PD status, we developed a highly accurate (92.3%), sensitive (90.0%), and specific (100%) random forest classification model (AUC = 0.92) that performed well across environmental and platform contexts. These findings provide robust support for further exploration of consumer-grade wearable devices and sensors for global population-wide PD screening and surveillance.
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Affiliation(s)
| | | | | | - Ray Dorsey
- Center for Health and Technology, University of Rochester Medical Center, Rochester, NY 14623, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14623, USA
| | - Jamie L Adams
- Center for Health and Technology, University of Rochester Medical Center, Rochester, NY 14623, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14623, USA
| | | | - Melissa A Kostrzebski
- Center for Health and Technology, University of Rochester Medical Center, Rochester, NY 14623, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY 14623, USA
| | - Allen Best
- Clinical Ink, Winston-Salem, NC 27101, USA
| | | | - Dan Amato
- Clinical Ink, Winston-Salem, NC 27101, USA
| | | | | | | | - Anna Keil
- Clinical Ink, Winston-Salem, NC 27101, USA
| | - Josh Cosman
- AbbVie Pharmaceuticals, North Chicago, IL 60064, USA
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Kangarloo T, Latzman RD, Adams JL, Dorsey R, Kostrzebski M, Severson J, Anderson D, Horak F, Stephenson D, Cosman J. Acceptability of digital health technologies in early Parkinson's disease: lessons from WATCH-PD. Front Digit Health 2024; 6:1435693. [PMID: 39253055 PMCID: PMC11381495 DOI: 10.3389/fdgth.2024.1435693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Accepted: 08/02/2024] [Indexed: 09/11/2024] Open
Abstract
Introduction Digital health technologies (DHTs) have the potential to alleviate challenges experienced in clinical trials through more objective, naturalistic, and frequent assessments of functioning. However, implementation of DHTs come with their own challenges, including acceptability and ease of use for study participants. In addition to acceptability, it is also important to understand device proficiency in the general population and within patient populations who may be asked to use DHTs for extended periods of time. We thus aimed to provide an overview of participant feedback on acceptability of DHTs, including body-worn sensors used in the clinic and a mobile application used at-home, used throughout the duration of the Wearable Assessments in the Clinic and at Home in Parkinson's Disease (WATCH-PD) study, an observational, longitudinal study looking at disease progression in early Parkinson's Disease (PD). Methods 82 participants with PD and 50 control participants were enrolled at 17 sites throughout the United States and followed for 12 months. We assessed participants' general device proficiency at baseline, using the Mobile Device Proficiency Questionnaire (MDPQ). The mean MDPQ score at Baseline did not significantly differ between PD patients and healthy controls (20.6 [2.91] vs 21.5 [2.94], p = .10). Results Questionnaire results demonstrated that participants had generally positive views on the comfort and use of the digital technologies throughout the duration of the study, regardless of group. Discussion This is the first study to evaluate patient feedback and impressions of using technology in a longitudinal observational study in early Parkinson's Disease. Results demonstrate device proficiency and acceptability of various DHTs in people with Parkinson's does not differ from that of neurologically healthy older adults, and, overall, participants had a favorable view of the DHTs deployed in the WATCH-PD study.
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Affiliation(s)
- T Kangarloo
- Takeda Pharmaceuticals, Cambridge, MA, United States
| | - R D Latzman
- Takeda Pharmaceuticals, Cambridge, MA, United States
| | - J L Adams
- Center for Health+Technology, University of Rochester, Rochester, NY, United States
- Department of Neurology, University of Rochester, Rochester, NY, United States
| | - R Dorsey
- Center for Health+Technology, University of Rochester, Rochester, NY, United States
- Department of Neurology, University of Rochester, Rochester, NY, United States
| | - M Kostrzebski
- Center for Health+Technology, University of Rochester, Rochester, NY, United States
- Department of Neurology, University of Rochester, Rochester, NY, United States
| | - J Severson
- Clinical Ink, Horsham, PA, United States
| | - D Anderson
- Clinical Ink, Horsham, PA, United States
| | - F Horak
- Balance Disorders Laboratory, Department of Neurology, Oregon Health and Science University, Portland, OR, United States
| | - D Stephenson
- Critical Path Institute, Tuscan, AZ, United States
| | - J Cosman
- Abbvie, North Chicago, IL, United States
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Goubault E, Duval C, Martin C, Lebel K. Innovative Detection and Segmentation of Mobility Activities in Patients Living with Parkinson's Disease Using a Single Ankle-Positioned Smartwatch. SENSORS (BASEL, SWITZERLAND) 2024; 24:5486. [PMID: 39275396 PMCID: PMC11398008 DOI: 10.3390/s24175486] [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: 07/18/2024] [Revised: 08/14/2024] [Accepted: 08/21/2024] [Indexed: 09/16/2024]
Abstract
BACKGROUND The automatic detection of activities of daily living (ADL) is necessary to improve long-term home-based monitoring of Parkinson's disease (PD) symptoms. While most body-worn sensor algorithms for ADL detection were developed using laboratory research systems covering full-body kinematics, it is now crucial to achieve ADL detection using a single body-worn sensor that remains commercially available and affordable for ecological use. AIM to detect and segment Walking, Turning, Sitting-down, and Standing-up activities of patients with PD using a Smartwatch positioned at the ankle. METHOD Twenty-two patients living with PD performed a Timed Up and Go (TUG) task three times before engaging in cleaning ADL in a simulated free-living environment during a 3 min trial. Accelerations and angular velocities of the right or left ankle were recorded in three dimensions using a Smartwatch. The TUG task was used to develop detection algorithms for Walking, Turning, Sitting-down, and Standing-up, while the 3 min trial in the free-living environment was used to test and validate these algorithms. Sensitivity, specificity, and F-scores were calculated based on a manual segmentation of ADL. RESULTS Sensitivity, specificity, and F-scores were 96.5%, 94.7%, and 96.0% for Walking; 90.0%, 93.6%, and 91.7% for Turning; 57.5%, 70.5%, and 52.3% for Sitting-down; and 57.5%, 72.9%, and 54.1% for Standing-up. The median of time difference between the manual and automatic segmentation was 1.31 s for Walking, 0.71 s for Turning, 2.75 s for Sitting-down, and 2.35 s for Standing-up. CONCLUSION The results of this study demonstrate that segmenting ADL to characterize the mobility of people with PD based on a single Smartwatch can be comparable to manual segmentation while requiring significantly less time. While Walking and Turning were well detected, Sitting-down and Standing-up will require further investigation to develop better algorithms. Nonetheless, these achievements increase the odds of success in implementing wearable technologies for PD monitoring in ecological environments.
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Affiliation(s)
- Etienne Goubault
- Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail (IRSST), 505 Boul. de Maisonneuve O, Montréal, QC H3A 3C2, Canada
| | - Christian Duval
- Département des Sciences de l'Activité Physique, Université du Québec à Montréal, Montréal, QC H2X 1Y4, Canada
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, QC H3W 1W6, Canada
| | - Camille Martin
- Centre de Recherche sur le Vieillissement, CIUSSS de l'Estrie-CHUS, Sherbrooke, QC J1H 4C4, Canada
| | - Karina Lebel
- Centre de Recherche sur le Vieillissement, CIUSSS de l'Estrie-CHUS, Sherbrooke, QC J1H 4C4, Canada
- Département de Génie Électrique et de Génie Informatique, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
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Sapienza S, Tsurkalenko O, Giraitis M, Mejia AC, Zelimkhanov G, Schwaninger I, Klucken J. Assessing the clinical utility of inertial sensors for home monitoring in Parkinson's disease: a comprehensive review. NPJ Parkinsons Dis 2024; 10:161. [PMID: 39164257 PMCID: PMC11335938 DOI: 10.1038/s41531-024-00755-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 07/24/2024] [Indexed: 08/22/2024] Open
Abstract
This review screened 296 articles on wearable sensors for home monitoring of people with Parkinson's Disease within the PubMed Database, from January 2017 to May 2023. A three-level maturity framework was applied for classifying the aims of 59 studies included: demonstrating technical efficacy, diagnostic sensitivity, or clinical utility. As secondary analysis, user experience (usability and patient adherence) was evaluated. The evidences provided by the studies were categorized and stratified according to the level of maturity. Our results indicate that approximately 75% of articles investigated diagnostic sensitivity, i.e. correlation of sensor-data with clinical parameters. Evidence of clinical utility, defined as improvement on health outcomes or clinical decisions after the use of the wearables, was found only in nine papers. A third of the articles included reported evidence of user experience. Future research should focus more on clinical utility, to facilitate the translation of research results within the management of Parkinson's Disease.
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Affiliation(s)
- Stefano Sapienza
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Luxembourg Institute of Health (LIH), Strassen, Luxembourg
| | - Olena Tsurkalenko
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Luxembourg Institute of Health (LIH), Strassen, Luxembourg
- Centre Hospitalier de Luxembourg (CHL), Rollengergronn-belair-nord, Luxembourg
| | - Marijus Giraitis
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Luxembourg Institute of Health (LIH), Strassen, Luxembourg
- Centre Hospitalier de Luxembourg (CHL), Rollengergronn-belair-nord, Luxembourg
| | - Alan Castro Mejia
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Luxembourg Institute of Health (LIH), Strassen, Luxembourg
| | - Gelani Zelimkhanov
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Luxembourg Institute of Health (LIH), Strassen, Luxembourg
- Centre Hospitalier de Luxembourg (CHL), Rollengergronn-belair-nord, Luxembourg
| | - Isabel Schwaninger
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Luxembourg Institute of Health (LIH), Strassen, Luxembourg
| | - Jochen Klucken
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg.
- Luxembourg Institute of Health (LIH), Strassen, Luxembourg.
- Centre Hospitalier de Luxembourg (CHL), Rollengergronn-belair-nord, Luxembourg.
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Bakker JP, Izmailova ES, Clement A, Hoffmann S, Leptak C, Menetski JP, Wagner JA. Regulatory Pathways for Qualification and Acceptance of Digital Health Technology-Derived Clinical Trial Endpoints: Considerations for Sponsors. Clin Pharmacol Ther 2024. [PMID: 39148198 DOI: 10.1002/cpt.3398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 07/15/2024] [Indexed: 08/17/2024]
Abstract
Despite widespread interest and substantial investment in the adoption of sensor-based digital health technologies (sDHTs) for remote data capture in drug development trials, no drug has been approved based on an sDHT-derived primary endpoint in the United States (US). One reason for this lack of advancement is the complexity of obtaining regulatory endorsement for those endpoints within current US regulatory pathways. The goal of our review is to describe the two choices currently available to pharmaceutical study Sponsors: (i) they may navigate the traditional route of compiling the evidence to support the sDHT-derived endpoint in their investigational new drug (IND) application, requiring specific expertise and substantial resources; or (ii) they may navigate the drug development tool (DDT) pathway with the goal of qualifying their sDHT-derived endpoint as a biomarker or clinical outcome assessment applicable to a broader context of use (COU), either alone or as part of a partnership or consortium. We describe the nuances of each pathway; the evidentiary requirements for supporting an sDHT-derived endpoint and the technology used to capture it; and the impact that an sDHT's regulatory status may have on a Sponsor's decision to use it for data capture. By systematically comparing the IND and DDT pathways, our over-arching goals are to support the increasing deployment of sDHTs within the clinical research setting and help advance regulatory science in the field of digital medicine.
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Affiliation(s)
| | | | | | - Steven Hoffmann
- Science Partnerships, Foundation for the National Institutes of Health, North Bethesda, Maryland, USA
| | | | - Joseph P Menetski
- Science Partnerships, Foundation for the National Institutes of Health, North Bethesda, Maryland, USA
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6
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Gala AS, Wilkins KB, Petrucci MN, Kehnemouyi YM, Velisar A, Trager MH, Bronte-Stewart HM. The digital signature of emergent tremor in Parkinson's disease. NPJ Parkinsons Dis 2024; 10:147. [PMID: 39112485 PMCID: PMC11306561 DOI: 10.1038/s41531-024-00754-7] [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: 10/19/2023] [Accepted: 07/22/2024] [Indexed: 08/10/2024] Open
Abstract
Emergent tremor in Parkinson's disease (PD) can occur during sustained postures or movements that are different from action tremor. Tremor can contaminate the clinical rating of bradykinesia during finger tapping. Currently, there is no reliable way of isolating emergent tremor and measuring the cardinal motor symptoms based on voluntary movements only. In this study, we investigated whether emergent tremor during repetitive alternating finger tapping (RAFT) on a quantitative digitography (QDG) device could be reliably identified and distinguished from voluntary tapping. Ninety-six individuals with PD and forty-two healthy controls performed a thirty-second QDG-RAFT task and the Movement Disorders Society - Unified Parkinson's Disease Rating Scale Part III (MDS-UPDRS III). Visual identification of tremor during QDG-RAFT was labeled by an experienced movement disorders specialist. Two methods of identifying tremor were investigated: 1) physiologically informed temporal thresholds 2) XGBoost model using temporal and amplitude features of tapping. The XGBoost model showed high accuracy for identifying tremor (area under the precision-recall curve of 0.981) and outperformed temporal-based thresholds. Percent time duration of classifier-identified tremor showed significant correlations with MDS-UPDRS III tremor subscores (r = 0.50, p < 0.0001). There was a significant change in QDG metrics for bradykinesia, rigidity, and arrhythmicity after tremor strikes were excluded (p < 0.01). The results demonstrate that emergent tremor during QDG-RAFT has a unique digital signature and the duration of tremor correlated with the MDS-UPDRS III tremor items. When involuntary tremor strikes were excluded, the QDG metrics of bradykinesia and rigidity were significantly worse, demonstrating the importance of distinguishing tremor from voluntary movement when rating bradykinesia.
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Affiliation(s)
- Aryaman S Gala
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Kevin B Wilkins
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | | | | | - Anca Velisar
- The Smith-Kettlewell Eye Research Institute, San Francisco, CA, USA
| | - Megan H Trager
- Columbia University College of Physicians and Surgeons, New York City, NY, USA
| | - Helen M Bronte-Stewart
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, US.
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7
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Tanner CM, Ostrem JL. Parkinson's Disease. N Engl J Med 2024; 391:442-452. [PMID: 39083773 DOI: 10.1056/nejmra2401857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/02/2024]
Affiliation(s)
- Caroline M Tanner
- From the Movement Disorders and Neuromodulation Center, Department of Neurology, Weill Institute for Neuroscience, University of California, San Francisco, San Francisco
| | - Jill L Ostrem
- From the Movement Disorders and Neuromodulation Center, Department of Neurology, Weill Institute for Neuroscience, University of California, San Francisco, San Francisco
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Mantovani E, Bressan MM, Tinazzi M, Tamburin S. Towards multimodal cognition-based treatment for cognitive impairment in Parkinson's disease: drugs, exercise, non-invasive brain stimulation and technologies. Curr Opin Neurol 2024:00019052-990000000-00188. [PMID: 39132779 DOI: 10.1097/wco.0000000000001310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
PURPOSE OF REVIEW Cognitive impairment is one of the most challenging non-motor symptoms of Parkinson's disease (PD) and may occur during all PD stages. There are no established pharmacological treatments for PD-related cognitive impairment, which may be improved by cognition-based interventions (i.e., cognitive stimulation, cognitive training, cognitive rehabilitation). Multimodal cognition-based interventions by adjunctive drugs, exercise, non-invasive brain stimulation and technologies may be effective in PD. RECENT FINDINGS Exercise combined with cognitive training may enhance global, memory, visuospatial and executive functioning, transcranial direct current stimulation delivered alongside cognitive training may improve attention and executive functioning, and exergames, semi-immersive virtual reality (VR) and telerehabilitation plus non-immersive VR combined with cognitive training may ameliorate global and executive functioning in PD patients. SUMMARY The evidence reviewed here, despite preliminary, is very encouraging and suggests strong rationale for combining pharmacological and non-pharmacological interventions with cognition-based treatments in PD. To overcome limitations of current studies, we propose some recommendations for future trials on drugs, exercise, non-invasive brain stimulation and technologies combined with cognition-based treatments for cognitive impairment in PD.
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Affiliation(s)
- Elisa Mantovani
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
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Illner V, Novotný M, Kouba T, Tykalová T, Šimek M, Sovka P, Švihlík J, Růžička E, Šonka K, Dušek P, Rusz J. Smartphone Voice Calls Provide Early Biomarkers of Parkinsonism in Rapid Eye Movement Sleep Behavior Disorder. Mov Disord 2024. [PMID: 39001636 DOI: 10.1002/mds.29921] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 06/03/2024] [Accepted: 06/21/2024] [Indexed: 09/05/2024] Open
Abstract
BACKGROUND Speech dysfunction represents one of the initial motor manifestations to develop in Parkinson's disease (PD) and is measurable through smartphone. OBJECTIVE The aim was to develop a fully automated and noise-resistant smartphone-based system that can unobtrusively screen for prodromal parkinsonian speech disorder in subjects with isolated rapid eye movement sleep behavior disorder (iRBD) in a real-world scenario. METHODS This cross-sectional study assessed regular, everyday voice call data from individuals with iRBD compared to early PD patients and healthy controls via a developed smartphone application. The participants also performed an active, regular reading of a short passage on their smartphone. Smartphone data were continuously collected for up to 3 months after the standard in-person assessments at the clinic. RESULTS A total of 3525 calls that led to 5990 minutes of preprocessed speech were extracted from 72 participants, comprising 21 iRBD patients, 26 PD patients, and 25 controls. With a high area under the curve of 0.85 between iRBD patients and controls, the combination of passive and active smartphone data provided a comparable or even more sensitive evaluation than laboratory examination using a high-quality microphone. The most sensitive features to induce prodromal neurodegeneration in iRBD included imprecise vowel articulation during phone calls (P = 0.03) and monopitch in reading (P = 0.05). Eighteen minutes of speech corresponding to approximately nine calls was sufficient to obtain the best sensitivity for the screening. CONCLUSION We consider the developed tool widely applicable to deep longitudinal digital phenotyping data with future applications in neuroprotective trials, deep brain stimulation optimization, neuropsychiatry, speech therapy, population screening, and beyond. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Vojtěch Illner
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Michal Novotný
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Tomáš Kouba
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Tereza Tykalová
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Michal Šimek
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Pavel Sovka
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Jan Švihlík
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
- Department of Computing and Control Engineering, Faculty of Chemical Engineering, University of Chemistry and Technology, Prague, Czech Republic
| | - Evžen Růžička
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Karel Šonka
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Petr Dušek
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Jan Rusz
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University, Prague, Czech Republic
- Department of Neurology and ARTORG Center, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
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10
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Bougea A. Digital biomarkers in Parkinson's disease. Adv Clin Chem 2024; 123:221-253. [PMID: 39181623 DOI: 10.1016/bs.acc.2024.06.005] [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] [Indexed: 08/27/2024]
Abstract
Digital biomarker (DB) assessments provide objective measures of daily life tasks and thus hold promise to improve diagnosis and monitoring of Parkinson's disease (PD) patients especially those with advanced stages. Data from DB studies can be used in advanced analytics such as Artificial Intelligence and Machine Learning to improve monitoring, treatment and outcomes. Although early development of inertial sensors as accelerometers and gyroscopes in smartphones provided encouraging results, the use of DB remains limited due to lack of standards, harmonization and consensus for analytical as well as clinical validation. Accordingly, a number of clinical trials have been developed to evaluate the performance of DB vs traditional assessment tools with the goal of monitoring disease progression, improving quality of life and outcomes. Herein, we update current evidence on the use of DB in PD and highlight potential benefits and limitations and provide suggestions for future research study.
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Affiliation(s)
- Anastasia Bougea
- Department of Neurology, Medical School, Aeginition Hospital, National and Kapodistrian University of Athens, Athens, Greece.
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Adams JL, Kangarloo T, Gong Y, Khachadourian V, Tracey B, Volfson D, Latzman RD, Cosman J, Edgerton J, Anderson D, Best A, Kostrzebski MA, Auinger P, Wilmot P, Pohlson Y, Jensen-Roberts S, Müller MLTM, Stephenson D, Dorsey ER. Using a smartwatch and smartphone to assess early Parkinson's disease in the WATCH-PD study over 12 months. NPJ Parkinsons Dis 2024; 10:112. [PMID: 38866793 PMCID: PMC11169239 DOI: 10.1038/s41531-024-00721-2] [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: 12/22/2023] [Accepted: 05/10/2024] [Indexed: 06/14/2024] Open
Abstract
Digital measures may provide objective, sensitive, real-world measures of disease progression in Parkinson's disease (PD). However, multicenter longitudinal assessments of such measures are few. We recently demonstrated that baseline assessments of gait, tremor, finger tapping, and speech from a commercially available smartwatch, smartphone, and research-grade wearable sensors differed significantly between 82 individuals with early, untreated PD and 50 age-matched controls. Here, we evaluated the longitudinal change in these assessments over 12 months in a multicenter observational study using a generalized additive model, which permitted flexible modeling of at-home data. All measurements were included until participants started medications for PD. Over one year, individuals with early PD experienced significant declines in several measures of gait, an increase in the proportion of day with tremor, modest changes in speech, and few changes in psychomotor function. As measured by the smartwatch, the average (SD) arm swing in-clinic decreased from 25.9 (15.3) degrees at baseline to 19.9 degrees (13.7) at month 12 (P = 0.004). The proportion of awake time an individual with early PD had tremor increased from 19.3% (18.0%) to 25.6% (21.4%; P < 0.001). Activity, as measured by the number of steps taken per day, decreased from 3052 (1306) steps per day to 2331 (2010; P = 0.16), but this analysis was restricted to 10 participants due to the exclusion of those that had started PD medications and lost the data. The change of these digital measures over 12 months was generally larger than the corresponding change in individual items on the Movement Disorder Society-Unified Parkinson's Disease Rating Scale but not greater than the change in the overall scale. Successful implementation of digital measures in future clinical trials will require improvements in study conduct, especially data capture. Nonetheless, gait and tremor measures derived from a commercially available smartwatch and smartphone hold promise for assessing the efficacy of therapeutics in early PD.
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Affiliation(s)
- Jamie L Adams
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA.
| | | | - Yishu Gong
- Takeda Pharmaceuticals, Cambridge, MA, USA
| | | | | | | | | | | | | | | | | | - Melissa A Kostrzebski
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Peggy Auinger
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Peter Wilmot
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Yvonne Pohlson
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Stella Jensen-Roberts
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | | | | | - E Ray Dorsey
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
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Podichetty JT, Sardar S, Henscheid N, Lee GV, Abrams JR, Anderson W, Ma SC, Romero K. Accelerating healthcare innovation: the role of Artificial intelligence and digital health technologies in critical path institute's public-private partnerships. Clin Transl Sci 2024; 17:e13851. [PMID: 38807460 PMCID: PMC11133960 DOI: 10.1111/cts.13851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 05/10/2024] [Accepted: 05/14/2024] [Indexed: 05/30/2024] Open
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Bhidayasiri R, Sringean J, Phumphid S, Anan C, Thanawattano C, Deoisres S, Panyakaew P, Phokaewvarangkul O, Maytharakcheep S, Buranasrikul V, Prasertpan T, Khontong R, Jagota P, Chaisongkram A, Jankate W, Meesri J, Chantadunga A, Rattanajun P, Sutaphan P, Jitpugdee W, Chokpatcharavate M, Avihingsanon Y, Sittipunt C, Sittitrai W, Boonrach G, Phonsrithong A, Suvanprakorn P, Vichitcholchai J, Bunnag T. The rise of Parkinson's disease is a global challenge, but efforts to tackle this must begin at a national level: a protocol for national digital screening and "eat, move, sleep" lifestyle interventions to prevent or slow the rise of non-communicable diseases in Thailand. Front Neurol 2024; 15:1386608. [PMID: 38803644 PMCID: PMC11129688 DOI: 10.3389/fneur.2024.1386608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 04/19/2024] [Indexed: 05/29/2024] Open
Abstract
The rising prevalence of Parkinson's disease (PD) globally presents a significant public health challenge for national healthcare systems, particularly in low-to-middle income countries, such as Thailand, which may have insufficient resources to meet these escalating healthcare needs. There are also many undiagnosed cases of early-stage PD, a period when therapeutic interventions would have the most value and least cost. The traditional "passive" approach, whereby clinicians wait for patients with symptomatic PD to seek treatment, is inadequate. Proactive, early identification of PD will allow timely therapeutic interventions, and digital health technologies can be scaled up in the identification and early diagnosis of cases. The Parkinson's disease risk survey (TCTR20231025005) aims to evaluate a digital population screening platform to identify undiagnosed PD cases in the Thai population. Recognizing the long prodromal phase of PD, the target demographic for screening is people aged ≥ 40 years, approximately 20 years before the usual emergence of motor symptoms. Thailand has a highly rated healthcare system with an established universal healthcare program for citizens, making it ideal for deploying a national screening program using digital technology. Designed by a multidisciplinary group of PD experts, the digital platform comprises a 20-item questionnaire about PD symptoms along with objective tests of eight digital markers: voice vowel, voice sentences, resting and postural tremor, alternate finger tapping, a "pinch-to-size" test, gait and balance, with performance recorded using a mobile application and smartphone's sensors. Machine learning tools use the collected data to identify subjects at risk of developing, or with early signs of, PD. This article describes the selection and validation of questionnaire items and digital markers, with results showing the chosen parameters and data analysis methods to be robust, reliable, and reproducible. This digital platform could serve as a model for similar screening strategies for other non-communicable diseases in Thailand.
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Affiliation(s)
- Roongroj Bhidayasiri
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
- The Academy of Science, The Royal Society of Thailand, Bangkok, Thailand
| | - Jirada Sringean
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Saisamorn Phumphid
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Chanawat Anan
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | | | - Suwijak Deoisres
- National Electronics and Computer Technology Centre, Pathum Thani, Thailand
| | - Pattamon Panyakaew
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Onanong Phokaewvarangkul
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Suppata Maytharakcheep
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Vijittra Buranasrikul
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Tittaya Prasertpan
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
- Sawanpracharak Hospital, Nakhon Sawan, Thailand
| | | | - Priya Jagota
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Araya Chaisongkram
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Worawit Jankate
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Jeeranun Meesri
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Araya Chantadunga
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Piyaporn Rattanajun
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Phantakarn Sutaphan
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Weerachai Jitpugdee
- Department of Rehabilitation Medicine, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Marisa Chokpatcharavate
- Chulalongkorn Parkinson's Disease Support Group, Department of Medicine, Faculty of Medicine, Chulalongkorn Centre of Excellence for Parkinson's Disease and Related Disorders, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Yingyos Avihingsanon
- Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Thai Red Cross Society, Bangkok, Thailand
| | - Chanchai Sittipunt
- Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Thai Red Cross Society, Bangkok, Thailand
| | | | | | | | | | | | - Tej Bunnag
- Thai Red Cross Society, Bangkok, Thailand
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Zubair AS. Utilization of wearable technology to track functional changes in a patient with myopathy. Brain Circ 2024; 10:190-191. [PMID: 39036302 PMCID: PMC11259317 DOI: 10.4103/bc.bc_104_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 03/06/2024] [Accepted: 03/13/2024] [Indexed: 07/23/2024] Open
Affiliation(s)
- Adeel S. Zubair
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
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Czech MD, Badley D, Yang L, Shen J, Crouthamel M, Kangarloo T, Dorsey ER, Adams JL, Cosman JD. Improved measurement of disease progression in people living with early Parkinson's disease using digital health technologies. COMMUNICATIONS MEDICINE 2024; 4:49. [PMID: 38491176 PMCID: PMC10942994 DOI: 10.1038/s43856-024-00481-3] [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: 08/10/2023] [Accepted: 03/08/2024] [Indexed: 03/18/2024] Open
Abstract
BACKGROUND Digital health technologies show promise for improving the measurement of Parkinson's disease in clinical research and trials. However, it is not clear whether digital measures demonstrate enhanced sensitivity to disease progression compared to traditional measurement approaches. METHODS To this end, we develop a wearable sensor-based digital algorithm for deriving features of upper and lower-body bradykinesia and evaluate the sensitivity of digital measures to 1-year longitudinal progression using data from the WATCH-PD study, a multicenter, observational digital assessment study in participants with early, untreated Parkinson's disease. In total, 82 early, untreated Parkinson's disease participants and 50 age-matched controls were recruited and took part in a variety of motor tasks over the course of a 12-month period while wearing body-worn inertial sensors. We establish clinical validity of sensor-based digital measures by investigating convergent validity with appropriate clinical constructs, known groups validity by distinguishing patients from healthy volunteers, and test-retest reliability by comparing measurements between visits. RESULTS We demonstrate clinical validity of the digital measures, and importantly, superior sensitivity of digital measures for distinguishing 1-year longitudinal change in early-stage PD relative to corresponding clinical constructs. CONCLUSIONS Our results demonstrate the potential of digital health technologies to enhance sensitivity to disease progression relative to existing measurement standards and may constitute the basis for use as drug development tools in clinical research.
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Affiliation(s)
| | | | | | | | | | | | - E Ray Dorsey
- University of Rochester Medical Center, Rochester, NY, USA
| | - Jamie L Adams
- University of Rochester Medical Center, Rochester, NY, USA
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16
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Kehnemouyi YM, Coleman TP, Tass PA. Emerging wearable technologies for multisystem monitoring and treatment of Parkinson's disease: a narrative review. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 4:1354211. [PMID: 38414636 PMCID: PMC10896901 DOI: 10.3389/fnetp.2024.1354211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 01/12/2024] [Indexed: 02/29/2024]
Abstract
Parkinson's disease (PD) is a chronic movement disorder characterized by a variety of motor and nonmotor comorbidities, including cognitive impairment, gastrointestinal (GI) dysfunction, and autonomic/sleep disturbances. Symptoms typically fluctuate with different settings and environmental factors and thus need to be consistently monitored. Current methods, however, rely on infrequent rating scales performed in clinic. The advent of wearable technologies presents a new avenue to track objective measures of PD comorbidities longitudinally and more frequently. This narrative review discusses and proposes emerging wearable technologies that can monitor manifestations of motor, cognitive, GI, and autonomic/sleep comorbidities throughout the daily lives of PD individuals. This can provide more wholistic insight into real-time physiological versus pathological function with the potential to better assess treatments during clinical trials and allow physicians to optimize treatment regimens. Additionally, this narrative review briefly examines novel applications of wearables as therapy for PD patients.
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Affiliation(s)
- Yasmine M. Kehnemouyi
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, United States
- Department of Bioengineering, Stanford University School of Engineering, Stanford, CA, United States
| | - Todd P. Coleman
- Department of Bioengineering, Stanford University School of Engineering, Stanford, CA, United States
| | - Peter A. Tass
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, United States
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17
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Mirelman A, Volkov J, Salomon A, Gazit E, Nieuwboer A, Rochester L, Del Din S, Avanzino L, Pelosin E, Bloem BR, Della Croce U, Cereatti A, Thaler A, Roggen D, Mazza C, Shirvan J, Cedarbaum JM, Giladi N, Hausdorff JM. Digital Mobility Measures: A Window into Real-World Severity and Progression of Parkinson's Disease. Mov Disord 2024; 39:328-338. [PMID: 38151859 DOI: 10.1002/mds.29689] [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: 09/05/2023] [Revised: 11/20/2023] [Accepted: 11/27/2023] [Indexed: 12/29/2023] Open
Abstract
BACKGROUND Real-world monitoring using wearable sensors has enormous potential for assessing disease severity and symptoms among persons with Parkinson's disease (PD). Many distinct features can be extracted, reflecting multiple mobility domains. However, it is unclear which digital measures are related to PD severity and are sensitive to disease progression. OBJECTIVES The aim was to identify real-world mobility measures that reflect PD severity and show discriminant ability and sensitivity to disease progression, compared to the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) scale. METHODS Multicenter real-world continuous (24/7) digital mobility data from 587 persons with PD and 68 matched healthy controls were collected using an accelerometer adhered to the lower back. Machine learning feature selection and regression algorithms evaluated associations of the digital measures using the MDS-UPDRS (I-III). Binary logistic regression assessed discriminatory value using controls, and longitudinal observational data from a subgroup (n = 33) evaluated sensitivity to change over time. RESULTS Digital measures were only moderately correlated with the MDS-UPDRS (part II-r = 0.60 and parts I and III-r = 0.50). Most associated measures reflected activity quantity and distribution patterns. A model with 14 digital measures accurately distinguished recently diagnosed persons with PD from healthy controls (81.1%, area under the curve: 0.87); digital measures showed larger effect sizes (Cohen's d: [0.19-0.66]), for change over time than any of the MDS-UPDRS parts (Cohen's d: [0.04-0.12]). CONCLUSIONS Real-world mobility measures are moderately associated with clinical assessments, suggesting that they capture different aspects of motor capacity and function. Digital mobility measures are sensitive to early-stage disease and to disease progression, to a larger degree than conventional clinical assessments, demonstrating their utility, primarily for clinical trials but ultimately also for clinical care. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Anat Mirelman
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
- Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Jana Volkov
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Amit Salomon
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Eran Gazit
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Alice Nieuwboer
- Department of Rehabilitation Science, KU Leuven, Neuromotor Rehabilitation Research Group, Leuven, Belgium
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, United Kingdom
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, United Kingdom
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Laura Avanzino
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal Child Health (DINOGMI), University of Genoa, Genoa, Italy
- Department of Experimental Medicine, Section of Human Physiology, University of Genoa, Genoa, Italy
| | - Elisa Pelosin
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal Child Health (DINOGMI), University of Genoa, Genoa, Italy
- IRCCS Policlinico San Martino Teaching Hospital, Genoa, Italy
| | - Bastiaan R Bloem
- Department of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands
| | - Ugo Della Croce
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Andrea Cereatti
- Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Avner Thaler
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
- Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | | | | | | | - Jesse M Cedarbaum
- Coeruleus Clinical Sciences, Woodbridge, Connecticut, USA
- Yale University School of Medicine, New Haven, Connecticut, USA
| | - Nir Giladi
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
- Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Jeffrey M Hausdorff
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
- Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Department of Physical Therapy, Tel Aviv University, Tel Aviv, Israel
- Department of Orthopedic Surgery, Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA
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Davila-Gonzalez S, Martin S. Human Digital Twin in Industry 5.0: A Holistic Approach to Worker Safety and Well-Being through Advanced AI and Emotional Analytics. SENSORS (BASEL, SWITZERLAND) 2024; 24:655. [PMID: 38276347 PMCID: PMC10818408 DOI: 10.3390/s24020655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 12/22/2023] [Accepted: 01/12/2024] [Indexed: 01/27/2024]
Abstract
This research introduces a conceptual framework designed to enhance worker safety and well-being in industrial environments, such as oil and gas construction plants, by leveraging Human Digital Twin (HDT) cutting-edge technologies and advanced artificial intelligence (AI) techniques. At its core, this study is in the developmental phase, aiming to create an integrated system that could enable real-time monitoring and analysis of the physical, mental, and emotional states of workers. It provides valuable insights into the impact of Digital Twins (DT) technology and its role in Industry 5.0. With the development of a chatbot trained as an empathic evaluator that analyses emotions expressed in written conversations using natural language processing (NLP); video logs capable of extracting emotions through facial expressions and speech analysis; and personality tests, this research intends to obtain a deeper understanding of workers' psychological characteristics and stress levels. This innovative approach might enable the identification of stress, anxiety, or other emotional factors that may affect worker safety. Whilst this study does not encompass a case study or an application in a real-world setting, it lays the groundwork for the future implementation of these technologies. The insights derived from this research are intended to inform the development of practical applications aimed at creating safer work environments.
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Affiliation(s)
- Saul Davila-Gonzalez
- Escuela Internacional de Doctorado, Universidad Nacional de Educación a Distancia (UNED), 28040 Madrid, Spain;
| | - Sergio Martin
- Industrial Engineering Faculty, Universidad Nacional de Educación a Distancia (UNED), 28040 Madrid, Spain
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19
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Esper CD, Valdovinos BY, Schneider RB. The Importance of Digital Health Literacy in an Evolving Parkinson's Disease Care System. JOURNAL OF PARKINSON'S DISEASE 2024; 14:S181-S189. [PMID: 38250786 PMCID: PMC11380271 DOI: 10.3233/jpd-230229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
Digital health technologies are growing at a rapid pace and changing the healthcare landscape. Our current understanding of digital health literacy in Parkinson's disease (PD) is limited. In this review, we discuss the potential challenges of low digital health literacy in PD with particular attention to telehealth, deep brain stimulation, wearable sensors, and smartphone applications. We also highlight inequities in access to digital health technologies. Future research is needed to better understand digital health literacy among individuals with PD and to develop effective solutions. We must invest resources to evaluate, understand, and enhance digital health literacy for individuals with PD.
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Affiliation(s)
| | | | - Ruth B Schneider
- Department of Neurology, University of Rochester, Rochester, NY, USA
- Center for Health + Technology, University of Rochester, Rochester, NY, USA
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20
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Sigcha L, Polvorinos-Fernández C, Costa N, Costa S, Arezes P, Gago M, Lee C, López JM, de Arcas G, Pavón I. Monipar: movement data collection tool to monitor motor symptoms in Parkinson's disease using smartwatches and smartphones. Front Neurol 2023; 14:1326640. [PMID: 38148984 PMCID: PMC10750794 DOI: 10.3389/fneur.2023.1326640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 11/21/2023] [Indexed: 12/28/2023] Open
Abstract
Introduction Parkinson's disease (PD) is a neurodegenerative disorder commonly characterized by motor impairments. The development of mobile health (m-health) technologies, such as wearable and smart devices, presents an opportunity for the implementation of clinical tools that can support tasks such as early diagnosis and objective quantification of symptoms. Objective This study evaluates a framework to monitor motor symptoms of PD patients based on the performance of standardized exercises such as those performed during clinic evaluation. To implement this framework, an m-health tool named Monipar was developed that uses off-the-shelf smart devices. Methods An experimental protocol was conducted with the participation of 21 early-stage PD patients and 7 healthy controls who used Monipar installed in off-the-shelf smartwatches and smartphones. Movement data collected using the built-in acceleration sensors were used to extract relevant digital indicators (features). These indicators were then compared with clinical evaluations performed using the MDS-UPDRS scale. Results The results showed moderate to strong (significant) correlations between the clinical evaluations (MDS-UPDRS scale) and features extracted from the movement data used to assess resting tremor (i.e., the standard deviation of the time series: r = 0.772, p < 0.001) and data from the pronation and supination movements (i.e., power in the band of 1-4 Hz: r = -0.662, p < 0.001). Conclusion These results suggest that the proposed framework could be used as a complementary tool for the evaluation of motor symptoms in early-stage PD patients, providing a feasible and cost-effective solution for remote and ambulatory monitoring of specific motor symptoms such as resting tremor or bradykinesia.
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Affiliation(s)
- Luis Sigcha
- Instrumentation and Applied Acoustics Research Group (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Madrid, Spain
- ALGORITMI Research Center, School of Engineering, University of Minho, Guimarães, Portugal
| | - Carlos Polvorinos-Fernández
- Instrumentation and Applied Acoustics Research Group (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Madrid, Spain
| | - Nélson Costa
- ALGORITMI Research Center, School of Engineering, University of Minho, Guimarães, Portugal
| | - Susana Costa
- ALGORITMI Research Center, School of Engineering, University of Minho, Guimarães, Portugal
| | - Pedro Arezes
- ALGORITMI Research Center, School of Engineering, University of Minho, Guimarães, Portugal
| | - Miguel Gago
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
| | - Chaiwoo Lee
- AgeLab, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Juan Manuel López
- Escuela Técnica Superior de Ingeniería y Sistemas de Telecomunicación (ETSIT), Universidad Politécnica de Madrid, Madrid, Spain
| | - Guillermo de Arcas
- Instrumentation and Applied Acoustics Research Group (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Madrid, Spain
| | - Ignacio Pavón
- Instrumentation and Applied Acoustics Research Group (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Madrid, Spain
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21
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Moreau C, Rouaud T, Grabli D, Benatru I, Remy P, Marques AR, Drapier S, Mariani LL, Roze E, Devos D, Dupont G, Bereau M, Fabbri M. Overview on wearable sensors for the management of Parkinson's disease. NPJ Parkinsons Dis 2023; 9:153. [PMID: 37919332 PMCID: PMC10622581 DOI: 10.1038/s41531-023-00585-y] [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: 05/25/2023] [Accepted: 10/02/2023] [Indexed: 11/04/2023] Open
Abstract
Parkinson's disease (PD) is affecting about 1.2 million patients in Europe with a prevalence that is expected to have an exponential increment, in the next decades. This epidemiological evolution will be challenged by the low number of neurologists able to deliver expert care for PD. As PD is better recognized, there is an increasing demand from patients for rigorous control of their symptoms and for therapeutic education. In addition, the highly variable nature of symtoms between patients and the fluctuations within the same patient requires innovative tools to help doctors and patients monitor the disease in their usual living environment and adapt treatment in a more relevant way. Nowadays, there are various body-worn sensors (BWS) proposed to monitor parkinsonian clinical features, such as motor fluctuations, dyskinesia, tremor, bradykinesia, freezing of gait (FoG) or gait disturbances. BWS have been used as add-on tool for patients' management or research purpose. Here, we propose a practical anthology, summarizing the characteristics of the most used BWS for PD patients in Europe, focusing on their role as tools to improve treatment management. Consideration regarding the use of technology to monitor non-motor features is also included. BWS obviously offer new opportunities for improving management strategy in PD but their precise scope of use in daily routine care should be clarified.
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Affiliation(s)
- Caroline Moreau
- Department of Neurology, Parkinson's disease expert Center, Lille University, INSERM UMRS_1172, University Hospital Center, Lille, France
- The French Ns-Park Network, Paris, France
| | - Tiphaine Rouaud
- The French Ns-Park Network, Paris, France
- CHU Nantes, Centre Expert Parkinson, Department of Neurology, Nantes, F-44093, France
| | - David Grabli
- The French Ns-Park Network, Paris, France
- Assistance Publique Hôpitaux de Paris, Department of Neurology, CIC Neurosciences, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
- Sorbonne University, Paris Brain Institute - ICM, Inserm, CNRS, Paris, France
| | - Isabelle Benatru
- The French Ns-Park Network, Paris, France
- Department of Neurology, University Hospital of Poitiers, Poitiers, France
- INSERM, CHU de Poitiers, University of Poitiers, Centre d'Investigation Clinique CIC1402, Poitiers, France
| | - Philippe Remy
- The French Ns-Park Network, Paris, France
- Centre Expert Parkinson, NS-Park/FCRIN Network, CHU Henri Mondor, AP-HP, Equipe NPI, IMRB, INSERM et Faculté de Santé UPE-C, Créteil, FranceService de neurologie, hôpital Henri-Mondor, AP-HP, Créteil, France
| | - Ana-Raquel Marques
- The French Ns-Park Network, Paris, France
- Université Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand University Hospital, Neurology department, Clermont-Ferrand, France
| | - Sophie Drapier
- The French Ns-Park Network, Paris, France
- Pontchaillou University Hospital, Department of Neurology, CIC INSERM 1414, Rennes, France
| | - Louise-Laure Mariani
- The French Ns-Park Network, Paris, France
- Assistance Publique Hôpitaux de Paris, Department of Neurology, CIC Neurosciences, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
- Sorbonne University, Paris Brain Institute - ICM, Inserm, CNRS, Paris, France
| | - Emmanuel Roze
- The French Ns-Park Network, Paris, France
- Assistance Publique Hôpitaux de Paris, Department of Neurology, CIC Neurosciences, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
- Sorbonne University, Paris Brain Institute - ICM, Inserm, CNRS, Paris, France
| | - David Devos
- The French Ns-Park Network, Paris, France
- Parkinson's Disease Centre of Excellence, Department of Medical Pharmacology, Univ. Lille, INSERM; CHU Lille, U1172 - Degenerative & Vascular Cognitive Disorders, LICEND, NS-Park Network, F-59000, Lille, France
| | - Gwendoline Dupont
- The French Ns-Park Network, Paris, France
- Centre hospitalier universitaire François Mitterrand, Département de Neurologie, Université de Bourgogne, Dijon, France
| | - Matthieu Bereau
- The French Ns-Park Network, Paris, France
- Service de neurologie, université de Franche-Comté, CHRU de Besançon, 25030, Besançon, France
| | - Margherita Fabbri
- The French Ns-Park Network, Paris, France.
- Department of Neurosciences, Clinical Investigation Center CIC 1436, Parkinson Toulouse Expert Centre, NS-Park/FCRIN Network and NeuroToul COEN Center, Toulouse University Hospital, INSERM, University of Toulouse 3, Toulouse, France.
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Zeissler ML, McFarthing K, Raphael KG, Rafaloff G, Windle R, Carroll CB. An International Multi-Stakeholder Delphi Survey Study on the Design of Disease Modifying Parkinson's Disease Trials. JOURNAL OF PARKINSON'S DISEASE 2023; 13:1343-1356. [PMID: 38007672 PMCID: PMC10741330 DOI: 10.3233/jpd-230109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/23/2023] [Indexed: 11/27/2023]
Abstract
BACKGROUND Design of disease modification (DM) trials for Parkinson's disease (PD) is challenging. Successful delivery requires a shared understanding of priorities and practicalities. OBJECTIVE To seek stakeholder consensus on phase 3 trials' overall goals and structure, inclusion criteria, outcome measures, and trial delivery and understand where perspectives differ. METHODS An international expert panel comprising people with Parkinson's (PwP), care partners (CP), clinical scientists, representatives from industry, funders and regulators participated in a survey-based Delphi study. Survey items were informed by a scoping review of DM trials and PwP input. Respondents scored item agreement over 3 rounds. Scores and reasoning were summarized by participant group each round until consensus, defined as≥70% of at least 3 participant groups falling within the same 3-point region of a 9-point Likert scale. RESULTS 92/121 individuals from 13 countries (46/69 PwP, 13/18 CP, 20/20 clinical scientists, representatives from 8/8 companies, 4/5 funders, and 1/1 regulator) completed the study. Consensus was reached on 14/31 survey items: 5/8 overall goals and structure, 1/8 Eligibility criteria, 7/13 outcome measures, and 1/2 trial delivery items. Extent of stakeholder endorsement for 428 reasons for scores was collated across items. CONCLUSIONS This is the first systematic multi-stakeholder consultation generating a unique repository of perspectives on pivotal aspects of DM trial design including those of PwP and CP. The panel endorsed outcomes that holistically measure PD and the importance of inclusive trials with hybrid delivery models. Areas of disagreement will inform mitigating strategies of researchers to ensure successful delivery of future trials.
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Affiliation(s)
| | | | - Karen G. Raphael
- College of Dentistry, New York University, New York, NY, USA
- Parkinson’s Research Advocate, USA
| | | | | | - Camille B. Carroll
- Faculty of Health, University of Plymouth, Plymouth, UK
- Translational and Clinical Research Institute, Newcastle University, Newcastle, UK
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